new behavioral paradigms to study taste-quality...

NEW BEHAVIORAL PARADIGMS TO STUDY TASTE-QUALITY

GENERALIZATION AND DISCRIMINATION IN RATS

By

CONNIE LYNN GROBE

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2006

Copyright 2006

by

Connie L. Grobe

This dissertation is dedicated to my brother, Robert.

ACKNOWLEDGMENTS

I thank my family, and friends. Their constant support has made it possible to

achieve my goals. At the University of Florida, I have been lucky to meet and interact

with some very talented people, who have each helped to shape my character along the

way. I especially recognize (in chronological order) Laura Tucker, Laura Geran, Cheryl

Vaughan, Shachar Amdur, Mary Clinton, Ginger Blonde, Shawn Dotson, Kathryn

Saulsgiver, Anaya Mitra, and Yada Treesukosol. They have provided me with

encouragement, assistance, advice, and countless other acts of kindness that I will never

fully understand, but always deeply appreciate.

I thank the entire faculty in the Behavioral Neuroscience area for contributing to

my education. I also gratefully acknowledge the help and guidance that I received from

Dr. Neil Rowland, my M.S. advisor and Dr. Alan Spector, my dissertation advisor. They

have each provided me with a perspective on science that I will continue to value and can

only hope to incorporate into my own future scientific approach.

Finally, I cannot thank my husband, Justin Grobe, enough for his unwavering love,

patience, and encouragement, but especially for his exemplary scholarship.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS ................................................................................................. iv

LIST OF TABLES............................................................................................................. ix

LIST OF FIGURES ........................................................................................................... xi

ABSTRACT..................................................................................................................... xiii

CHAPTER

1 LITERATURE REVIEW .............................................................................................1

Introduction...................................................................................................................1 Domains of Taste..........................................................................................................3

Sensory Discriminative Domain............................................................................4 Affective Domain ..................................................................................................4 Physiological Domain ...........................................................................................5

Taste Quality.................................................................................................................5 Animal Models Used to Study Taste Quality ...............................................................6

Discrimination Tasks.............................................................................................6 Generalization Tasks ...........................................................................................10 Ideal Psychophysical Task ..................................................................................13

Importance of Psychophysical Analysis in Animal Models.......................................13 Argument for the Development of Psychophysical Tasks .........................................14

2 RATS CAN LEARN A DELAYED MATCH/DELAYED NON MATCH TO SAMPLE TASK USING ONLY TASTE STIMULI .................................................16

Background.................................................................................................................16 Method........................................................................................................................17

Animals................................................................................................................17 Apparatus.............................................................................................................17 Stimuli .................................................................................................................18 Surgery ................................................................................................................19 Training and Testing Phases................................................................................20

Spout training ...............................................................................................20 Side training .................................................................................................21 Alternation....................................................................................................21

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Discrimination training I-II ..........................................................................21 Trial structure (final parameters)..................................................................22

Testing .................................................................................................................23 Adjustments to Testing Parameters .....................................................................23 Statistical Analyses..............................................................................................24

Results.........................................................................................................................24 Overall Performance............................................................................................24 Performance on Same Trials ...............................................................................25 Performance on Different Trials..........................................................................25 Performance on Same Trials versus Different Trials ..........................................25

Discussion...................................................................................................................26

3 A NEW METHOD OF ASSESSING TASTE QUALITY GENERALIZATION IN RATS.....................................................................................................................35

Introduction.................................................................................................................35 Experiment I ...............................................................................................................38

Method.................................................................................................................38 Subjects ........................................................................................................38 Training Stimuli ...........................................................................................38 Procedure......................................................................................................38

Data Analysis.......................................................................................................40 Results .................................................................................................................41 Discussion............................................................................................................42

Experiment II ..............................................................................................................43 Method.................................................................................................................44

Subjects ........................................................................................................44 Apparatus .....................................................................................................44 Task overview ..............................................................................................44 Stimuli ..........................................................................................................45 Groups ..........................................................................................................45 Trial structure ...............................................................................................45 Training ........................................................................................................46 Test compounds............................................................................................48 Retraining water as a comparison stimulus..................................................49 Negative control test.....................................................................................49 Data analysis ................................................................................................49 Generalization score .....................................................................................50

Results .................................................................................................................51 Novel concentrations: NaCl .........................................................................52 Novel concentrations: Sucrose .....................................................................53 Novel concentrations: Quinine.....................................................................53 Novel concentrations: Citric acid.................................................................54 Mixtures between NaCl and sucrose: 1.07 M NaCl + 0.421 M sucrose ......55 Mixtures between NaCl and Sucrose: 1.07 M NaCl + 0.077 M Sucrose.....55 Mixtures between NaCl and Sucrose: 0.376 M NaCl + 0.421 M Sucrose...56 Novel test compound: Water........................................................................56

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Retraining water as a comparison stimulus..................................................57 Negative control session...............................................................................58

Discussion............................................................................................................58

4 APPLICATION OF A NEW BEHAVIORAL PARADIGM TO ASSESS TASTE QUALITY GENERALIZATION...............................................................................76

Introduction.................................................................................................................76 Method........................................................................................................................76

Subjects................................................................................................................76 Apparatus.............................................................................................................77 Task Overview.....................................................................................................77 Stimuli .................................................................................................................77 Trial Structure......................................................................................................78 Training ...............................................................................................................78

Spout training ...............................................................................................79 Side training .................................................................................................79 Alternation....................................................................................................79 Discrimination training I-III.........................................................................80

Test Compounds..................................................................................................81 Data Analysis.......................................................................................................81

Results.........................................................................................................................82 Test Stimulus: Sodium Gluconate .......................................................................82

0.376 M sodium gluconate ...........................................................................82 0.668 M sodium gluconate ...........................................................................83

Test Stimulus: Denatonium .................................................................................83 0.131 mM denatonium .................................................................................83 0.360 mM denatonium .................................................................................84

Test Stimulus: Maltose ........................................................................................84 0.077 M maltose...........................................................................................84 0.148 M maltose...........................................................................................85

Test Stimulus: Potassium Chloride (KCl) ...........................................................86 0.376 M KCl.................................................................................................86 0.668 M KCl.................................................................................................86

Test Stimulus: Monosodium Glutamate ..............................................................87 0.077 M MSG...............................................................................................87 0.148 M MSG...............................................................................................87

Test Stimulus: Fructose .......................................................................................88 0.077 M fructose ..........................................................................................88 0.148 M fructose ..........................................................................................89

Performance of Water Group ..............................................................................89 Discussion...................................................................................................................91

Sodium Gluconate ...............................................................................................91 Denatonium .........................................................................................................92 Maltose ................................................................................................................93 Potassium Chloride..............................................................................................94 Monosodium Glutamate ......................................................................................95

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Fructose ...............................................................................................................97

5 GENERAL DISCUSSION .......................................................................................115

Introduction...............................................................................................................115 Delayed Match/Non-Match to Sample .....................................................................115 Novel Taste Quality Generalization .........................................................................118

Future Validation of the Procedure ...................................................................122 Potential Uses of the New Generalization Procedure........................................124

Neurobiological applications......................................................................124 Behavioral data support analytic processing rather than synthetic ............128

Perspectives ..............................................................................................................129

LIST OF REFERENCES.................................................................................................130

BIOGRAPHICAL SKETCH ...........................................................................................139

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LIST OF TABLES

Table page 3-1. Training compounds selected from Experiment I. ....................................................62

3-2. Experimental groups..................................................................................................62

3-3. Results from one-sample t-tests for a novel concentration of NaCl..........................62

3-4. Performance to training stimuli during novel NaCl testing.......................................62

3-5. Results from one-sample t-tests for a novel concentration of sucrose. .....................63

3-6. Performance to training stimuli during novel sucrose testing ...................................63

3-7. Results from one-sample t-tests for a novel concentration of quinine .......................63

3-8. Performance to training stimuli during novel quinine testing ...................................64

3-9. Results from one-sample t-tests for a novel concentration of citric acid ..................64

3-10. Performance to training stimuli during novel citric acid testing .............................64

3-11. Results from one-sample t-tests for 1.07 M NaCl + 0.421 M sucrose ....................65

3-12. Performance to training stimuli during high NaCl + high sucrose testing ..............65

3-13. Results from one-sample t-tests for 1.07 M NaCl + 0.077 M sucrose ....................65

3-14. Performance to training stimuli during high NaCl + low sucrose testing ...............66

3-15. Results from one-sample t-tests for 0.376 M NaCl + 0.421 M sucrose ..................66

3-16. Performance to training stimuli during low NaCl + high sucrose testing ...............67

3-17. Results from separate one-sample t-tests for water .................................................67

3-18. Performance to training stimuli during water testing ..............................................67

4-1. Overview of experimental groups .............................................................................99

4-2. Training schedule for N, S, Q, and C groups ............................................................99

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4-3. Training parameters for W group. ...........................................................................100

4-4. Results from one-sample t-tests for 0.376 M NaGluconate ....................................101

4-5. Results from one-sample t-tests for 0.668 M NaGluconate ....................................101

4-6. Performance to training stimuli during sodium gluconate testing...........................101

4-7. Results from one-sample t-tests for 0.131 mM denatonium ...................................102

4-8. Results from one-sample t-tests for 0.360 mM denatonium ...................................102

4-9. Performance to training stimuli during denatonium testing ....................................102

4-10. Results from one-sample t-tests for 0.077 M maltose ...........................................103

4-11. Results from one-sample t-tests for 0.148 M maltose ...........................................103

4-12. Performance to training stimuli during maltose testing.........................................103

4-13. Table of t-test statistics for 0.376 M KCl ..............................................................104

4-14. Table of t-test statistics for 0.668 M KCl ..............................................................104

4-15. Performance to training stimuli during KCl testing ..............................................104

4-16. Table of t-test statistics for 0.077 M MSG ............................................................105

4-17. Table of t-test statistics for 0.148 M MSG ............................................................105

4-18. Performance to training stimuli during MSG testing ............................................105

4-19. Table of t-test statistics for 0.077 M fructose........................................................106

4-20. Table of t-test statistics for 0.148 M fructose........................................................106

4-21. Performance to training stimuli during fructose testing ........................................106

4-22. Performance to training stimuli for W group during dt3-5 through dt3-8.............107

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LIST OF FIGURES

Figure page 2-1. Trial structure for DMTS/DNMTS (same/different) task. ........................................30

2-2. The mean overall performance to all trial types is shown.........................................31

2-3. Mean performance to same trials...............................................................................32

2-4. Mean overall performance to different trials .............................................................33

2-5. Mean performance on same versus different trials....................................................34

3-1. Mean (n=8) unconditioned licking to NaCl in a brief access test .............................68

3-2. Mean (n=8) unconditioned licking to sucrose in a brief access test..........................68

3-3. Mean (n=8) unconditioned licking to quinine in a brief access test..........................69

3-4. Mean (n=8) unconditioned licking to citric acid in a brief access test ......................69

3-5. An overview of the trial structure..............................................................................70

3-6. The generalization profile obtained when 0.847 M NaCl was used as a test compound .................................................................................................................71

3-7. The generalization profile obtained when 0.068 M sucrose was used as a test compound .................................................................................................................71

3-8. The generalization profile obtained when 0.546 mM quinine was used as a test compound .................................................................................................................72

3-9. The generalization profile obtained when 42.56 mM citric acid was used as a test compound .................................................................................................................72

3-10. The generalization profile obtained when 1.07 M NaCl + 0.421 M sucrose was used as a test stimulus ..............................................................................................73

3-11. The generalization profile obtained when 1.07 M NaCl + 0.077 M sucrose was used as a test stimulus. .............................................................................................73

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3-12. The generalization profile obtained when 0.376 M NaCl + 0.421 M sucrose was used as a test stimulus ..............................................................................................74

3-13. The generalization profile obtained when water was used as a test stimulus..........74

4-1. Profile for 0.376 M NaGluconate ............................................................................108

4-2. Profile for 0.668 M NaGluconate ............................................................................108

4-3. Profile for 0.131 mM denatonium ...........................................................................109

4-4. Profile for 0.360 mM denatonium ...........................................................................109

4-5. Profile for 0.077 M maltose.....................................................................................110

4-6. Profile for 0.148 M maltose.....................................................................................110

4-7. Profile for 0.376 M KCl ..........................................................................................111

4-8. Profile for 0.668 M KCl ..........................................................................................111

4-9. Profile for 0.077 M MSG ........................................................................................112

4-10. Profile for 0.148 M MSG ......................................................................................112

4-11. Profile for 0.077 M fructose ..................................................................................113

4-12. Profile for 0.148 M fructose ..................................................................................113

4-13. Summary of performance for W group during training with water and quinine...114

4-14. Diagram outlining two possibilities for the level (peripheral or central) at which convergence of taste signal processing leading to the same behavioral output might occur.............................................................................................................114

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

NEW BEHAVIORAL PARADIGMS TO STUDY TASTE-QUALITY GENERALIZATION AND DISCRIMINATION IN RATS

By

Connie Lynn Grobe

August 2006

Chair: Alan C. Spector Major Department: Psychology

Questions regarding the nature of perceivable taste qualities remain: Is taste quality

perception analytic or synthetic? Specifically, are tastes comprised of mixtures of a

discrete number of basic qualities? Currently, there are no appropriate animal models

that allow repeated assessments of the qualitative features of taste stimuli. Because it is

not possible to directly measure taste perception in animals, such sensory experiences

must be inferred on the basis of results from specially designed behavioral tasks.

Here, an operant-conditioning based behavioral paradigm was used to train rats to

taste two samples within a trial and then to make one response if presentations of the taste

stimuli (NaCl or sucrose) matched and another response if they did not match. Rats

performed similarly on matching and non-matching trials. Overall performance reached

an asymptote at ~74%. This approach could provide a means of testing discrimination

and generalization as well as exploring the temporal capacities of short term memory in

the taste system.

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Another study used operant techniques to train four groups of rats to distinguish the

taste quality of a single (standard) compound representing one of the putative four basic

tastes (“salty,” “sweet,” “sour,” “bitter”) from compounds representing the three other

taste qualities (comparisons). Prototypical stimuli were used to represent basic tastes

(NaCl, sucrose, citric acid, quinine). This task was then used to quantify how animals in

each group generalized their responses when presented with novel taste stimuli, providing

a way to assess how NaCl-like, sucrose-like, citric acid-like , and quinine-like the quality

of the solution was. Stimulus control of training compounds was maintained at high

levels, and behavioral responses to test stimuli generalized in predictable ways, providing

a non-invasive method for repeatedly assessing taste quality in the same animals.

Interestingly, the profile of monosodium glutamate is both NaCl-like and sucrose-like.

Overall, results suggest that taste processing is analytic.

Additionally, these paradigms could provide a functional context to interpret the

outcomes of anatomical, pharmacological, and genetic manipulations of the gustatory

system. They are also compatible with existing techniques that are crucial for linking

neural activity with behavior, which is essential for understanding gustatory processing.

xiv

CHAPTER 1 LITERATURE REVIEW

Introduction

Many questions remain concerning the organization of the gustatory system and the

neural mechanisms underlying taste function. How are tastes detected in the mouth and

appropriate signals sent to the brain? Specifically, how are the relevant features of a

chemical stimulus coded by the nervous system? What portions of the gustatory pathway

are necessary for the maintenance of particular functions, like taste intensity

discrimination or taste quality detection and/or discrimination?

Before one can approach these questions, it is important to resolve fundamental

concepts concerning the perceptual characteristics of taste stimuli in the animal models

chosen to study issues pertaining to taste. For example, it is not fully known whether

rats, a commonly used animal model, perceive taste stimuli as categorical or falling along

a continuum of possible qualities. These two possibilities represent theoretically

opposing viewpoints of how gustatory processing occurs: The analytic view and the

synthetic view, respectively. Erickson (1968) stated that color vision is a synthetic

system whereas audition is an analytic system. The difference being that the synthetic

system appears to involve the same set of neurons and the analytic system appears to

involve different sets (Erickson, 1968). Erickson (1968) further pointed out that this key

difference might be at the heart of the debate about whether signals regarding taste

quality are processed through devoted labeled-lines or in an across-fiber pattern.

1

2

Therefore, the purpose of the current experiments was for the development and

application of psychophysical tasks that may yield an answer to the question of whether

there might be a few taste primaries or an indefinite number of them. At issue is whether

a few discrete categories of taste quality are sufficient to encompass all taste experiences

in our animal model (Sprague-Dawley rat) or whether there is a continuum of possible

taste perceptions.

The aim of the first experiment was to design a versatile task that would provide

insight into the ability of rats to discriminate differences between 2 stimuli, whether of

the same compound (intensity discrimination) or between different compounds (quality

discrimination). A second goal of the first experiment was to determine if the same

protocol could be used to investigate the temporal properties of short-term memory for

taste solutions.

The overall goal of the second and third experiments was to examine whether rats

can reliably discriminate taste compounds thought to fall into different qualitative

perceptual classes, and whether they will reliably categorize novel stimuli as possessing

characteristics similar to the training stimuli. The existence of such a paradigm would

offer researchers the opportunity to observe the effects that manipulations made to the

gustatory system have on performance in a behavioral task that was specifically aimed at

measuring taste quality identification. In addition, the task could be used to gain insight

into the gustatory perceptual experience of the animal generated by novel taste

compounds.

In order to conduct these experiments, it was assumed that rats treat taste stimuli as

being composed of (at minimum) the same 4 basic qualitative classes that humans report

3

perceptually: “salty,” “sweet,” “sour,” and “bitter.” Nowlis, Frank, and Pfaffman (1980)

found evidence supporting this assumption using a behavioral approach. Moreover, work

examining the peripheral transduction mechanisms in rodents to prototypical compounds,

those identified by humans as representing the four basic tastes, suggest that animals may

have receptors devoted to the four taste qualities (Chandrashekar et al., 2000; Gilbertson

& Boughter, 2003; Scott & Giza, 1990; Zhang et al., 2003; Zhao et al., 2003). A

controversial fifth taste quality, referred to as “umami” (Yamaguchi, 1991), has been

identified in the literature and is described as the taste quality associated with a savory or

delicious sensation in humans. Support for the existence of “umami” taste in rodents is

mixed; some sources indicate that the taste of monosodium glutamate (MSG) (the

prototypical compound for the “umami” taste) generalizes to sucrose and NaCl, “sweet”

and “salty,” respectively (Heyer, Taylor-Burds, Tran, & Delay, 2003), but other data

suggest that rats can nonetheless discriminate MSG from sucrose even when the

contribution of the sodium ion is reduced (Heyer, Taylor-Burds, Mitzelfelt & Delay,

2004). The strategy of using representative compounds from the classic 4 basic tastes

does not detract from the possibility that there could be more taste qualities; in fact, it

could even provide support for such a notion.

How does one measure taste function in non-human animals? To appreciate this, it

is important to understand each of the identified taste domains and how they may be

measured in animals (including humans).

Domains of Taste

The functional aspects of taste can be classified into at least three broad domains:

sensory-discriminative, affective, and physiological (see Spector, 2003b, for review).

The discriminative domain deals with identification of a stimulus, and the affective

4

domain refers to the hedonic aspects of a compound, whereas the physiological domain

consists of the physiological reflexes that a stimulus elicits. Each of the three domains

describes a different facet of taste function, and possibly represents different aspects

related to ingestive behavior.

Sensory Discriminative Domain

Briefly, sensory-discriminative function, the identification of a stimulus, can be

dissociated from the affective/hedonic domain by use of several different operant and

classical conditioning procedures aimed at measuring both detection thresholds and

quality discriminations in animals (Spector, 2003b). These procedures do not rely on the

hedonic aspects of the taste solution to drive responses because the taste serves as a

signal for other reinforcing or punishing events. Consequently, the inherent motivational

properties of the stimulus are irrelevant in the animal’s identification of the stimulus.

Affective Domain

Briefly, the affective domain refers to the hedonic attributes of taste stimuli (i.e.,

the palatability of a compound). The most commonly used methods to describe the

affective responses of animals regarding taste compounds include operant tasks aimed at

assessing appetitive/avoidance behavior and those aimed at measuring consummatory

responses, which are the reflex-like behavior stimulated by a tastant contacting its

sensory receptors (Spector, 2003a). The two-bottle intake test, a variation of it termed

the brief-access task, and various operant response measurements (e.g., progressive ratio

breakpoints, rates of responding) have been used to quantify the reinforcement efficacy

of a taste stimulus (Clark & Bernstein, 2006; Guttman, 1953; Hodos, 1961; Reilly, 1999;

Sclafani, 2006; Sclafani & Ackroff, 2003; Starr & Rowland, 2006). Consummatory

responses, on the other hand, have been measured through use of the taste reactivity

5

paradigm (Grill & Berridge, 1985; Grill et al., 1987), which is a procedure that involves

the quantification of oromotor reflexes elicited by taste stimuli infused directly into the

oral cavity.

Physiological Domain

The physiological domain, often referred to as cephalic phase responses (e.g.,

Berthoud, et al., 1981; Grill, Berridge, & Ganster, 1984; Mattes, 1997; Pavlov, 1902;

Powley, 1977; Spector 2000), consists mainly of salivation and other predigestive

responses that are elicited by taste stimuli. The increased salivation to food/fluids and

other physiological reflexes related to contact with taste receptors are proposed to be

adaptive as they likely contribute both to digestion/assimilation of food and protection of

the oral epithelium (e.g., salivation) (Spector, 2000).

Taste Quality

The quality of a taste falls under the rubric of sensory-discriminative function.

According to Bartoshuk (1978), Aristotle was first to suggest that the taste of all foods

and fluids was a combination of only a few discrete perceptual qualities. He suggested

that there were 7 basic tastes: sweet, bitter, sour, salty, astringent, pungent, and harsh

(Bartoshuk, 1978). It was not until 1927, however, that Hans Henning formally asserted

that the four basic tastes (salty, sweet, sour, and bitter) can be conceived as representing

the corners of a tetrahedron with combinations of two qualities along the edges, and

combinations of three on the face (Bartoshuk, 1978). This idea has been commonly

accepted despite occasional evidence suggesting additional qualities exist; the most

notable is the claim of a fifth quality, the umami taste which is said to arise from

glutamate salts and is described as “savory” by humans (Galindo-Cuspinera, & Breslin,

2006; Schiffman, 2000; Yamaguchi, 1991). Support for the existence of “umami” taste

6

in rodents, however, is mixed with some sources indicating that the taste of MSG (the

prototypical compound for the “umami” taste) generalizes to sucrose and NaCl, “sweet”

and “salty”, respectively (Heyer, Taylor-Burds, Tran, & Delay, 2003), but other data

suggest that rodents can nonetheless discriminate MSG from sucrose (Ninomiya &

Funakoshi, 1989a) even when the contribution of the sodium ion is reduced (Heyer,

Taylor-Burds, Mitzelfelt & Delay, 2004).

Animal Models Used to Study Taste Quality

Many researchers assume that the same basic taste qualities that are identified by

humans also extend to other animals. Support for this statement is based on the fact that

animals respond to prototypical compounds putatively representing the 4 basic tastes as

would be expected. That is, animals ingest and avoid taste solutions in a manner that

appears similar to human descriptions of pleasantness and aversion. Suppression of

intake of a solution, however, does not necessarily indicate qualitative similarity to other

avoided compounds in sensory-discriminative terms. In other words, when an animal

avoids two compounds equally, there is no way of knowing whether the animal also

perceives them as possessing the same taste quality. For example, an animal might avoid

drinking very concentrated NaCl to the same extent as it avoids drinking a quinine

solution, but data in animals and humans suggest that the two compounds are

qualitatively dissimilar. Accordingly, other methods are necessary for inferences on taste

quality in nonhuman animals to be established. Indeed, that is a primary theme of this

dissertation.

Discrimination Tasks

Operant discrimination procedures have been useful for determining whether two

compounds are perceptually distinct. If an animal cannot discriminate between two

7

different solutions, then it is plausible that both give rise to a perceptually identical

experience (Spector, 2003a). Alternatively, if an animal can reliably discriminate two

compounds from one another, then there must be some identifiable cue (e.g., differential

neural signals generated by the two stimuli) that can be used by the animal to guide its

behavior accordingly. It is critical in these experimental designs that intensity cues be

minimized so that discriminative responding comes under the explicit control of taste

quality. For example, it is known that a rat can discriminate a relatively lower

concentration of NaCl from a higher concentration of NaCl (Colbert, Garcea, & Spector,

2004), but this does not necessarily imply that the taste quality of the sensation between

strong and weak NaCl solutions is different. Therefore, when conducting studies of

quality discrimination, it is important to use a range of concentrations of the respective

training stimuli so as to render intensity a relatively irrelevant cue (Spector, 2003a;

Spector & Grill, 1992; Spector et al., 1996, 1997; St. John et al., 1995, 1997, 1998).

Spector (2003b) has identified important assumptions associated with this strategy:

the selected range of concentrations must have overlapping intensities and the relevant

taste quality of each compound delivered is assumed to remain constant across the

concentration range tested. If the first assumption were not met and two compounds

were of the same quality but all of the concentrations of one compound were perceived as

weaker than all of the concentrations of the other compound, then the animals would

likely be able to discriminate between the two compounds based on the differences in

intensity. One could identify the basis for such a discrimination, for example, if rats

performed well when lower concentrations of the “weak” compound were added to the

test stimulus array, but performed poorly to additional low concentrations of the “strong”

8

compound. Conversely, the opposite would be true. That is, if greater concentrations

were included in the discrimination task for both compounds, then as the ‘weaker-tasting’

one became more salient, performance would decrease because the rats would incorrectly

respond as if it were the “stronger-tasting” compound. In contrast, performance would be

expected to improve for the “stronger-tasting” compound because a greater concentration

would only serve to distinguish it more from the “weaker-tasting” compound.

A typical stimulus discrimination paradigm involves training an animal to make

one response after tasting one compound and to make a different response after tasting a

different compound. As stated earlier, it is best when the concentration of each

compound is varied to render intensity an irrelevant cue, which should make taste quality

the salient signal. Typically, the animal is water deprived (< 24 h) to encourage

sampling, and correct responses are reinforced with brief access to water.

At least two studies have been published suggesting the occurrence of perceptual

identity as evidenced by rats being unable to discriminate between two different taste

stimuli. In one study, Spector and Kopka (2002) found that rats could not discriminate

quinine hydrochloride (a prototypical “bitter” compound) from denatonium benzoate (a

substance that rats also avoid consuming and that humans report as “bitter”). The same

rats were able, however, to discriminate quinine from KCl (judged to be a complex

“bitter-sour-salt” by humans), and NaCl from KCl. Interestingly, the rats appeared to be

able to substitute denatonium for quinine after being trained to discriminate quinine from

KCl, signifying the two compounds were similar. These results support the claim that

quinine and denatonium likely generate a unitary qualitative percept in rats.

9

The other study which demonstrates perceptual identity between taste compounds

in rodent models is provided by Spector, Guagliardo, and St. John (1996). In that study,

amiloride, an epithelial sodium channel blocker, was used to remove the specific NaCl

taste cues necessary to discriminate NaCl and KCl. With application of 100 µM

amiloride, the remaining gustatory cues were not sufficient for rats to distinguish between

the two salts and they performed at chance levels in a discrimination task. Moreover, an

analysis of the errors in responding showed that mistakes primarily occurred on NaCl +

amiloride trials. This observation suggests that the rats responded as if NaCl + amiloride

was perceptually similar to KCl. Adding support to the hypothesis that amiloride

changes the perceptual taste qualities of NaCl to be more similar to KCl are data from

Hill, Formaker, & White (1990), showing that when NaCl, adulterated with amiloride,

was used as a conditioned stimulus (see below for definition) in a conditioned taste

aversion paradigm, rats generalized their aversion to non-sodium salts (specifically the

halogenated salts tested) including KCl.

Because there are many factors which might potentially serve as cues in a

discrimination task, results from studies using this approach are more compelling when

rats cannot discriminate between two compounds, provided that learning and intensity

effects can be rule-out. For example, the rise and decay time of the signal may differ

between two compounds that share a similar quality. Such temporal cues alone may be

sufficient to allow an animal to distinguish between the stimuli in a discrimination task.

Another possible signal, as mentioned earlier, may be the relative intensity of the tastants

selected. If the experimenter does not know the relevant concentration ranges to include

10

in the test stimulus panel and includes some that do not overlap in intensity, the animal

may be able to use those cues to guide performance.

Generalization Tasks

Guttman & Kalish, (1956) are credited with associating the concept of

discriminability with generalization gradients. A typical study in which a generalization

gradient is obtained consists of a scenario in which appropriate responses are reinforced,

when a specific training stimulus is present. Once stimulus-contingent responding is

established, a generalization test is presented during which no responses are reinforced.

The stimulus is varied on some physical dimension and the rate of responding is

recorded. These experiments generally produce response gradients that decrease as a

function of the difference between the training and test stimuli (Guttman & Kalish,

1956).

This concept has been adapted for use to study similarities between taste

compounds in the conditioned taste aversion (CTA) paradigm. Tapper & Halpern (1968)

innovatively applied the CTA procedure to make inferences on how animals classify taste

stimuli. They exposed experimental animals to radiation (2.5 min exposure of 80 r/min)

20 minutes before a scheduled session in which the rats normally consumed their daily

supply of water; after the radiation, however, a novel taste compound (the conditioned

stimulus; CS) was presented in place of water. This procedure resulted in a robust

avoidance to the CS, evidenced by the fact that after the pairing occurred, rats consumed

less of the CS upon subsequent re-exposure to the tastant. In the procedure, Tapper &

Halpern (1968) assumed: “ i) the [CS] becomes the quality standard against which the

animals compare other solutions; ii) the test solutions will be aversive, that is, associated

with the [CS], as long as their taste to the animal is qualitatively similar to the [CS], iii)

11

the magnitude of rejection indicates the degree of similarity in taste between the test

solution and the [CS].” By means of multiple cross-generalization pairings, they could

construct functions of aversion with which to compare compounds. Their rationale for

inferring that two compounds were of the same quality was based on the assumption that

similar generalization profiles would emerge for the respective stimuli (Tapper &

Halpern, 1968).

This approach was comprehensively extended later by Nowlis, Pfaffmann, and

Frank (1980). They conditioned aversions to a large number of compounds in hamsters

and rats and then measured intake to the four prototypical taste compounds, NaCl,

sucrose, HCl, and quinine, which served as test stimuli. As such, like Tapper and

Halpern (1968), it was assumed that the response profiles obtained related to the

qualitative properties of the prototypical taste compounds, thus allowing them to make

conclusions about the degree to which, a compound was sucrose-like, NaCl-like, HCl-

like, and quinine-like. With some exceptions, these data became the basis for many to

consider that rodents likely share the same perceptual taste experience as humans do.

Later, the same technique was applied to the study of mixtures (Frank, Formaker, &

Hettinger, 2003; Smith & Theodore, 1984), and researchers showed that rats could

identify the CS in a mixture in a concentration-dependent manner.

There are some limitations associated with this approach, which include effects of

stimulus familiarity, extinction, concentration, and stimulus hedonics. First, the CTA

procedure is only successful if the CS is novel to the animal, thus limiting the choice of

CSs to unfamiliar compounds. Additionally, typically only one CS is used per

experimental group, which means that each concentration of the compound included in

12

the study also requires a devoted set of animals. It follows that this substantially

increases the number of animals required for a comprehensive study of taste quality

generalization. In addition, an inclusive design would require the researcher to also test

for cross-generalization of each of the concentrations selected, and therefore, it is

necessary to use a large number of animals.

A second limitation of the CTA approach relates to the strength of conditioning.

Testing occurs in extinction, meaning that the animal does not experience the

unconditioned-stimulus-induced consequences previously paired with intake, and so the

effects of learning can diminish over time. Often the strength of the conditioning is

reassessed periodically, and the avoidance to the CS indeed lessens. This complicates

interpretation of results and limits the number of potential test stimuli (Nowlis, Frank,

and Pfaffmann, 1980).

A third limitation of the CTA procedure is associated with stimulus intensity

dynamism, which must be carefully considered in the interpretation of generalization

profiles. Intensity dynamism refers to the observation that conditioning to a CS will

generalize similarly to all higher concentrations of that compound, rather than as an

inverted-V gradient, peaking at the CS, as might be expected (see Guttman & Kalish,

1968; Hull, 1949). In other words, there is a steep gradient beginning at some

concentration below the CS, but the behavioral profile obtained reveals that increasing

the intensity of the compound results in a greater or at least similar conditioned response.

For example, if conditioning occurred to 0.05 M NaCl it would generalize to higher

concentrations, including 0.5 M NaCl but conditioning to 0.5 M NaCl might not

13

generalize to the lower concentration. That is, cross-generalization does not necessarily

occur between high and low concentrations of the same compound.

Finally, the inherent affective properties of a taste stimulus can influence the

interpretation of CTA results. If a solution is unconditionally aversive, an animal will not

readily consume much, if any, of the compound. This could compromise both the

effectiveness of the pairing and the ability to measure behavior (i.e., floor effect).

Therefore, some compounds, which are preferably ingested, better lend themselves to a

procedure like CTA. In fact, this is a key problem facing those who study taste

classification. Some compounds, especially “bitter” and “sour” solutions are not readily

consumed by rats and attempts to incorporate them into CTA designs can result in

problems associated with floor effects. That is, it is difficult to quantify changes in intake

for an experimental versus control group when intakes of the taste stimuli for both groups

are low.

Ideal Psychophysical Task

An ideal psychophysical task would have the following characteristics: it would 1)

be compatible with assessing discriminability and generalization within the same

animals, 2) allow for repeated test (probe) trials within the same animals, 3) yield clear,

interpretable results, 4) be highly replicable within and between animals (i.e., have little

variance in responses), and 5) circumvent the potential confounding of stimulus intensity.

Importance of Psychophysical Analysis in Animal Models

Advances in our understanding of taste function can be optimally achieved through

a combination of experimental approaches. Arguably, it is the innovation of rigorous

behavioral techniques that facilitates the confirmation or refutation of predictions about

gustatory function that are based on more reduced levels of analysis (i.e.,

14

electrophysiology, molecular biology, etc.). In addition, carefully executed

psychophysical experiments produce results that generate new hypotheses regarding how

the gustatory system is organized. Psychophysical tasks, though time consuming, provide

invaluable data on the sensory capacities of both humans and non-human animals.

Psychophysical analysis of non-verbal subjects is challenging but can be achieved

through the use of operant and classical conditioning procedures.

Chief among the benefits of using a psychophysical approach with non-human

animals is that invasive procedures, in which the gustatory system can be manipulated,

are possible. Taste function is complex; therefore, the design and application of a variety

of psychophysical measures is necessary to obtain a comprehensive assessment of

function.

The failure to develop appropriate tasks can lead to misguided conclusions. For

example, the two-bottle preference test has been, and continues to be, the most common

behavioral measure of taste responsiveness in animals. This measure, however, only

assesses the motivational characteristics of a taste stimulus. Moreover, postingestive

events can influence the behavior. Certainly, the use of this procedure masked for many

years the understanding of the contribution of gustatory nerves in the processing of taste

input (e.g., Pfaffmann, 1952; Richter, 1939; see Spector, 2003a for discussion).

Argument for the Development of Psychophysical Tasks

The use of appropriate behavioral procedures directed at measuring taste function

in animal models has been indispensable in the analysis of the neural organization of the

gustatory system (e.g., Flynn, Grill, Schulkin, & Norgren, 1991; Flynn, Grill, Schwartz,

& Norgren, 1991; Kopka & Spector, 2001; Kopka, Geran, & Spector, 2000; Slotnick,

Sheelar, & Rentmeister-Bryant, 1991; Spector, Schwartz, & Grill, 1990; Spector & Grill,

15

1992; St. John, Markison, & Spector, 1995; Shimura, Grigson, & Norgren, 1997). It

follows, therefore, that the development of new behavioral paradigms that are aimed at

yet unexplored aspects of gustatory function promise to lead to further important

discoveries.

CHAPTER 2 RATS CAN LEARN A DELAYED MATCH/DELAYED NON MATCH TO SAMPLE

TASK USING ONLY TASTE STIMULI

Background

A major goal of this project was to develop a novel behavioral task that would

address whether rats can accurately assess when two samples, tasted in sequence, differ

or whether they are the same. The paradigm combines two procedures, a match to sample

and non-match to sample task. Potentially, such a task could be used to assess the degree

of qualitative discriminability between two taste stimuli. Another possible benefit of this

paradigm is that once the animal has sufficient training in the contingencies of the task,

various compounds or concentrations could be added for testing.

Such a procedure was introduced by Konorski, in 1959, who apparently suggested

it could be used with olfactory or auditory stimuli because they both were sensory

modalities which were incompatible with simultaneous delivery of test stimuli (in Shimp

& Moffit, 1977). The taste system is also incompatible with simultaneous delivery of

two comparison stimuli. This inherent delay between samples, as a consequence of the

rat sequentially sampling two separate stimuli within a single trial, provides the

opportunity to allow one to assess the properties of short-term memory processes

involving taste stimuli – a phenomenon that has not been previously approached. To

date, only long-term memory has been studied in the taste system via conditioned taste

aversion (CTA), which is not optimally designed for multiple trial analyses.

16

17

Method

Animals

Nine adult male Sprague-Dawley rats weighing 555 +/- 20 g at the start of training

were used as subjects. Two of the animals were euthanized within the first two weeks of

training: one demonstrated a response bias within the first few discrimination training

(see below) sessions and was removed from the study to allow for an increase in session

length for the other rats, and the other rat removed his surgically implanted intraoral

cannulae (see below) and thus required immediate euthanization. Therefore, seven rats

served as subjects in the experiment. The rats came from Charles River (Wilmington,

SC) and were maintained on Purina (5001) laboratory rat chow ad libitum (except during

experimental test sessions) in a vivarium that had the lights and temperature

automatically controlled. Lights were programmed to be on a 12:12 hour light:dark cycle

with lights on at 0700 h, but due to an undiscovered timer malfunction the animals were

in constant light during the first 110 days of training and testing. A contingency was in

place so that rats would receive supplemental water if body weight decreased to 85% of

the ad libitum weight calculated each week; this contingency was only necessary for one

of the animals on three separate occasions. All procedures were approved by the

University of Florida Institutional Animal Care and Use Committee.

Apparatus

In the present experiment a gustometer, which is a specially designed stimulus

delivery and response measurement device, was modified from an earlier version

described in detail elsewhere (Spector, Andrews-Labenski, and Letterio, 1990), and was

used in training and testing. Briefly, the test chamber had two response spouts which

flanked either side of a central slot through which the animal could access a sample spout

18

controlled by a stepping motor. There were two cue lights positioned 4.2 cm above the

response spouts which could be activated at the appropriate time in the trial. The

response spouts served as the source for water reinforcement when the animal performed

the appropriate behavior (licked the appropriate response spout after tasting a specific

combination of solutions). Fluid stimuli and the water reinforcer were contained in 11

pressurized reservoirs connected to solenoid valves to regulate the amount of fluid

deposited into the spout. Background masking noise was present during each session, and

the test cage was enclosed in a sound-attenuating chamber housed within a dimly lit room

to minimize possible extraneous cues related to stimulus delivery. A Polyethylene (PE)-

100 tube, covered by a spring, was connected via a swivel to a solenoid valve which was,

in turn, connected to a water reservoir. This tube was inserted through a small hole in the

ceiling of the sound attenuation chamber where it was connected to an intraoral cannula

implanted in the rat. This was used to provide water rinses between stimuli as described

below.

Stimuli

All solutions were prepared daily with purified water (Elix 10; Millipore, Billerica,

MA) and reagent grade chemicals, and were presented at room temperature. Initially, we

attempted to use 0.1 M NaCl and 0.5 M NaCl as training stimuli, but the overall

performance of the rats remained at chance. Consequently, the rats never progressed out

of the training phase and it was deemed necessary to change the training stimuli after two

months (35 sessions). Two solutions were used in the second phase of training: 0.1 M

NaCl and 0.1 M sucrose. We reasoned that a discrimination between two compounds

that are of different qualities might be easier to learn. Although we know that rats can

discriminate NaCl on the basis of concentration (Colbert, Garcea, & Spector, 2004), we

19

believed it might reduce the acquisition time if the two stimuli differed chemically and

were putatively members of different perceptually qualitative classes so as to render them

more distinct from each other. The initial training results with 0.1 M and 0.5 M NaCl

will be ignored for the remainder of this chapter.

Taste stimuli were prepared fresh daily from reagent grade chemicals (NaCl and

sucrose: Fisher Scientific, Atlanta) and purified water (Elix 10; Millipore, Billerica, MA);

they were presented at room temperature

Surgery

Rats were anesthetized with a mixture of 125 mg/kg body wt ketamine, 5 mg/kg

body WT xylazine (injection given intramuscularly) and two intraoral (IO) cannulae were

surgically implanted so that water could be infused directly into the mouth. The rats were

placed in a surgical head holder and an incision was made along the midline of the scalp.

The fascia was cleared and four small machine screws were inserted into holes drilled

into the skull. The rat was then removed from the head holder and placed in a supine

position. The blunt end of a 19g needle shaft was attached to the opposite end of heat-

flared PE-100 tubing. A small Teflon washer was slipped onto the cannulae and placed

against the heat-flared end. The beveled end of the needle was then placed between the

cheek and gum, anterolateral to the first upper molar on either side of the mouth, and the

needle was pushed through the tissue in a trajectory that passed beneath the zygomatic

arch close to the skull until the Teflon washer and heat-flared end of PE tubing rested

against the roof of the mouth lateral to the maxillary molars. The needle was separated

from the PE tubing, the excess was trimmed, and a blunt piece (~10 mm) of 19 gauge

stainless steel tubing, with a bead of solder attached, was securely fitted into the PE

tubing. Both cannulae were placed in the same manner. Once in place, dental acrylic

20

was added so that it created a mound over the screws and secured the cannulae (PE

tubing + 19 G stainless steel tubing with bead of solder for extra anchoring) firmly in

place. All rats were injected with a prophylactic dose of penicillin G Procaine suspension

(30,000 units, s.c.) and the analgesic ketorolac tromethamine (2 mg/kg body mass, s.c.)

immediately before surgery and on the following 3 days. At least three months passed

before animals began training.

The intraoral cannulae were cleaned out every day by passing a smaller diameter

(polyethylene-10) tubing through the cannulae until it exited into the oral cavity. The

intra-oral cannulae were implanted so that water could be infused into the oral cavity

between taste samples in order to reduce the potential for adaptation to occur to the first

stimulus in the pair.

Training and Testing Phases

Training and testing sessions took place Monday through Friday of each week

during the regularly scheduled lights-on phase. Rats were water restricted beginning

Sunday night and received all daily fluid within the session. At the end of the last session

on Friday, water bottles were returned to the home cages until the following Sunday.

Spout training

The rats had access to only one spout (either the sample spout, the left response

spout, or the right response spout) and each spout was connected to a reservoir that

contained water. The purpose of this phase was to train the rats to approach and gain

familiarity with getting fluid from each of the spouts. Eventually, the sample spout

would contain a taste stimulus and only the response spouts would contain water. The

rats had to learn to lick from the sample spout and then select one of the response spouts

21

by licking it. There were a total of 6 days of spout training so that the rats experienced

two sessions with each spout.

Side training

Only one trial type was presented within a given session during side training. If the

rats were trained with same trials then, the trials within the session alternated between 1)

0.1 M NaCl followed by 0.1 M NaCl, and 2) 0.1 M sucrose followed by 0.1 M sucrose.

During the next session, the rats received only different trials in which the first sample

differed from the second (0.1 M NaCl followed by 0.1 M sucrose or 0.1 M sucrose

followed by 0.1 M NaCl). After sampling, rats had 180 s (limited hold period) during

which they were required to respond. If they made the correct response, they had limited

access to water (20 licks or 10 s, whichever occurred first). Side training lasted a total of

4 days.

Alternation

During alternation training, the rats started out with a single trial type (either same

or different). Upon completion of a set criterion of correct responses, the program

automatically switched to delivery of the opposite trial type. The correct responses did

not have to be consecutive. The limited hold was changed from 180 s to 15 s.

Additionally, if the rat failed to initiate the second sample within 15 s of the spout

becoming available, the trial terminated and punishment (timeout) was delivered. During

the decision phase, if a rat failed to make any response, or made the incorrect response, a

10-s timeout was presented.

Discrimination training I-II

Trials were delivered in a block with a random pattern selected by the computer

program. Therefore, the rats had no indication from the prior trial, which solutions would

22

be offered on the current trial. The block size was 8; consequently, every trial was

repeated twice within the block before a new block of randomized presentations

occurred. Reinforcement licks were changed from 20 to 25, and timeout increased to 20

s during this phase. Session length was increased to 50 min.

Trial structure (final parameters)

During the 65-min test session, each rat was allowed to complete as many trials as

possible within the time allotted. Each trial (see Figure 2-1) consisted of six different

phases: sample 1, inter-stimulus interval, sample 2, decision, consequence, and inter-trial

interval. The sample phase began when the rat made contact with the dry sample spout

and initiated licking. The rat was required to lick the dry drinking spout twice within 250

ms, upon which the shaft of the drinking spout was filled with the stimulus and each

subsequent lick resulted in an additional deposit of 5 µl into the fluid column. The rat was

allowed 3 s access to the stimulus or five additional licks, whichever came first. A 6-s

interstimulus delay followed the first sample during which 30µl of fluid was infused into

the mouth through the left intraoral cannula. Additionally, during this inter-stimulus

interval, the sample spout was rotated over a funnel, rinsed with purified water, and air-

dried in preparation for the second sample, which followed the same initiation

requirements as stated above. If the rat failed to initiate the second sample within 2 s of

the spout becoming available, the spout rotated away from the access port and the trial

moved immediately into the consequence phase during which the rat received a timeout.

In a trial in which the rat properly initiated both samples, the houselights in the

gustometer were turned off and the cue lights above each lever were illuminated,

signaling the start of the decision phase. Concurrently, the sample spout was rotated out

of position so that it could no longer be accessed. During the decision phase, rats were

23

allowed a prescribed period of time (5 s during the testing phase, referred to as the

limited hold) to respond by licking the correct response spout. If the correct spout was

licked, the houselights were reactivated and the rat had the opportunity to receive 10 s or

40 licks access to water, whichever came first. If the incorrect spout was selected or no

response was made within the limited hold period, the cue lights were extinguished and

the rat was given a 40-s timeout, during which fluid was unavailable. The trial terminated

with a 48-s intertrial interval, during which all lights were off until the next trial began.

Testing

Testing began 46 sessions after the very first spout training day. The parameters

were the same as those used at the end of Discrimination Training II.

Adjustments to Testing Parameters

Initially, the trial parameters were set during the Discrimination Training II phase.

There were, however, some adjustments made to the trial parameters, during the 21-week

testing phase, in an attempt to increase performance in the rats. In the fifth week, the

magnitude of the reinforcer was increased from 25 licks to 40 licks. In the sixth week,

the timeout was increased from 20 s to 40 s. In the twelfth week, the inter-trial interval

was increased from 10 s to 48 s. In the sixteenth week, the session length was increased

from 60 min to 65 min. During the seventeenth week, session length was increased from

65 min to 70 min, but was reduced again because the rats stopped responding near the

end of the session. Finally, beginning in the nineteenth week, the intraoral rinses were

discontinued because of problems with intraoral cannulae coming loose. Consequently,

two of the rats had to be euthanized because of this problem during the nineteenth and

twentieth weeks of testing.

24

Statistical Analyses

For data analyses, repeated measures analysis of variance (ANOVA), one-sample t-

tests, and paired t-tests were used; Bonferroni adjustment was applied where appropriate.

The mean weekly performance score for each trial type for every animal during the first

eighteen weeks of testing were used in the analyses. This time period was chosen for

analysis because it spanned the testing period in which all rats had intraoral rinses and it

also included the weeks for which data were available from all subjects. For each of the

weeks tested, every animal had six performance scores: the two same trial types, the two

different trial types, and an integrated score for both same and different trials.

Results

Overall Performance

Results are shown in Figures 2-2 through 2-5. As shown in Figure 2-2, the mean

performance on the task did not exceed 75%.

A repeated measures ANOVA of overall performance across testing weeks

revealed that the rats performed significantly better over the 18 testing weeks analyzed

(F(17,102) = 10.070, p < 0.001). Multiple one-sample t-test comparisons (null

hypothesis is 50%) of performance during each week, revealed that performance was

better than chance levels initially (t(6) = 2.680, p = 0.037), but a Bonferroni adjustment

eliminated the statistical significance of the comparison (p = 0.658). Beginning at the

third week of testing, however, both the p-value and the Bonferroni adjusted p-value

revealed significant differences (all ps < 0.035), which remained so for the duration of

testing (all Bonferroni adjusted ps < 0.03). Of note is a drop in performance at week 14,

which was attenuated by recalibration of the apparatus to deliver the appropriate volume

per lick.

25

Performance on Same Trials

A graph representing performance on same trials when the trial was NaCl-NaCl or

sucrose-sucrose is shown in Figure 2-3.

A repeated measures ANOVA was used to analyze performance to both types of

same trials. There was a main effect of time (F(17, 102) = 4.52, p < 0.001), but no main

effect of trial type and no interaction was present (both p-values > 0.2). Therefore, these

data could be used to support the claim that rats may have learned to respond to the trial

type regardless of what the chemical compound was.

Performance on Different Trials

Figure 2-4 depicts the performance to different trial types. A repeated measures

ANOVA was used to compare performance on NaCl-sucrose trials to sucrose-NaCl trials.

There was a main effect of time (F(17,102) = 3.290, p < 0.001), but no evidence of a

main effect of trial type (p > 0.34) nor an interaction (p > 0.80). Therefore, when both

compounds are presented within a trial, it does not appear to matter whether the first

sample is NaCl or sucrose.

Performance on Same Trials versus Different Trials

Figure 2-5 shows the performance of same trials collapsed across compounds

versus performance of different trials also combined together. It would appear (Figure 2-

5) that rats perform better on different trials, especially initially, but a statistical analysis

of the performance between same and different trials does not support such a claim. A

repeated measures ANOVA comparing the 18 weeks of testing revealed a main effect of

time (F(17,102) = 3.303, p < 0.001), but no effect of trial type and no interaction was

present (both p-values > 0.50). Additionally, a paired t-test examining the first week of

26

testing did not provide evidence that performance on the two trial types differed (t(6) = -

2.153, p = 0.084).

Discussion

Results from the present study indicate that rats are able to reliably respond to two

taste stimuli, separated by a 6-s delay, and sampled within a single trial, on the basis of

whether they are the same or different. This is the first known report of its kind involving

the taste modality. Below, the performance of the rats in this taste behavioral paradigm is

placed in context with other sensory modalities.

Steckler, Drinkenburg, Sahgal, and Aggleton (1998) published a series of three

articles outlining the ability of rodents at, what they termed, “recognition memory” tasks

and the underlying neuroanatomical substrates mediating such performance. Overall,

they claimed that rodents can acquire these tasks, but do not typically perform at high

levels. Their work, however, focuses on particular tasks using objects or spatial stimuli.

It is interesting that the rats in this experiment did not perform better on the

different trials. Wright and Delius (2005) reported that pigeons performing a matching-

and oddity-to-sample task acquire the oddity-to-sample most rapidly. In fact, there are

published data that suggest a preference for stimuli that do not match (the oddity-

preference effect) (Ginsburg, 1957). There is also a previous account in which matching

performance begins at or below chance (50%) and non-matching performance begins

higher than chance, though these studies used pigeons and differed procedurally from the

task presented here (Zentall Edwards, Moore, & Hogan, 1981).

An experiment by Wallace, Steinart, Scobie, and Spear (1980) might also provide

information worth considering regarding the difficulty the rats had performing at high

levels in this task. In their study, rats performed better in a delayed matching task on

27

trials that contained auditory sample stimuli rather than visual (an illuminated light)

stimuli. The differences in performance between the two modalities disappeared when

the delay was 0 s, but emerged when delays were longer. Perhaps taste stimuli are not as

salient as stimuli from other modalities.

Interestingly, Slotnick and colleagues (1993) reported that rats can learn an odor

matching task and perform at very high levels (>90%) even with a delay of 10 s and

presentation of a masking odor between samples. The reason for the disparity in

performance between their rats and those in the present task are unknown, but there are

procedural differences that may explain some of them. They used a conditional go/no-go

discrimination task, which allowed many more trials and far fewer reinforcers to be

delivered; that difference may have helped acquisition of the task in their case.

Additionally, they used a learning set of stimuli, consisting of several different scents;

thus, it is possible that experience with a variety of training stimuli would improve

acquisition of the task. If such an approach was adopted with taste stimuli, it remains

possible that higher levels of performance would be seen.

Finally, one reason that the mean performance did not surpass 75% might be

related to the ratio between the interstimulus delay and the intertrial interval. One

published study, using pigeons in a visual discrimination paradigm, showed that the

overall correct responding changed when the experimenter varied the ratio of

interstimulus delay to intertrial interval (Roberts & Kraemer, 1982). Specifically, they

tested ratios of 0.5, 2, 8, 16, 32, and 64 and reported that when the delay between trials in

their experiment was the greatest, the highest levels of performance occurred (Roberts &

Kraemer, 1982). In the present study, design limitations of the gustometer restricted the

28

minimum interstimulus interval to 6 s. According to Roberts & Kraemer’s 1982 study,

with a delay this long, it would have been optimal to use an intertrial delay of 386 s. This

was not practical because either the number of trials or the number of sessions possible

per day would have been dramatically reduced. In light of these findings, one might even

conclude that the rats in the present experiment performed as well as would be expected;

this statement is based on the fact that the subjects in Roberts & Kraemer’s (1982) task

performed at 77% when the ISI/ITI ratio was 8, as it was in the present study. Therefore,

reducing the delay or lengthening the intertrial interval would be predicted to improve

performance. Perhaps in contrast to that statement, however, is evidence from Sargisson

and White (2001), who showed that delay appears to become part of the training stimulus

and shares a portion of discriminative control, thus lowering the delay in testing might

actually decrease performance if the animal acquired the task at a higher interstimulus

delay. These are potentially addressable issues empirically.

It might have been insightful to include different test compounds at the end of the

testing period to establish if the rats would be able to apply the concept of sameness or

difference. It is possible that the performance in this test was contingent on prior training

with these compounds, and the learning would not generalize to novel compounds. Thus,

it would have been informative to discern whether such a transfer would have occurred.

If rats acquired high levels of performance to the new set of stimuli more quickly than

with the first set, then it might support the claim that rats could learn to perform the

conceptual task of sameness and/or difference. We felt that the current level of

performance was not sufficiently high to pursue this question. Nevertheless, in the

future, especially if optimal testing parameters can be achieved to increase overall

29

performance levels, adding a variety of test compounds should be included in the

experimental design. Perhaps using multiple training compounds would actually help to

establish higher levels of performance (see Slotnick et al., 1993).

Overall, the results of the present study were encouraging that such a procedure

could be used to study rodent discrimination ability. It certainly seems reasonable that

lowering the delay between stimuli would increase the overall task performance and

allow more options for discrimination (e.g., solutions that vary in intensity).

Additionally, this approach also shows promise for the investigation of short-term

memory in the gustatory neuraxis, which might ultimately provide information about the

properties of the system, the structures involved, and how taste short-term memory

compares with other forms of taste memory and memory processes involving other

sensory modalities. Further development of this task could reveal properties of

neurobiological mechanisms underlying certain forms of behavior.

Unfortunately, because the performance of the rats on the task was not optimal for

continuing in the same research direction, an alternative avenue to assess taste quality in

rats was required. This, however, does not detract from the potential success of the task

outlined above, but because the technical limitations could not be overcome at present, it

was decided to move ahead in a different direction.

30

Figure 2-1. Trial structure for DMTS/DNMTS (same/different) task.

31

TOTAL PERFORMANCE

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Time (Weeks)

Per

form

ance

(% C

orre

ct)

Figure 2-2. The mean overall performance to all trial types is shown. Performance on the task increased over the course of the experiment and became significantly different from chance during the 3rd week of testing.

32

NaCl-NaCl vs.

Sucrose-Sucrose

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Time (Weeks)

Per

form

ance

(% C

orre

ct)

NaCl Same

Sucrose Same

Incr

ease

d re

inck

s

out off

Incr

ease

ngth

Incr

ease

ngth

Figure 2-3. Mean performance to same trials. The performance of the rats did not differ depending on the stimulus that was included in the same trials. The rats improved over the course of the experiment.

forc

er to

40

li

Incr

ease

d tim

e

Two

wee

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d se

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d se

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n le

33

NaCl-Sucrosevs.

Sucrose-NaCl

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Time (Weeks)

Per

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NaCl - Sucrose

Sucrose - NaCl

Incr

ease

d re

info

rcer

to 4

0 lic

ks

Incr

ease

d tim

e ou

t

Two

wee

ks o

ff

Incr

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d se

ssio

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ngth

Incr

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d se

ssio

n le

ngth

Figure 2-4. Mean overall performance to different trials. Rats did not perform significantly differently on trials containing both compounds regardless of the order that the stimuli were sampled.

34

SAME vs. DIFFERENT

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Time (Weeks)

Per

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Same

Different

Incr

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d re

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to 4

0 li

Incr

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d tim

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d se

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cks

out off

Incr

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ngth

Incr

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ngth

Figure 2-5. Mean performance on same versus different trials. There was no statistical evidence that the performance on different trials was better than performance on same trials.

CHAPTER 3 A NEW METHOD OF ASSESSING TASTE QUALITY GENERALIZATION IN

RATS

Introduction

With some exceptions, the most common method used to assess taste quality in

rodent models is the conditioned taste aversion (CTA) generalization paradigm. In this

procedure an animal is presented with a taste solution, which serves as the conditioned

stimulus (CS), followed by induction of visceral malaise. After such a conditioning trial,

animals will avoid ingesting the CS as well as compounds that are thought to possess a

similar taste quality. Although this procedure has provided useful information to

researchers interested in taste processing, it has some interpretive and methodological

limitations. One constraint is that a novel CS must be used with each group. Thus, a

large number of animals are required to comprehensively assess taste quality

generalization. Another key problem is that some stimuli (e.g., quinine or HCl) are

inherently avoided by rats, hence making it difficult to differentiate conditioned from

unconditioned suppression of intake (e.g., “floor effect”). Additionally, as described in

Chapter 1, stimulus intensity dynamism presents another caveat for data interpretation

that must be considered. Because an animal will show an increased conditioned response

to concentrations higher than the CS, it becomes important to know what the relative

intensity differences elicited by different compounds might be. Finally, given that-testing

occurs in extinction, the number of test stimuli and test sessions possible is restricted.

For the reasons outlined above, a major goal of this experiment was to develop a

35

36

procedure that circumvents the interpretive and methodological limitations associated

with the CTA approach.

Morrison (1967) introduced a unique behavioral procedure that examined taste

generalization in a different manner. He trained a group of rats to press one lever if the

compound sampled was 0.1 M NaCl (the standard), and another lever if the sample was

0.1 M sucrose. He trained another group of rats to discriminate that same concentration

of NaCl from 0.01 M HCl. Finally, he trained a third group of rats to discriminate the 0.1

M NaCl from 0.5 mM quinine. Next, he was able to determine which response each

group made when given a novel test salt. Profiles, based on whether they responded on

the standard (NaCl) lever or the comparison lever, were derived. This design included all

four prototypical taste compounds split across the three groups, so by placing the

proportion of responses made on the comparison lever together on the same graph, it

represented how sucrose-like, quinine-like, and hydrochloric acid-like the test salt was.

If the profile was not any of the three, then the compound was assumed to be entirely

NaCl-like.

Though this approach is clever, it still has some limitations. First, within a single

group, it is not intuitively obvious how to interpret a compound that is similar to neither

of the two compounds. If the basic tastes are indeed different from one another,

presenting a compound from a separate taste quality would not be expected to fall

exclusively on either one of the training stimuli for a given group, yet a score of 0.5

would indicate that the test compound shared similarities with both. Morrison does not

address this possibility (Morrison, 1967). Perhaps a better paradigm would involve

training the rats to discriminate a taste compound putatively representing one quality

37

from taste stimuli thought to represent all other proposed qualities. In this approach, the

rat might learn to focus solely on one taste in order to separate the features of that

compound from all others. If that occurred, then when a rat responded to a novel

compound as if it were the standard, it would indicate that the test compound was similar

to the standard.

Secondly, Morrison (1967) used only a single concentration of each prototypical

stimulus. In that study intensity was not varied to make it an irrelevant cue. Therefore, it

is unknown whether the rats in Morrison’s (1967) experiment were responding on the

basis of intensity differences or quality differences. A better approach would be to

include several concentrations of each training stimulus to decrease the relevance of

intensity making taste quality the only reliable cue.

The present study was undertaken to expand upon Morrison’s (1967) design and to

incorporate improvements to overcome his experimental shortcomings. Namely, the

differences include an attempt to train the rats to focus on discriminating a single

prototypical compound, representing the putative four basic taste qualities, from the

remaining three. Additionally, inclusion of a broader array of concentrations of the

standard stimulus is intended to circumvent problems that might occur with

generalizations based on intensity features.

In order to choose a broad range of concentrations that represent the prototypical

stimuli and include overlapping intensities, a brief-access taste test was conducted with

one prototypical representative from each of the putative 4 basic taste qualities. The goal

of Experiment I was to identify concentrations of NaCl, sucrose, quinine, and citric acid

that span the dynamic range of intensity, which would be used in Experiment II.

38

Experiment I

Method

Subjects

Eight naïve, adult, male Sprague-Dawley (Charles River Breeders; Wilmington,

MA) rats were used. The rats were housed individually in polycarbonate shoe-box style

cages in a room where temperature, humidity, and light cycle (lights on 7am – 7pm) were

controlled automatically. All manipulations were performed during the light phase. The

rats had ad libitum access to Purina Rat Chow (5001) in the home cage. Purified (Elix

10; Millipore, Billerica, MA) water was also available ad libitum except where indicated.

All procedures were approved by the University of Florida Institutional Animal Care and

Use Committee.

Training Stimuli

All solutions were prepared daily with purified water (Elix 10, Millipore, Billerica,

MA) and reagent grade chemicals, and were presented at room temperature. Test stimuli

consisted of six concentrations of sucrose (0.01, 0.03, 0.06, 0.1, 0.3, and 1.0 M; Fisher

Scientific, Atlanta, GA), NaCl (0.03, 0.1, 0.2, 0.3, 0.5, and 1.0 M; Fisher Scientific,

Atlanta, GA), citric acid (0.3, 1, 3, 10, 30, and 100 mM; Fisher Scientific, Atlanta, GA),

quinine (0.01, 0.03, 0.1, 0.3, 1.0, and 3.0 mM; Sigma-Aldrich, St Louis, MO) and

purified water.

Procedure

A brief-access procedure similar to that described by others (e.g., Glendinning,

Gresack, and Spector, 2002; St. John, Garcea, and Spector, 1994; Spector, Redman, and

Garcea, 1996) was used. Testing took place in the gustometer, which was described in

Chapter 2. The sample phase began when the rat made contact with the dry sample spout

39

and initiated licking. The rat was required to lick the drinking spout twice within 250 ms,

upon which the shaft of the drinking spout was filled with the stimulus and each

subsequent lick resulted in an additional deposit of 5 µl into the fluid column. During the

session, the rat was allowed access to a single concentration for a brief period of time (5

s) and then after a 6-s inter-presentation interval during which the sample spout was

rotated over a funnel and rinsed with clean water, a different solution was offered. The

stimulus array for each compound tested included the six different concentrations

detailed above and purified water. A given trial started after the first lick. Trials were

presented in randomized (without replacement) blocks so that every concentration of a

stimulus and water was presented exactly once before the initiation of the subsequent

block. Unconditioned licking responses were recorded for later analysis. Sessions were

30 min in duration during which rats could initiate as many trials as possible. The

animals were first trained to lick a stationary spout delivering water for 30 min in the

gustometer after being placed on 23.5-h restricted water access schedule. For sucrose

testing, animals then received 2 days of testing with six stimulus concentrations and

purified water while maintained on the water-restriction schedule. During this period of

training, the sample spout rotated away from the access slot between trials. The two days

of sucrose training under a water-restriction schedule was done to familiarize the animals

to approaching and licking the spout. Water bottles were then returned to the home cages

for three days, following which, the rats were tested for three days under conditions of

non-deprivation. After the last sucrose session, water bottles were again returned to the

home cages for a rehydration period before the next-testing week. When the test

compound was not sucrose, rats were placed on a water restriction schedule on a Sunday

40

night, placed into the gustometer for two days of testing with water from a spout which

rotated between trials, and then tested for three days under water-restriction. During the

three 3-day test sessions with NaCl, citric acid, and quinine, respectively, water rinses

were presented between each taste stimulus. A rehydration period always occurred

between test compounds.

Data Analysis

A Tastant/Water Lick Ratio was calculated for the data that were collected during

sessions with water-restricted rats. This ratio was computed by taking the average

number of licks per trial for each concentration and dividing it by the average number of

licks per trial when water was delivered as a taste stimulus. This ratio standardizes the

data to control for individual differences in lick rates. In the non-deprived condition, the

average number of licks per trial for each concentration was divided by that animal’s

estimated maximal lick rate (licks/5 s) yielding a Standardized Lick Ratio. The maximal

lick rate was calculated using the reciprocal of the mean of the inter-lick interval (ILI)

distribution (in s) that was measured during training (only inter-lick intervals >50 ms and

<200 ms were used) and multiplying this value by 5. Standardizing the licking response

in this fashion controls for individual differences in lick rates.

These data were used to select concentrations of NaCl, quinine, and citric acid

which elicit similar lick suppression relative to water. The mean lick data for each

concentration were plotted and then a three-parameter logistic equation was used to fit a

curve to the data: f(x) = a/(1+10b(x-c)), where a is the asymptote (note, for NaCl, quinine,

and citric acid, a was a constant set at 1), b is the slope and c is the point of inflection.

The resulting curve was used to guide the choice of concentrations for Experiment II.

41

Results

Results from the brief-access test are shown in Figures 3-1 – 3-4. Table 3-1 lists

the concentrations selected to represent training stimuli for each prototypical compound.

Unfortunately, the incorrect lowest concentration of quinine was included in the proposed

training array through a typographical error. Instead of using the intended concentration

of 0.0827 mM of quinine, 0.027 mM was recorded. Consequently, that low concentration

became incorporated into training array of Experiment II. The lowest training

concentration of quinine, 0.027 mM is only about twice the most conservative measure of

detection threshold for quinine. (0.012 mM , Koh & Teitelbaum, 1961; 0.005 mM, Thaw

& Smith, 1994; 0.003 mM , Shaber, Brent, & Rumsey, 1970; 0.010 and 0.018 mM, St.

John, & Spector).

In order to determine which concentrations resulted in a reduction in licking at a

specific level (i.e., 20%, 40%, and 60% suppression), the equation was rewritten to solve

for x, such that x = (log10((a/y)-1)/b)+c). Following the selection of concentrations

associated with 20%, 40%, and 60% suppression rates, a concentration that was one order

of magnitude (i.e., 1 log10 unit) below the highest concentration of NaCl (which was

associated with a 60% reduction in licking as compared with water) was identified. For

sucrose, the opposite strategy was taken and concentrations that were 40%, 60% and 80%

of their maximal licking rate to water were used along with the concentration that was

approximately 1 log10 unit above the lowest concentration selected. The lowest

concentration for citric acid was selected to be ~1.5 log10 units below the concentration

associated with a 60% reduction in licking because otherwise, there would have been

little difference in behavioral responding for the concentration associated with a 40%

reduction of licking and the intended one that was 1 log unit below the highest

42

concentration. For quinine, it was our intention to choose a concentration that was 1.0

log10 unit lower than highest concentration selected (i.e., 0.0827 mM), but an erroneous

value was selected (0.027 mM) that was actually ~1.5 log10 units lower. Regardless, all

concentrations spanned at least 1 log10 unit and incorporated the dynamic range of

responsiveness measured in this task.

Discussion

The selection of training stimuli suitable for Experiment II was based on the three

isoresponsive concentrations and the additional concentration for each compound that

allowed for the range of concentrations to span at least 1 log10 unit. For the aversive

stimuli (NaCl, quinine, and citric acid), intensities at which rats reduced their licking to

the same benchmark level of performance were selected. The three compounds are

referred to as aversive because the rats decreased their licking monotonically as

concentration was raised. For the appetitive stimulus, sucrose, the concentrations that

resulted in alterations in licking were similarly selected except that the changes in

concentration resulted in increased levels of licking rather than suppression. Thus, we

attempted to match the three highest concentrations of aversive compounds with the three

lowest concentrations of sucrose, with respect to the effect that increasing concentration

has on behavior. Although this procedure likely does not result in exactly matching

intensities between compounds, we assume that it is a good approximation and

importantly provides some confidence that the concentrations chosen at least are

overlapping. Here, the same rats were used to determine the dynamic range of

concentrations for which licking is modulated across four compounds representing the

basic taste qualities.

43

It is plausible that there were order effects associated with the curves obtained for

each compound, considering that sucrose was the first stimulus to be tested, which was

followed by NaCl, citric acid, and then quinine. The nature of the prior experience with

sucrose may have trained the animal to accept stronger concentrations of the taste stimuli,

thus inflating the range of concentrations selected. Perhaps using a naïve set of rats, or

randomizing the order of presentation between the rats, for each of the four compounds

would have yielded different results. An examination of the literature revealed that

comparison of the midpoint of the concentration-dependent curve for quinine obtained

here (approximately 0.4 mM) with those from two published studies examining brief-

access using quinine (approximately 0.3 and 0.2 mM) suggests that these rats did perhaps

accept higher concentrations than naïve rats do (Spector and Kopka, 2002; St. John,

Garcea, and Spector, 1994). Nevertheless, potential parametric influences aside, the

experiment provided some basis upon which to choose a broad range of concentrations

for each stimulus that at the very least overlap in intensities.

Experiment II

The following experiment attempted to adapt Morrison’s (1967) procedure,

described above, but incorporated a broader array of training concentrations and

comparison stimuli in order to test the following two hypotheses: 1) rats can learn to

discriminate prototypical compounds, characteristic of the putative basic taste qualities,

when a variety of concentrations are used to represent each compound, and 2) rats will

generalize the responses learned with training stimuli to novel untrained test stimuli.

44

Method

Subjects

Forty-eight naïve adult male Sprague-Dawley (Charles River Breeders;

Wilmington, MA) rats served as subjects. The rats were housed individually in

polycarbonate shoe-box style cages in a room where temperature, humidity, and light

cycle (lights on 7am – 7pm) were controlled automatically. All manipulations were

performed during the light phase. The rats had ad libitum access to Purina Rat Chow

(5001) in the home cage. Purified (Elix 10; Millipore, Billerica, MA) water was also

available, but was removed approximately 16 hours before (~4:00 pm the night before)

the first behavioral session of the week and was replaced at the completion of the last

session of the week. A contingency was in place that would allow rats to receive

supplemental water if body weight decreased to 85% of the ad libitum weight calculated

each week, but no rat dropped below that criterion in this experiment. One of the animals

was removed before side training (see below) began because it exhibited self-injurious

behavior. All procedures were approved by the Institutional Animal Care and Use

Committee at the University of Florida.

Apparatus

The apparatus was the same as that described in Chapter 2. There was, however,

no cannula lead entering the chamber from the port in the ceiling of the sound attenuation

chamber.

Task overview

The prototypical taste compounds NaCl, sucrose, quinine HCl, and citric acid were

used to represent the putative 4 basic tastes, salty, sweet, bitter, and sour, respectively.

Four groups of rats were trained to respond by licking one response spout after sampling

45

any of the 4 training concentrations of a particular standard, which for each group was

one of the prototypical compounds, and they were trained to lick a different response

spout after sampling any of the comparison stimuli (the remaining three compounds).

Stimuli

All solutions were prepared daily with purified water (Elix 10, Millipore, Billerica,

MA) and reagent grade chemicals, and were presented at room temperature. Test stimuli

consisted of four concentrations each of NaCl (0.107 M, 0.376 M, 0.668 M, and 1.07 M;

Fisher Scientific, Atlanta, GA), sucrose (0.042 M, 0.077 M, 0.148 M, and 0.421 M;

Fisher Scientific, Atlanta, GA), citric acid (2.04 mM, 10.4 mM, 28.2 mM, and 64.3 mM;

Fisher Scientific, Atlanta, GA), quinine (0.027 mM, 0.131 mM, 0.360 mM, and 0.827

mM; Sigma-Aldrich, St Louis, MO) and purified water.

Groups

For overview of the four groups (N, S, Q, and C) and their associated standard and

comparison stimuli, see Table 3-2. Each of the groups was named for their standard

stimulus and was trained to discriminate four concentrations of that compound from four

concentrations each of the comparison stimuli (those from the remaining three

prototypical compounds).

Trial structure

On any given trial (see Figure 3-5), rats were trained to lick a centrally positioned

stimulus delivery spout. Initially, the sample spout was dry, but when the rat licked two

times with an inter-lick interval < 250 ms, then the predetermined solution filled the shaft

of the spout, after which the rat could receive up to 5 licks (~5µl was deposited into the

fluid column upon each lick) before the spout was rotated out of position. Next, a

decision phase was initiated, during which the rat was required to lick one response spout

46

after sampling the standard stimulus or the other response spout after sampling a

comparison stimulus. During the consequence phase, if the rat responded correctly to the

stimulus, water reinforcement was delivered directly through the response spout (20 licks

@ ~5µl per lick or a total of 10 s access, whichever occurred first). If the rat failed to

respond, or responded on the incorrect response spout, then the rat was punished with a

20-s timeout. After either consequence of the decision phase, the trial moved into an

intertrial interval that lasted 6 s.

Training

Spout training. The rats had access to only one spout (either the sample spout, the

left response spout, or the right response spout) and each spout was connected to a

reservoir that contained water. The purpose was to train the rats to approach and gain

familiarity with obtaining fluid from each of the spouts. Eventually, the sample spout

would contain a taste stimulus and only the response spouts would contain water.

Side training. Only one trial type was presented within a given session during side

training so that the solutions available alternated with each session. That is, if the rats

were trained with their standard compound in the first session, then during the next

session, the rats received only comparison compounds. After sampling, rats had 180 s

(limited hold period) during which they were required to respond. Side training lasted a

total of 4 days. Only the third highest concentration of each stimulus was presented. The

rats were required to lick the sample spout to obtain a small volume of the stimulus and

then select one of the response spouts by licking it. If the rat responded correctly, then

water reinforcement was available (10 s access or 20 licks, whichever came first). The

intertrial interval during this phase was 6 s.

47

Alternation. During alternation training, the rats started out with either a standard

or one of the comparison stimuli. Upon completion of a set criterion of correct

responses, the program switched to the opposite trial type. Each time the rat completed

the criterion of correct responses, the program automatically switched to delivery of the

other trial type. When the trial type consisted of comparison stimuli, the computer

randomly selected (without replacement) the solution to deliver. The correct responses

did not have to be consecutive. The limited hold was changed from 180 s to 15 s.

During the decision phase, if a rat failed to make any response, or made an incorrect

response, a 10-s timeout was initiated.

Discrimination training I-II. Stimuli were delivered in a block with a random

pattern selected by the computer program. Therefore, the rats had no indication from the

prior trial, which solutions would be offered on the current trial. All four training

concentrations were used in this phase, but because the gustometer had a limited number

of fluid reservoirs, only two concentrations (always one of the highest two and one of the

lowest two) of each prototypical compound were included per session. The block size

was 12; consequently, every standard concentration for a given session was repeated

three times within the block so that the number of standard stimuli matched the number

of comparison stimuli available (which were each only presented once per block). The

timeout period was increased to 20 s during this phase. After 12 days of discrimination

training, a partial schedule of reinforcement was introduced. During the session, two

trials (one standard and one comparison) from each block of 12 trials were randomly

selected to have neither reinforcement nor punishment delivered contingent on the

animal’s response. That is, the animal did not receive reinforcement if it made the

48

correct response and it did not receive punishment if it made the incorrect response on

those selected trials. There was, however, a punishment contingency in place if the rat

failed to make a response. The partial schedule of reinforcement was introduced in

anticipation of the eventual inclusion of test stimuli, which would make up approximately

16% of the total trials in a session. The limited hold period (the time the animal was

allowed to make a response after sampling) was 5 s for this phase.

Test compounds

There was no correct response associated with a test stimulus, so the animal would

not receive reinforcement, but it also did not receive punishment for a response, unless it

failed to make the response before the limited hold period expired. In order to validate

whether rats would generalize untrained test stimuli to the standard compound, novel

concentrations of the training stimuli were presented. The following novel

concentrations of the training compounds and mixtures of NaCl and sucrose compounds

served as test stimuli:

• 0.847 M NaCl

• 0.068 M Sucrose

• 0.546 mM Quinine

• 42.56 Mm Citric acid

• 1.07 M (high) NaCl + 0.421 M (high) Sucrose

• 1.07 M (high) NaCl + 0.077 M (low) Sucrose

• 0.376 M (low) NaCl + 0.421 M (high) Sucrose

• Water

49

Retraining water as a comparison stimulus

Water was selected as a test compound because it has an interesting history in the

literature. Conceptually, water should represent the absence of a taste. The literature,

however, reveals that some humans (Anderson, 1959) report water as having a “bitter”

taste and animals respond to water as if it were quinine-like (Bartoshuk, 1977; Morrison,

1967).

Here, the profile for water as a test stimulus showed that water appeared to

generalize to quinine (see Results). It was not clear whether this was a result of the

erroneous inclusion of the very weak concentration of quinine in the training array, or if

water indeed has a quinine-like taste (note, these are not mutually exclusive).

Consequently, we attempted to train the rats to identify the difference between water and

quinine by adding water to the comparison group.

Negative control test

A water control session was included at the end of the experiment, in which all of

the reservoirs were filled with water. Two reservoirs were arbitrarily assigned to the

“standard” spout, and another six were designated as the “comparison” spout. This was

done to examine whether the rats were using non-chemical cues to guide their behavior.

Data analysis

A 1-way analysis of variance (ANOVA) was conducted for each test stimulus to

determine the presence of differences among groups followed by more detailed

Bonferroni-adjusted paired comparisons. Separate one-sample t-tests against both of the

null hypotheses 1.0 (i.e., the test compound was similar to the standard stimuli) and 0

(i.e., the test compound was similar to the comparison stimuli) were performed. The

conventional p ≤ 0.05 value was used as the statistical rejection criteria.

50

Data for the negative control test were analyzed using a one-sample Binomial

analysis with null hypothesis = 0.5, which corresponds with the chance level of

performance.

Generalization score

A Generalization Score was calculated for each animal, which essentially

quantified the degree to which the test compound was similar to the standard stimulus.

The following equation was used to calculate the Generalization Score: [P(T)-P(C)] /

[P(S)-P(C)]; where, P(T) = proportion of times the rats responded on the standard

response spout when presented with a test stimulus; P(C) = proportion of times the rat

responded on the standard response spout when presented with a comparison stimulus;

and P(S) = proportion of times the rat responded on the standard response spout when

presented with a standard stimulus. Performance (reported as errors) to the comparison

stimuli was included in the equation in an attempt to account for response bias that may

have developed for individual animals, thus the Generalization Score serves to

standardize performance scores for each animal.

The data are presented as Generalization Scores for each group. Each vertical bar,

represents a different group and shows the degree to which the test compound was

behaviorally treated like the standard. A Generalization Score of 0 indicates that the rat

responded to the test compound as if it were a comparison stimulus. A score of 1.0

indicates that the rat responded to the test compound as if it were a standard stimulus. A

Generalization Score of 0.5 indicates that the compound was no more like the standard

than it was the comparison. A score of 0.5, therefore, could indicate that the test

compound shares some similarities with both the standard and one (or more) of the

comparison compounds. Alternatively, a score of 0.5 could indicate that the test

51

compound is completely unlike any of the trained stimuli (standard and comparison) and

the score is obtained because the rat is randomly placing its behavior between the trained

responses.

Results

The generalization profiles for each test compound are shown in Figures 3-6

through 3-13. These figures are used to reveal the proportion of responding to the test

stimulus as compared with the standard stimulus. This format is similar to that used by

Morrison (1967), except that the Generalization Score is plotted on the ordinate instead of

proportion of responses to the standard; the group names are listed along the horizontal

axis. Tables 3-4, 3-6, 3-8, 3-10, 3-12, 3-14, 3-16, and 3-18 list the performance for each

group to individual concentrations of the training stimuli for each test compound. Data

reported in these tables can be used to support the conclusion that rats in this experiment

were reliably able to discriminate between training compounds and that stimulus control

was maintained during the testing period. Each table reflects the data for those particular

sessions that contained the test stimulus. It is noteworthy that these scores were generally

high and the variance was low. Interestingly, the scores for the lowest concentration of

citric acid in the quinine group for many of the test stimuli were lower than the other

concentrations, which implies that the group had more trouble discriminating that

concentration of citric acid from their standard (quinine concentrations). Indeed, results

from studies using electrophysiological and CTA approaches suggest that the signals for

“bitter” and “sour” stimuli may overlap to some extent (e.g., Frank, Contreras, and

Hettinger, 1983; Lemon and Smith, 2005; Nowlis, Frank, and Pfaffmann, 1980), but

clearly the generally high levels of behavioral performance seen here would argue against

that. Besides, similarly poor performance to the lowest sucrose concentration can be seen

52

during some of the same test weeks, which might suggest a problem with an overall

ability to maintain stimulus control for the weakest solutions in that group.

Novel concentrations: NaCl

Figure 3-6 depicts the untrained responses to the novel concentration of NaCl,

0.847 M. An ANOVA comparing performance in the 4 groups revealed that there was a

significant difference between one or more of the groups (F(3, 43) = 2607.5, p < 0.01).

Subsequent post-hoc analysis with Bonferroni adjustment revealed that the

Generalization Scores for the different groups could be ordered in the following way: N>

S > Q > C. Separate one-sample t-test tests (see Table 3-3) showed that the

Generalization Scores for the N group were actually greater than 1.0, indicating that

novel NaCl is more standard-like than the standard concentrations used to maintain

stimulus control, but the actual value was indeed very close to unity. Conversely, the

Generalization Scores from the S and C groups were significantly less than 0, indicating

that those groups treated the novel NaCl as more comparison-like than their actual

comparison stimuli. Both of these types of findings can probably be explained as

statistical artifacts.

In general, it is fair to say that the N group responded as if novel NaCl was

standard-like and rats in the S, Q, and C groups treated the test compound as if it were

comparison-like; this was expected given that NaCl is one of the comparison compounds

for each of these latter three groups. The overall performance on training stimuli, which

were used to maintain stimulus control during testing sessions, is listed in table 3-4; the

performance values during the sessions with the test compound present are shown.

53

Novel concentrations: Sucrose

Figure 3-7 depicts the untrained responses to the novel concentration of sucrose,

0.068 M. An ANOVA comparing Generalization Scores obtained for the 4 groups

revealed that there was a significant difference between one or more of the groups (F(3,

43) = 1587.9, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that

the Generalization Scores for the different groups could be ordered in the following way:

S> N > Q > C. Separate one-sample t-test analyses of the Generalization Scores (see

Table 3-5) revealed that in the S group, novel sucrose was not different from the standard

(sucrose) training stimuli and that the N and Q groups were statistically not different from

comparison training stimuli. The C group did, however, treat the novel sucrose as more

comparison-like than their comparison training compounds. Again, this can likely be

explained by statistical artifact. Overall, there is statistical evidence to support the claim

that the novel concentration of sucrose generalizes to sucrose in the S group, and not at

all to the standards for the N, Q, and C groups. Table 3-6 includes the performance data

for all of the animals during this phase of testing.

Novel concentrations: Quinine

Figure 3-8 describes the untrained responses to the novel concentration of quinine,

0.546 mM. An ANOVA comparing Generalization Scores obtained from the 4 groups




Q> C > N > S. Of interest, separate one-sample t-test tests of the Generalization Scores

(see Table 3-7) showed that the Generalization Scores to novel quinine in the Q group

were not statistically different from their standard stimulus and that performance in the N

54

and S groups was not different from comparison stimuli. Analysis of the C group

revealed that Generalization Scores were statistically greater than 0, but this difference

did not survive a Bonferroni correction and it was minor in magnitude. Therefore,

performance to the novel concentration of quinine appears to generalize completely to the

trained concentrations of quinine in the Q group and all other groups respond as if the

stimulus were comparison-like. Table 3-8 lists the performance data for all of the

animals during this phase of testing.

Novel concentrations: Citric acid

Figure 3-9 shows the untrained responses to a novel concentration of citric acid,

42.56 mM. An ANOVA comparing Generalization Scores obtained from the 4 groups




C> N > Q > S. Of interest, separate one-sample t-tests of the Generalization Scores (see

Table 3-9) showed that the novel citric acid test stimulus was statistically more standard-

like for the C group than the training concentrations used, though that effect disappeared

with Bonferroni correction. Also of note is that the S and Q groups responded as if the

novel concentration of citric acid was more comparison-like than the training compounds,

though Bonferroni adjustment resulted in the Q group failing to reach significance. The

N group responded as if the test stimulus was not different from the comparison training

stimuli. Overall, the rats in the C group responded as if the novel concentration of citric

acid were similar to the training concentrations, while the rats in the other groups

responded as if it were a comparison stimulus. Table 3-10 contains the performance to

all concentrations of training stimulus for all of the rats.

55

Mixtures between NaCl and sucrose: 1.07 M NaCl + 0.421 M sucrose

Figure 3-10 shows the untrained responses to a mixture of 1.07 M NaCl and 0.421

M sucrose. An ANOVA comparing Generalization Scores obtained from the 4 groups


43) = 89.2, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the

Generalization Scores for the different groups could be ordered in the following way: N =

S > Q > C. Of interest, separate one-sample t-tests of the Generalization Scores (see

Table 3-11) showed that all of the groups differ statistically from 1.0 (the test compound

is standard-like), and only the C group responds as if the test stimulus is not statistically

different than the comparison stimuli. Consequently, the N and S groups report that the

mixture is also not comparison-like, while the Q group responded as if the mixture was

more comparison-like than the training compounds. The performance in the N and S

groups showed similar levels of responding (ANOVA post hoc between N and S p =

1.000), which suggests that both qualities (NaCl-like and sucrose-like) contributed to the

overall experience of the solution. Table 3-12 contains performance data for the training

stimuli.

Mixtures between NaCl and Sucrose: 1.07 M NaCl + 0.077 M Sucrose






N > = C = S =Q. Of interest, separate one-sample t-test analyses of the Generalization

Scores (see Table 3-13) showed that all groups were statistically different from 1.0 (i.e.,

56

the test stimulus was standard-like) and only the N group differed significantly from 0

(the test stimulus is comparison-like). Taken together, these data indicate that a NaCl-

like taste appears to be the only quality present in the mixture. It would seem that the

relatively strong concentration of NaCl overshadows the relatively weak concentration of

sucrose. Table 3-14 contains data for performance to training stimuli.

Mixtures between NaCl and Sucrose: 0.376 M NaCl + 0.421 M Sucrose




43) = 78.0, p < 0.01). A post-hoc analysis with Bonferroni adjustment revealed that the

Generalization Scores for the different groups could be ordered in the following way: S >

N > C > Q. Of interest, separate one-sample t-test analyses of the Generalization Scores

(see Table 3-15) showed that all groups differed significantly from 1.0 (i.e., that the test

stimulus was standard-like) and that the S and N groups also differed significantly from 0

(the test stimulus is comparison-like). Both the Q and C groups responded as if the test

compound was comparison-like. The post hoc test of the ANOVA indicated that the S

component was statistically greater than the N component. This suggests that rats can

distribute their behavior according to the relative contribution of each compound that is

present in a mixture. The overall performance to training stimuli, which were used to

maintain stimulus control during testing sessions, is listed in Table 3-16.

Novel test compound: Water

Figure 3-13 shows the untrained responses to water as a test compound. An

ANOVA comparing Generalization Scores obtained from the 4 groups revealed that there

was a significant difference between one or more of the groups (F(3, 43) = 386.4, p <

57

0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalization

Scores for the different groups could be ordered in the following way: Q > C > S > N. Of

interest, separate one-sample t-tests of the Generalization Scores (see Table 3-17) showed

that all groups were statistically different from 1.0 (the test compound is standard-like)

and only the N group was not statistically different from 0 (the test stimulus is

comparison-like). Taken together, these results show that, under these testing conditions,

there is a strong quinine-like component, followed by citric acid-like and sucrose-like

components to water. The significance of this will be addressed in the discussion section,

but briefly it may have occurred because the lowest training concentration of quinine was

only about twice the most conservative measure of detection threshold reported and also

because water might actually possess a weak quinine taste. Performance to all stimulus

control concentrations are shown in Table 3-18.

Retraining water as a comparison stimulus

The results of the phase in which we attempted to retrain the rats include water with

the comparison stimuli are shown in figure 3-14. The results for the overall performance

when all training compounds were present were poor for the water stimulus in the Q

group (data not shown). Because the percentage of the trials in the session that were

water was very low when all training stimuli were presented, we reasoned that to increase

the ability of the rats to specifically learn the discrimination, it was necessary to limit the

types of training stimuli encountered to only water and quinine. Consequently, to

increase the animals overall experience with discriminating water from quinine, only

water and 0.360 mM & 0.827 mM quinine were present in training sessions shown in

Figure 3-14; for reference, the performance during the first day of retraining is included.

Clearly, the rats were unable to perform this discrimination well. Although it can be seen

58

that their performance to water improved over the course of training, the ability of the

rats to correctly detect the presence of quinine worsened, indicating that the stimuli were

unable to maintain the high levels of stimulus control previously seen with the other

training compounds.

Negative control session

The results for the negative (water) control session are shown in Figure 3-15.

When all of the testing reservoirs which were normally filled with chemical stimuli were

filled instead with water, performance dropped to chance levels for most of the animals.

There were 8 rats that performed significantly worse than chance. If a Bonferroni

correction is applied to control for multiple tests, then the same rats fail to reach

significance. These data support the claim that rats used only chemical cues to guide

their behavior.

Discussion

The rats in this experiment readily learned to discriminate several concentrations of

one prototypical compound representing one of the putative basic taste qualities from

various concentrations of prototypical compounds representing the three remaining taste

qualities. Moreover, the results from test stimuli support the claim that responses to

training stimuli generalized to novel compounds that likely shared similar taste qualities

with the training compounds. The fact that these trials were presented without

consequence allows the assertion that the behavior generalized on the basis of the training

history of the animal. Additionally, other evidence to support that claim is based on the

performance to the novel concentrations of training compounds; the rats performed as if

the novel concentrations were similar to the training compounds.

59

The novel concentrations of training stimuli were taken from the midpoint of each

of the curves obtained during the brief-access experiment. Because the concentrations

included were within the range of training stimuli, it remains possible that more intense

compounds would not have generalized as well, but given what is known about stimulus

intensity dynamism, it is likely that higher concentrations would be identified

appropriately. Nevertheless, it remains an empirical question which could be addressed

by further experiments.

The data on the mixtures of NaCl and sucrose were insightful. These data showed

that rats do not fully generalize to their standard concentration just because it is present

within the mixture. When the standard compound is included in a solution with another

compound to which the rat has been trained to make a competing response, a

Generalization Score of 0.5 may result. Therefore, depending on the relative

concentrations of the standard and comparison solutions used, the behavior of the rats

seems to reflect which compound(s) is/are dominant in the solution. It suggests, then,

that profiles of this type would be helpful in the identification of the components of

complex stimuli (either naturally complex, or through mixtures).

Overall, the data from this novel paradigm suggest that this testing method has the

potential to provide information similar to that obtainable using the conditioned taste

aversion approach with respect to the way rats categorize taste stimuli, presumably on the

basis of their qualitative feature. The most obvious benefit of this procedure over the

CTA approach, however, is that the same test animals can be used repeatedly to report on

essentially an unlimited number of test compounds. The initial training that is required

60

could be described by some as rather lengthy, but the potential for information return is

quite large, and arguably worthwhile.

This paradigm could serve as a more efficient method of obtaining information

about the taste quality of several compounds. It is fair to state that the procedure has

promise as an alternative or complementary testing protocol to the study of taste quality

in animal models. Clearly, more test compounds should be used to extend previous

findings and to identify similarities between this method and other existing procedures.

While it is feasible that this paradigm would yield different findings (e.g., because of

different-testing parameters), it is also possible that this method would provide

converging lines of evidence for results obtained using the conditioned taste aversion

approach and taste discrimination tasks. Such an outcome would increase the confidence

that these different approaches are tapping into similar principles.

Even if this paradigm, however, resulted in conflicting findings from those

generated with other procedures such as CTA, it still does not undermine the information

that this method could potentially provide. As long as the results are reproducible some

aspect of taste behavior is being measured. Perhaps the unique strengths of this

procedure will be realized with further development. One possible avenue which sets this

approach apart from the CTA method is that extinction of learning is not a factor.

Theoretically, the same compound could be tested weeks apart and the animal would

respond to it in the same manner, provided the training stimuli still maintained stimulus

control. The usefulness of this aspect of the task is that it is compatible with

manipulations of the gustatory system in which subsequent re-testing in the same animal

61

subjects is required by design. This feature (i.e., the strength of within subject designs

for data interpretation) is a benefit that the CTA approach does not offer.

When water served as a test compound, the profile generated was unexpected. In

the planning stages of the experiment, the wrong concentration of quinine was included

in the proposed training array through an unfortunate typographical error. Because the

lowest training concentration of quinine, 0.027 mM is only about twice the most

conservative measure of detection threshold for quinine reported in the literature (0.012

mM , Koh & Teitelbaum, 1961; 0.005 mM , Thaw & Smith, 1994; 0.003 mM , Shaber,

Brent, & Rumsey, 1970; 0.010 and 0.018 mM, St. John, & Spector), it remains possible

that animals generalized water responses to quinine because, of all the stimuli, quinine

had the weakest of the low concentrations. It is also possible that water might have a

quinine-like taste as has been reported for both humans (Anderson, 1959), and rats

(Bartoshuk, 1977; Morrison, 1967). These two possibilities are not mutually exclusive,

but as the next experiment will suggest, however, the latter explanation seems to have

some merit.

62

Table 3-1. Training compounds selected from Experiment I. Compound 1 2 3 4 NaCl (M) 0.107 0.376 0.668 1.07 Sucrose (M) 0.042 0.077 0.148 0.421 Quinine (mM) 0.027 0.131 0.360 0.827 CitricAcid (mM) 2.04 10.4 28.2 64.3

Table 3-2. Experimental groups Group Standard Comparison Solutions 1) N NaCl Sucrose, Quinine, Citric Acid 2) S Sucrose NaCl, Quinine, Citric Acid 3) Q Quinine NaCl, Sucrose, Citric Acid 4) C Citric Acid NaCl, Sucrose, Quinine

Table 3-3. Results from one-sample t-tests for a novel concentration of NaCl. Test against 1.0 Test against 0

Grp df t p-

value Adjusted p-value t p-value

Adjusted p-value

N 11 5.0 < 0.01 < 0.01 228.62 < 0.01 < 0.01S 10 -148.2 < 0.01 < 0.01 -4.85 < 0.01 < 0.01Q 11 -67.3 < 0.01 < 0.01 -1.77 0.11 0.84C 11 -93.5 < 0.01 < 0.01 -7.12 < 0.01 < 0.01

Table 3-4. Performance to training stimuli during novel NaCl testing Group

NaCl Sucrose Quinine Citric Acid Solution Conc. Mean SE Mean SE Mean SE Mean SE

0.107 0.107 92.1 1.4 96.0 1.6 88.9 1.9 97.5 0.376 0.376 97.8 0.7 96.7 1.3 96.0 2.2 98.9 0.668 0.668 97.9 0.6 97.0 0.9 97.9 1.1 97.6

NaCl (M)

1.07 1.07 97.9 0.6 97.6 1.3 98.1 1.6 97.4 0.042 0.042 98.7 0.9 90.5 2.8 87.6 3.3 94.7 0.077 0.077 98.0 1.1 96.3 1.1 93.5 1.7 98.9 0.148 0.148 100.0 0.0 96.8 0.7 96.7 2.1 98.2

Sucrose (M)

0.421 0.421 99.0 0.5 98.8 0.8 96.8 1.4 97.6 0.027 0.027 96.1 1.3 87.4 3.3 94.8 1.0 79.3 0.131 0.131 97.9 0.9 92.4 1.5 94.3 1.2 80.7 0.360 0.360 96.2 0.7 94.4 0.9 94.7 1.0 86.2

Quinine (mM)

0.827 0.827 98.4 0.7 93.1 1.3 95.3 0.6 85.6 2.04 2.04 98.6 0.8 96.1 1.4 79.8 3.6 89.2 10.4 10.4 96.3 1.2 97.9 0.8 90.9 1.6 94.4 28.2 28.2 97.3 1.1 99.1 0.9 96.9 1.2 97.7

Citric Acid (mM)

64.3 64.3 98.2 1.0 99.0 0.7 95.6 2.5 99.1

63

Table 3-5. Results from one-sample t-tests for a novel concentration of sucrose. Test against 1.0 Test against 0

Grp df t p-


Adjusted p-value

N 11 -112.36 < 0.01 < 0.01 0.19 0.85 1.00S 10 -0.25 0.81 1.00 77.92 < 0.01 < 0.01Q 11 -54.94 < 0.01 < 0.01 0.51 0.62 1.00C 11 -146.99 < 0.01 < 0.01 -7.88 < 0.01 < 0.01

Table 3-6. Performance to training stimuli during novel sucrose testing Group


0.107 97.0 0.8 97.0 0.6 90.2 2.6 97.9 0.7 0.376 97.8 0.6 99.6 0.4 97.7 1.1 99.2 0.6 0.668 98.5 0.7 99.3 0.7 97.8 1.0 98.6 0.6

NaCl (M)

1.07 98.0 0.7 98.4 0.7 94.6 1.7 94.1 1.7 0.042 98.9 0.6 89.9 1.2 78.8 3.1 95.3 1.2 0.077 97.8 0.9 96.4 0.6 94.0 1.9 98.1 0.9 0.148 96.8 1.6 98.7 0.6 98.8 0.7 96.9 1.1

Sucrose (M)

0.421 99.7 0.3 98.9 0.8 99.7 0.3 97.8 0.7 0.027 97.7 0.9 90.0 2.5 96.3 0.6 83.3 3.9 0.131 100.0 0.0 95.5 1.6 95.8 1.4 88.0 2.7 0.360 98.0 1.5 96.3 1.3 97.2 0.7 92.6 3.1

Quinine (mM)

0.827 96.9 0.8 98.0 0.5 96.4 0.6 90.8 1.6 2.04 97.6 1.7 96.1 1.7 79.4 2.4 89.8 1.3 10.4 97.8 0.8 98.5 0.5 92.8 1.2 95.0 0.9 28.2 98.8 0.6 99.5 0.5 96.4 1.0 99.1 0.3

Citric Acid (mM)

64.3 96.7 2.2 97.8 0.8 91.6 2.5 94.8 0.9

Table 3-7. Results from one-sample t-tests for a novel concentration of quinine Test against 1.0 Test against 0

Grp df t p-


Adjusted p-value

N 11 -153.10 < 0.01 < 0.01 -0.62 0.55 1.00S 10 -122.15 < 0.01 < 0.01 -1.80 0.10 0.82Q 11 2.66 0.02 0.16 109.65 < 0.01 < 0.01C 11 -61.17 < 0.01 < 0.01 3.11 0.01 0.08

64

Table 3-8. Performance to training stimuli during novel quinine testing Group


0.107 92.1 1.4 96.0 1.6 88.9 1.9 97.5 1.1 0.376 97.8 0.7 96.7 1.3 96.0 2.2 98.9 0.6 0.668 97.9 0.6 97.0 0.9 97.9 1.1 97.6 1.0

NaCl (M)

1.07 97.9 0.6 97.6 1.3 98.1 1.6 97.4 1.0 0.042 98.7 0.9 90.5 2.8 87.6 3.3 94.7 1.6 0.077 98.0 1.1 96.3 1.1 93.5 1.7 98.9 0.6 0.148 100.0 0.0 96.8 0.7 96.7 2.1 98.2 1.0

Sucrose (M)

0.421 99.0 0.5 98.8 0.8 96.8 1.4 97.6 0.8 0.027 96.1 1.3 87.4 3.3 94.8 1.0 79.3 3.6 0.131 97.9 0.9 92.4 1.5 94.3 1.2 80.7 3.2 0.360 96.2 0.7 94.4 0.9 94.7 1.0 86.2 2.9

Quinine (mM)

0.827 98.4 0.7 93.1 1.3 95.3 0.6 85.6 2.6 2.04 98.6 0.8 96.1 1.4 79.8 3.6 89.2 1.3 10.4 96.3 1.2 97.9 0.8 90.9 1.6 94.4 1.1 28.2 97.3 1.1 99.1 0.9 96.9 1.2 97.7 0.4

Citric Acid (mM)

64.3 98.2 1.0 99.0 0.7 95.6 2.5 99.1 0.2

Table 3-9. Results from one-sample t-tests for a novel concentration of citric acid Test against 1.0 Test against 0

Grp df t p-


Adjusted p-value

N 11 -178.99 < 0.01 < 0.01 -0.21 0.84 1.00S 10 -145.84 < 0.01 < 0.01 -6.11 < 0.01 < 0.01Q 11 -68.01 < 0.01 < 0.01 -32.85 0.01 0.08C 11 3.50 0.01 0.08 109.77 < 0.01 < 0.01

Table 3-10. Performance to training stimuli during novel citric acid testing Group


0.107 97.0 0.8 97.0 0.6 90.2 2.6 97.9 0.7 0.376 97.8 0.6 99.6 0.4 97.7 1.1 99.2 0.6 0.668 98.5 0.7 99.3 0.7 97.8 1.0 98.6 0.6

NaCl (M)

1.07 98.0 0.7 98.4 0.7 94.6 1.7 94.1 1.7 0.042 98.9 0.6 89.9 1.2 78.8 3.1 95.3 1.2 0.077 97.8 0.9 96.4 0.6 94.0 1.9 98.1 0.9 0.148 96.8 1.6 98.7 0.6 98.8 0.7 96.9 1.1

Sucrose (M)

0.421 99.7 0.3 98.9 0.8 99.7 0.3 97.8 0.7 0.027 97.7 0.9 90.0 2.5 96.3 0.6 83.3 3.9 0.131 100.0 0.0 95.5 1.6 95.8 1.4 88.0 2.7 0.360 98.0 1.5 96.3 1.3 97.2 0.7 92.6 3.1

Quinine (mM)

0.827 96.9 0.8 98.0 0.5 96.4 0.6 90.8 1.6 2.04 97.6 1.7 96.1 1.7 79.4 2.4 89.8 1.3 10.4 97.8 0.8 98.5 0.5 92.8 1.2 95.0 0.9 28.2 98.8 0.6 99.5 0.5 96.4 1.0 99.1 0.3

Citric Acid (mM)

64.3 96.7 2.2 97.8 0.8 91.6 2.5 94.8 0.9

65

Table 3-11. Results from one-sample t-tests for 1.07 M NaCl + 0.421 M sucrose Test against 1.0 Test against 0

Grp df t p-


Adjusted p-value

N 11 -6.89 < 0.01 < 0.01 12.88 < 0.01 < 0.01S 10 -5.72 < 0.01 < 0.01 9.47 < 0.01 < 0.01Q 11 -104.61 < 0.01 < 0.01 -6.29 < 0.01 < 0.01C 11 -62.61 < 0.01 < 0.01 -0.56 0.59 1.00

Table 3-12. Performance to training stimuli during high NaCl + high sucrose testing Group


0.107 89.9 2.2 94.6 1.4 78.2 3.2 96.1 1.3 0.376 96.6 1.4 95.8 1.2 93.6 1.6 95.9 2.6 0.668 97.0 1.3 96.1 1.1 96.2 2.7 88.2 8.1

NaCl (M)

1.07 98.8 0.4 100.0 0.0 98.0 1.0 95.2 1.6 0.042 98.7 0.5 86.9 1.2 80.0 3.0 89.6 2.7 0.077 99.6 0.4 94.7 1.8 91.3 2.6 97.7 0.9 0.148 99.3 0.5 98.4 0.4 97.2 1.2 97.2 1.4

Sucrose (M)

0.421 97.5 1.2 99.0 0.5 98.0 1.2 97.0 1.5 0.027 98.0 0.9 88.1 2.5 94.9 1.0 81.5 1.9 0.131 99.0 0.5 94.7 1.6 95.2 1.0 87.7 2.4 0.360 98.5 0.6 95.7 1.6 97.1 0.8 89.7 2.3

Quinine (mM)

0.827 97.5 1.0 99.0 0.5 97.0 0.9 89.3 3.8 2.04 99.0 0.7 97.3 1.3 80.2 2.5 87.0 1.4 10.4 99.2 0.5 97.8 0.8 88.7 2.6 94.0 1.1 28.2 99.2 0.5 98.6 0.8 96.4 0.8 98.4 0.6

Citric Acid (mM)

64.3 98.6 1.1 97.8 1.1 97.9 0.6 99.5 0.3


Grp df t p-


Adjusted p-value

N 11 -4.91 < 0.01 < 0.01 98.04 < 0.01 < 0.01S 10 -110.81 < 0.01 < 0.01 0.48 0.63 1.00Q 11 -63.78 < 0.01 < 0.01 -0.68 0.51 1.00C 11 -51.85 < 0.01 < 0.01 -0.95 0.36 1.00

66

Table 3-14. Performance to training stimuli during high NaCl + low sucrose testing Group


0.107 94.6 1.6 95.9 1.3 89.0 2.6 95.2 1.9 0.376 97.4 1.0 98.4 0.7 98.1 1.0 98.6 0.5 0.668 98.6 0.4 98.3 0.9 99.7 0.3 98.8 0.7

NaCl (M)

1.07 98.6 0.3 98.2 0.8 97.7 0.6 98.0 0.8 0.042 99.7 0.3 92.2 1.9 87.2 2.3 96.5 2.0 0.077 100.0 0.0 97.9 0.8 95.1 1.6 98.1 0.7 0.148 98.6 0.8 97.7 1.3 99.1 0.5 97.4 1.1

Sucrose (M)

0.421 99.4 0.4 98.5 1.0 97.3 1.1 96.4 1.2 0.027 99.0 0.5 87.5 3.9 93.7 1.3 79.7 4.0 0.131 99.1 0.5 94.2 1.0 96.8 0.5 84.8 3.3 0.360 98.9 0.6 94.3 1.4 97.5 0.5 90.1 2.5

Quinine (mM)

0.827 98.0 0.8 96.0 2.1 96.1 0.8 92.3 1.8 2.04 99.7 0.3 94.3 1.7 71.9 2.6 88.9 1.6 10.4 99.6 0.4 96.8 1.2 89.8 1.8 94.3 0.9 28.2 99.0 0.7 98.3 1.0 97.2 1.4 98.3 0.5

Citric Acid (mM)

64.3 99.0 0.5 99.7 0.3 98.8 0.7 99.1 0.3


Grp df t p-


Adjusted p-value

N 11 -17.58 < 0.01 < 0.01 15.07 < 0.01 < 0.01S 10 -4.43 < 0.01 < 0.01 14.87 < 0.01 < 0.01Q 11 -51.13 < 0.01 < 0.01 -1.17 0.27 1.00C 11 -18.29 < 0.01 < 0.01 3.18 0.01 0.08

67

Table 3-16. Performance to training stimuli during low NaCl + high sucrose testing Group


0.107 92.2 1.5 95.0 1.5 86.2 2.2 98.1 0.6 NaCl 0.376 99.2 0.3 98.5 0.6 98.6 0.6 98.7 0.5 (M) 0.668 98.9 0.5 99.3 0.5 98.1 1.1 96.7 1.6 1.07 98.7 0.4 99.2 0.5 90.6 8.3 97.6 0.7 0.042 98.1 0.6 88.9 1.9 81.7 3.4 91.2 2.3 Sucrose 0.077 99.7 0.3 93.0 1.7 87.0 2.2 93.3 3.1 (M) 0.148 99.6 0.4 98.7 0.7 98.0 1.0 97.6 1.0 0.421 99.6 0.4 98.9 0.5 97.8 0.8 98.9 0.6 0.027 97.5 1.2 87.5 2.0 93.1 1.0 78.8 3.4 Quinine 0.131 97.5 1.2 95.1 1.6 95.1 1.0 85.0 3.5 (mM) 0.360 98.9 0.6 96.8 1.2 88.4 8.0 91.4 1.9 0.827 98.3 0.6 98.4 0.7 96.3 0.8 90.1 2.3 2.04 98.3 0.8 93.9 1.4 57.3 4.2 79.3 2.5 Citric

Acid (mM)

10.4 98.9 0.8 98.7 0.7 81.4 7.2 91.8 1.4 28.2 99.7 0.3 99.5 0.5 99.3 0.7 99.0 0.4 64.3 97.7 1.0 98.5 0.9 97.1 1.0 99.4 0.3

Table 3-17. Results from separate one-sample t-tests for water Test against 1.0 Test against 0

Grp df t p-


Adjusted p-value

N 11 -130.33 < 0.01 < 0.01 3.01 0.01 0.08S 10 -30.60 < 0.01 < 0.01 7.19 < 0.01 < 0.01Q 11 -4.73 < 0.01 < 0.01 90.67 < 0.01 < 0.01C 11 -24.67 < 0.01 < 0.01 7.880 < 0.01 < 0.01

Table 3-18. Performance to training stimuli during water testing Group


0.107 97.3 0.6 99.2 0.8 94.5 1.0 97.9 0.8 NaCl 0.376 98.3 0.5 98.9 0.6 98.6 0.6 98.4 0.8 (M) 0.668 98.7 0.4 96.7 0.9 99.2 0.5 98.5 0.6 1.07 99.0 0.3 98.3 0.9 99.3 0.5 99.7 0.3 0.042 96.7 1.0 90.7 2.4 87.3 2.5 94.2 1.9 0.077 98.3 1.4 95.2 1.3 96.7 1.1 98.0 1.0 0.148 99.2 0.5 98.4 0.4 98.4 0.7 97.9 1.2

Sucrose (M)

0.421 99.3 0.5 98.1 0.8 97.4 0.8 96.9 1.2 0.027 97.2 1.6 89.1 2.7 92.3 1.2 81.6 2.2 0.131 96.9 1.2 94.0 2.3 94.7 0.9 88.7 2.5 0.360 97.5 1.0 92.4 2.3 94.3 1.2 91.0 2.6

Quinine (mM)

0.827 98.0 0.7 97.1 1.1 94.0 1.2 90.1 1.6 2.04 98.3 0.7 97.2 1.1 90.9 1.6 93.4 1.0 10.4 97.4 1.1 98.8 0.8 95.8 1.1 93.9 1.6 28.2 97.2 1.1 99.2 0.5 92.8 1.6 93.4 1.2

Citric Acid (mM)

64.3 98.6 0.6 99.5 0.5 97.9 1.0 98.6 0.6

68

NaCl

Concentration (M)0.01 0.1 1

0.0

0.2

0.4

0.6

1.0

0.8

Figure 3-1. Mean (n=8) unconditioned licking to NaCl in a brief access test. Rats monotonically decreased licking as concentration increased.

Sucrose

Concentration (M)0.001 0.01 0.1 1 10

0.0

0.2

0.4

1.0

0.8

0.6

Figure 3-2. Mean (n=8) unconditioned licking to sucrose in a brief access test. Rats monotonically increased licking as concentration increased.

69

Quinine

Concentration (mM)0.001 0.01 0.1 1 10

0.0

0.2

0.4

0.6

0.8

1.0

Figure 3-3. Mean (n=8) unconditioned licking to quinine in a brief access test. Rats monotonically decreased licking as concentration increased.

Citric Acid

Concentration (mM)0.01 0.1 1 10 100 1000

0.0

0.2

0.4

0.6

0.8

1.0

Figure 3-4. Mean (n=8) unconditioned licking to citric acid in a brief access test. Rats monotonically decreased licking as concentration increased.

70

Figure 3-5. An overview of the trial structure.

71

0.847 M NaCl

GroupsN S Q C

-0.20.00.20.40.60.81.01.2

Figure 3-6. The generalization profile obtained when 0.847 M NaCl was used as a test compound. The novel concentration generalized to the trained standard.

0.068 M Sucrose

GroupsN S Q C

-0.20.00.20.40.6

1.2

0.81.0

Figure 3-7. The generalization profile obtained when 0.068 M sucrose was used as a test compound. The novel concentration generalized to the trained standard.

72

0.546 mM Quinine

GroupsN S Q C

-0.20.00.20.40.60.81.01.2

Figure 3-8. The generalization profile obtained when 0.546 mM quinine was used as a test compound. The novel concentration generalized to the trained standard.

42.56 mM Citric Acid

GroupsN S Q C

-0.20.00.20.40.60.81.01.2

Figure 3-9. The generalization profile obtained when 42.56 mM citric acid was used as a test compound. The novel concentration generalized to the trained standard.

73

1.07 M NaCl + 0.421 M Sucrose

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 3-10. The generalization profile obtained when 1.07 M NaCl + 0.421 M sucrose was used as a test stimulus. The profile obtained was equally NaCl- and sucrose-like.


GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 3-11. The generalization profile obtained when 1.07 M NaCl + 0.077 M sucrose was used as a test stimulus. The profile obtained was NaCl-like.

74


Groups

Gen

eral

izat

ion

Scor

e

N S Q C-0.2

0.20.0

0.40.6

1.01.2

0.8

Figure 3-12. The generalization profile obtained when 0.376 M NaCl + 0.421 M sucrose was used as a test stimulus. The profile obtained was more sucrose-like than NaCl-like, but there was no statistical evidence for quinine-like or citric acid-like components.

Water

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.2

0.20.40.6

1.01.2

0.0

0.8

3. The generalization profile obtained when water was used as a test stimulus. The profile obtained was predominantly quinine-like, indicating that either

quinine had the weakest of the low concentrations.

Figure 3-1

water has a quinine-like taste quality and/or because, of all of the stimuli,

75

Retraining 0.360 mM & 0.827 mM Concentrations of Quinine Versus Water

0.4

0.5

0.6

0.8

0.9

1

% C

orre

c

01

0

0.3

0.7

14 15 16 17 18 19 20 21 22 23 24 25 26 27

t

.1

0.2 Quinine

Wa

Days

ter

f water Figure 3-14. Performance of the Q group during retraining for discrimination o

from the two mid-range concentrations of quinine.

Overall Performance During Negative Control Test

70

80

90

100

% C

orre

ct

0

10

0

1 2 3 4 5 6 7 8 9

Rat Number

2

40

50

60

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

* * * * * ** *

30

Figure 3-15. Performance on water control test. Individual performance scores for all

rats indicate that taste did not serve as a cue to guide behavior.

CHAPTER 4 APPLICATION OF A NEW BEHAVIORAL PA

QUALITY GENERALIZATION RADIGM TO ASSESS TASTE

Introduction

s,

d

s that were completely novel to the rats.

s. The rats were housed individually in polycarbonate shoe-

Chapter 3 explored whether rats would be able to perform a task in which they

were required to discriminate one prototypical taste compound, thought to be

representative of one of the putative four basic taste qualities, from the other three

prototypical taste compounds. Furthermore, we wanted to determine whether we could

use the untrained responses of the animals, when presented with novel taste compound

to generate profiles which would indicate how NaCl-like, sucrose-like, quinine-like, and

citric acid-like each novel test compound is. A few questions and confounds were not

addressed in that particular paradigm. Therefore, the following experiment was modifie

by 1) increasing the lowest concentration of quinine from 0.027 mM to 0.083 mM, and 2)

including a water (W) group specifically trained to discriminate the four prototypical

stimuli (comparison stimuli) from their water standard in an attempt to overcome the

pitfalls encountered in Experiment II of Chapter 3. Additionally, the current experiment

was also designed to extend the findings of the previous chapter by including test

compound

Method

Subjects

Thirty naïve adult male Sprague-Dawley (Charles River Breeders; Wilmington,

MA) rats served as subject

76

77

box style cages in a room where temperature, humidity, and light cycle (lights on 7am –

7pm)

e HCl, and citric acid,

present the putative 4 basic tastes, salty, sweet, bitter, and sour,

respe

out

g

ere

were controlled automatically. All manipulations were performed during the light

phase. The rats had ad libitum access to Purina Rat Chow (5001) in the home cage.

Purified (Elix 10; Millipore, Billerica, MA) water was also available, but was removed

approximately 16 hours before (4:00 pm the day before) the first behavioral session of the

week and was replaced at the completion of the last session of the week.

Apparatus

The apparatus was the same as that described in Experiment II of Chapter 3.

Task Overview

The prototypical taste compounds, NaCl, sucrose, quinin

were used to re

ctively. Five groups of rats were trained in a manner similar to that in Chapter 3.

Briefly, they were trained to respond by licking one response spout after sampling any of

the 4 training concentrations of a particular standard, which for each group was one of the

prototypical compounds or water, and they were trained to lick a different response sp

after tasting any of the comparison stimuli, which included the remaining compounds

(see Table 4-1).

Stimuli

The same concentrations of each of the prototypical compounds were used durin

training and were based on the results of Experiment I in Chapter 3. All solutions w

prepared daily with purified water (Elix 10, Millipore, Billerica, MA) and reagent grade

chemicals, and were presented at room temperature. Test stimuli consisted of four

concentrations each of NaCl (0.107 M, 0.376 M, 0.668 M, and 1.07 M; Fisher Scientific,

Atlanta, GA), sucrose (0.042 M, 0.077 M, 0.148 M, and 0.421 M; Fisher Scientific,

78

Atlanta, GA), citric acid (2.04 mM, 10.4 mM, 28.2 mM, and 64.3 mM; Fisher Scien

Atlanta, GA), quinine (0.083 mM, 0.131 mM, 0.360 mM, and 0.827 mM; Sigma-A

St Louis, MO) and purified water. Note the originally intended lowest

tific,

ldrich,

concentration of

d in this experiment.

Trial

t

parison

s,

If the rat failed to respond, or

respo out .

al

he

,

r

quinine, 0.083 mM, was include

Structure

On any given trial (see flow chart), rats were trained to lick a centrally positioned

stimulus delivery spout. Initially, the sample spout was dry, but when the rat licked two

times with an interlick interval < 250 ms, then the shaft of the spout was filled with the

stimulus solution, after which the rat could sample up to 5 licks (~5µl was deposited into

the fluid column upon each lick) before the spout was rotated out of position. Next, a

decision phase was initiated, during which the rat was required to lick one response spou

after tasting the standard stimulus or the other response spout after tasting a com

stimulus. During the consequence phase, if the rat responded correctly to the stimulu

water reinforcement was delivered directly through the response spout (20 licks @ ~5µl

per lick or a total of 10 s access, whichever occurred first).

nded on the incorrect response spout, then the rat was punished with a 20-s time

After either consequence of the decision phase, the trial moved into an inter-trial interv

that lasted 6 s. See Figure 3-5 in Chapter 3 for an overview of the trial structure.

Training

Table 4-2 contains the training parameters associated with this experiment. T

inclusion of water as a comparison had to be abandoned in order to proceed with training

but the water group was maintained, albeit on a different training schedule than the othe

4 groups (see Table 4-3).

79

Spout training

This phase was the same as that described in Experiment II Chapter 3. The rats had

pout (either the sample spout, the left response spout, or the right

respo

ty with getting fluid

m each of the spouts. Eventually, the sample spout would contain a taste stimulus and

re ntain water. The rats had to learn to lick from the

mp ponse spouts by licking it. If the rat responded

ment was available (10 s access or 20 licks, whichever

me ial interval during this phase was 6 s.

s the same as that described in Experiment II Chapter 3. Briefly,

ly o as presented within a given session during side training. If the rats

r standard in the first session, then during the next session, the rats

ison trials. After sampling, rats had 180 s (limited hold period)

rin required to respond. Side training lasted a total of 4 days. Only

th ration of each stimulus was presented.

access to only one s

nse spout) and each spout was connected to a reservoir that contained water. The

point of this phase was to train the rats to approach and gain familiari

fro

the sponse spouts would only co

sa le spout and then select one of the res

correctly, then water reinforce

ca first). The inter-tr

Side training

This phase wa

on ne trial type w

were trained with thei

received only compar

du g which they were

the ird highest concent

Alternation

This phase was the same as that described in Experiment II Chapter 3. Briefly,

during alternation training, the rats started out with either a standard or one of the

comparison stimuli. Upon completion of a set criterion of correct responses, the program

switched to the opposite trial type. The criterion was set at 6 the first day, 4 the second

day, and 2 the third day of alternation training. Each time the rat completed the criterion

of correct responses, the program automatically switched to delivery of the other trial

80

type. The correct responses did not have to be consecutive. The limited hold was

changed from 180 s to 15 s. During the decision phase, if a rat failed to make any

respo

ere delivered in a block with a random pattern selected by the computer

program. Therefore, the rats had no indi m the prior trial, which solutions would

be off

reservoirs, only two concentrations

e of the lowest two) of each prototypical compound

he block size was 16 to accommodate the water standard;

conse

shment

e

t

owever, a punishment contingency if the rat

failed to make a response. There was no correct response associated with a test stimulus,

nse, or made the incorrect response, a 10-s timeout was initiated.

Discrimination training I-III

Trials w

cation fro

ered on the current trial. All 4 training concentrations were used in this phase, but

because the gustometer had a limited number of fluid

(always one of the highest two and on

were included per session. T

quently, every standard concentration for a given session was repeated three times

within the block so that the number of standard stimuli matched the number of

comparison stimuli available (which were each only presented once per block). The

timeout period was increased to 20 s during this phase. The training schedules differed

for the W group and the N, S, Q, & C groups at this point.

Once performance reached an asymptote for all animals in the N, S, Q, and C

groups (85% or better two consecutive days), a partial schedule of reinforcement was

introduced. During the session, 2 trials (one standard and one comparison) from each

block of 12 trials were randomly selected to have neither reinforcement nor puni

delivered contingent on the animal’s response. That is, the animal did not receiv

reinforcement if it made the correct response but it also did not receive punishment if i

made the incorrect response. There was, h

81

so the animal would not receive reinforcement, but it also did not receive punishment for

a response, unless it failed to make the response before the limited hold (5 s) timed out.

Test Compounds

In order to extend the results from the last experiment, only novel taste compounds

were tested. The following novel compounds served as test stimuli:

• 0.376 M sodium gluconate

• 0.668 M sodium gluconate

• 0.131 mM denatonium

• 0.360 mM denatonium

• 0.077 M maltose

• 0.148 M maltose

• 0.376 M KCl

• 0.668 M KCl

• 0.077 M MSG

• 0.148 M MSG

• 0.077 M fructose

• 0.148 M fructose

Data Analysis

The same calculation and interpretation for the Generalization Score was used as

described in Experiment II of Chapter 3. One-way analyses of variance (ANOVAs) were

conducted for each test stimulus to determine the presence of differences among groups

followed by detailed Bonferroni-adjusted paired comparisons. Separate one-sample t-

tests testing group means against both of the null hypotheses 1.0 (the test compound was

similar to the standard stimuli) and 0 (the test compound was similar to the comparison

82

stimuli) were performed. The conventional p < 0.05 value was used as the statistical

rejection criteria.

Data for the negative control test were analyzed using a one-sample Binomial

analysis with null hypothesis = 0.5, which corresponds with the chance level of

performance.

Results

, S, Q, and C for the two concentrations of each of the 6 test

comp

C.

standard stimulus, the profile was still

In addition, Bonferroni post hoc comparisons of the

re revealed that S, Q, and C groups did not differ from each other,

while

Results for groups N

ounds can be seen in Figures 4-1 through 4-10.

Test Stimulus: Sodium Gluconate

0.376 M sodium gluconate

Figure 4-1 shows the behavioral profile obtained for 0.376 M sodium gluconate.

An ANOVA comparing Generalization Scores obtained from the 4 groups revealed that

there was a significant difference between one or more of the groups (F(3, 20) = 68.7, p <


Scores for the different groups could be ordered in the following way: N > S = Q =

Separate one-sample t-test analyses of the Generalization Scores (see Table 4-4) showed

that all groups were statistically different than 1.0. In addition, the N and S groups were

statistically different than 0. These results show that although the N group did not treat

0.376 M sodium gluconate exactly like a

predominantly NaCl-like.

Generalization Sco

the N group differed from all three.

83

0.668 M sodium gluconate

Figure 4-2 shows the behavioral profile obtained for 0.668 M sodium gluconate.

An ANOVA comparing Generalization Scores obtained from the 4 groups revealed

there was a significant difference between one or more of the groups (F(3, 20) = 44.4, p

0.01). A post-hoc analysis with Bonferroni adjustment indicated that the Generalization

Scores for the different groups could be ordered in the following way: N > Q = S = C.

Separate one-sample t-test analyses of the Generalization Scores (see Table 4-5) showed

that all groups were statistically different than 1.0, although with a Bonferroni adjustme

for multiple t-tests, the N group failed to reach statistical significance. Thus, statistical

evidence exists to support the claim th

that

<

nt

at the N group was standard-like. After Bonferroni

lied to the results from the t-test aimed at discerning which groups

differ sis

-

there

Separate one-sample t-tests of the Generalization Scores (see Table 4-7) determined that

correction was app

ed statistically from 0 (that the test stimulus was comparison-like), the analy

revealed that only the N and Q groups differed from 0. Thus, there is a predominant

NaCl-like component in 0.668 M sodium gluconate and possibly also a slight quinine

like component. Performance to the training stimuli used during testing for 0.376 M and

0.668 M sodium gluconate is shown in Table 4-6.

Test Stimulus: Denatonium

0.131 mM denatonium

Figure 4-3 shows the behavioral profile obtained for 0.131 mM denatonium. An

ANOVA comparing Generalization Scores obtained from the 4 groups revealed that


0.01). A post-hoc analysis with Bonferroni adjustment showed that the Generalization

Scores for the different groups could be ordered in the following way: Q > S > C = N.

84

all groups except the Q group differed significantly from 1.0 (the test compound was

standard-like). Thus, denatonium is statistically not different than the training

, the t-test comparing the Generalization

ealed that both the Q and S groups were statistically greater than 0,

indica

tion

S.

led

different than 0, indicating that the N, S, and C groups had performance that

n-like. This test compound, 0.360 mM denatonium, was clearly quinine-

like.

there

concentrations of quinine. On the other hand

Scores to 0 rev

ting that 0.131 mM denatonium is treated behaviorally as predominantly quinine-

like, and very slightly sucrose-like.

0.360 mM denatonium

Figure 4-4 shows the behavioral profile obtained for 0.360 mM denatonium. An



0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generaliza

Scores for the different groups could be ordered in the following way: Q > C = N =

Separate one-sample t-test analyses of the Generalization Scores (see Table 4-8) revea

that the Q group is not statistically different than 1.0 (i.e., the test compound is standard-

like), while all of the other groups are different. Furthermore, only the Q group is

statistically

was compariso

Performance to the stimulus control concentrations for both 0.131 mM and 0.360

mM denatonium is shown in Table 4-9.

Test Stimulus: Maltose

0.077 M maltose

Figure 4-5 shows the behavioral profile obtained for 0.077 M maltose. An

ANOVA comparing Generalization Scores obtained from the 4 groups revealed that


85



Separate one-sample t-test analyses of the Generalization Scores (see Table 4-10)

revealed that all of the groups differed significantly from 1.0 but that only the S and Q

groups differed from 0. This indicates that there was both a sucrose-like and quinine-like

component to

the maltose. Since the post hoc analyses of the ANOVA revealed that Q >

r Q component to the compound than an S

hould be stated again that the Generalization Score does not reflect the

intens

ere

N.

that the post hoc analysis of the ANOVA showed no differences

d Q group means. These results, taken together, indicate that there is an

equal

n

S, it can be concluded that there is a stronge

component. It s

ity of the taste quality, but it is an indicator of how similar the test compound is to

the standard stimulus concentrations.

0.148 M maltose

Figure 4-6 shows the behavioral profile obtained for 0.077 M maltose. An

ANOVA comparing Generalization Scores obtained from the 4 groups revealed that th



Scores for the different groups could be ordered in the following way: S = Q > C >


revealed that all of groups differed statistically from a hypothesized mean of 1.0.

Additionally, the S and Q groups also differed significantly from a hypothesized mean of

0. It is interesting

between the S an

sucrose-like and quinine-like component arising from 0.148 M maltose. These data

might reveal the basis of taste cues which allow discrimination of maltose and sucrose i

86

rats. Performance to the training stimuli for both concentrations of maltose can be seen

in Table 4-12.

Test Stimulus: Potassium Chloride (KCl)

0.376 M KCl

Figure 4-7 shows the behavioral profile obtained for 0.376 M KCl. An ANOV

comparing Generalization Scores obtained from the 4 groups revealed that there was a

significant difference between one or more of the groups (F(3, 20) = 6.2, p < 0.01). A

post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores for

the different groups could be ordered in the following way: Q

A

> C = N = S (also Q>N=S)

Separate one-sample t-test analyses of the Generalization Scores (see Table 4-13) show

that all groups are statistically different than 1.0 (standard-like). After Bonferroni

adjustment for multiple comparisons, only the performance of the Q and N groups

differed from 0 (comparison-like). Collectively, these data indicate that while KCl is

pred

.

ominantly quinine-like there is also a NaCl-like component. The profile is that of a

alities contributing at least some portion to the overall

0.668

a

r

complex taste, with two qu

experience

M KCl

Figure 4-8 shows the behavioral profile obtained for 0.668 M KCl. An ANOVA

comparing Generalization Scores obtained from the 4 groups revealed that there was


post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scores fo

the different groups could be ordered in the following way: Q > C = N = S. Separa

one-sample t-test analyses of the Generalization Scores (see Table 4-14) show that

performance in all groups was statistically different than it was for their respective

te

87

standards. Performance for each of the groups, N, Q, and C, was significantly different

than 0, indicating that portions of those three qualities contributed to the taste of KCl.

The predominant aspects are quinine and citric acid followed by NaCl, which coincid

nicely with data from previous studies, suggesting KCl has a ‘bitter’, ‘sour’, ‘salty

(Morrison, 1967). Data concerning the performance to the training stimuli are shown i

es

’ taste

n

Test

A

es for

te

y

s result

o a

pect.

A

Table 4-14.

Stimulus: Monosodium Glutamate

0.077 M MSG

Figure 4-9 shows the behavioral profile obtained for 0.077 M MSG. An ANOV



post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scor

the different groups could be ordered in the following way: S > N > Q = C. Separa


performance for all groups differed from the standard (hypothesized mean of 1.0). Onl

the S and N groups differed from 0, indicating that there was both a NaCl-like and

sucrose-like component to the 0.077 M MSG. The post hoc analysis of the ANOVA

indicated that there was a greater sucrose-like component than NaCl-like. Thi

suggests that MSG, at this concentration, is predominantly sucrose-like but there is als

NaCl-like as

0.148 M MSG

Figure 4-10 shows the behavioral profile obtained for 0.148 M MSG. An ANOV



88

post-hoc analysis with Bonferroni adjustment revealed that the Generalization Scor

the different groups could be ordered in the following way: N > S > C = Q. Separate


performance for all groups differed from the standard (hypothesized mean of 1.0). O

the S and N groups differed from 0, indicating that there was both a NaCl-like and

sucrose-like component to the 0.077 M MSG. The post hoc analysis of the ANOVA

indicated that there was a greater NaCl-like component than sucrose-like. This res

suggests that MSG, at this concentration, is predominantly NaCl-like but there is also a

sucrose-like aspect. Taken together, both profiles for MSG would suggest there is good

evidence to postulate that the taste of MSG is more sucrose-like or NaCl-like, depen

on the concentration, than anything else, but that it is definitely a combination of the two

compounds and there is little evidence to support the claim that MSG is representative of

a fifth taste quality in rats. The performance to the training stimuli is shown in Table 4-

18.

Test Stimulus: Fructose

es for

nly

ult

ding

0.077

ion

the S, Q, and C groups differed from 0. This indicates that there was a sucrose-like,

M fructose

Figure 4-11 shows the behavioral profile obtained for 0.077 M fructose. An



0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Generalizat



revealed that all of the groups differed significantly from 1.0 except the Q group, but that

89

quinine-like, and citric acid-like component to the fructose. Since the post hoc analyses

of the ANOVA revealed that Q > S > C, it can be concluded that ther

e is a stronger Q

component to the compound than an S It should be stated again that the

Gene

.

re

alization

from a hypothesized mean

that the post hoc analysis of the ANOVA showed no differences

betwe s an

e

component.

ralization Score does not reflect the intensity of the taste quality, but it is an

indicator of how similar the test compound is to the standard stimulus concentrations

0.148 M fructose

Figure 4-12 shows the behavioral profile obtained for 0.077 M fructose. An

ANOVA comparing Generalization Scores obtained from the 4 groups revealed that the


0.01). A post-hoc analysis with Bonferroni adjustment revealed that the Gener

Scores for the different groups could be ordered in the following way: S = Q > C = N.


revealed that all of groups differed statistically from a hypothesized mean of 1.0.

Additionally, the S, Q and C groups also differed significantly

of 0. It is interesting

en the S and Q group means. These results, taken together, indicate that there i

equal sucrose-like and quinine-like component arising from 0.148 M fructose. Thes

data might indicate that fructose and sucrose would be discriminable to rats.

Performance to the training stimuli for both concentrations of fructose can be seen in

Table 4-21.

Performance of Water Group

The water (W) group was on a different schedule than the other groups because

discrimination of water from quinine proved a difficult task. Results for the different

phases of training are shown in Figure 4-13. First, the W group was performing poorly

90

overall when only the third-highest concentrations of all compounds were used in

training. When comparing their performance to all compounds, it was clear that the

problem primarily occurred between water and quinine (data not shown). Only the

highest concentration of quinine and water were next used to help rats discriminat

between the compounds. See summary of training schedule (Table 4-3) for number of

days at each manipulation. Next, the number of sample licks and reinforcement licks was

increased and appeared to improve the performance slightly (see Figure 4-13). A

different water source (Publix purified water) was used, and that appeared also to help th

rats learn the discrimination (see Figure 4-13), but we cannot rule-out that this could ha

been based on potential chemical cues arising from the storage container (Song, Al-

Taher, & Sadler, 2003). In fact, high levels of discriminability remained when the

source was switched back to the in-house Millipore-purified water. A drop in the nu

e

e

ve

water

mber

of sam ee

e

g that

ows that there was

iability in the group (Figure 4-13). The overall performance to training

comp

ple licks and reinforcement licks resulted in a decrease in performance levels (s

point dt1-27 on Figure 4-13). Finally, returning to the 10 sample licks and 40

reinforcement licks appeared to increase levels of performance.

Next, one of the two highest concentrations of each prototypical compound was

used to retrain the rats to discriminate water from all 4 prototypical compounds; th

concentration present depended on which concentrations the other groups were usin

session. Finally, all of the concentrations were rotated through the training sessions and

overall performance was better than chance, though the graph sh

substantial var

ounds is listed in Table 4-22; from the table, it can be seen that the rats did not

perform as well as the other rats on many of the compounds, suggesting that maintaining

91

stimulus control under these conditions is difficult. The training history of the ani

cannot be ruled out as a contributing factor to the poor performance.

Discussion

The results from the test compounds reveal that the rats can generalize the behavi

learned from the prototypical training compounds to completely novel compounds.

Presumably, if the behavior generalizes to untrained (i.e., no consequence was delivered

for responses on these trials) compounds, then it supports the conclusion that the anima

is responding on the basis of a shared feature between the standard stimuli and the test

stimulus, most likely related to taste quality. The fact that performance to the tra

stimuli remained high and the profiles obtained were distinct for the different

compounds, suggests the animals were not just arbitrarily responding to test compounds.

Some of the profiles were complex, which further

mals

or

l

ining

demonstrates the strength of this

parad

ach

size of

ate anion size limiting passage of sodium through tight junctions; NaCl, with

the re ons

igm to capture not only pure taste qualities, but also compounds which appear to

possess two or more distinct taste qualities (e.g., MSG, KCl).

Sodium Gluconate

The profiles for both concentrations of sodium gluconate were similar to e

other, although there was more of a quinine-like component present for the highest

concentration tested. These data may help to explain differences found between NaCl

and sodium gluconate in other behavioral data in the literature. Sodium gluconate and

NaCl are thought to activate different salt transduction pathways due to the large

the glucon

latively smaller anion is thought to be capable of passing through the tight juncti

resulting in activation of paracellular receptor sites (Elliot & Simon, 1990; Formaker &

Hill, 1988; Ye et al., 1991, 1993, 1994). Therefore, it was assumed that when amiloride,

92

an epithelial sodium channel (ENaC) blocker, was applied to the oral cavity, the

transduction of both salts would be impaired differentially because sodium gluconat

theoretically would not have a pathway to activate. Geran and Spector (2000), howe

showed that although amiloride treatment significantly shifted sodium gluconate

detection thresholds more than it did for NaCl thresholds, rats were still able to pe

e

ver,

rform

above ter

m

l.,

e and high concentrations of sodium gluconate, that is responsible for

onent present in the higher concentration.

Dena

.

chance at higher concentrations (0.1, 0.2 and 0.4 M) of the organic salt. They la

concluded that it did not seem likely that the higher levels of performance to sodiu

gluconate was related to Na+ reaching the transcellular receptors (see Geran & Spector,

2000, 2004 for further discussion). Their conclusion leaves open the possibility that a

non-sodium cue was being detected at the high sodium gluconate concentrations. The

data in this experiment, showing that there is a quinine-like component to sodium

gluconate, however slight, might serve as a basis for the difference in performance.

It would be interesting to know which gustatory receptors sodium gluconate, at

various concentrations, activates. Perhaps it additionally activates taste receptors

belonging to the T2R family, which have been shown to be involved in “bitter” taste

transduction (Chandrashekar et al., 2000; Gilbertson & Boughter, 2003; Zhang, et a

2003). If not, then perhaps there is some convergence that occurs downstream of

receptor signaling, such as a shared component in the signal transduction pathway

activated by quinin

the quinine-like comp

tonium

That denatonium was treated as similar to the Q group standard was not surprising

In fact, this was predicted based on the work of Spector and Kopka (2002) that showed

Sprague-Dawley rats cannot discriminate between the two purported “bitter” tasting

93

compounds. Their results were controversial in light of other published findings

suggesting that rats could discriminate between the two compounds (Caicedo & Roper,

2001) because their application to taste receptor cells in situ resulted in differential

calcium responses in separate subpopulations of cells, which they interpreted to imply

discrimination at the cellular level. The data presented here together with the data from

Spector and Kopka (2002) could argue that even if different populations of receptor cells

are activated with exposure to the two ligands, the signal that is used by the animal to

guide

The

ped

rences

th

e two compounds,

havioral data here and in Spector and Kopka (2002).

Malto

of

ther

, &

behavior is apparently the same for both quinine and denatonium. Alternatively,

activation of potentially separate signal processing pathways results in the same

behavioral outcome. See Figure 4-14 for a diagram outlining both possibilities.

designs of the current experiment, and that of Spector and Kopka (2002) are not equip

to determine which of the two might be the case.

These two findings could be reconciled if one considers that there were diffe

between levels of investigation. That is, the behavior of the rats represents the output of

the entire gustatory system, whereas the findings from measurement of calcium

responding (Caicedo & Roper, 2001) are based on the initial stages of stimulus

processing, which occur in a discrete subpopulation of receptor cells. Therefore, it is

possible that convergence of information somewhere in the gustatory neuraxis from bo

denatonium and quinine plays a role in the perceptual similarity of th

which was supported by be

se

The fact that maltose did not fully generalize to sucrose makes sense in light

previous studies showing that maltose and sucrose are discriminable from one ano

(Nissenbaum and Sclafani, 1987; Spector and Grill, 1988; Spector, Markison, St. John

94

Garcea, 1997). The results from this paradigm, along with those from previous work

suggests that the discrimination of maltose from sucrose is not based merely using

intensity cues, but is likely guided by other “sideband” tastes, described here. This

finding, above all others, might demonstrate the true strength of this approach. It gives

insight into how similar the test compound is to each of the prototypical stimuli.

It is interesting, however, that there is a substantial quinine-like (assumed to be

inherently aversive) component to the maltose profiles because maltose has been

established as a preferred stimulus in rats (Davis, & Smith, 1992; Richter & Campbell,

1940; Sclafani, & Clyne, 1987; and Sclafani, & Mann, 1987; Sc

,

lafani & Nissenbaum,

1987) s

r

at

ulus is related to the extensive use of this

salt a

times in rats that NaCl is behaviorally discriminable from KCl (St. John, Markison, &

. It has also been shown to cross-generalize to other sugars, like sucrose, in studie

employing the conditioned taste aversion approach in the rat (Sako, Shimura, Komure,

Mochizuki, Matsuo, & Yamamoto, 1994; Spector & Grill, 1988), but not in the hamste

(MacKinnon, Frank, Hettinger, & Rehnberg, 1999). The issue at hand highlights an

interpretive requirement concerning the profiles obtained here. It must be stressed th

the height of the bar does not imply intensity of the compound, but merely indicates the

presence of the component. That is, when a test compound fully generalizes to the

standard of a given group, it says nothing of the intensity of that signal, but only that the

taste arising from the test compound fits into the range that was trained to define the

standard stimulus.

Potassium Chloride

The choice to include KCl as a taste stim

s a taste stimulus in other studies. Morrison (1967) showed in his study with rats

that KCl produced a profile that was distinct from NaCl and it has also been shown many

95

Spector, 1997; Kopka, Geran, & Spector, 2000; Spector, Guagliardo, & St. John, 1996

St. John, Markison, Guagliardo, Hackenberg, & Spector, 1995). Potassium chloride

salt that tastes “salty-bitter” to humans (e.g., van der Klaauw & Smith, 1995). In

employing behavioral generalization (CTA), non-sodium salts and acids are catego

similarly by rats (Nowlis, Frank, & Pfaffmann, 1980). One of the goals here was to use

the present paradigm to obtain a behavioral description of KCl in rats, which mi

to determine the qualitative characteristics used by rats to identify the taste of KCl.

The profiles obtained for both concentrations of KCl were indicative of a complex

taste. There were components of quinine-like, citric acid-like, and NaCl-like tastes,

which may pr

;

is a

studies

rized

ght help

ovide insight into the differential taste cues that a rat might use to

te the salt from NaCl. It would be interesting to know if adulteration with

amilo ted

hn,

this

discrimina

ride would cause NaCl to yield a profile that looked like KCl, as would be predic

from behavioral work showing the two compounds are treated similarly with oral

amiloride application (Hill, Formaker, & White, 1990; Spector, Guagliardo, & St. Jo

1996). Technically, this would be difficult because it would be important to maintain

stimulus control of the NaCl training stimuli, and if everything was adulterated with

amiloride (as is commonly done to assure constant exposure to the ENaC blocker),

would be impossible. It might be feasible, however to present the animals with a few

trials at the end of the session in which amiloride is added to NaCl.

Monosodium Glutamate

The inclusion of MSG as a test compound was in response to the growing

acceptance for “umami” taste as a distinct fifth quality. As stated previously, the

evidence for a separate MSG-like taste quality is mixed for rodents, but there are

examples supporting the existence of this taste quality where MSG is distinguishable

96

from sucrose and NaCl (Heyer, Taylor-Burds, Mitzelfelt, & Delay, 2004; Stapleton,

Luellig, Roper, & Delay, 2002). The NaCl-like aspect of MSG is often controlled for

using amiloride to suppress the taste of NaCl or adding NaCl to sucrose solutions to

account for the salt taste present in MSG (e.g., Heyer, Taylor-Burds, Mitzelfelt, & Delay,

2004). Heyer, Taylor-Burds, Mitzelfelt, and Delay (2004) conclude that “sweet”

(sucrose) and “umami” (MSG) afferent signaling may share a similar signaling pathway

either through a common taste receptor with high affinity for both prototypical

compounds, some similar downstream transduction mechanism, or possibly through cell-

cell interactions (e.g., see Figure 4-14 for similar explanation).

Some of those possibilities have been supported by work using the mouse model

that indicates that the two transduction processes do share similar components (Zhang et

al., 2003; Zhao et al., 2003). Taken together, it is not surprising that we found both a

NaCl-like and sucrose-like profile for the concentrations of MSG tested. The data from

the present experiment extend previous findings suggesting that, in the rat, the taste

quality associated with MSG is not uniquely different than that arising from sucrose and

NaCl, but is likely a combination of the two. It would be interesting to test more amino

acids to uncover whether they are similarly categorized by rats to be a combination of the

putative four basic tastes, or if they will yield a profile as yet unseen. Moreover, perhaps

different amino acids would fall into categories, based on similarity of responding, that

would match those interpreted to be “sweet” tasting and “bitter” tasting (Iwasaki,

Kasahari, & Sato, 1985; Nelson et al., 2002).

In summary, the findings from the present study have provided evidence for the

usefulness of this paradigm to examine the perceptual taste qualities of novel compounds

97

for which the animals have never received explicit training. That rats will respond to

novel stimuli the level to w stimulus (e.g.,

q and d aton aC ts that the two

. Additionally, the behavioral

paradigms can potentially be used to indicate other taste qualities that might play a part in

the overall perceptual experience gen pound. This is especially

rmati hemical stimuli that have comp any

g a rch to pursu n the finding ent study, but first the

tion e must be lly defined. Chapter 5 provides a discussion

po

ked nearly identical to maltose

was unexpected. Again, it is interesting that there is a substantial quinine-like (assumed

to be inherently aversive) component to the fructose profiles because fructose, like

maltose, is a preferred stimulus and it is a component of the sucrose molecule. It has

been shown to cross-generalize to other sugars, like sucrose, in studies employing the

conditioned taste aversion approach in the rat (e.g., Nissenbaum & Sclafani, 1987;

Nowlis, Frank, & Pfaffmann, 1980). On the other hand, Ramirez (1994) showed that rats

differentially avoided consuming sucrose and fructose following aversion training of

each, indicating that the two sugars differ in some aspect of quality after he attempted to

control for intensity. Additionally, experimental evidence from a human psychophysical

task claims that with many sugars (including fructose), the bitterness of sweeteners

decreases as concentration increases (Sciffman et al., 1995). Finally, it stands repeating

that the height of the bar does not imply intensity of the compound, but merely indicates

at same hich they respond to the standard

uinine en ium, and N l and sodium gluconate) sugges

compounds likely share similar qualitative features

erated by a com

info ve regarding c lex tastes. There are m

excitin venues of resea e give s of the pres

limita s of the procedur carefu

to that int.

Fructose

The fact that fructose generated a profile that loo

98

the presence of the component. That is, when a test compound fully generalizes to the

of a giv up, it says the in al, that the

ta ising f compound fits into the range that was trained to define the

s d stimu

standard en gro nothing of tensity of that sign but only

ste ar rom the test

tandar lus.

99

Table 4-1. Overview of experimental groups Group N St omparison Stimuli andard C1) N 6 NaCl Su nine, Citric Acid, Water* crose, Qui2) S 6 Sucro Na e, Citric Acid, Water) Q 6 inine NaCl, S , Citric A ater* ) C 6 ric Aci NaCl, , Quinine, * ) W 6 ter NaCl, , Quinine, Acid ate as ultim ndoned mparis

se Cl, Quinin * 3 Qu ucrose cid, W4 Cit d Sucrose Water5 Wa Sucrose Citric

*W r w ately aba as a co on stimulus

Table 4-2. Training schedule for N, S, Q, and C groups

Sessions Phase Limited hold (s),

t (s) Schedule timeou1-6 Spout ning N N/A trai /A 7-10 Side trai 180, tant

-13 Altern 15, ating 25 Discrim I 10, dom37 Only Q s 10, dom

38-40 Disc. I. (N,S,Q,C) 10, 20 Semi-random 41-47 Discrimination II 5, 20 Semi-random

Lim d hold i he maximu ount of time al or ns alternatited ly u ertain numb ct respons ade. This d w the ssi the secon , the es muli were

p d in ize ks dur sem om s ule

ning 0 Cons11 ation 10 Altern14- ination 20 Semi-ran 26- , W grp 20 Semi-ran

48-64 Partial Reinforcement 5, 20 Semi-random 65-90 Testing 5, 20 Semi-random

ite s t m amntil a c

lotted fer of corre

a respo e. Duringes were m

on, a stimulus predeterminewas presen

alternation repeated

criterion as 6 in first seing a

on, 4 in dched

and 2 in final s sion. Stiresente random d bloc i-rand

100

Table 4-3. Training parameters for W group.

Sessio Phase ple licks,

RLimited hold

tim out (s) ns Sam

einf. licks (s),

e Stimuli 1-6 Spout training 5, N/A Millipore wa 20 ter

7-10 S ng 5, 20 1 3rd conc., e wate

1-1 ion 5, 20 1 conM e water

14-3 Discrimination I 5, 20 10, 20 3rd highest conc., Millipore water

re water & 0.827 nine

53-61 Discrimin 5, 20 rified water & 0.827 M q

Di tion 5, ore water & 0.827 m ne

0-7 Di tion I 10, 4 M e water & 0.827 nine

77-8 Discrimination II 10, 40 5, 20 M e water &0.827 mM or 0.360 mM quinine

re water, all trations all

compounds

ide traini 80, 0 highestMillipor r

1 3 Alternat 5, 10 3rd highest c., illipor

7

38-52 Discrimination I 10, 40 10, 20 MillipomM qui

ation I 10, 40

20

Publix puuinine

62-69 scrimina I 5, 20 MillipM quini

7 6 scrimina 0 5, 20 illipormM qui

illipor either 1

82-90 Discrimination III 10, 40 5, 20 Millipoconcen

101

Table 4-4. Results from one-sample t-tests for 0.376 M NaGluconate Test against 1.0 Test against 0

Grp df t p-value Adjusted p-value

-t p-value

Adjusted pvalue

N 5 -3.34 0.02 0.17 14.01 - -

< 0.01 < 0.01S 5 58.84 < 0.01 < 0.01 6.62 < 0.01 < 0.01Q 5 15.48 < 0.01 < 0.01 0.49 0.64 1.00C 5 -26.81 < 0.01 < 0.01 -1.06 0.34 1.00

Table 4-5. Results from one-sample t-tests for 0.668 M NaGluconate Test against 1.0 Test against 0

Grp df t p-value Adjusted p-value

-t p-value

Adjusted pvalue

N 5 -3.06 0.03 <

0.225 10.06 < 0.01 0.01S 5 -19.13 < 0.01 < 0.01 4.38 < 0.01 0.06Q 5 -32.01 < 0.01 < 0.01 5.71 < 0.01 0.02C 5 -28.43 < 0.01 < 0.01 1.53 0.19 1.00

Table 4-6. Performance to training stimuli during sodium gluconate testing Group


0.107 86.5 2.5 89.5 4.3 88.2 3.1 96.4 1.5 0.376 96.0 0.7 97.0 1.5 96.4 0.9 92.6 1.3 0.668 98.0 0.4 99.1 0.9 98.6 0.6 94.0 2.0

NaCl (M)

1.07 93.2 1.5 92.6 3.0 96.0 1.0 93.5 2.4 0.042 97.4 0.7 87.3 1.7 84.8 2.7 88.6 1.7 0.077 96.8 1.4 94.6 2.2 92.2 3.0 85.7 3.2 0.148 96.5 2.4 97.2 0.9 98.1 0.6 95.6 1.4

Sucrose (M)

0.421 97.8 1.0 96.0 0.9 81.3 4.8 83.0 3.5 0.027 91.5 1.6 86.1 3.0 90.6 2.1 83.8 2.5 0.131 95.9 1.2 84.0 1.3 89.2 1.0 78.2 5.1 0.360 91.6 3.0 86.3 1.9 91.6 2.1 83.0 3.3

Quinine (mM)

0.827 95.1 0.9 92.3 2.3 95.8 0.8 78.9 4.6 2.04 97.6 1.6 96.6 1.7 70.5 4.8 78.3 3.3 10.4 93.6 2.8 95.4 1.8 84.3 2.7 88.9 2.0 28.2 96.7 1.0 95.2 3.2 93.3 2.2 94.9 1.0

Citric Acid (mM)

64.3 98.8 0.8 93.5 5.4 99.3 0.5 93.5 2.4

102

Table 4-7. Results from one-sample t-tests for 0.131 mM denatonium Test against 1.0 Test against 0 Grp df t p-value t p-value Adjusted p-

value Adjusted p-

value N 5 -68.14 < 0.01 < 0.01 0.26 0.80 1.00 S 5 -38.43 < 0.01 < 0.01 7.06 < <

4 <-

0.01 0.01 Q 5 -3.32 0.02 0.17 5.38 < 0.01 0.01 C 5 48.70 < 0.01 < 0.01 1.71 0.15 1.00

Table 4-8. Results from one-sample t-tests for 0.360 mM denatonium Test against 1.0 Test against 0

Grp df t p-value Adjusted p-

value t p-value Adjusted p-

value N 5 -69.45 < 0.01 < 0.01 1.10 0.32 1.00 S 5 -34.50 < 0.01 < 0.01 0.69 0.52 1.00 Q 5 0.29 0.79 1.00 3 <

- <5.38 < 0.01 0.01

C 5 21.55 < 0.01 0.01 1.70 0.15 1.00

Table 4-9. Performance to training stimuli during denatonium testing Group


0.107 86.5 2.5 89.5 4.3 88.2 3.1 96.4 1.5 0.376 96.0 0.7 97.0 1.5 96.4 0.9 92.6 1.3 0.668 98.0 0.4 99.1 0.9 98.6 0.6 94.0 2.0

NaCl (M)

1.07 93.2 1.5 92.6 3.0 96.0 1.0 93.5 2.4 0.042 97.4 0.7 87.3 1.7 84.8 2.7 88.6 1.7 0.077 96.8 1.4 94.6 2.2 92.2 3.0 85.7 3.2 0.148 96.5 2.4 97.2 0.9 98.1 0.6 95.6 1.4

Sucrose (M)

0.421 97.8 1.0 96.0 0.9 81.3 4.8 83.0 3.5 0.027 91.5 1.6 86.1 3.0 90.6 2.1 83.8 2.5 0.131 95.9 1.2 84.0 1.3 89.2 1.0 78.2 5.1 0.360 91.6 3.0 86.3 1.9 91.6 2.1 83.0 3.3

Quinine (mM)

0.827 95.1 0.9 92.3 2.3 95.8 0.8 78.9 4.6 2.04 97.6 1.6 96.6 1.7 70.5 4.8 78.3 3.3 10.4 93.6 2.8 95.4 1.8 84.3 2.7 88.9 2.0 28.2 96.7 1.0 95.2 3.2 93.3 2.2 94.9 1.0

Citric Acid (mM)

64.3 98.8 0.8 93.5 5.4 99.3 0.5 93.5 2.4

103

Table 4-10. Results from one-sample t-tests for 0.077 M maltose T Test against 1.0 est against 0

Grp df t p-value A

pA

vdjusted p-

value t -value djusted p-

alue N 5 -42.68 < 0.01 < 0.01 0.

5 <11 < 0. < 0.

2 0. 0.

158 0.88 1.00 S 5 -9.89 < 0.01 < 0.01 .79 0.01 0.02 Q 5 -6.98 < 0.01 < 0.01 .37 01 01 C 5 -14.34 < 0.01 < 0.01 .98 03 25

Table 4-11. Results from one-sample t-tests for 0.148 M maltose T Test against 1.0 est against 0

Grp df t p-value A

pA

vdjusted p-

value t -value djusted p-

alue N 5 - < 0

1 < <6 < 0. < 0.4 < 0. 0.

56.27 < 0.01 0.01 .69 0.52 1.00 S 5 -11.23 < 0.01 < 0.01 3.75 0.01 0.01 Q 5 -10.01 < 0.01 < 0.01 .93 01 01 C 5 -16.59 < 0.01 < 0.01 .21 01 07

Table 4-12. Performance to training stimuli during maltose testing Group

NaCl Sucrose Quinine Citric Acid Solution Conc.

1 1

10.4 98.7 0.9 98.2 1.2 97.2 1.0 95.0 1.1 28.2 99.0 0.7 96.9 1.5 97.7 1.1 96.5 0.6 64.3 98.0 0.8 98.4 1.0 97.8 1.5 98.3 0.7

Mean SE Mean SE Mean SE Mean SE 0.107 92.5 1.4 96.3 2.3 91.6 2.2 91.9 1.7 0.376 96.6 1.4 97.4 0.8 94.9 1.1 94.5 1.9 0.668 98.8 0.6 98.9 1.1 99.3 0.5 96.5 1.6

NaCl (M)

1.07 94.4 2.3 96.6 1.1 93.6 1.8 92.8 1.6 0.042 94.6 1.9 89.7 2.7 89.7 3.0 91.9 3.3 0.077 93.3 1.2 94.6 1.8 95.4 0.8 95.3 2.6 0.148 94.9 1.5 97.9 0.8 00.0 0.0 00.0 0.0

Sucrose (M)

0.421 94.4 1.3 96.8 1.0 96.5 2.5 94.4 1.1 0.027 92.5 3.8 93.4 2.3 89.0 2.0 90.7 1.5 0.131 92.8 0.9 93.6 3.3 94.3 0.7 89.2 3.0 0.360 91.7 2.8 95.1 1.7 97.1 0.5 86.9 3.5

Quinine (mM)

0.827 93.3 1.8 96.1 2.0 93.2 2.2 77.2 3.2 2.04 99.2 0.5 97.5 1.1 82.5 1.6 90.4 1.5 Citric

Acid (mM)

104

Table 4-13. Table of t-test statistics for 0.376 M KCl Test against 1.0 Test against 0

Grp df t p-value adjusted p-


value N 5 -24.52 < 0.01 < 0.01 8.70 < 0.01 < 0.01 S 5 -14.24 < 0.01 < 0.01 2.58 0.05 0.40 Q 5 -4.99 < 0.01 0.03 6.06 < 0.01 0.01 C 5 -9.56 < 0.01 < 0.01 4.20 < 0.01 0.07

Table 4-14. Table of t-test statistics for 0.668 M KCl Test against 1.0 Test against 0

Grp df t p-value Adjusted p-


value N 5 -18.08 < 0.01 < 0.01 6.38 < 0.01 0.01 S 5 -13.37 < 0.01 < 0.01 2.98 0.03 0.25 Q 5 -4.88 < 0.01 0.04 6.35 < 0.01 0.01 C 5 -6.37 < 0.01 0.01 4.67 < 0.01 0.04

Table 4-15. Performance to training stimuli during KCl testing Group


0.107 93.5 1.3 95.2 1.8 87.2 1.6 97.5 1.2 0.376 95.9 1.3 96.5 1.8 99.4 0.4 97.6 0.8 0.668 99.7 0.2 93.7 3.5 96.7 1.3 94.7 1.3

NaCl (M)

1.07 96.8 1.2 97.3 1.8 91.3 4.5 92.8 2.0 0.042 95.4 1.8 87.4 2.2 93.0 1.4 95.8 1.2 0.077 88.5 1.7 93.6 1.6 95.5 2.5 98.2 0.8 0.148 94.7 1.6 96.9 1.4 96.0 1.5 99.0 0.7

Sucrose (M)

0.421 94.0 1.7 93.6 3.2 90.7 3.6 94.7 1.1 0.027 98.1 0.8 90.8 3.1 93.1 1.7 86.2 2.4 0.131 96.5 0.6 87.7 2.6 93.4 0.7 84.0 4.7 0.360 98.9 0.7 92.6 4.2 93.8 1.5 89.2 1.9

Quinine (mM)

0.827 95.2 1.0 92.8 3.0 95.8 0.5 86.3 1.6 2.04 97.2 0.8 95.2 2.2 87.0 1.3 84.7 2.2 10.4 94.3 1.3 87.8 6.1 87.5 4.6 92.6 2.0 28.2 97.8 0.9 91.6 7.7 99.0 0.7 99.4 0.4

Citric Acid (mM)

64.3 98.1 0.8 96.9 2.3 98.7 0.6 95.4 2.0

105

Table 4-16. Table of t-test statistics for 0.077 M MSG Test against 1.0 Test against 0

Grp df t p-value Adjusted p-value t p-value

Adjusted p-value

N 5 -25.21 < 0.01 < 0.01 9.31 < 0.01 < 0.01 S 5 -6.85 < 0.01 < 0.01 8.91 < 0.01 < 0.01 Q 5 -28.00 < 0.01 < 0.01 0.86 0.43 1.00 C 5 -22.09 < 0.01 < 0.01 0.16 0.88 1.00

Table 4-17. Table of t-test statistics for 0.148 M MSG Test against 1.0 Test against 0


Adjusted p-value

N 5 -5.00 < 0.01 0.03 7.84 < 0.01 < 0.01 S 5 -18.56 < 0.01 < 0.01 12.66 < 0.01 < 0.01 Q 5 -38.35 < 0.01 < 0.01 0.87 0.43 1.00 C 5 -21.62 < 0.01 < 0.01 1.93 0.11 0.89

Table 4-18. Performance to training stimuli during MSG testing Group


0.107 87.9 2.5 91.3 3.5 90.9 1.7 92.7 1.9 0.376 94.5 2.2 95.8 1.8 96.3 0.9 91.9 1.4 0.668 98.6 0.5 94.9 5.1 100.0 0.0 90.7 2.6

NaCl (M)

1.07 95.7 1.0 97.0 3.0 95.7 1.1 96.7 1.2 0.042 94.1 1.7 85.6 1.9 83.9 3.0 87.9 2.3 0.077 93.7 1.2 95.0 0.8 86.7 2.4 94.4 1.4 0.148 98.1 0.8 96.8 1.1 95.4 1.2 92.2 2.1

Sucrose (M)

0.421 98.7 0.9 97.6 1.2 95.2 1.8 96.6 1.5 0.027 95.3 1.5 92.9 2.5 94.1 1.2 80.0 2.5 0.131 93.4 1.4 92.7 2.2 93.6 1.5 85.7 2.9 0.360 100.0 0.0 95.3 1.8 96.5 1.2 82.5 1.2

Quinine (mM)

0.827 96.1 1.6 94.4 3.5 96.0 1.0 82.8 2.7 2.04 91.4 2.4 99.3 0.7 69.1 5.8 83.3 1.7 10.4 97.0 0.9 95.5 1.7 81.1 2.5 90.2 1.7 28.2 97.6 1.6 98.2 1.1 91.0 3.6 93.9 1.9

Citric Acid (mM)

64.3 94.3 1.9 98.3 1.7 98.2 1.2 97.7 0.5

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Table 4-19. Table of t-test statistics for 0.077 M fructose Test against 1.0 Test against 0


Adjusted p-value

N 5 -68.25 > 0.01 > 0.01 1.28 0.26 1.00 S 5 -15.90 > 0.01 > 0.01 9.59 > 0.01 > 0.01 Q 5 -4.00 0.01 0.08 7.52 > 0.01 > 0.01 C 5 -30.18 > 0.01 > 0.01 6.08 > 0.01 0.01

Table 4-20. Table of t-test statistics for 0.148 M fructose Test against 1.0 Test against 0


Adjusted p-value

N 5 -44.44 > 0.01 > 0.01 0.01 0.99 1.00 S 5 -11.08 > 0.01 > 0.01 17.41 > 0.01 > 0.01 Q 5 -7.88 > 0.01 > 0.01 6.85 > 0.01 > 0.01 C 5 -17.51 > 0.01 > 0.01 2.58 0.05 0.40

Table 4-21. Performance to training stimuli during fructose testing Group


0.107 92.8 1.3 97.0 1.0 89.9 2.4 95.9 1.2 0.376 95.4 1.8 97.4 1.2 97.7 0.7 94.3 2.2 0.668 95.5 1.9 97.7 1.0 99.2 0.5 94.2 2.3

NaCl (M)

1.07 97.4 0.5 98.3 1.1 98.6 1.0 93.3 1.4 0.042 96.9 0.6 90.3 2.2 80.9 2.3 92.0 3.2 0.077 92.7 1.6 94.0 2.0 93.3 2.0 95.2 1.2 0.148 96.1 1.8 98.1 1.2 96.5 1.9 95.2 2.0

Sucrose (M)

0.421 96.2 1.1 87.6 4.5 89.2 5.5 98.9 0.7 0.027 92.3 2.4 92.0 2.4 94.2 0.8 86.4 4.2 0.131 96.0 2.2 90.8 3.2 92.2 1.2 82.8 2.2 0.360 93.8 1.4 82.5 6.2 93.5 1.2 78.3 2.6

Quinine (mM)

0.827 96.0 2.7 100.0 0.0 96.0 0.8 93.6 1.3 2.04 97.5 1.7 77.9 7.8 28.3 2.7 60.0 4.4 10.4 97.1 1.9 98.5 0.9 94.1 1.9 90.5 2.6 28.2 100.0 0.0 100.0 0.0 95.3 2.8 99.0 0.3

Citric Acid (mM)

64.3 98.8 0.8 100.0 0.0 99.1 0.7 99.0 0.5

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Table 4-22. Performance to training stimuli for W group during dt3-5 through dt3-8.

Group Water Solution Conc. Mean SE

0.107 69.3 5.9 0.376 82.1 2.8 0.668 96.6 1.4

NaCl (M)

1.07 85.8 6.2 0.042 97.4 0.7 0.077 77.4 5.1 0.148 92.9 2.1

Sucrose (M)

0.421 86.1 7.6 0.083 53.4 6.0 0.131 70.1 4.4 0.360 73.5 6.2

Quinine (mM)

0.827 100.0 0.0 2.04 89.0 3.6 10.4 80.7 6.0 28.2 87.1 5.7

Citric Acid (mM)

64.3 98.0 1.3 Water Water 86.1 2.0

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0.376 M NaGLUCONATE

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-1. Profile for 0.376 M NaGluconate. Mean Generalization Scores for each group are plotted. The novel concentration of NaCl generalized to NaCl training concentrations

0.668 M NaGLUCONATE

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-2. Profile for 0.668 M NaGluconate. Mean Generalization Scores for each group are plotted. The novel concentration of NaCl generalized to NaCl training concentrations

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0.131 mM DENATONIUM

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-3. Profile for 0.131 mM denatonium. Mean Generalization Scores for each group are plotted. The novel concentration of denatonium generalized to quinine training concentrations.

0.360 mM DENATONIUM

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-4. Profile for 0.360 mM denatonium. Mean Generalization Scores for each group are plotted. The novel concentration of denatonium generalized to quinine training concentrations.

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0.077 M MALTOSE

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-5. Profile for 0.077 M maltose. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to sucrose and quinine training concentrations.

0.148 M MALTOSE

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-6. Profile for 0.148 M maltose. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to sucrose and quinine training concentrations.

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0.376 M KCl

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-7. Profile for 0.376 M KCl. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, quinine, and citric acid training concentrations.

0.668 M KCl

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-8. Profile for 0.668 M KCl. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, quinine, and citric acid training concentrations.

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0.077 M MSG

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-9. Profile for 0.077 M MSG. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, and sucrose training concentrations.

0.148 M MSG

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-10. Profile for 0.148 M MSG. Mean Generalization Scores for each group are plotted. The novel concentration of maltose generalized to NaCl, and sucrose training concentrations

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0.077 M FRUCTOSE

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-11. Profile for 0.077 M fructose. Mean Generalization Scores for each group are plotted. The novel concentration of fructose generalized to sucrose training concentrations.

0.148 M FRUCTOSE

GroupsN S Q C

Gen

eral

izat

ion

Scor

e

-0.20.00.20.40.60.81.01.2

Figure 4-12. Profile for 0.148 M fructose. Mean Generalization Scores for each group are plotted. The novel concentration of fructose generalized to sucrose training concentrations.

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Water Group Performance

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Figure 4-13. Summary of performance for W group during training with water and quinine.

Figure 4-14. Diagram outlining two possibilities for the level (peripheral or central) at which convergence of taste signal processing leading to the same behavioral output might occur.

CHAPTER 5 GENERAL DISCUSSION

Introduction

The findings presented in the current collection of studies are novel to the field of

taste quality research. Chapters 2 and 3 presented data from two experimental procedures

that have not been previously used with taste stimuli, and the results suggest that they

could be applied as an alternative method to examine taste quality discrimination and

generalization. These paradigms could provide a functional context to interpret the

outcomes of anatomical, pharmacological, and genetic manipulations of the gustatory

system. Additionally, they may afford a means of testing hypotheses proposing how

signals generated from taste stimuli give rise through some central process to the

perception of taste quality. Moreover, these paradigms extend existing techniques that

are crucial for linking neural activity with behavior, which is essential for understanding

gustatory processing. Perhaps of most theoretical interest, however, is that the data

presented in Chapters 3 and 4 provide evidence that the perception of taste quality is

analytic. In other words, the behavior of the rats in this task was unambiguously

categorical, and at least one of the four putative taste qualities, respectively represented

by four prototypical stimuli, or a combination of them, was sufficient to describe each of

the novel test compounds, including MSG.

Delayed Match/Non-Match to Sample

The experiment employing the delayed matching and non-matching

(DMTS/DNMTS) approaches (Chapter 2) was successful in that rats indeed learned to

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respond to all types of trials presented at above chance levels. Although performance of

the rats was statistically better than chance, applying the paradigm, as it currently stands,

to address questions such as the temporal capacity of taste memory in the same/different

discrimination would be complicated by the fact that the range between asymptotic

performance and chance levels of responding is too limited to adequately measure

changes in behavior. A strategy to lower the interstimulus delay would be the most

plausible solution. Reliably higher levels of correct responding would be necessary to

pursue any aims focused on attributing changes in performance to specific gustatory

components.

As mentioned in Chapter 2, asymptotic performance could potentially be improved

by decreasing the delay period between the two sample stimuli. There have been

accounts showing that animals acquire a similar task at a faster rate when the delay

between the sample and a comparison stimulus is shorter, with a 0 s delay often yielding

the best performance (e.g., Sargisson & White, 2001). Currently, the design of the

gustometer prevents a delay shorter than 6 s. A substantial modification would be

necessary to enable the delivery of two taste compounds in a shorter time period. In

recent weeks, an adapter was constructed offsite to allow two sample spouts to be

controlled by the same stepping motor; consequently, a reduced delay between samples

would be achievable. There are still some technical aspects to overcome in order to use

such an adaptor, however, and therefore empirical testing is not yet possible. Further

development of this paradigm is almost certain to provide results indicating higher levels

of performance.

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Another change that could be made to the design of the experiment, as it was

presented, would be to increase the number of trials the rats were able to experience in a

given session. Reducing both the interstimulus interval and the intertrial interval would

help to achieve this; with shorter delays rats would be able to initiate more trials in a

session. Another potential problem that may have decreased the performance of the rats

in the present experiment was the likely loss of motivation through satiation. A water-

restriction schedule was used to potentiate the reinforcer efficacy of water, but as animals

sampled fluid in the trials, received fluid during intraoral rinses, and had access to water

after correct responses, they became sated. Perhaps introducing a partial schedule of

reinforcement might help improve performance while reducing the amount of fluid

received during each trial. One problem with this suggestion, however, is that it would

interfere with a benefit of the design. Typically, the water obtained during the

reinforcement phase serves to rinse the oral cavity between trials so that adaptation to a

particular compound does not occur. There have been prior accounts in the literature

showing that adaptation to a stimulus can affect subsequent responses to taste stimuli in

both rodents and humans (e.g., Bartoshuk, 1977; Galindo-Cuspinera et al., 2006).

An alternative, though not mutually exclusive, explanation for the overall

performance levels not surpassing 75% in this task might be related to the malfunction

that occurred in the light timer during the first 110 days of training and testing. Published

studies have used continuous exposure to lighted conditions as a chronic mild stressor

(CMS) (Grippo & Johnson, 2002; Grippo, Beltz, & Johnson, 2003; Grippo, Moffitt, &

Johnson, 2002), which results in various physiological changes in the animal, including

an increase in circulating corticosterone and decreased behavioral responsiveness to

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sucrose (experimentally induced anhedonia). This might lead some research groups to

speculate that hypothalamic–pituitary–adrenal dysregulation following CMS may result

in altered cognitive function. This remains an untested hypothesis.

The development of the DMTS/DNMTS paradigm with taste stimuli has potential

benefits for those interested in studying cognitive processes in animal models. For

example, this task could be used to address the concepts of working memory, or short-

term memory processing, as has been attempted in other sensory modalities. Evidence

exists in audition and olfaction that shows that differential neuronal activity can be found

during tasks requiring the animal to compare one stimulus to a second before making a

response (Sakurai, 1990; Wiebe & Staubli, 2001). A process, that some have termed

olfactory recognition memory, has been shown to have neural activity correlates in

hippocampal theta cells (Wiebe & Staubli, 2001). Therefore, the potential for identifying

similar underlying explanations and neural structures for taste behavior exists, especially

using a task such as the DMTS/DNMTS task outlined here.

Further development of the DMTS/DNMTS task could also facilitate efforts to

assess intensity discrimination in animal models. In the same way that tasks similar to

the one outlined here helped understanding of hue discrimination (e.g., see Wright,

1972), this task could help gustatory researchers realize the limits of taste quality and

intensity discrimination of their animal models.

Novel Taste Quality Generalization

In Chapter 3, it was demonstrated that rats can learn to discriminate between

stimuli thought to typify the four classic basic tastes. Further, they are capable of

responding to test stimuli that have not been explicitly trained, in ways that would be

predicted. When mixtures of two of the prototypical stimuli are presented, the behavioral

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responses of the rats can be used to generate behavioral profiles that indicate which of the

compounds was present in the highest overall concentration. The fact that a rat does not

respond entirely on the standard response spout when a familiar concentration of the

standard has been mixed with a familiar concentration of the comparison reveals that the

animal can detect that there is more than one qualitative taste component. This is a

remarkable aspect of this paradigm because it gives insight into the relative features of a

complex stimulus. The results from the mixtures indicate that rats will distribute their

behavior according to how prevalent a taste component is within a mixture.

There was also a major caveat of the paradigm that was highlighted when water

was used as a test compound. Together with the findings in Chapter 4 which examined

the extent that water can be discriminated from characteristic stimuli of the putative four

basic tastes, we now know that the number of sample licks is likely a critical factor in the

ability of the rats to make some discriminations in this task. Additionally, data from

Chapter 4 support the view that this paradigm can be used to obtain behavioral profiles

which may describe the qualitative features of novel taste compounds. Moreover, rats do

not need to be trained explicitly with these novel compounds, the training received using

the prototypical stimuli appears to generalize to new taste compounds. Presenting the

data from all groups reveals the degree to which the four basic taste qualities are

generated by a given test stimulus. The extent to which this holds true should (and could)

be examined by varying the relative concentrations of different pairings so that mixtures

for all possible combinations at various concentrations are thoroughly explored.

If one wanted the animals to respond completely on their standard response spout

when the taste was present within a compound, it might be possible to train the rats to

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identify the standard in mixtures during training. The concentration in the mixture could

be varied in much the same way that the training concentrations were varied to render

intensity an irrelevant cue, which should result in better discriminatory control.

Conceivably, the rats would be able to perform quite well with feedback encountered

during training. If the groups of rats could learn to identify, at high levels of

performance, the presence of the standard in many different combinations of the four

prototypical taste compounds, then it would increase the confidence that responses on the

standard response spout after presentation of unknown test compounds indicate detection

of a standard-like taste. This would be an alternative strategy for using behavioral

profiles to describe a compound’s qualitative features. Unfortunately, the current design

of the gustometer delivery system prevents such a strategy due to a limited number of

fluid reservoirs, but if such technical limitations could be overcome it would be useful in

the future to explore the use of complex mixtures as standards.

It is interesting that in the experiment where water was used as a test stimulus, a

quinine-like profile was obtained. Additionally, it is remarkable that the rats in the Q

group of the experiment in Chapter 3 were unable to learn to discriminate quinine from

water. Studies measuring absolute detection threshold for quinine are possible, and

thresholds have even been obtained using the same equipment, albeit with a

methodologically different task. Additionally, Experiment I in Chapter 3 (brief-access

test) showed that rats will alter their licking behavior in a concentration dependent

manner to quinine, though in that experiment the rats could initiate as many licks in a 5-s

period as possible (which could result in as many as 35 licks) compared to the 5 licks

they were allowed during sampling in Experiment II. In fact, the average (+/- SE) licks

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to the concentration closest to 0.027 mM quinine (0.03 mM) presented during brief-

access testing was 27.93 (+/-1.18). The highest concentration used as a training stimulus

in the generalization experiment (0.827 mM) falls between two of the concentrations

used in the brief-access test, 1 mM and 0.3 mM. At the higher concentration, 1 mM

quinine, rats in the brief access test licked an average of 9.5 (+/- 1.2) and at the next

lowest concentration, 0.3 mM quinine, rats licked an average of 21.7 (+/- 1.1).

Therefore, it is plausible that the number of sample licks associated with the testing

parameters were too low in the original design of this task. Clearly they were sufficient

to maintain high levels of performance with the other compounds, but apparently water

and/or quinine are different from the other three compounds. The experiment in Chapter

4 helped to clarify the ability of rats to discriminate quinine and water under these

conditions, but interpretation of those results are complicated by the different training

histories encountered by animals between these two experiments.

Surprisingly, the naïve rats in the W and Q groups from Chapter 4 had difficulty

discriminating 0.360 mM quinine and water also. After 22 days of discrimination

training, the average performance was near the mid-to-low 60% range. This is in stark

contrast with the rate at which the N, S, and C groups learned to discriminate their

training compounds. It was decided at that point in the experiment to remove water from

the comparison stimuli for the N, S, Q, and C groups. The W group received more

discrimination training with the water and 0.827 mM quinine so that eventually the group

might be useful for assessing whether a test compound would generate a water-like

profile. It was clear, however, that when all of the training concentrations were added to

the training array, there was evidence of loss of stimulus control. It is possible that with

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more explicit training using all of the training compounds, high levels of performance

should be achievable. The explanation for the impaired performance is elusive, unless

one considers that water may have a quinine-like taste in rats (Bartoshuk, 1977,

Morrison, 1967).

Future Validation of the Procedure

For this paradigm to be useful to researchers, it should be further validated with

respect to understanding the limitations of the information provided by the profiles. For

example, it would be instructive to examine how the rats would respond if they were

given a concentration completely unrelated to the range of the training compounds

encountered. This could most easily be accomplished by using very high concentrations

of the standards and comparisons. Not only would this provide information about the

ability for the training to generalize to concentrations completely outside the range of

training compounds, but it might also provide information about the constancy of a

quality at high concentrations.

Another important issue would be to understand what would happen if a compound

from a new distinct taste quality was encountered during testing. One way to approach

that issue would be to train three groups of rats to discriminate only three of the

prototypical compounds and use the fourth as a test compound. If the generalization

profile obtained did not resemble any of the standard stimuli (or it resembled all of them

equally, i.e., Generalization Score = 0.5), then it would provide evidence that rats were

capable of indicating when a stimulus was unlike any of the familiar training stimuli. All

possible combinations of comparisons should be tested to identify which, if any, qualities

might generalize most to others.

123

It would not be surprising if the rats generalized one of the prototypical compounds

to another. Morrison (1967) showed this in his study, for example, when he used quinine

as a test compound so that the rats in the HCl and sucrose groups had to distinguish

whether it was more NaCl-like or more like their comparison stimulus (HCl or sucrose,

respectively). He showed that when rats were forced to choose between HCl and NaCl to

“behaviorally describe” quinine taste, that the responses were more HCl-like than NaCl-

like. This has been the basis for some to interpret that rats have difficulty discriminating

between quinine and HCl (Lemon & Smith, 2005). It would be interesting to know what

the generalization profile might reveal using this paradigm if we trained rats in the citric

acid group to discriminate citric acid from sucrose and NaCl (in this example, the only

two comparison stimuli) and then presented quinine as a test stimulus. It might look like

the profile obtained using Morrison’s (1967) procedure, or this paradigm might offer

more flexibility for responding. Either way, it is an interpretably important piece of

information to consider.

Another recommended validation procedure would be to understand what happens

to responding when the animal is made to no longer experience a specific taste quality.

For example, if specific gustatory nerve transections were performed, and they resulted in

the complete inability to detect one (or more) training compound(s), would responding to

the other stimuli then be normal? What would the profile for that taste compound look

like? Perhaps such an outcome would result in loss of stimulus control, especially if the

quality is the standard. This is an interesting and important interpretive issue that is

revisited below.

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It would also be important to extend the test stimulus array to include many

examples of compounds, especially those with complex tastes. The limited number of

test compounds incorporated into these studies may bias the conclusions drawn about the

utility of the profiles. Each of the test stimuli were carefully chosen because of a certain

expectation about what the outcome should look like as we anticipated that it would

probably be most informative to first include compounds that have been tested using

other behavioral methodology (e.g., the conditioned taste aversion approach) to provide

an external validation. The only unanticipated results came from the maltose profile

where we found evidence of a more dominant quinine-like component than a sucrose-like

component for the putative sweetener. This was surprising because rats are known to

prefer maltose, so one would not imagine that it would contain a dominant quinine-like

quality. If the results from this paradigm and others are not in agreement, however, it

would not necessarily suggest that this paradigm (or another) is flawed, but it certainly

would warrant further investigation.

This list of suggested means to further validate the procedure is likely not

exhaustive, but it is meant to indicate that the interpretation of profiles should be done

with these caveats in mind. Addressing each of the issues would only serve to strengthen

any conclusions about the taste quality of a test compound determined using this

procedure.

Potential Uses of the New Generalization Procedure

Neurobiological applications

The behavioral testing paradigm presented in Chapters 3 and 4 has the potential to

provide great insight into the study of the peripheral gustatory system. For example, it

would be possible to employ gustatory nerve transections in order to understand the

125

necessity and sufficiency of specific nerves to maintain gustatory function. Such studies

might reveal that a particular nerve (or combination of nerves) is necessary for the

transduction of specific taste qualities. Alternatively, it might be the case that a single

nerve is important for all quality discrimination, indicating that the signal for taste quality

is channeled through a specific pathway. Currently, such studies have not been

attempted, likely because the conditioned taste aversion approach is not compatible with

such a design, or at least not one that would yield such straightforward results.

The peripheral gustatory system. The known presence of narrowly tuned N-

fibers, in the chorda tympani (CT) nerve, which respond to sodium salts (and LiCl),

suggested that the anterior tongue taste receptor cells (which are innervated by the CT)

were critical in NaCl sensibility (Frank, Contreras, & Hettinger, 1983). Researchers were

surprised, however, when transection of the CT did not affect NaCl intake or preference

in an overnight test as compared with intact rats (Akaike, Hiji, & Yamada, 1965;

Pfaffmann, 1952; Richter, 1939; Vance, 1967). It was not until later, when the application

of more detailed and rigorous behavioral testing was used, that severe consequences of

CT transection on salt taste perception were revealed (Slotnick, Sheelar, & Rentmeister-

Bryant, 1991; Spector, Schwartz, & Grill, 1990). Transection of the CT increases the

detection threshold for NaCl by at least 1–2 orders of magnitude (Kopka & Spector,

2001; Slotnick, Sheelar, & Rentmeister-Bryant, 1991; Spector, Schwartz, & Grill, 1990),

resulting in decreased sensitivity to Na+ salts. Chorda tympani nerve transection also

attenuates salt discrimination performance (Kopka, Geran, & Spector, 2000; Spector &

Grill, 1992; St. John, Markison, & Spector, 1995). These results suggest that the CT is

126

necessary to maintain normal sodium detectability and recognition even though its

transection denervates only 15% of taste buds in the oral cavity.

Conversely, when the glossopharyngeal (GL) nerve is severed, thereby removing

input from roughly 60% of the total taste bud complement (Miller, 1977), salt

discrimination and sodium recognition remain normal (Markison, St. John, & Spector,

1995; Spector, Schwartz, & Grill, 1990). Thus, it appears that the GL, which innervates

taste buds on the posterior 1/3 of the tongue, is not necessary for the maintenance of these

particular functions (St. John, & Spector, 1998).

Given what is known about the importance of the CT in taste discrimination

behavior, it would be enlightening to design an experiment in which half of the animals

in each group receive bilateral transection of the CT nerve and their subsequent ability to

discriminate between the training compounds is assessed. If, they were still able to

perform at high levels, then it might be equally exciting to see whether the generalization

profiles for test compounds (either novel and/or those experienced prior to surgery)

compare to the other half of each group, which would receive sham surgery.

Likewise, it would be interesting to perform the same experiment with GL

transection as the surgical manipulation because although the GL has not been shown to

be highly involved with any behavioral tasks assessing the discrimination of compounds,

it is responsive to all four prototypical stimuli. The GL is highly responsive to quinine,

responds well to acids, and also has a somewhat weaker response to salts and sugars

(Oakley, 1967; Boudreau, et al., 1987; Frank, 1991; Dahl et al., 1997). Therefore, it is

possible that the GL would be involved in carrying information specifically about

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quinine-like taste qualities, or possibly even all four putative basic tastes. It remains a

conceptually interesting question that can now be addressed.

As pointed out earlier, it may first be important to understand what happens to

intact rats if one of their training stimuli suddenly disappears. The loss of a specific taste

quality could be mimicked through providing sham licks. If the gustometer was

programmed to proceed normally allowing a rat to lick the dry sample spout, but not

deliver a taste sample contingent on the licks, and then otherwise treat the trial normally,

it is conceivable that that condition might mimic loss of a specific taste quality.

Unfortunately, it would not mimic the other sensory cues associated with sampling (e.g.,

somatosensory) and so is not the most ideal; although it would be a better alternative than

presenting water, which has been associated with a quinine-like taste in this paradigm.

Additionally, the current generalization paradigm would be well-suited for a within

subject design that could assess function before, during, and after recovery from specific

nerve transection. Following regeneration of the nerve after a surgical lesion, it would be

interesting to know if function returns to the same levels seen prior to the insult. Such a

procedure might not be possible given the likelihood of loss of stimulus control that

would occur in those animals. This point could be addressed, however, by including

other groups that do not receive testing during the period of time that regeneration occurs.

Perhaps findings of this ilk would be useful to predicting recovery of function after

human injury.

Finally, another exciting avenue of study that is possible with this paradigm would

be to use an inducible knockout preparation. The rationale for such a statement is that the

knockout technology could be key in understanding whether specific taste receptors are

128

necessary for a specific taste quality (or just specific compounds). For example, the

experimenter could train the animal to discriminate the prototypical compounds as used

here, and then they could “knockout” function of a specific receptor and observe the level

of discrimination behavior. Next, the experimenter could restore the function of the

receptor and note the effects on performance. Likely, the animal would perform poorly

without the proper signal transduction machinery, but would be able to perform the task

once the receptor was restored. It might be more interesting, however, to find out that the

animal can compensate for non-functional receptors, suggesting redundancy in the

system. Obviously the success of the proposed study depends on a lot of technical factors

working properly, but theoretically, the behavioral testing paradigm opens the doors to a

lot of currently unachievable inquiries.

Behavioral data support analytic processing rather than synthetic

Results from the novel taste quality generalization experiments suggest that taste

quality signals may undergo analytic, rather than synthetic, processing in the gustatory

system. The fact that rats could learn to discriminate between the four prototypical taste

compounds, representing the four putative basic taste qualities, and then respond to novel

test compounds in terms of how NaCl-like, sucrose-like, quinine-like, or citric acid-like

they were provides support for this claim. The profiles generated from sodium gluconate

and quinine looked like the profiles generated from the novel concentration of NaCl and

quinine, respectively, used in Chapter 3. Although maltose and fructose did not generate

a strictly sucrose-like profile, it was still consistent with an analytic viewpoint because

there was also a quinine-like component to the response profiles. Even KCl, which has

been argued as a complex taste which is distinct from NaCl gave rise to a profile that

consisted of a combination of the 4 prototypical stimuli. Moreover, when MSG was

129

presented, the profile generated appeared to be a combination of two of the training

compounds, NaCl and sucrose, which is also consistent with the assertion that taste

quality is analytic. This is especially remarkable given that some researchers argue that

the taste of MSG is representative of a distinct fifth taste quality referred to as “umami.”

If all of the novel test compounds had generated profiles indicative of a separate

taste quality then it would have refuted the analytic claim. For a test compound to have

generated a profile suggestive of a separate taste quality, either all of the groups

Generalization Scores would have been 0.5 or they would all have been 0. Therefore, if

the rats did not recognize distinct components comprising the novel test compounds but

instead responded as if the test compounds were novel qualities, then it would have

suggested synthetic processing (that continua of taste qualities exist rather than a few

discrete qualities) (see Erickson, 1968).

Perspectives

The present collection of studies introduced and utilized two novel behavioral

paradigms to study aspects of taste processing in rats. Each paradigm has unique

strengths that attempt to circumvent shortcomings associated with the commonly used

conditioned taste aversion technique. This dissertation provides significant groundwork

towards the characterization of these new behavioral paradigms for assessing taste quality

in rodents, and indicates future lines of investigation necessary to fully elucidate the

strengths and limitations of these paradigms. The two approaches will likely prove useful

in future investigations of taste quality coding in rodents, especially with respect to

answering questions about whether taste coding is governed by analytic or synthetic

processing. Thus far, results support analytic processing, but further testing is

recommended.

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BIOGRAPHICAL SKETCH

Connie Lynn Colbert was born in Miami, Florida, on May 5, 1977, to Robert and

Debora Colbert. She has a twin brother and had an older brother (deceased), both of

whom fostered a healthy competitive spirit regarding school. Connie always enjoyed

school and knew at a very young age that she would enjoy a career in the sciences. She

graduated high school in 1995 with an AA degree from Miami Dade Community College

and then attended Florida International University in Miami and received her Bachelor of

Science in psychology in 1998. She spent the next year continuing her research at F.I.U.,

and in 1999, Connie started graduate school in psychobiology at the University of

Florida, and received her M.S. degree in August 2002. She also met her husband, Justin

L. Grobe, while obtaining her Ph.D. After obtaining her Ph.D., Connie will move to

Iowa for postdoctoral training with her husband.

new behavioral paradigms to study taste-quality...

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