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Resistance of Response Accuracy to Distraction 1

Running head: RESISTANCE OF RESPONSE ACCURACY TO DISTRACTION

The Effects of Relative Rates of Reinforcement on

the Resistance of Response Accuracy to Distraction

Mary Scamman

University of Southern Maine

Correspondence: Mary Scamman

25 Two Rod Rd

Scarborough, Me 04074

[email protected]


Abstract

Basic research on behavioral momentum has shown that resistance to change is a positive

function of relative reinforcement rate. Resistance to change tests such as pre-feeding,

intercomponent food (ICI), delay of reinforcement, and extinction generally demonstrated that

rates of responding decreased overall but were more resistant in the component with the higher

rate of reinforcement. The present study translated basic research procedures on resistance to

disruption to an applied setting with the socially relevant behavior of completing math

worksheets accurately under conditions of distraction by video with middle school students.

Distraction by video generally demonstrated that rates of responding and rates of responding

accurately were more resistant in the component with the higher rate of reinforcement. These

results compare to basic research with nonhumans and provide support for the species generality

of this research. The applied implications of these findings are discussed.

KEY WORDS: behavioral momentum, resistance to change, response accuracy, relative rates of

reinforcement, resistance to distraction


The Effects of Relative Rates of Reinforcement on the Resistance of

Response Accuracy to Distraction

Learning refers to the durability of performance following repeated exposure to

contingent reinforcement (Mace, Gritter, Johnson, Malley, & Steege, 2006; Nevin & Grace,

2000a). Persistence of learned prosocial behavior is desirable when there is a change in the

response-reinforcer relationship that could result in a degradation of performance. Some factors

that promote persistence or resistance to change include rate, immediacy, delay, and quality of

reinforcement and duration of reinforcement history (Nevin, Mandell, & Atak, 1983; Mace et al.,

1990; Nevin, Tota, Torquato, & Shull, 1990; Mace, Mauro, Boyajian, & Eckert, 1997; Nevin &

Grace, 2000b; Nevin, Milo, Odum, & Shahan, 2003).

Social behavior is typically learned from modeling and direct instruction with feedback

and reinforcement. However, learning is not complete until the individual can continue to engage

in the specific behavior when the modeling and reinforcement is lessened, delayed, challenged,

or terminated. The same is true for academic skills. Learning to read involves knowing the letter

names and sounds in the presence of a visual stimulus such as printed material. Initially, a

student receives frequent feedback regarding the accuracy of their reading skills in the form of

praise, social interaction, or tangible reinforcers such as stickers or a grade. In a school setting, as

the student demonstrates consistent accuracy, the rate or type of feedback typically lessens and

the goal is for the reading skills to persist when arranged reinforcing consequences are changed

or terminated.

Operant behavior has been observed to vary in behavioral persistence or resistance to

change when conditions maintaining a response class are altered (Nevin et al., 1983). Resistance

to change is a fundamental property of reinforced behavior in which the rate of responding tends


to persist when the response-reinforcer relation maintaining the behavior is challenged by some

external variable (Nevin, 1992; Nevin et al, 1983; Nevin & Grace, 2000a). Operations that

challenge or disrupt the response-reinforcer relationship such as extinction, satiation, response-

independent reinforcers, reinforcer delay, alternative reinforcement, punishment, or distraction

have been the subject of many basic research studies conducted primarily in laboratories with

nonhumans. Because disruptions such as response-independent or alternative reinforcement

occur routinely in educational settings and can contribute to learning difficulties (Dube &

McIlvane, 2001), continuing these basic studies into applied settings with humans is essential.

Examining the factors that strengthen or weaken resistance to change could lead to empirically-

based interventions that benefit many students in school settings and especially those students

who have emotional, learning, and attention difficulties.

The basic unit of analysis in behavior analysis is the discriminated operant. Many

definitions have been offered since Skinner defined it in 1938 as the three-term contingency with

the antecedent discriminative stimulus, the response, and a reinforcing or punishing

consequence. Catania (2007) defined it as an operant that occurs and is reinforced in the presence

of a stimulus or stimulus class. The stimulus sets the occasion for the response to occur, thus the

operant is defined in terms of the stimuli during which it occurs as well as by the environmental

effect the response has. Thus, operant behavior is behavior that is maintained by contingent

reinforcement.

Reinforced behavior is said to have a degree of response strength. Nevin (1974) reported

that response strength refers both to response rate and the resistance of responding to change

when the relationship between the response and the reinforcer is challenged. Resistance to

change has typically been studied using a two-component, multiple variable-interval, variable


interval (VI VI) schedule of reinforcement with different rates arranged in alternating

components and correlated with distinctive stimuli for each component. Once responding is

stable, a disrupting operation can be introduced equally to each component, and the relative

resistance of baseline response rates to this change can be examined. After stable responding by

pigeons on multiple VI VI schedules with different rates of reinforcement in two components,

Nevin (1974) varied frequency, magnitude, delay, and differential reinforcement of low-rate

(DRL) and differential reinforcement of high-rate (DRH) schedules of reinforcement. He then

examined the relative resistance of baseline response rates to response independent delivery of

reinforcers (RIRD) between components (dark key) and by extinction (EXT). He found that

resistance to change was greater in the component with the higher rate of reinforcement, the

longer duration, the shorter delay of reinforcement, and the DRL condition.

Nevin, Mandell, & Atak (1983) introduced the notion that the persistence of behavior,

despite the presence of various response-reinforcer disruptors, suggests that learned behavior

possesses momentum. This metaphor between the momentum of objects in motion subject to an

opposing force and the momentum of operant behavior when the response-reinforcer relationship

is disrupted is known as behavioral momentum. Newton’s First Law of Motion states that the

momentum of objects in motion is a product of mass and velocity. A heavy body and a light

body moving at the same speed differ in momentum. It is observed that the velocity of the

heavier body changes less than that of the lighter body when an external force opposes motion.

In behavioral terms, the behavior with greater response strength or mass is analogous to the

heavy body and thus more resistant to change when a disruptor is introduced as an opposing

force.


The Nevin et al. (1983) study was concerned with the assessment of behavioral mass and

its relation to the rate of reinforcement and the effect of a disruptor on resistance to change. The

purpose of their study was to determine how log response rate changes relative to its baseline

value (proportion of baseline) when an external variable is applied equally to schedule

components. They believed that it was possible to measure the mass of one performance relative

to the performance of another when the same external variable is applied equally to both. Their

analog of change in velocity was the logarithm of the proportion of baseline. Six pigeons were

trained on three pairs of VI schedules in all possible orders. Once key pecking on two-

component multiple VI VI schedules was stable, resistance to change was assessed by presenting

response-independent food during periods between components and by extinction. The results

from response-independent reinforcement (RIRD) suggested that the ratio of behavioral masses

is a power function of the ratio of reinforcement rates and that behavioral mass may be measured

in a ratio scale. Although Nevin et al. (1983) was the seminal article that introduced the

behavioral momentum metaphor, the experiment contained an important limitation. That is, the

number of stimulus-reinforcer contingencies was confounded with the number of response-

reinforcer contingencies in the multiple schedule design, thereby making it unclear whether

resistance to change was a function of response-reinforcer contingencies or stimulus-reinforcer

contingencies.

Nevin et al. (1990) attempted to resolve this issue in two interrelated experiments. Prior

research showed that the addition of reinforcers from an alternative source weakens an existing

response-reinforcer contingency and reduces the target response rate. Nevin et al. (1990)

suggested that if a target response rate and resistance to change are correlated manifestations of

the effect of reinforcement on operant behavior (response strength), then degrading the operant


contingency should reduce response rate as well as resistance to change if resistance to change is

a function of response-reinforcer contingencies. This should hold true if the operant

contingencies are degraded by the addition of alternative reinforcement whether by response

independent delivery or by contingency on a different, concurrent response. By contrast, if

persistence is a function of stimulus-reinforcer contingencies, the opposite finding would be

expected. That is, resistance to change should be strengthened by added reinforcement to the

context in which behavior occurs and is reinforced (Nevin et al., 1990). Thus, although the

addition of alternative reinforcement degrades the operant contingency, it increases the overall

rate of reinforcement received in the presence of the stimulus in which the operant occurs and

thus enhances the stimulus-reinforcer correlation. According to this account, the stimulus-

reinforcer contingency determines a response’s resistance to change.

Nevin et al. (1990) attempted to demonstrate that behavioral momentum is a function of

stimulus-reinforcer relations by examining the effect of alternative reinforcers on the rate of a

target response and the resistance of that response rate to satiation and extinction. In two

experiments, a target response was reinforced at a specific rate in different and independent

components of a multiple schedule of reinforcement. Alternative reinforcers were presented in

one component either response-independently (Experiment 1) or contingent on a concurrent

response (Experiment 2). Nevin et al. (1990) hypothesized that relative reinforcement of a

response should determine its response rate, while the overall rate of reinforcement, including

alternative and contingent reinforcement, correlated with a stimulus should determine the

resistance to change.

Experiment 1 included seven conditions: 3 identical green-key VI 1-min conditions

(baseline); 2 identical red-key VI 1-min & 2 different VT conditions; and 2 different red-key VI


VT conditions. This allowed for the comparison of the effects on both absolute and relative

numbers of VT food presentations on response rate in the red component with response rate in

the constant green component. The effect of satiation was examined by including various rates of

prefeeding in the 4 experimental red conditions. The effect of extinction was examined after

prefeeding and the pigeons received 12 additional baseline sessions followed by 7 to 11 sessions

of extinction in which food was never presented. This provided a separate assessment of

resistance to change under the different response disruptors. Additional tests for key-color biases

were conducted by applying extinction to the 7th condition.

The overall results from Experiment 1 showed that resistance to extinction was a positive

function of the overall food rate in a schedule component during training and was unaffected by

the proportion of VT food presentation when overall food rates were equal. Adding response-

independent food to a schedule component decreased the rate and increased resistance to change.

Similar findings were obtained when satiation was used as a response disruptor.

Experiment 2 further examined the possibility that adding food contingent on an alternative

response would increase the resistance of a target response to change just as freely delivered

food did. Three pairs of concurrent schedules each signaled by different colored keys were

arranged (i.e., a multiple concurrent schedule). Two of the concurrent schedule pairs arranged

equal VI food schedules for pecks at the RIGHT or target key. In the first pair (GREEN), food

was also delivered for pecks on the LEFT or alternative response key, while in the second pair

(RED), left-key pecks never resulted in food. In the third (WHITE) concurrent schedule pair,

food was scheduled for the RIGHT or target key but at a rate similar to the sum of the two food

rates in the first component. These arrangements were similar to the Green and Red key

experimental conditions in Experiment 1. The hypothesis was that if food for a specific


alternative response functions the same as response-independent food, then the target response

should be more resistant to change in the first than in the second component and equally resistant

to change in the first and third schedule pairs. This outcome would provide further evidence that

overall food rate in the presence of a stimulus determines a response’s resistance to change. In

addition, using a specific response for alternative food allows for the measurement of its

resistance to change. The Extinction condition consisted of a single session that continued until

twelve minutes without a key peck. Satiation consisted of long-session and short-session

methods. Long-session consisted of prefeeding 15 g 30 to 60 min before the start of a session. In

the short-session method, sessions were reduced to 9 presentations for each component.

Prefeeding consisted of 0g, 30g, 45g, and sometimes 52.5g. in its home cage over 3 or 4

consecutive days. Prior to resistance tests, baseline training sessions were conducted.

In most cases, response rates were highest in Condition C which had the highest absolute

food rate for the target response (60/hr) and no alternative food; next highest in Condition B with

15/hr for the target response and no alternative food; and lowest for Condition A with 15/hr for

target response and 45/hr alternative food on the left key. The results supported the conclusion

that the baseline rate of a target response maintained by a VI food schedule is lower when

alternative food is available either independently or contingent upon an alternative concurrent

response than when there is no alternative food. The second and most important conclusion was

that resistance to satiation or extinction of a target response maintained by a VI food schedule is

greater when alternative food is available than when there is no alternative food. The final

conclusion was that resistance to satiation or extinction of a target response in one component of

a multiple schedule is maintained when VI food schedule and alternative food is available does

not differ from that of a second condition in which the response is maintained by a VI schedule


at an equal sum of the VI and alternative schedules in the first component. Resistance to change

may be unaffected by the fundamental operant contingency but was affected by the overall rate

of reinforcement in a signaled component such that resistance to change was greater in the

component with the higher overall reinforcement rate. Resistance to change depended directly

upon the stimulus-reinforcer contingency.

Mace et al. (1990) tested the species generality of Nevin et al. (1990) with two adults

with mental retardation in a group home setting. The participants sorted different colored plastic

dinner and correct sorting responses were reinforced with food according to a multiple VI 60 s

VI 240 s schedule of reinforcement. Following baseline, sorting occurred in the presence of a

distracting stimulus (videotaped TV programs). The results were consistent with Nevin et al.’s

(1990) findings and showed that human performance during distraction is a positive function of

the frequency of reinforcement signaled by task-related stimuli but is independent of baseline

response rates and response-reinforcer contingencies.

Research on behavioral momentum and resistance to change has largely examined

quantitative dimensions of behavior such as response rate and changes in the proportion of

baseline response rate resulting from various response disruptors. Nevin, Milo, Odum, &

Shahan (2003) extended this research by examining a qualitative dimension of a response,

response accuracy. Pigeons were trained to make conditional discriminations of vertical and

horizontal lines using a matching-to-sample procedure. Accurate discriminations were

reinforced according to a two-component multiple schedule with two rates of reinforcement

probabilities, .80 and .20. Resistance tests included prefeeding, response-independent

reinforcement during intercomponent intervals (dark key), delay between sample and

comparison stimuli, and extinction. In this manner, the effects of reinforcer rate on the relative


resistance to disruption of both response rate and matching accuracy, within subjects and

sessions, was assessed in a way that was similar to past research on resistance to change.

The traditional measure of accuracy called proportion correct or p(C) was calculated as

well as Log d, which is the logarithm of the geometric mean of the ratios of correct to incorrect

responses on trials. Log d was used because it is independent of biases between comparison

stimuli that may occur during disruption by condition changes. Matching accuracy was found to

be noticeably more resistant to change in the richer component in 12 out of 20 tests for both Log

d and p(C). Within subject agreement was found between the resistance of response rate and

matching accuracy to three disruptors (satiation, RIRD, and extinction).The findings suggest that

response accuracy may be strengthened by reinforcement in the same way as free-operant

responding and that the quality of performance, defined by the extent to which responding

conforms to the different contingencies signaled by different stimuli, may be strengthened by

reinforcement in the same way as quantity of performing has been measured by response rate.

Thus, the accuracy of a conditional discrimination has mass-like qualities and is more resistance

to change in the richer of two conditions of a multiple schedule.

The present study attempted to establish the species generality of Nevin et al. (2003) by

applying parametric resistance tests to two conditions with different rates of reinforcement with

human subjects. We translated the basic nonhuman studies of Nevin (1974, 1992) and Nevin et

al. (1990, 2003) and extended the basic human studies of Dube and McIlvane (2002, 2003) and

Mace et al. (1990), by examining the effects of relative reinforcement on the resistance to change

of both problem completion rate and of accurate completion of math worksheets by children of

low average to average cognitive development and who have been identified as having learning


and attention difficulties. Previous human studies have applied resistance tests to adults with

developmental disabilities and mental retardation.

Method

Participants

Two boys, ages 11 and 14, diagnosed with Autistic Disorder and Asperger’s syndrome

served as participants in this study. Hank was a 14 year-old, eighth-grade student who attended a

day-treatment program that provided assessment and treatment of problem behavior. He was

diagnosed with Asperger’s syndrome, had a FSIQ of 75, identified under the special education

categories of Autism and Emotional Disorder (ED), and was taking Clonidine and Melatonin at

bedtime for sleep difficulties. His behavioral goals included improving attendance and work

completion. Luke was an 11-year-old, sixth-grade student who attended a public school autism

program. He was diagnosed with Autistic Disorder, communicated using his natural voice, had a

Global Fluid-Crystallized Index or general ability level of 78, and was identified under the

special education category of Autism. He was supported in mainstream classes by an Educational

Technician and was in the process of transitioning to the middle school. Each participant

demonstrated stable preference for at least 5 food items and 2 video choices.

Setting and Materials

All sessions were conducted one-on-one in separate conference rooms or small

classrooms. The conference rooms were furnished with a large table, desk, and several chairs.

The small classroom was furnished with books in several bookshelves, several tables, chairs, a

desk, file cabinets, and a sink. The experimenter, the participant, and occasionally the doctoral

supervisor were present in the room during sessions.


Responses were recorded on red or blue math worksheets. For Hank, the problems

consisted of mixed single-digit addition and subtraction on the same page. Distraction by video

consisted of Star Wars CD’s on an HP laptop computer placed on the table directly in front of

Hank. For Luke, the problems consisted of one page each of single-digit addition, multiplication,

and subtraction in that order. Distraction by video consisted of SpongeBob video’s on a

television on a cart placed directly in front of Luke. The edible reinforcers were placed on

another table within each student’s visual field.

Target Behavior, Data Collection and Interobserver Agreement

The target behavior was the completion of single-digit addition, subtraction, or

multiplication problems. Data collection consisted of the count of problems completed and

problems completed accurately at the end of sessions. Single-digit addition, subtraction, or

multiplication problems were generated randomly from a math worksheet site

(themathworksheetsite.com) and randomly assigned to Red or Blue worksheets. The problems

were of comparable difficulty but with no duplication within sessions. Red and Blue poker chips

were used as conditioned reinforcement and were exchanged for pre-selected food after the

session was completed. The dependent measures were problems completed per minute and

problems completed accurately per minute..

Interobserver agreement (IOA) was obtained on 41% of worksheets scored by two

independent observers and balanced across conditions. Exact occurrence, nonoccurrence, and

total agreement on a problem-by-problem basis were calculated by dividing the number of

agreements by the number of agreements plus disagreements multiplying by 100%. Mean

interobserver agreement was 86% for worksheets and 99% for problems-by-problems.


Rate of reinforcement was also recorded as tokens delivered within the 10-s limited hold

during the VI 20-s and VI 80-s schedules. The rate was recorded as delivered or not delivered on

a tally sheet at each interval.

Sessions at the day treatment program were 22 minutes in duration and included two 10-

min components separated by one 2-min intercomponent interval (ICI). Sessions at the public

school were 13 min in duration and included two 5-min components separated by one 3-min ICI.

For both students, one component arranged red poker chips on a VI 20-s schedule for accurate

responses during the 10-s limited hold on a red worksheet and the second component arranged

VI 80-s reinforcement with blue poker chips and worksheets.

Procedure

Preference assessment. The ACHIEVE version of the Reinforcer Assessment for

Individuals with Severe Disabilities (RAISD) (Fisher, Piazza, Bowman, & Amari, 1997;

ACHIEVE Manual, 2008) was administered to parents, caregivers, teachers, and students by

interview to identify a menu of highly preferred food items and videos. The participant was

allowed to sample each food item and a list was created of preferred edibles. A list of preferred

videos was also created.

Demonstration of conditioned reinforcement. A concurrent Fixed Ratio 1, Extinction

(Conc FR1 EXT) schedule with reversal to a concurrent Extinction, Fixed Ratio 1 (Conc EXT

FR1) schedule using poker chips as consequences, was used to demonstrate that poker chips

function as conditioned reinforcers. Tokens were exchanged for back-up edible reinforcers (from

the list created after the preference assessment) at the end of the session. Identical problems were

presented concurrently and schedules were correlated with red or blue worksheets and poker

chips. Both students demonstrated they understood the conditioned reinforcement system by


choosing the worksheet paired with the poker chip and not choosing the worksheet without the

poker chip in 4 out of 4 trials.

Baseline. A multiple variable-interval variable interval (VI VI) schedule of reinforcement

with a10-s limited hold was used during baseline. Poker chips were delivered contingent on

accurate completion of math problems within 10 s of the scheduled reinforcement. The poker

chips were exchanged for food items at the end of the session with one poker chip equal to one

edible from a list generated by the RAISD-ACHIEVE preference assessment.

Test of resistance to change. A parametric analysis of proportion of sessions was

conducted with distraction by videotape in the following randomly assigned proportions: 25%,

50%, 75%, and 100% of the session. Each session began with a verbal instruction, “You will

have 10 (Hank) (or 5 Luke) minutes to complete these worksheets. Work as quickly and

carefully as you can. A poker chip will be presented from time to time for correct answers. You

will be able to exchange these for food after the assignment. Sometimes a video will be playing

and sometimes it will not. Work until I tell you to stop please.” Sessions terminated with the

instruction, “stop working and put your pencil on the desk”. Students then passed in the

worksheets to the teacher/examiner.

Experimental design

Phase 1 of the study consisted of a preference assessment to identify edible reinforcers

and videos. Phase 2 establish tokens as conditioned reinforcers for the edibles available at the

end of the sessions. The schedule consisted of concurrent FR1 EXT with reversal to concurrent

EXT FR1 with poker chips and EXT correlated with different colored math worksheets. Poker

chips were exchangeable for the identified food reinforcers at the end of each session.


In Phase 3, baseline was established with multiple VI 20-s VI 80-s schedules with a 10-s

limited hold with poker chips delivered contingent on accurate completion of math problems. In

the final Phase 4, resistance tests consisted of a parametric analysis of proportion of sessions by

adding a videotape to the environment for 25%, 50%, 75%, or 100% of the session randomly

assigned and during the middle segment of each component.

Data Analysis

At the end of each session, the experimenter counted the number of problems completed

and completed accurately. Proportion of baseline response rates was calculated by dividing the

number of problems completed and the number of problems completed accurately during each

level of distraction by the average of the previous baseline sessions. Data are displayed

graphically representing the proportion of baseline for each percentage of session with

distraction.

Results Table 1 presents the raw session data on problems completed for both participants

obtained during the baseline and distraction by video phases of the study. Table 2 presents

similar raw session data on problems completed accurately. These data are analyzed and

presented graphically in Figures 1 through 5 and discussed separately for the two participants.

Hank

Figure 1 shows problems completed per min (top panel) and problems completed

accurately per min (bottom panel) for Hank during baseline and distraction by video conditions

under the multiple VI 20-s VI 80-s schedule of reinforcement. In baseline, problems completed

per min were consistently higher in the VI 20-s schedule (M = 18.1; range = 14.0 to 22.1) than in

the VI 80-s schedule (M = 16.0; range = 13.3 to 18.7). An increasing trend was evident in both


schedule components. During distraction by video, problems completed per min were higher in

the VI 20-s schedule than in the VI 80-s schedule for all four levels of distraction by video (25%,

50%, 75%, and 100%). The increasing trend continued during distraction for both schedule

components.

In baseline, problems completed accurately per min were also higher in the VI 20-s

schedule (M = 17.9; range = 14.0 to 21.8) than in the VI 80-s schedule (M = 15.8; range = 13.0

to 18.7). An increasing trend was evident in both schedule components. During distraction by

video, problems completed accurately per min were higher in the VI 20-s schedule than in the VI

80-s schedule for the four levels of distraction by video. Response patterns for problems

completed and problems completed accurately were similar for Hank.

Figure 2 shows the proportion of baseline problems completed by Hank during the four

levels of distraction by video (25%, 50%, 75%, and 100%, top panel) and proportion of baseline

problems completed accurately for the four levels of distraction by video (bottom panel). For 3

of the 4 levels of distraction, problems completed per min were more resistant to distraction by

video in the VI 20-s schedule than in the VI 80-s schedule, although this difference was small for

the 25% and 50% level of distraction. The average proportion of baseline for the VI 20-s

schedule was 1.31. The average proportion of baseline for VI 80-s was 1.24. For 4 of the 4 levels

of distraction, problems completed accurately per min were more resistant to distraction by video

in the VI 20-s schedule than in the VI 80-s schedule. The average proportion of baseline for the

VI 20-s schedule was 1.31. The average proportion of baseline for the VI 80-s schedule was

1.25.

Figure 3 expresses the difference in resistance to distraction by video relative to baseline

between the VI 20-s and the VI 80-s schedules as percentage of greater resistance in the VI 20-s


schedule for Hank. For problems completed, the percentage of greater resistance to distraction in

the VI 20-s schedule was 29%, 5%, 61%, and 18% in the 25%, 50%, 75%, and 100% levels of

distraction by video, respectively. For problems completed accurately, the percentage of greater

resistance to distraction in the VI 20-s schedule was 13%, 11%, 52%, and 25% in the four levels

of distraction by video, respectively.

Luke

Figure 4 shows problems completed per min (top panel) and problems completed

accurately per min (bottom panel) for Luke during baseline and distraction by video conditions

under the multiple VI 20-s VI 80-s schedule of reinforcement. In baseline, problems completed

per min were higher in the VI 20-s schedule for 1 out of 5 sessions (M = 11.4; range = 10.2 to

13.2) than in the VI 80-s schedule (M = 12.0; range = 11.4 to 12.6). During distraction by video,

the average problems completed per min were slightly higher in the VI 80-s schedule (M = 12.3;

range = 10.4 to 15.6) than in the VI 20-s schedule (M = 11.7; range = 7.8 to 16.0) for the four

levels of distraction by video. However, the data were generally undifferentiated.

Baseline problems completed accurately per min were higher in the VI 20-s schedule for

2 out of 5 sessions (M = 9.6; range = 9 to 11) than in the VI 80-s schedule (M = 9.8; range = 9.2

to 10.2). During distraction by video, average problems completed accurately per min were

higher in the VI 80-s schedule (M = 68.8; range = 8.2 to 14) than in the VI 20-s schedule (M =

65.1; range = 6.4 to 13) for the four levels of distraction by video. Data during distraction was

also undifferentiated for both problems completed and problems completed accurately; there was

no discernable change from baseline to distraction for Luke.

Figure 5 shows the average proportion of baseline problems by Luke completed during

the four levels of distraction by video (25%, 50%, 75%, and 100%, top panel) and the average


proportion of baseline problems completed accurately for the four levels of distraction by video

(bottom panel). For 2 of the 4 levels of distraction, problems completed per minute were more

resistant to distraction by video in the VI 20-s schedule than in the VI 80-s schedule. The average

proportion of baseline for the VI 20-s schedule was 1.2. The average proportion of baseline for

VI 80-s was also 1.2. In none of the 4 levels of distraction was problems completed accurately

per min more resistant to distraction by video in the VI 20-s schedule than in the VI 80-s

schedule. The average proportion of baseline for the VI 20-s schedule was 1.1. The average

proportion of baseline for the VI 80-s schedule was 1.2. Resistance of problems completed

accurately was similar for the two components at the 25% and 50% levels of distraction. At the

75% and 100% levels, resistance to distraction was greater in the VI 80-s component.

Overall, there were two tests each of resistance to distraction by video for problems

completed and problems completed accurately. For Hank, resistance to distraction was generally

greater in the VI 20-s schedule for both dependent measures. For Luke, resistance of problems

completed to distraction was mixed across distraction levels. For problems completed accurately,

there was no difference at two of four levels of distraction, and resistance was greater in the VI

80-s schedule for the other two levels.

Discussion

The purpose of the present study was to further establish the species generality of

behavioral momentum research conducted with pigeons to children who completed math

worksheets. The study attempted to demonstrate that both the quantitative dimension of response

rate and the qualitative dimension of response accuracy would show greater resistance to change

in the richer reinforcement component of a two-component multiple schedule of reinforcement

when challenged parametrically by video distraction during the completion of math worksheets


by middle school students. Resistance to change was assessed by video distraction, one form of

distraction that is present in many natural human settings.

For Hank, rates of problems completed were higher in the schedule component with the

higher rate of reinforcement during both baseline and distraction sessions. During distraction

sessions, greater resistance occurred in the richer component at the higher levels of distraction.

The difference in resistance to distraction was small for the lower levels. Resistance tests for

Luke were mixed with resistance slightly greater for problems completed in the richer

component at the lower levels of distraction, but then were reversed at the higher levels of

distraction, favoring the leaner schedule of reinforcement. For math problems completed

accurately by Hank, the response pattern was similar to that seen with problems completed.

Resistance of response accuracy to distraction by video was greater in the richer component for

all four levels of distraction, but was greatest at the two higher levels of distraction. Math

problems completed accurately by Luke were also similar to problems completed. Relative rates

of resistance to distraction were again mixed for response accuracy with little difference at lower

levels and greater resistance for the leaner schedule at the higher levels of distraction.

When considering each level of video distraction as a test, for problems completed, 5 of 8

favored the VI 20-s component and no difference was seen for 1 of 8 of the tests. For problems

completed accurately, 4 of 8 tests favored the VI 20-s component and no difference was seen for

2 of 8 tests.

The current study translated basic research procedures on resistance to change to an

applied setting with humans. It is a translation of Nevin et al. (2003) with the socially relevant

behavior of completing math worksheets accurately under conditions of distraction by video.

Nevin et al. examined the effects of relative reinforcer rate on the resistance to disruption of


both response rate and matching accuracy with pigeons. They arranged identical discrimination

tasks consisting of a matching-to-sample procedure with two-component multiple schedules of

reinforcement (0.8 or 0.2 probabilities) for accurate responses signaled by distinctive stimuli.

Following this baseline, accuracy of discrimination was assessed during short-term disruptions of

pre-feeding, intercomponent food (ICI), delayed discrimination, and extinction.

Figure 3 from the Nevin et al. (2003) is reproduced in Figure 6. The left column shows

response rates as proportion of baseline and the right column shows the log d values as

proportion of baseline of matching accuracy. The top three rows show the results of five sessions

each of disruptions by prefeeding, ICI food, and delayed reinforcement and are expressed as

proportion of the immediately preceding baseline for each pigeon. For the bottom row, response

rate (left column) and log d of matching accuracy (right column) are shown in two successive

five-session blocks of extinction. Proportion of baseline response rates were resistant to

prefeeding in 2 of 4 pigeons, resistant to ICI food in 4 of 4 pigeons, resistant to delayed

discrimination in 2 of 4 pigeons, and resistant to extinction in 4 of 8 tests for 3 of 4 pigeons.

Thus, 10 of the 20 tests (50%) of resistance to change on response rate across 4 pigeons and 4

response disrupters favored the richer schedule. Proportion of baseline matching accuracy was

resistant to prefeeding in 3 of 4 pigeons, resistant to ICI food in 4 of 4 pigeons, resistant to

delayed reinforcement in 1 of 4 pigeons, and resistant to extinction in 5 of 8 tests for 3 of 4

pigeons. Of the 20 tests of resistance to change on matching accuracy across 4 pigeons, 12 of 20

tests favored the richer schedule (i.e., 60%).

The current findings can be compared to the results from Nevin et al. (2003). Overall, for

each student in the present study, there were 4 tests each of resistance to distraction by video for

problems completed and 4 tests for problems completed accurately, each signaled by distinctive


stimuli similar to the Nevin et al. study. For Hank, resistance to distraction was greater in the

richer component for 3 out of 4 tests for problems completed. For Luke, resistance to distraction

was mixed across all distraction levels for problems completed. The resistance was slightly

higher in the richer schedule for 2 of the 4 tests of distraction and reversed in favor of the leaner

component in the other 2 tests. For problems completed by both students, 5 out of 8 tests were

more resistant in the richer component, or 63%, and 1 out of 8 tests showed no difference. This

compares with 50% of resistance to change of response rates tests with pigeons in Nevin et al.

(2003).

Resistance of problems completed accurately to distraction was greater in all 4 tests for

Hank. The results for problems completed accurately by Luke were mixed with no difference in

resistance at 2 of 4 levels of distraction by video and in favor on the leaner component at 2 of 4

levels. For problems completed accurately by both students, 4 out of 8 tests, 50%, were more

resistant in the richer component and 2 out of 8 showed no difference. In Nevin et al. (2003) with

pigeons, 60% of the tests of resistance to change of discrimination accuracy favored the richer

schedule. Thus, results of the present study are generally consistent with those using pigeons as

subjects and support the species generality of this research.

To the extent that the present findings support a functional relation between relative rates

of reinforcement and the persistence of a qualitative dimension of behavior, this study may

provide direction for further applied research and practice. Looking at accuracy of responses as a

qualitative dimension of behavior that is independent to some extent from the quantitative

dimension of response rate can be helpful in educational settings. Since response accuracy is

central to academic performance, and since disruptions are common in educational settings


(Dube & McIlvane, 2001), understanding how to strengthen the persistence of accurate

responding in the face of disruption may contribute significantly to educational outcomes.

Common distracters found in schools include distractions from peers and teachers inside

and outside the classroom, lapses in treatment integrity, high rates of reinforcement prior to

academic instruction, and delay between accurate responses, performance feedback and

reinforcer deliveries. Planning to strengthen the persistence of response accuracy in the face of

these disruptions can be accomplished by gaining a thorough understanding of how

reinforcement rates impact performance in general. This can lead to the development of effective

strategies such as including rates of reinforcement into teacher plans that will be just rich enough

to ensure that accurate responding can withstand disruptions such as peers being off-task and

noisy. Other ways to support the persistence of response accuracy is to implement plans and

strategies as designed, avoid the provision of high rates of similar reinforcement prior to

instruction, and avoid delays in reinforcement delivery that often result from high student to

teacher ratios.

Existing rates of reinforcement in a typical classroom are often low, poorly planned, and

focus on work completion or productivity rather than on response accuracy. For example, in

most classrooms when students complete assignments, they independently put the paper product

in a designated area and then can have access to a preferred activity such as a break or less

demanding schoolwork. Only later, when the teacher has time, is the accuracy of the responses

reviewed. Feedback and/or reinforcement for accuracy are typically delivered hours to days later

if at all. This delay can be viewed as a performance disruptor just as distractions from peers and

merits future study (Mayhew, G., & Crow, R. (1977); Nevin, 1974; Nevin et al. 2003). The

present study suggests that richer rates of reinforcement delivered as soon as possible for


accurate responding can and should be used by practitioners to not only increase the rate of

accurate responding but also to strengthen the persistence of accurate responding when

disruptions occur.

The present study has a number of limitations that warrant caution in the interpretation of

the findings. First, a significant limitation was the practice effect from completion of many math

worksheets in an attempt to establish a stable baseline. Rather than reducing response rate and

accuracy during distraction by video, rates continued for both conditions in an upward trend

during distraction. Dube and colleagues have addressed the difficulty of establishing a stable

baseline with human subjects by introducing a distributed-sessions procedure of conducting a

predetermined number of baseline sessions prior to a series of disrupter-test sessions consisting

of both baseline and disrupter components (Dube & McIlvane, 2002; Dube et al., 2003). Future

studies may wish to adopt this procedure as well as conducting baseline sessions without

reinforcement being offered. This would allow for a no reinforcement baseline condition to be

compared to the baseline with reinforcement..

There were also some limitations with the study population and the math worksheets with

up to 36 problems per page. The first participant appeared to try harder in the face of distraction.

He seemed to compensate by completing more problems during the non-distraction phase before

and after each parametric presentation of the video. With the second participant, attention to the

video distraction decreased over time and with longer proportions of distraction (i.e., 75% and

100% levels). It was also difficult for the experimenter to view the calculations as the student

completed them and to see which responses were accurate versus inaccurate. To this extent,

inaccurate responses may have inadvertently received token reinforcement. Any inaccuracies in

reinforcer deliveries may not have strengthen the resistance of response accuracy to distraction


independent of response rate. Future research may consider use of an automated program that

presents math problems one at a time, controls for problem difficulty, records responses, and

presents reinforcement such as tokens or edibles automatically for accurate responses to control

for the present limitations.

Limits to the access of the study participants also compromised the experimental control

that was realized in this study. Replication of the levels of distraction was not obtained for one

participant due to the end of the school year. The education plan of transitioning to the middle

school for the other participant interrupted the study before experimental control could be

determined. In addition, neither response rate nor accuracy decreased as a result of video

distraction in some sessions suggesting that video might not have been an optimal response

disruptor. Lionello-DeNolf, Dube and McIlvane (in press) examined the effect of four response

disruptors with children: prefeeding, presentation of a concurrent alternative stimulus,

presentation of a movie, and the presence of a researcher dispensing response-independent

reinforcement. Presentation of a movie resulted in less response disruption than was seen with

the prefeeding and the alternative stimulus. The present results suggest that future studies should

allocate more time for the study and consider alternative distracter conditions.

The present research illustrates a recent trend in behavior analytic research. That is,

advances in basic behavioral research are being translated into new conceptualizations of human

behavior problems and, in turn, are leading to innovations in behavioral interventions (Mace,

1994; Mace & Critchfield, in press). If qualitative dimensions of behavior, such as response

accuracy, are found to be lawfully related to relative reinforcement rate, instructional practices

can be designed to explicitly strengthen this property of behavior and hold promise to

substantially improve educational outcomes.


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Table 1. Raw data on a session-by-session basis for Hank and Luke during the multiple VI 20-s

VI 80-s schedule for Problems Completed per minute for the Baseline and Distraction Test

phases.

Phase/Session Hank Luke VI 20-s VI 80-s VI 20-s VI 80-s

Baseline

1 15.2 12.6 2 13.3 11.0 3 14.0 11.4 4 14.9 11.6 5 15.9 12.4 6 17.1 10.2 7 13.6 11.4 8 16.1 12.2 9 18.6 13.2 10 16.7 11.4 11 18.7 12 19.9 13 18.2 14 15.1 15 18.2 16 21.7 17 22.1 18 17.8 Condition Mean 18.1 16.0 11.4 12.0 Distraction Test (%)

1 19.1 (50%) 12.8 (25%) 2 21.8 (50%) 13.4 (25%) 3 23.0 (25%) 10.6 (50%) 4 19.4 (25%) 11.0 (50%) 5 25.2 (100%) 12.4 (75%) 6 21.3 (100%) 11.8 (75%) 7 24.8 (75%) 10.8 (100%) 8 19.7 (75%) 13.6 (100%) 9 9.2 (75%) 10 11.0 (75%) 11 13.0 (100%)


12 11.6 (100%) 13 11.0 (50%) 14 12.4 (50%) 15 11.8 (25%) 16 12.6 (25%) 17 7.8 (25%) 18 11.6 (25%) 19 10.5 (75%) 20 12.2 (75%) 21 10.8 (50%) 22 11.0 (50%) 23 9.6 (100%) 24 11.4 (100%) 25 13.8 (25%) 26 16.0 (25%) 27 10.4 (100%) 28 11.2 (100%) 29 12.2 (50%) 30 11.4 (50%) 31 14.0 (75%) 32 12.4 (75% Condition Mean NA NA 12.3 (25%) 12.65 (25%) 11.3 (50%) 11.7 (50%) 11.4 (75%) 12.38 (75%) 10.6 (100%) 12.3 (100%)


Table 2. Raw data on a session-by-session basis for Hank and Luke during the multiple VI 20-s

VI 80-s schedule for Problems Completed Accurately per minute for the Baseline and

Distraction Test phases.

Phase/Session Hank Luke VI 20-s VI 80-s VI 20-s VI 80-s

Baseline

1 15.0 10.2 2 13.0 9.2 3 14.0 10.4 4 14.8 9.2 5 15.9 9.8 6 16.9 8.2 7 13.3 9.0 8 15.7 10.2 9 18.2 9.6 10 16.4 11.0 11 18.7 12 19.6 13 17.9 14 15.0 15 18.1 16 21.7 17 21.8 18 17.1 Condition Mean 17.9 15.8 9.6 9.8 Distraction Test (%)

1 18.8 (50%) 10.2 (25%) 2 21.6 (50%) 10.0 (25%) 3 22.6 (25%) 8.6 (50%) 4 19.4 (25%) 9.2 (50%) 5 25.1 (100%) 10.4 (75%) 6 20.9 (100%) 10.0 (75%) 7 24.2 (75%) 7.8 (100%) 8 19.4 (75%) 12.0 (100%) 9 6.4 (75%) 10 8.6 (75%) 11 11.0 (100%)


12 8.2 (100%) 13 8.6 (50%) 14 10.8 (50%) 15 9.6 (25%) 16 10.2 (25%) 17 6.4 (25%) 18 9.0 (25%) 19 9.0 (75%) 20 11.0 (75%) 21 9.2 (50%) 22 9.0 (50%) 23 8.6 (100%) 24 9.2 (100%) 25 12.1 (25%) 26 13.0 (25%) 27 8.6 (100%) 28 9.4 (100%) 29 9.4 (50%) 30 11.0 (50%) 31 14.0 (75%) 32 11.0 (75%) Condition Mean NA NA 11.65 (25%) 11.8 (25%) 10.85 (50%) 11.2 (50%) 10.25 (75%) 11.28 (75%) 9.85 (100%) 11.9 (100%)


Figure Captions

Figure 1. Problems completed per minute (top panel) and problems completed accurately per

minute (bottom panel) for Hank during baseline and distraction by video conditions under the VI

20-s VI 80-s schedule of reinforcement.

Figure 2. Proportion of baseline problems completed by Hank during the four levels of

distraction by video (25%, 50%, 75%, and 100%, top panel) and proportion of baseline problems

completed accurately during the four levels of distraction by video (bottom panel).

Figure 3. The difference in resistance to distraction by video relative to baseline between the VI

20-s and the VI 80-s schedules as percentage of greater resistance in the VI 20-s schedule for

Hank.

Figure 4. Problems completed per minute (top panel) and problems completed accurately per

minute (bottom panel) for Luke during baseline and distraction by video conditions under the VI

20-s VI 80-s schedule of reinforcement.

Figure 5. Average proportion of baseline problems completed by Luke during the four levels of

distraction by video (25%, 50%, 75%, and 100%, top panel) and average proportion of baseline

problems completed accurately during the four levels of distraction by video (bottom panel).

Figure 6. Reproduction of Figure 3 in Nevin et al. (2003). Results of resistance to disruption

tests for 4 pigeons, with response rates as proportions of baseline in the left column and log d

values as proportions of baseline in the right column. On the top three rows, response rates and

accuracy of matching are pooled over five sessions of disruption (prefeeding, ICI food, and

delay) and expressed as proportions of immediately preceding baseline values for each pigeon.

On the bottom row, response rates and log d values are presented from two successive 5-session

blocks of extinction.


Author Note

The first author thanks Lora Perry, Kim Stowers, Jamie Pratt, Aaron Gritter, and Providence’s

ACHIEVE! Staff and students as well as Suzanne Lucas, Phil Potenziano, Diane Gagne, Danielle

Crystal, and MSAD6 Hollis Elementary School staff and students who supported and

participated in the experimental procedure. Appreciation is also extended to Dr. F. Charles Mace,

Dr. Mark Steege, and Dr. Michael Kelly for their valuable input in the preparation of this

manuscript.


Figure 1

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Figure 2

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Figure 5

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Figure 6.

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