burgos (2003) - latent inhibition

8/9/2019 Burgos (2003) - Latent Inhibition

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Behavioural Processes 62 (2003) 183192

Theoretical note: simulating latent inhibitionwith selection neural networks

Jos E. Burgos

Centro de Estudios e Investigaciones en Comportamiento, University of Guadalajara, 12 de Diciembre 204,

Col. Chapalita, CP 45030-Guadalajara, Jalisco, Mexico

Received 1 September 2002; accepted 8 December 2002

Abstract

The selection neural-network model proposed by Donahoe et al. [J. Exp. Anal. Behav. 60 (1993) 17] was used to simulate

latent inhibition (LI). The model can simulate increases of LI by the number, intensity, and duration of preexposed conditioned

stimulus (CS). It can also simulate dependence on total CS preexposure time, CS specificity, and attenuation by preexposure

to a compound that includes the to-be-trained CS. It also predicts a potentially new phenomenon: acquisition facilitation by

preexposure to a stimulus that is orthogonal to and synaptically competitive with the to-be-trained CS. The basic mechanism

is the same through which the model simulates extinction, namely, weight decrement. The realization of this mechanism in

the present simulations required two conditions. First, networks had to come to the experimental situation with substantial

initial connection weights in the sensory-association subnetwork (0.15, compared to the 0.01 value we have used in all previous

simulations). Second, the discrepancy threshold for deciding whether to increase or decrease weights had to be larger than zero

(the value we have used in all published simulations). A value of 0.001 was sufficient to produce all the effects.

2003 Elsevier Science B.V. All rights reserved.

Keywords: Latent inhibition; Neural networks

1. Introduction

Latent inhibition (LI) is a significant retardation in

the acquisition of a conditioned response (CR) after a

preexposure to the putative conditioned stimulus (CS)

(Lubow and Moore, 1959).All accounts of this phe-nomenon have relied on the learningperformance dis-

tinction, where learning typically refers to a covert

process that causally affects performance, which de-

fined as overt behavior. Accordingly, Schmajuk et al.

(1996, Table 1) organize such accounts into two cat-

Present address: 413 Interamericana Blvd., WH1, PMB 30-189,

Laredo, TX 78045-7926, USA.

E-mail addresses: [email protected], [email protected]

(J.E. Burgos).

egories. In one category, LI is viewed as a learning

failure, consisting in a disruption of the formation of

associations between a CS and an unconditioned stim-

ulus (US), after preexposure. In the other category,

LI is viewed as a performance failure, consisting in

a disruption of the retrieval of such associations. Athird category arises from the neural-network model

advanced by the authors, where LI is viewed both as

a learning and a performance failure.

My aim in the present note is two-fold. First, I

want to demonstrate that the selection neural-network

model proposed byDonahoe et al. (1993)can simu-

late at least the basic LI phenomenon and some of its

behavioral properties. Second, I want to show how the

model accounts for those effects in terms of a kind

of learning failure that differs from other proposed

0376-6357/03/$ see front matter 2003 Elsevier Science B.V. All rights reserved.

doi:10.1016/S0376-6357(03)00025-1


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184 J.E. Burgos / Behavioural Processes 62 (2003) 183192

accounts. In the next section, I describe the features

that are relevant to the way in which the model simu-

lates LI. Then, I describe four simulation experiments

that show how the model simulates the basic LI effect,some of its behavioral properties, and even predicts

a potentially new phenomenon. I end with a general

discussion.

2. The selection neural-network (SNN) model

The SNN model is a discrete-time model consist-

ing of two sub-models, namely, neuro-computational

and network. The neurocomputational sub-model con-

sists of an activation rule, which determines the ac-

tivation state of a neural processing element (NPE)

at a time-step (ts) t, and a learning rule, which de-

scribes how connection weights change. Activations

and weights take values within the [0, 1] interval. The

network model is a taxonomy of NPEs plus rules on

how they are to be connected. A typical SNN architec-

ture, shown inFig. 1,is a feedforward (cf.Donahoe

and Burgos, 2000)fully-connected (cf.Burgos, 1997,

2000)network consisting of one input, two or more

hidden (labeled as sa and ma; see below), and one

output layer. Thin lines represent variable connections

Fig. 1. A typical DBP neural network. The small empty circles

represent CS input elements and the small filled circle represents

the US input element. Activations of input elements represent

stimuli. The large open circles represent neural processing ele-

ments (NPEs). The thin lines represent variable connections and

the thick arrows innate, fixed, maximally-strong connections. Ac-

tivations are transmitted unidirectionally, from input elements to

output NPEs; sa: sensory association; ca1: Cornu Ammon 1; ma:

motor association;vta: ventral tegmental area; CR/UR: conditioned

response/unconditioned response.

and thick arrows fixed maximally-strong connections.

All activations are conveyed from input to hidden to

output elements.

Larger-than-zero activations of the input elements(small circles labeled as CS1, CS2, CS3, US) rep-

resent stimuli (CSs, represented by the activation of

the small open circles, and USs, represented by the

activation of the small filled circle). Hidden layers

consist of at least one sensory-association (sa) and

one motor-association (ma) layer, and are constituted

by NPEs (large empty circles) that mediate between

input and output activations. Activations of the output

NPE represent either conditioned responses (CRs), if

caused by the activation of one or more CS elements

through the sa and ma NPEs, or unconditioned re-

sponses (URs), if caused by the activation of the US

input element (hence the label CR/UR). A selection

neural network can also have output NPEs that do

not receive connections from the US element (e.g.

Donahoe et al., 1993; Burgos and Donahoe, 2000;

Donahoe and Burgos, 1999), but they are relevant

for operant conditioning. Since my emphasis here is

on Pavlovian conditioning, I shall ignore that kind

of NPE. Other NPEs are the ca1 (for Cornu Am-

mon 1, a hippocampal field) and vta (for ventral

tegmental area, a dopaminergic nucleus) NPEs. Their

activations generate a discrepancy reinforcement sig-nal that modulates changes in connection weights

(see below).

The part that is relevant to the present study is the

learning rule (for a description of the activation rule,

seeDonahoe et al., 1997,Appendix), which is given

by:

wi,j,t=

jaj,tpi,trj,tdt, ifdt

jwi,j,t1ai,taj,t, ifdt < (1)

As in any neural-network model, connection weightsgate the efficacy with which presynaptic input signals

activate postsnaptic NPEs. The rule consists of two

mutually exclusive modes, namely, incremental and

decremental. In the incremental mode, the weightwi,j,tfrom presynaptic process i (which can be either

an input element or another NPE) to postsynaptic

process j at ts t is increased according to the first

conditional. The rate of increase depends on a free

parameter (),js activation (aj),pi (the proportion of

total weight allotted toi),rj(the remaining portion of


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J.E. Burgos / Behavioural Processes 62 (2003) 183192 185

weight left on j that is acquirable by any presynaptic

process), and the reinforcement signal (dt), where

pi,t=ai,twi,j,t1

N(2)

whereNdenotes the inner product of the activation and

the weight vectors that include iand impinge onj, and

rj,t= 1

si=1

wi,j,t (3)

wheres denotes the total number of connections that

impinge onj.Eqs. (2) and (3)make weight increment

a competitive process. ThroughEq. (2), more strongly

activated stronger connections gain weight faster than

less strongly activated weaker connections. ThroughEq. (3), the sum of all connection weights on j is

bounded to 1.0 and an increase in the weight of any

connection leaves less free weight available for other

connections to gain.

In the decremental mode,w i,jdecreases according

to the second conditional. This mode typically func-

tions whenever a CS is presented without reinforce-

ment, which is the critical feature for simulating LI

with this model. The rate of decrease depends on a

free parameter (), the amount of weight gained thus

far byi, and the activations ofi and j.

The two modes are not symmetric because themodel assumes that presynaptic competition and re-

inforcement discrepancy play a role only in the incre-

ment of synaptic efficacies. Whether the rule is in the

incremental or the decremental mode depends on dt,

which is given by

dt=

t+ t(1 t), ifjisan sa or a ca1NPE

totherwise

(4)

wheret= |aca1,t aca1,t1| (5)

t= avta,t avta,t1 (6)

whereaca 1is the activation of the ca1 NPE andavt ais

the activation of thevtaNPE.Eq. (4)assumes that the

reinforcement signal that modulates weight increases

in sa and ca1 NPEs arises from the latter and is am-

plified by the one arising from the vta NPE. This as-

sumption is based on the fact that the functioning of

the hippocampus is amplified by dopamine (e.g. Stein

et al., 1993).Eq. (5) assumes that the ca1 reinforce-

ment signal is always positive, based on the hypoth-

esis that the hippocampus is a change detector (i.e.it affects learning equally, whether the activity of its

constituting cells increases or decreases across time).

In contrast,Eq. (6)assumes that thevtareinforcement

signal can be positive or negative. This assumption

is based on the hypothesis that dopaminergic nuclei

affect learning differentially, depending on whether

the activity of their constituting cells increases or de-

creases across time.

The ca1 and vta reinforcement signals are diffuse

in that they reach all of their associated connection

weights (CS1-sa and sa-ca1, in the case of the ca1

signal, and sa-ma, ma-vta, and ma-CR/UR weights,

in the case of the vta signal), in a way analogous to

dopaminergic modulation of synaptic plasticity. Pa-

rameterinEq. (1)is a threshold for deciding whether

the learning rule is in the incremental or decremen-

tal mode. In all previous simulations, = 0, but this

had to be changed in order to obtain LI and its basic

behavioral properties (see below).

3. Simulation experiments

Tokens of the architecture shown in Fig. 1 were

used. The basic procedure consisted in preexpos-

ing some networks (the PE networks) to some LI

treatment, which involved non-reinforced CSi trials

(CSi). Other networks (the SIT networks) were

not pretreated in any way. Then, all networks were

trained in a forward-delay Pavlovian procedure con-

sisting of 300 reinforced CS1 trials (CS1+), where

CS1 was the activation of the CS1 input element

(see Fig. 1) with a magnitude of 1.0 for 8 ts, and

the US was the activation of the US input elementalso with a magnitude of 1.0 at ts 8. The interstim-

ulus interval during CS1+, thus, was 7 ts. As in all

previous simulations, the intertrial interval (ITI) was

not directly simulated. Instead, it was assumed to be

sufficiently long to make activations to decrease to

a spontaneous near-zero level, which was given by

a logistic function with a mean of 0.5, a standard

deviation of 0.1, and an argument of zero (the re-

sulting spontaneous activation is approximately equal

to 0.066928; for further discussion on the role of


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Fig. 3. Weight changes in the connection from CS1 to the first

sa NPE from top to bottom in the architecture shown in Fig. 1.

Upper panel: a SIT net during CS1+. Lower panel: a PE net

during CS1+, after preexposure.

Fig. 3 shows changes in the weight of the connection

from CS1 to the first sa NPE from top to bottom in

the architecture depicted in Fig. 1 (that NPE is now

labeled as sa1), for a SIT (upper panel) and a PE

(lower panel) network, during CS1+. As can be seen,

the weight increased noticeably more slowly than the

corresponding weight in the SIT network. The weights

did not exceed 0.7 because the other two connections

(from CS2 and CS3; seeFig. 1) controlled 30% of thetotal weight available on that NPE.

Why did the CS1-saweights tend to increase more

slowly in the PE networks? CS1 preexposure caused

a weight loss in the initial CS1-sa connections.3 An

example is shown inFig. 4, which depicts changes in

the same CS1-sa weight of the same PE-200 during

CS1. As can be seen, the initial weight decreased

from 0.15 to almost 0.05. This decrease reduced the ac-

tivation ofj (aj ,t inEq. (1))and increased the remain-

ing portion of weight acquirable by i (rj ,t inEq. (1))

from 0.55 to approximately 0.65.

4

Parameter was

3 The sa-ca1 connections did not change significantly during

CS1, so they played no a role in the observed differences.

Subsequent simulations, not shown here, demonstrated that an

initial value of 0.15 in these connections only accelerated learning

in SIT and PE networks equally, thus leading to comparable results.4 Before preexposure, CS1, in virtue of its initial weights, con-

trolled 0.15 of the maximum weight available on any of the sa

NPEs, while CS2 and CS3 controlled 0.15 2 = 0.30, for a total

used weight of 0.45. This weight subtracted from the maximum

weight possible of 1 results in a total free weight of 0.55. After

the same for all networks, so it did not play a role

in the observed differences. The same applies to pi ,t,

for only one input element (CS1) was active through-

out the entire experiment. Consequently, there was nodynamic competition among the three input elements.

Further analyses showed that dtdid not differ signif-

icantly, so it did not play a role in the present results

either.

In order to assess the true level of activation of

the sa NPEs, in the absence of weight changes, fur-

ther simulations were run where the learning rule

was disabled (seeDonahoe et al., 1997,p. 207). The

results showed that, in effect, the mean activations

of the sa NPEs in the presence of CS1 tended to be

significantly lower in the PE networks after CS1

preexposure than in the SIT networks. For instance,

the firstsaNPE from top to bottom in the architecture

ofFig. 1, had a mean activation across all ts of 0.014

for a randomly chosen SIT network and 0.008 for a

randomly chosen PE network. Multiplying by r, one

obtains 0.014 0.55 = 0.007 for the SIT network

and 0.008 0.65 = 0.005 for the PE network. The

advantage provided by rin the PE networks was thus

overcompensated by the decrease in the sa activation

levels. This decrease, then, was sufficient to retard

weight increments in the CS1-sa connections in the

PE networks, thus causing them to take more trials toachieve acquisition.

3.2. Experiment 2: effect of preexposed-CS intensity

The experimental evidence has shown that LI in-

creases with preexposed CS intensity (e.g.Schnur and

Lubow, 1976,Experiment 2). To simulate this effect,

two new groups of 20 nave networks were used.

Group PE-0.2 received 200 CS1 of input element

CS1 of 0.2. Group PE-0.8 received 200 CS1 trials

with a CS1 intensity of 0.8. CS1 duration was 8 tsfor both groups. Then, both received 300 CS1+ trials.

AsFig. 5shows, in agreement to the evidence, LI was

directly proportional to the intensity of the preexposed

CS.

preexposure, the CS1-sa weights in the PE networks decreased to

almost 0.05 (seeFig. 3), which increased the free available weight

on any sa NPE to approximately 0.65. Consequently, after CS1

preexposure and before CS1+ training, the value of r for any sa

tended to be larger in the PE than in the SIT networks.


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Fig. 4. Weight changes in the same CS1-sa connection of the same PE net shown in Fig. 3, but during CS1 preexposure.

A two-tail unpaired t-test showed that the dif-

ference between the two group means was signif-

icant (t = 4.295, with 38 degrees of freedom and

P = 0.0001). The difference between the two stan-

dard deviations was not significant (F= 1.683, P=

0.1326) and both groups passed the normality test

according to the KS method [KS(PE-0.2) = 0.1325

and KS(PE-0.8) = 0.1322, with P > 0.1 for both

groups].The basic mechanism was the same one described

in Experiment 1. Preexposure to a more intense

Fig. 5. Results of Experiment 2. LI was increased by preexposed-CS intensity. Error bars represent standard deviations.

CS1 caused a more substantial decrease in the ini-

tial CS1-sa weights. Such a decrease was due to the

fact that weight decrements are as much activation-

dependent as weight increments, so the larger the ac-

tivation of the pre- and/or postsynaptic elements (ai ,tandaj ,tin the decremental mode ofEq. (1), respec-

tively), the more substantial the weight loss. This loss,

in turn, caused sa activations in the PE-0.8 networks

to be smaller, and hence, their CS1-sa weights to in-crease more slowly, thus requiring more CS1+ trials

to achieve acquisition.


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3.3. Experiment 3: dependence on total

preexposure time

The evidence has shown that LI is also increasedby the number of preexposed CSs (Lantz, 1973, Ex-

periment 1) and CS duration (Lubow, 1989, p. 63;

Westbrook et al., 1981, Experiment 3). Further re-

search indicated that the critical variable here is total

CS preexposure time, regardless of how it is dis-

tributed across trials (e.g.Ayres et al., 1992).Specif-

ically, LI increases with total preexposure and groups

with the same total exposure time, but different CS

durations, demonstrate comparable amounts of LI.

To simulate these effects, four new groups of

20 nave networks were used. Groups PE-100/5,

PE-50/10, PE-200/10, and PE-400/5 were preexposed

to 100 5-ts, 50 10-ts, 200 10-ts, and 400 5-ts CS1

trials, respectively. The total preexposure times were

500 ts for the first two groups and 2000 ts for the

last two. CS1 intensity was 1.0 for all groups. After

preexposure, all groups received 300 CS1+ trials. As

Fig. 6shows, in agreement with the evidence, LI was

directly proportional to total preexposure time.

A one-way four-level ANOVA showed significant

differences among the group means (F= 12.155, with

a total of 79 degrees of freedom and P


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To simulate these effects, two new groups of 20 nave

networks were used and compared to Groups SIT and

PE-200 (the latter now called PE-CS1, for ease of

comparison) from Experiment 1. Group PE-CS1/CS2was preexposed to 200 CS1/CS2 trials consisting

in the non-reinforced concurrent activation of input

elements CS1 and CS2. Group PE-CS2/CS3 was

preexposed to 200 non-reinforced CS2/CS3 trials

consisting in the non-reinforced simultaneous activa-

tion of input elements CS2 and CS3. All CSs during

preexposure had a duration of 8 ts and a magnitude

of 1.0. Then, both groups received 300 CS1+ trials.

As Fig. 7 shows, in agreement with the evi-

dence, preexposure to CS1/CS2 produced less LI

than preexposure to CS1 (compare PE-CS1 to

PE-CS1/CS2). CS specificity was also obtained (com-

Fig. 7. Results of Experiment 4. The effect of preexposure was

specific to the preexposed CS (compare PE-CS1 to PE-CS2/CS3),

LI to CS1 was attenuated by non-reinforced preexposure to the

CS1/CS2 compound (compare PE-CS1 to PE-CS1/CS2), and ac-

quisition under CS1+ was facilitated by non-reinforced preexpo-

sure to the CS2/CS3 compound (compare SIT to PE-CS2/CS3).

Error bars represent standard deviations.

pare PE-CS1 to PE-CS2/CS3). Furthermore, preexpo-

sure to CS2/CS3 substantially facilitated acquisition

(compare SIT to PE-CS2/CS3).

A one-way four-level ANOVA showed that themean differences were significant (F= 189.15, with

a total of 79 degrees of freedom and P < 0.0001).

A TukeyKramer multiple comparisons test showed

that, except for SIT and PE-CS1/CS2 (q = 3.08,P >

0.05), the differences among all the relevant means

were significant (for PE-CS1 and PE-CS1/CS2, q =

15.122, P < 0.001; for SIT and PE-CS2/CS3, q =

21.226, P < 0.001; for PE-CS1 and PE-CS2/CS3,

q = 33.268, P < 0.001; for PE-CS1/CS2 and

PE-CS2/CS3, q = 18.146, P < 0.001). The two

new groups passed the normality test, according

to the KS method [KS(PE-CS1/CS2) = 0.15 and

KS(PE-CS2/CS3) = 0.1728, with P > 0.1 for

both groups]. However, the differences between the

standard deviations of PE-CS1 and PE-CS1/CS2

(F= 5.196, P= 0.0004), PE-CS1 and PE-CS2/CS3

(F= 7.661, P


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a consequence of CS preexposure. Except for the

PE-CS2/CS3 networks in Experiment 4, CS1 pre-

exposure caused a reduction in CS1-sainitial weights

in all the PE networks, which, in turn, caused sa ac-tivations to be smaller early in CS1+ training, thus

decelerating CS1-saweight increments and retarding

acquisition. The amount of weight reduction during

CS1 (and, to that extent, of sa activations early in

CS1+) was directly proportional to the CS1 in-

tensity (Experiment 2) and total preexposure time

(Experiment 3).

In the first three experiments, the critical factor in

Eq. (1)was a reduction the postsynaptic (in this case,

thesa) activations. This reduction decelerated weight

increases in the CS1-sa weights during CS1+, and was

caused by a decrement in the initial CS1-sa weights,

due to CS1 preexposure. Under the SNN model,

then, LI arises as a learning failure. The way in which

this failure occurs, however, differs from the way it

does in other accounts, such as the one found in the

SchmajukLamGray (SLG) model, for instance.

Like the SNN model, decelerated weight increase

in the SLG model is due to a reduction in an ac-

tivation parameter (called novelty in the latter

model) that affects the rate of weight increments.

In contrast to the SLG model, the reduction of that

activation in the SNN model is due to a reductionin initial weights. Furthermore, in the SLG model,

the reduction in novelty takes place in a separate

subnetwork (the so-called novelty system), which

consists only of fixed connections. In the SNN model,

in contrast, the relevant reduction occurs in the same

subnetwork where subsequent learning occurs (viz.

thesa subnetwork).

Additionally, the minimal architecture necessary to

obtain the present effects is simpler in the SNN than

the SLG model (compare Fig. 1 of the present pa-

per with Fig. 1, p. 324, of the SLG paper). Also,the SLG model has 11 free parameters, whereas the

SNN model has only five (the two of the learning

rule plus three in the activation rule). The SNN ac-

count thus seems to be more parsimonious than the

SLG one, at least regarding minimal architecture and

number of free parameters. To be sure, there are many

other LI features that await simulation by the SNN

model (e.g. LI of conditioned inhibition; dependence

on ITI duration, context, and other compound-CS ma-

nipulations; etc.), so they may well require larger and

perhaps even non-fully connected selection networks.

This possibility raises the issue of the role of the archi-

tecture in the SNN model. This issue has been previ-

ously discussed in relation to handcrafted architectures(Burgos, 2000; Burgos and Donahoe, 2000; Donahoe

and Burgos, 2000)and implications for the evolution

of learning, behavior, and nervous systems, as ex-

plored through the use of genetic algorithms (Burgos,

1997).

The facilitation of acquisition that was observed in

the PE-CS2/CS3 networks in Experiment 4 is a po-

tentially new effect that, to the best of my knowledge,

is not predicted by any other conditioning model. It

represents a sort of negative LI, for which it can be

called latent disinhibition, or LD. I am not aware

of research with living organisms that is directly rel-

evant to LD, so the prediction awaits verification,

which raises the issue of how to design a conditioning

experiment to test for it. In the present simulations,

LD arose through preexposure to a stimulus that was

orthogonal to andsynaptically competitive with the

trained pattern. Orthogonality seems to be relatively

unproblematic to obtain in a conditioning experiment.

For example, a light and a tone presumably qualify

as orthogonal stimuli, in that they represent differ-

ent sensory modalities. Orthogonality, however, does

not ensure the absence of synaptic competition. TheSNN model asserts that there can be synaptic com-

petition among orthogonal input patterns and that it

occurs in sa cortex. However, it is unclear exactly of

which stimulus parameters synaptic competition is a

function. One possibility is frequency. For example,

a higher-pitch tone may be synaptically more com-

petitive with a shorter- than with a longer-wavelength

light.

Preliminary simulations demonstrated that if the

initial CS1-saweights were set to 0.01 and/or = 0,

none of the explored properties of LI was obtained.Substantial initial CS1-sa weights and > 0 thus

were both necessary to obtain the present results,

which has implications for the neural substrates of

learning. If one assumes that the present networks

were experimentally nave, the first condition implies

that experimentally nave need not mean tabula

rasa. Experimentally nave organisms may well come

to the experimental situation with substantial sen-

sory learning. Conditioning thus could involve larger

synaptic changes in motor than in sensory brain areas,


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at least in vertebrates (see Donahoe and Palmer,

1994, pp. 177210, for a discussion of this possibility

within a broader approach to learning and behavior).

The second condition suggests that the physiologicalthreshold for changes in dopaminergically-modulated

synaptic efficacies is larger than zero, although its pre-

cise cellular and molecular nature is unclear. Further

experimental and theoretical analyses are needed.

Acknowledgements

I thank John W. Donahoe, Peter R. Killeen, and an

anonymous reviewer for useful comments.

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