Response-outcome contingency: Behavioral and judgmental effects of appetitive and aversive outcomes with college students

LEARNING

AND

MOTIVATION

16, 1-34

(1985)

Response-Outcome Contingency: Behavioral and Judgmental Effects of Appetitive and Aversive Outcomes with College Students D. L. CHATLOSH,

D. J. NEUNABER, The University

AND E. A. WASSERMAN

of Iowa

In two experiments, positive, negative, and zero response-outcome contingencies were responded to and rated by college students under a free-operant procedure. In Experiment 1, outcomes were either neutral or were associated with point gain. In Experiment 2, subjects were administered different outcome treatments: neutral outcomes, outcomes associated with money gain, or outcomes associated with money loss. In both experiments, subjects’ judgments of response-outcome contingency and their operant responses were each strong linear functions of A P, the difference between the probability of an outcome given a response and the probability of an outcome given no response. Appetitive and aversive outcomes produced opposite and symmetrical response patterns. In Experiment 1, no differences in ratings occurred with neutral or appetitive outcomes; however, in Experiment 2, more potent appetitive outcomes led to somewhat more extreme ratings than either neutral or aversive outcomes. increasing outcome probability produced only a slight bias in ratings of noncontingent problems in Experiment 1 and no bias in Experiment 2. Contrary to predictions derived from an analysis of superstitious behavior, increasing outcome probability in noncontingent problems decreased operant responding when outcomes were appetitive and increased operant responding when outcomes were aversive. Trend analyses revealed that A P was superior to several other metrics in predicting subjects’ estimates of contingency and the behavioral effects of contingency. Operant responding was in closer accord with matching predictions than with maximizing predictions. 0 1985 Academic

Press, Inc.

Contingency-based accounts of operant performance in reinforcement paradigms have been profluent in the animal learning literature (Rescorla & Skucy, 1969; Seiigman, Maier, & Soiomon, 1971). Certainly, Ferster and Skinner’s (1957) exhaustive investigation of operant behavior docThis research Mental Health. to this research. The University

was supported in part by Grant MH 15773 from the National Institute of The authors thank M. W. O’Hara and A. Schneider for their contributions Reprints are available from E. A. Wasserman. Department of Psychology, of Iowa, Iowa City, IA 52242.

0023.%90/85

$3.00

Copyright 0 1985 by Academic Press, Inc. All rights of reproduction in any form reserved.

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uments animals’ sensitivity to reinforcement schedules. Nevertheless, analysis of this sensitivity by contingency theories would necessitate the manipulation of reinforcement probabilities given the presence and absence of a response; but in the free-operant paradigm, complications arise due to the inherent confounding of response rate with these two conditional probabilities. Because the animal’s behavior makes direct contact with the programmed reinforcement schedule, and therefore determines the actual degree of contingency present, precise a priori specification of local response-outcome probabilities cannot be made (Gibbon, Berryman, & Thompson, 1974). This procedural confounding may not be insurmountable, however. Hammond (1980) devised an experimental technique designed to forestall the problematic interaction between response rate and outcome probability. In Hammond’s study, independence of these two variables was achieved by programming conditional reinforcement probabilities for a “response” and “no response” on a second-by-second basis. The scanning of successive l-s intervals for the presence or absence of responding determined which conditional reinforcement probability would be probed at the end of each interval. Thus, local reinforcement probabilities were specified in advance, and were unaffected by overall response rates. Contingencies were defined by A P, the difference between the probability of an outcome given a response, p(O/R), and the probability of an outcome given no response, p(O/no R) (Allan, 1980). Using this procedure with appetitive reinforcers, response rate decrements were observed when rats initially trained with positive response-reinforcer contingencies were later shifted to zero and negative contingencies. While we may thus be convinced that animals are indeed responsive to experimenter-determined response-reinforcer contingencies, independent of ongoing behavior patterns, an additional question surfaces in reference to C. Lloyd Morgan’s (1894) subtle distinction between being affected by the contingencies present in one’s environment and actually perceiving these cause-effect relationships. Do organisms accurately perceive causal relationships between events? To answer this somewhat more intriguing question, it is necessary to examine subjects’ reflective reports of interevent relations. Although the desirability of obtaining reflective reports certainly need not exclude nonhuman subjects from investigation (e.g., Killeen, 1978); it would seem particularly profitable to exploit the communication skills of human subjects via their verbal and/or mathematical judgments of contingency. Early research into contingency perception by humans led to the disconcerting conclusion that adult subjects often err in estimating interevent correlations or contingencies (Jenkins & Ward, 1965; Smedslund. 1963). More recent investigations have, however, been more encouraging (for a review see Alloy & Tabachnik, 1984). Reasonably accurate judgments

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of influence, connection, or control-defined as the absolute value of the difference between p(O/R) and &O/no R)-were obtained when various degrees of contingency were arranged between joystick responses and the appearance of a photograph of the Loch Ness Monster (Allan & Jenkins, 1980) and between button presses and light onsets (Alloy & Abramson, 1979; for more on the question of assessing judgment accuracy, see Shaklee, 1983). Although reassuring, even these judgments were strongly influenced by the overall probability of the outcome, a variable irrelevant to response-outcome contingency. Given that the discrete-trial (e.g., Alloy & Abramson, 1979) and continuous-trial (e.g., Allan & Jenkins, 1980) methods used previously either failed to support demonstrable agreement between scheduled contingencies and their subjective estimates (Jenkins & Ward, 1965), or uncovered measurable biases due to such irrelevant factors as the valence of the correlations (Allan & Jenkins, 1983; Erlick & Mills, 1967) or overall outcome probability (Allan & Jenkins, 1980, 1983; Alloy & Abramson, 1979), the present experiments utilized Hammond’s (1980) variant of the free-operant paradigm to explore the two-dimensional contingency space with humans; this contingency space is defined by a unit square, with axes representing p(O/R) and p(O/no R) (Seligman et al., 1971). The free-operant procedure has been successful in producing contingency judgments consonant with A P (Wasserman, Chatlosh. & Neunaber. 1983). Wasserman ef al. (1983) designated a factorial array of outcome probabilities given the presence or absence of responding; this array resulted in five different contingency levels nested within nine problems. Subjects instructed to estimate the magnitude and direction of the effect of their telegraph key responding on the occurrence of a white light made accurate and symmetrical judgments of positive and negative contingencies and were not significantly biased by overall outcome probability on noncontingent problems. An additional advantage of the free-operant procedure is shared by other judgment tasks, but has seldom been exploited. If appetitive or aversive events are associated with outcome occurrences, the behavioral effects of various degrees of response-outcome contingency should be manifested in subjects’ operant responding. Jenkins and Ward (1965) gave this type of task to one group of subjects receiving “score” instructions: Subjects were told to respond on two buttons to maximize outcome occurrences. Although the two-response procedure failed to yield accurate estimates of contingency, in the stronger of the two nonzero contingency problems subjects allocated much more than half of their responses to the button associated with the higher outcome probability. Response probability in the noncontingent problems was not affected by outcome probability.

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A supplementary behavioral measure was also introduced into two of Alloy and Abramson’s (1979, Experiments 1 and 2) judgment of control experiments. Subsequent to rating the degree of control that button presses exerted over light onsets, subjects were monetarily reinforced for outcome occurrences during 10 additional trials. Under these circumstances, subjects’ button-press responses, as well as their control ratings, reflected the objective nonzero contingencies (Experiment 1). And although nondepressed subjects’ control ratings of zero contingencies were related to outcome probability, no performance differences due to outcome probability were obtained for either depressed or nondepressed subjects with these problems (Experiment 2). While these studies provide a glimpse of how contingency affects subjects’ behavior, in neither investigation was this problem a major focus. Performance measures were only reported for groups exposed to positive outcomes; comparisons between these groups and those presented with neutral outcomes could further elucidate subjects’ behavioral sensitivity to response-outcome contingencies. Furthermore, changes in behavior over time were not carefully examined. The aim of the present experiments, then, was to utilize Hammond’s relatively unbiased free-operant paradigm and human subjects to investigate simultaneously their behavioral sensitivity to and perceptions of a variety of positive, negative, and zero response-outcome contingencies. Betweengroup manipulations of outcome valence were instituted to address several questions: Does providing subjects with reinforcement for responding to maximize outcome occurrences produce any significant biases in their estimates of contingency (Experiments 1 and 2)? Do response probabilities under these conditions differ from those in which outcomes are neutral and in a manner consistent with the degree of contingency between responses and outcomes (Experiments 1 and 2)? Do response probabilities exhibit an orderly acquisition function (Experiment l)? Does making outcomes unfavorable produce any biases in subjects’ contingency estimates and do the behavioral effects of aversive outcomes mirror the effects of appetitive outcomes (Experiment 2)? Do subjects under various outcome conditions exhibit any preexperimental biases, as evidenced by initial response tendencies (Experiment 2)? Are there any significant sex differences (Experiment l)? In addition, the possibility that ratings and/or operant responding are biased by overall outcome probability was examined (Experiments 1 and 2). Finally, we studied the adequacy of arbitrarily demarcated time windows for defining “response” and “no response” intervals, as well as the effects of varying the amount of information available to subjects in terms of the number of sampling intervals within a problem or total problem time (Experiment 1).

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In our initial experiment, we were primarily concerned with two issues. First, because few behavioral data have been reported in human contingency studies, one objective was to look at operant performance under different levels of response-outcome contingency. The performance of subjects for whom outcomes were either neutral or positive was compared. Given that the free-operant procedure yields accurate ratings of responseoutcome contingencies, we were optimistic regarding its potential for providing a sensitive measure of subjects’ operant response to different contingencies. Our expectation was that, in contrast to a neutral outcome group, response probabilities in an appetitive outcome group would reflect the programmed response-outcome contingencies. That is, subjects instructed to respond to maximize outcome occurrences should be more likely to respond during positive contingency problems and less likely to respond during negative contingency problems, relative to noncontingent problems; control subjects, on the other hand, should not display these response tendencies. Furthermore, recording response probabilities in successive blocks of sampling cycles within each problem for the positive outcome group furnished a means for examining response acquisition in a detailed fashion. The manipulation of outcome valence had a secondary purpose as well. Alloy and Abramson (1979, Experiments 3 and 4) suggested that desirable or undesirable outcomes may differentially affect subjects’judgments of control. However, in actuality, these authors failed to vary outcome valence between groups of subjects. Half of their subjects lost money when the outcome did not occur; the remaining subjects gained money with each outcome occurrence. In our estimation, the outcome valence is positive in both of these cases, as light onsets were either associated with no loss of money or money gain-both favorable events. Therefore, our objective was to present subjects with a variety of both contingent and noncontingent problems and to compare ratings obtained from a positive outcome group with those from a neutral outcome group, to determine whether judgment biases occurred when outcomes were made favorable. The second major issue this first experiment addressed relates to the problem of response definition. With Hammond’s procedure, each l-s sampling interval containing one or more responses is regarded as an interval with a response; each l-s sampling interval containing no responses is regarded as an interval without a response. Are these temporally defined sampling cycles adequate for specifying “response” and “no response” alternatives? To see how varying the cycle duration affects performance, groups of subjects in each of the outcome conditions were tested with either l- or 4-s sampling intervals for determining the presence or absence of responding. Because the amount of information available

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to subjects could confound any obtained differences, comparing these two time windows necessitated three experimental subgroups such that, while sampling interval duration was varied between subgroups, either total problem time or number of sampling cycles within each problem was held constant. If substantial differences in either ratings or operant behavior were found among these information subgroups, a more complete investigation of the appropriate temporal parameters to adopt would seem warranted. Finally, the experiment was designed to provide information concerning two subsidiary matters. As mentioned previously, the overall probability of the outcome, p(O), has sometimes been found to bias subjects’ ratings on contingency problems. The inclusion of three noncontingent problems, which differed in terms of overall outcome probability, allowed us to assess the effects of this variable on both ratings and operant performance. Based on the human judgment research of Allan and Jenkins (1980) and Alloy and Abramson (1979), we expected increasing inaccuracy of contingency ratings as p(O) rose. Furthermore, the operant performance of subjects receiving appetitive response-independent outcomes speaks to the issue of superstitious behavior. Based on Skinner’s (1948) analysis of this phenomenon in pigeons, our prediction was that, during noncontingent problems, more frequent positive outcomes would result in increased operant responding. This prediction notwithstanding, previous studies have reported that responding was not related to the frequency of noncontingent outcomes (Alloy & Abramson, 1979, Experiment 2; Jenkins & Ward, 1965, Experiment 1). Sex differences in contingency ratings are not unprecedented (Shaklee & Hall, 1983; Wasserman et al., 1983, Experiment 3). Therefore, gender was included as a factor in analyses of both contingency ratings and operant performance. Method Subjects. The subjects were 108 elementary psychology students who participated in the experiment as one option for fulfilling a course requirement. Apparatus. Two stimulus-response panels were used and were located in individual running rooms. Each panel consisted of a black plywood base (22.8 x 18.8 cm) on which a telegraph key was mounted and a black Masonite upright (27.4 x 18.8 cm) affixed to the rear edge of the base. The upright contained two small, red and white jewel light fixtures which could be illuminated from behind by small bulbs (Sylvania 1820, 24 V dc). A relay (Potter & Brumfield KRP 11 DN, 24 V dc) was mounted on the back of the upright, out of the subject’s view. The onset and offset of a telegraph key response first engaged and then disengaged the relay, providing additional external feedback in the form of a sharp “click” with each operation and release of the key. Scheduling and data

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collection were managed by a DEC PDP 8/L minicomputer equipped with a relay interface and SKED software system (Snapper, Stephens, & Lee, 1974) located in a nearby room. Procedure. In an attempt to examine the justifiability of an arbitrarily selected time window for defining “response” and “no response” periods, three information conditions were designated in which sampling intervals were either 1 or 4 s long. In the l-s (60) condition, 60 cycles of l-s sampling intervals were presented in each problem; in the 4-s (60) condition, 60 cycles of 4-s sampling intervals constituted each problem. A third information condition was included for two purposes. The first was to provide another control comparison for assessing the effects of sampling interval duration. While the number of sampling cycles is equal in the l-s (60) and 4-s (60) conditions, total problem time varies (i.e., 1 min vs 4 min). Therefore, in this third condition, each problem consisted of 4 min of l-s sampling intervals (i.e., 240 cycles). Second, an examination of performance under the two l-s conditions afforded a means for evaluating the influence of the number of sampling intervals within each problem (i.e., 60 vs 240 cycles). Upon entering the laboratory, subjects were assigned to one of the three information conditions, so that an equal number of males and females served in each. These subjects were trained under one of two outcome conditions. Half were from a previous study (Wasserman et al., 1983, Experiment 2) and were given neutral or no point instructions (condition NP): In this experiment, your task is to find out whether tapping a telegraph key has any effect on the occurrence of a white light. At any time you may choose to tap the key or not tap it. You may tap the key as many times as you like, or you may refrain from tapping it for as long as you like. However. because of the nature of the task, it is to your advantage to tap some of the time and not tap some of the time. In order to make accurate estimates. you must know what happens when you tap the key and when you don’t tap it. In addition, because your task is to find out whether tapping the key has any effect on the occurrence of the white light, please do not hold the key down at any time during the experiment. I would like you to try nine problems, each of which will be a few minutes long. In each case, your task will be to find out whether tapping the telegraph key has any effect on the occurrence of the white light. Each problem will begin when the red light comes on and end when it goes off. After each problem, choose the number between - 100 and + 100 on the rating scale that best characterizes the degree to which your tapping of the telegraph key affected the occurrence of the white light, from prevents the lighrfiom occurring to cuuses the light ro occur. Then write that number in the space provided below for each problem. In addition, because the problems differ from one another, it is important that you not let your judgment on any given problem affect your judgment on any of the other problems. The problems will be separated by a minute or two to allow you time to make your ratings. I’ll return when the problems are over.

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The other half of the subjects received similar instructions, but were encouraged to respond to maximize outcome occurrences by being told that with each light onset, one point would be accumulated (condition P). Nine males and nine females were run under each of the six experimental conditions. All subjects were presented with nine problems to respond to and rate. The problems factorially combined three levels of the two conditional probabilities, p(O/R) and p(O/no R). The three levels were .125, 500, and .875. The nine problems in turn defined five different responseoutcome contingencies, p(O/R) - p(O/no R): + .750 (.875 - .125), +.375 (.875 - .500 and .500 - .125), 0 (.875 - .875, .500 - .500, and .125 - .125), -.375 (.500 - .875 and .125 - .500), and -.750 (.125 - .875). Note that the positive and negative contingencies were less than perfect and were symmetrical around zero. Note further that the three noncontingent problems varied according to the overall likelihood of outcome occurrence, p(O), from .125 to .500 to .875. Probabilities were scheduled such that if the subject responded on the telegraph key at least once during the designated sampling interval, p(O/R) was probed at the end of the interval; otherwise, p(O/no R) was probed. If the probe was positive, a 0.10-s illumination of the white light was given, after which another sampling interval began; if the probe was negative, no light was presented and another sampling interval immediately began. Each problem was signaled by illumination of the red light, with the number of cycles per problem being determined by the subject’s assigned experimental condition. The nine problems were presented in a random order and in such a way that, over all subjects, each problem occurred equally often in each of the nine ordinal positions in a session. After each problem, subjects were asked to pick the number from - 100 (prevents light from occurring) to 0 (has no effect on light) to + 100 (causes light to occur) that best characterized the effect of telegraph key responding and to do so independently of their ratings on prior problems. Approximately 1 to 2 min separated each of the nine problems in a session, thus allowing subjects ample time to make their ratings. A more complete account of the purposes and procedures of the experiment was provided to subjects after the session ended. Experimental design. The experimental conditions outlined above describe a 2 x 3 x 2 x 5 factorial design, with the following factors: two outcome conditions (conditions P and NP); three information subgroups within each outcome condition (groups l-s (60), 4-s (60), and l-s (240)); two sexes (Male and Female); and five levels of response-outcome contingency (+ .750, + .375, 0, - .375, - .750), obtained from the original set of nine problems. The factor of contingency level was manipulated within subjects; all other factors were varied between subjects. Nine males and nine females constituted each of the six experimental subgroups.

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The three noncontingent problems permitted an assessment of the possible effects of overall outcome probability. These three problems are unique in that the probability of the outcome (.125, SOO, or .875) was not affected by the subject’s behavior. Separate analyses of these problems entailed a 2 x 3 x 2 x 3 factorial design, with outcome probability, which was manipulated within subjects, as the last factor. Statistical analyses. Analyses of variance, including trend tests, were conducted on both contingency judgments (response-outcome contingency ratings) and operant performance (telegraph key responding). Contingency ratings were made on a continum of - 100 to + 100, and were scaled by dividing by 100, in order to equate the ranges over which both ratings and contingencies could vary. Analyses of operant responding were based on the probability of one or more responses occurring within the time window specified by each information condition, defined as the proportion of sampling intervals containing at least one telegraph key response. Overall response probabilities for each problem were determined for both outcome conditions. Analyses of telegraph key response acquisition and terminal performance were restricted to condition P, as only in this condition was operant responding expected to reflect sensitivity to the contingencies; pertinent data were not collected from condition NP. Unless otherwise indicated, (Y was set equal to .05. Results and Discussion Contingency

Ratings

Figure 1 (upper panels) illustrates the mean scaled rating scores obtained with each scheduled contingency, across both sexes, for each information group, under both outcome conditions. Ratings rose as a direct function of response-outcome contingency across both sexes and under all experimental conditions and were quite symmetrical around zero. The linear trend of the statistically significant main effect of contingency was reliable, F(1, 96) = 709.36, p < .OOl. The slight inverted S shape of the functions was verified by the statistically significant cubic component, F( 1, 96) = 11.04, p = .OOl. No reliable main effects of information or outcome conditions were found, F < 1 in both cases. Although not shown, the rating function for females in the l-s (240) group, across both outcome conditions, exhibited a steeper slope than that for males; however, in the other two information groups, the opposite tendency was manifested (i.e., the slopes of males’ rating functions were somewhat steeper). A reliable gender x information condition interaction was found, F(2, 96) = 4.86, p = .OlO, as well as the reliable linear trend of the statistically significant contingency x gender X information condition interaction, F(2, 96) = 4.24, p < .025. The reasons for these sex differences are not clear.

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FIG. 1. Upper panels: Mean scaled contingency ratings (divided by 100) for 4-s (60 samples), l-s (60 samples), and l-s (240 samples) subgroups in no points and points conditions at each of the five levels of response-outcome contingency. Lower panels: Mean scaled contingency ratings (divided by 100) on noncontingent problems for 4-s (60 samples), l-s (60 samples), and l-s (240 samples) subgroups in no points and points conditions at each of the three levels of outcome probability.

As seen in Fig. 1 (upper panels), subjects’ mean scaled rating scores were very near approximations of the experimenter-programmed contingencies, defined by A P. Contingency judgments were a strong linear function of A P across both genders, all information conditions, and both outcome conditions, While analysis of variance revealed a reliable cubic component, the linear trend accounted for 98.11% of the contingency variance, and the additional variance accounted for by the cubic component was less than 1%. Reinforcing subjects for maximizing outcome occurrences had no biasing effect on causal judgments. Furthermore, although the rating functions for group l-s (240) appear to coincide most closely with the positive diagonal in Fig. l-demonstrating that ratings corresponded very closely with A P-lengthening the duration of the sampling interval from 1 to 4 s did not significantly disrupt judgment accuracy. Figure 1 (lower panels) depicts a slight rise in contingency ratings for

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the three noncontingent problems as the outcome probability increased from .125, where ratings were typically of slight negative valence, to near zero at an outcome probability of .875. The linear trend of the statistically significant main effect of outcome probability was reliable, F(1, 96) = 7.90, p < .Ol. Thus, overall outcome probability produced a small bias in rating scores for noncontingent problems, with higher outcome probabilities resulting in higher contingency ratings. Despite this slight rise, in all experimental groups at all outcome probabilities, mean contingency ratings were within .16 of zero. Ratings on these problems were uninfluenced by sex, information condition, or outcome condition. Telegraph

Key Responding

Overall response probability. Figure 2 (upper panels) illustrates that in condition P, overall response probabilities rose with increases in the scheduled response-outcome contingency in all three information groups, demonstrating that subjects’ telegraph key responding was sensitive to the scheduled contingencies, Such increases were not found in condition NP. (In fact, slight decreases were found in condition NP-see Wasserman et al., 1983, Experiment 2, for a more detailed analysis of response probability in this condition.) Reliable linear and quadratic components of the statistically significant contingency main effect were found, F(1, 96) = 33.17, p < .OOl and F(1, 96) = 18.13, p < .OOl, respectively. More importantly, the reliable linear trend of the stastistically significant contingency x outcome condition interaction, F(1, 96) = 72.61, p < .OOl, reflects subjects’ appropriate responsiveness to the contingencies in condition P, but not in condition NP. Relative to noncontingent reinforcement, subjects in condition P responded more often with positive response-outcome contingencies and less often with negative contingencies, bringing about many more light onsets than would have occurred had they responded randomly. Neither the sex of the subject nor the information condition reliably affected overall response probabilities. As shown in Fig. 2 (lower panels), a comparison of overall response probabilities in the three noncontingent problems revealed that in two of the information groups, 4-s (60) and l-s (240), response levels were relatively stable across outcome probabilities in condition NP; however, in condition P, response probabilities decreased with increasing outcome probabilities. Thus, in the 4-s (60) and l-s (240) information groups, increases in the occurrence of a favorable event decreased operant responding. This pattern was not present in the l-s (60) information group. Analysis of variance yielded the following significant effects: the linear trend of the reliable main effect of outcome probability, F(1, 96) = 5.54, p < .025; the main effect of information condition, F(2, 96) = 4.11, p < .025; the quadratic component of the reliable outcome probability x outcome condition interaction, F( 1,96) = 4.84, p < .05; and the quadratic

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FIG. 2. Upper panels: Mean overall probability of making a recorded response under no points and points conditions for group 4-s (60 samples), group l-s (60 samples), and group l-s (240 samples) at each of the five levels of response-outcome contingency. Lower panels: Mean overall probability of making a recorded response on noncontingent problems under no points and points conditions for group 4-s (60 samples), group I-s (60 samples), and group I-s (240 samples) at each of the three levels of outcome probability.

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component of the reliable outcome probability x information condition x outcome condition interaction, F(2, 96) = 3.11, p < .05. Recall that, during these noncontingent problems, subjects in condition P were given favorable response-independent outcomes, a situation which provides an opportunity for the development of superstitious responding. Consideration of Skinner’s (1948) hypothesis that more frequent responseindependent reinforcers would be more likely to generate superstitious behavior led us to expect that the response probabilities of subjects in condition P would be an increasing function of overall outcome probability. Indeed, contingency ratings were slightly biased in this direction (Fig. 1, lower panels). However, as illustrated in Fig. 2 (lower panels), in two of the information groups of condition P (groups 4-s (60) and l-s (240)). response probabilities actually decreased with increasing outcome probability. This finding directly contradicts our expectations. Thus, while a satisfactory explanation for the complex pattern of effects reported above does not immediately present itself, there was little indication of superstitious responding by our subjects. Acquisirion. Response acquisition was analyzed by making pairwise comparisons between information groups in condition P. Because this comparison process entailed repeated use of the same data, a! was adjusted to .Ol for these contrasts. First, to determine whether the duration of the sampling interval affected the acquisition of telegraph key responding, successive response probabilities in the 4-s (60) and l-s (60) information groups were analyzed for differences and trends. In these groups, the number of sampling cycles per problem was identical; each problem contained 60 sampling intervals. Response probabilities were determined for successive blocks of six sampling cycles. As shown in Fig. 3, throughout training the performance functions in both groups were generally ordered according to contingency level, with the most positive contingency producing the most responding, the intermediate contingencies producing intermediate levels of responding, and the most negative contingency producing the least responding. The linear trend of the statistically significant contingency main effect was reliable, F(1, 32) = 30.97, p < .OOl. Differences in response levels with increased exposure to the problems were confirmed by the reliable blocks main effect, F(9, 288) = 2.73, p < .Ol. Although the contingency x blocks interaction was not statistically significant, Fig. 3 illustrates that the contingency functions of these two information groups generally diverged as training continued. The lack of a significant interaction appears to be attributable to the fact that signs of contingency-appropriate behavior were evident in the very Jirst block of training. No significant effects of sex or information group were obtained. Second, performance comparisons were made between the 4-s (60) and l-s (240) information groups. Both the total number of sampling

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30

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FIG. 3. Mean probability of making a recorded response in blocks of six cycles for each of the five response-outcome contingency levels in group 4-s (60 samples), group Is (60 samples), and group l-s (240 samples). The data shown are from the points condition only.

intervals in each problem and the duration of the sampling intervals varied in these groups. However, unlike the previous comparison, total problem time remained constant (i.e., 4 min). Response probabilities were determined for successive 24-s blocks. Figure 4 shows that, in each of these two groups, the performance functions for the various levels of contingency diverged as training continued. And again, one can see the almost perfect ordering of response probabilities for the various contingency levels. The linear trend of the statistically significant main effect of contingency was reliable, F(1, 32) = 65.41, p < .OOl, and unlike the previous comparison, a reliable quadratic component was also found, F( 1, 32) = 12.05, p < .Ol. As in the prior comparison, a significant blocks main effect was found, F(9, 288) = 3.49, p < ,001; however, in addition, the

CONTINGENCY DELTA P 0 -,7M ,-,375 0 .ooo .+.375

l.O0.9> o-af ;I 0.72 g ! 0.6E 0.5% T5 0.4s 25 go.3-

15

AND BEHAVIOR

1-S

(240)

P--A A/+A-\A/-A

l,--*\,b-o-*’ 6/= K*.

0.2-

o-.-o

O-lO.Or 0

2

4 TIME IN 24-W

6

8 BLOCKS

10

o.oi

0

2 TlME

4 6 8 IN 24-SEC BLOCKS

10

FIG. 4. Mean probability of making a recorded response in 24-s blocks for each of the five response-outcome contingency levels in group 4-s (60 samples) and group l-s (240 samples). The data shown are from the points condition only.

data of these two groups also yielded a statistically significant linear trend of the reliable contingency x blocks interaction, F(9, 2304) = 7.06, p < .OOl. There were no significant effects of sex or information condition. The third acquisition analysis compared response levels in the l-s (60) and l-s (240) information groups. In these groups, sampling intervals were the same duration; however, the number of sampling intervals within each problem differed, as did the total problem time. Therefore. response probabilities during the first 60 cycles of each problem in the l-s (240) group were compared to those of the l-s (60) group, in blocks of six cycles. Figure 3 shows that response levels in the two groups were a function of the different levels of response-outcome contingency. The linear trend of the statistically significant contingency main effect was reliable, F( 1, 32) = 28.41, p < .OOl. Because of the lack of a statistically significant blocks main effect or a blocks x contingency interaction, one can only assume that although the functions appear increasingly to diverge as training progresses, response levels were sufficiently differentiated during Block 1 to render further changes statistically nonsignificant. Preliminary work had suggested that it would be necessary to finely divide problems to detect performance changes, as subjects are very astute at learning the task. Thus, blocks of only six cycles were chosen. However, it appears that even this reiatively fine division may be large enough to conceal the process of response acquisition. No significant sex or information condition differences were found. Finally, a trend analysis was conducted on response probabilities during the &zst 180 cycles of each problem in the l-s (240) information group, again in blocks of 6 cycles, to see if the additional training this group

16

CHATLOSH,

NEUNABER,

AND WASSERMAN

received significantly improved performance. Figure 3 shows that the curves for the different contingency levels remained ordered throughout training, with most of the divergence occurring very early on. The linear trend of the statistically significant contingency main effect was reliable, F(1, 16) = 39.78, p < .OOl. No significant sex or blocks effects were found. Terminal performance. Terminal performance in the three information groups of condition P was analyzed using response probabilities from the last block of six cycles. Figure 5 (left panel) shows that terminal response probabilities generally rose as a function of response-outcome contingency in the three groups. A reliable linear trend, F( 1, 48) = 64.18, p < .OOl, of the statistically significant contingency main effect was found. Also reliable was the smaller quadratic trend, F(1, 48) = 6.23, p < .025; this trend may be attributable to the fact that response probabilities were below SO for noncontingent problems in all groups (i.e., a slight floor effect may have been operating for negative contingencies). No significant differences due to sex or information group were found. To examine the role of outcome probability on terminal performance in condition P, the three noncontingent problems were analyzed for differences. No significant effects were found; terminal response probabilities were not systematically influenced by overall outcome probability (Fig. 5, right panel). Before proceeding to our second experiment, a note seems warranted concerning the lack of significant differences in either contingency judg;; 1.0, 3 0.9. ?

-1.0

GROUP

-0.5 0.0 0.5 RESPONSE-OUTCOME CONTINGENCY

1.0

d-0

015 OUTCOME PROBABILITY

1

FIG. 5. Left panel: Mean probability of making a recorded response during the last six cycles of training for group 4-s (60 samples), group l-s (60 samples), and group l-s (240 samples) at each of the five levels of response-outcome contingency. Right panel: Mean probability of making a recorded response during the last six cycles of training on noncontingent problems for group 4-s (60 samples), group l-s (60 samples), and group l-s (240 samples) at each of the three levels of outcome probability. The data shown are from the points condition only.

CONTINGENCY

AND

BEHAVIOR

17

ments or response probabilities as a result of our manipulation of sampling interval duration. This result was somewhat surprising given the historical importance placed on temporal contiguity as a contributing factor in cause-effect relationships. Hume (1739/1%2) included close spatial and temporal contiguity as one of three main rules by which causal judgments are made, and Gruber, Fink, and Damm (1957) found temporal contiguity to be a major determinant of causal perceptions of college women. Regarding operant behavior, Skinner’s (1948) analysis of superstition in pigeons led him to conclude that operant conditioning is entirely dependent upon the temporal relation between response and reinforcer. It seems possible, then, that the temporal variations chosen here (l- and 4-s cycles) may have been too slight to support behavioral differences. In addition, no effects attributable to the number of sampling cycles in each problem or to total problem time were significant. Neither contingency ratings nor operant performance differences were found between the l-s (60) and l-s (240) groups. Here again, it is probable that the parameters employed were not sufIiciently disparate to produce differences. Indeed, the finding that in two of the the three pairwise comparisons made between information groups, operant performance levels for the various degrees of contingency showed no statistically significant divergence after the first six sampling cycles suggests that it may be necessary to present subjects with very short problems in order to test the informational limits of their sensitivity to response-outcome contingencies. EXPERIMENT

2

In Experiment 1, we demonstrated that subjects’ ratings of responseoutcome contingency were strongly related to the scheduled contingencies and were relatively unaffected by either of two variables often cited as potent biasing factors in contingency perception: namely, the valence and frequency of the outcome (Allan & Jenkins, 1980; Alloy & Abramson, 1979). These findings essentially replicate our earlier investigation (Wasserman et al., 1983) which established the free-operant paradigm as a sensitive and unbiased procedure for exploring the perception of responseoutcome relations. In addition, in Experiment 1 we demonstrated that the isomorphism found between the scheduled response-outcome contingencies and subjects’ perceptions of them was reflected in the behavioral effects of these contingencies. When light onset was associated with a favorable outcome (the accumulation of a point), telegraph key responding increased as a function of increases in the scheduled contingency. In the present experiment, we sought to extend these findings (a) by comparing the effects of making outcomes neutral, favorable, or unfavorable

18

CHATLOSH,

NEUNABER,

AND

WASSERMAN

and (b) by choosing a different array of contingent and noncontingent problems. Although Experiment 1 failed to disclose any judgment bias due to reinforcing subjects with points for maximizing outcome occurrences, a potentially more potent reinforcer, like money, might support significant biases in either initial response tendencies or contingency ratings. To test this proposition, subjects assigned to the “gain money” condition in the present experiment were reinforced with money for maximizing light onset. In addition, previous workers have suggested that aversive outcomes may affect contingency judgments (Alloy & Abramson, 1979). However, as noted earlier, Alloy and Abramson did not actually create unfavorable outcome conditions. Subjects assigned to their “unfavorable” outcome condition lost money when the outcome did not occur. Hence, this condition actually involved a favorable outcome, with outcome occurrence signaling no loss of money. To test whether truly aversive outcomes bias contingency ratings, a “lose money” condition was included in the present experiment, in which light onset signaled the loss of money, and subjects were instructed to minimize light onset. Both the gain money and lose money groups were compared with a no money control, in which subjects were simply instructed to learn the relationship between responding and outcomes. Whereas in Experiment 1 the contingencies were chosen to sample widely the two-dimensional contingency space, the contingencies for the present experiment were chosen to examine small gradations of both contingency and outcome probability along the positive and negative diagonals of the contingency space. As noted in Experiment 1, examination of subjects’ performance in the noncontingent problems under reinforcement conditions addressed the issue of superstitious behavior. In Experiment 1, it was predicted that superstitious behavior would increase as the probability of the favorable outcome increased. However, recall that in two of the information groups, response probabilities actually decreased with increasing outcome probability. The inclusion of a wider range of noncontingent problems in the present experiment allowed us a closer look at this interesting finding. Futhermore, while Skinner’s (1948) description of superstition focused on noncontingent appetitive events, Herrnstein (1966) suggested that analogous effects could be expected with noncontingent aversive events. Accordingly, the prediction follows that the more frequent the occurrence of an aversive event, the more likely it is to be contiguous with the response and, thus to result in a superstitious decrease in responding. Given the lack of evidence for superstitious responding with appetitive outcomes in Experiment 1, we were interested in testing this additional prediction. The lose money condition afforded us this opportunity.

CONTINGENCY

AND BEHAVIOR

19

A final issue addressed in the present experiment concerns the symmetry of the behavioral effects of appetitive and aversive outcomes. In Experiment 1, associating light onset with the accumulation of points produced increases in response probability with increases in the response-outcome contingency. A similar effect was expected in the gain money condition of the present experiment, in which light onset was associated with the accumulation of money. However, in the lose money condition, because light onset was associated with an aversive outcome (the loss of money), the opposite effect on response probability was expected; increases in the contingency should be associated with decreases in response probability. Method Subjects and apparatus. The subjects were 39 elementary psychology students like those in Experiment 1. The apparatus was the same as that in Experiment 1. Procedure. Subjects were randomly assigned to one of three different outcome conditions (n = 13), with the restriction that each group contain seven females and six males. Subjects assigned to the no money condition were given instructions like those given to subjects in the no points condition of Experiment 1, except that they were told they would receive 13 problems rather than 9. Subjects assigned to the gain money condition received similar instructions, but they were encouraged to respond to maximize light onset by being told that with every fourth light onset, lc would be accumulated. Subjects assigned to the lose money condition received instructions similar to those used in the no money condition, but they were encouraged to respond to minimize light onset by being told that w&h every fourth light onset, lc would be subtracted from an initial amount of $3. Subjects were informed that they would be allowed to keep the amount of money they had gained or avoided losing, and were paid at the end of the experiment. All subjects received 13 problems to respond to and rate. The problems included four positive and four negative contingencies, p(O/R) - &O/no R): +.80 (.90 - .lO), +.60 (.80 - .20), +.40 (.70 - .30), -t-.20 (.60 - .40), -.20 (.40 - .60), -.40 (.30 - .70), -.60 (.20 - .80), and -.80 (. 10 - .90>. Also included were five noncontingent problems that varied in outcome probability, p(0): .lO, .30, SO, .70, and 90. The probabilities of light occurrence associated with response and nonresponse options in each problem were scheduled as in Experiment 1, except that each problem comprised 150 l-s sampling intervals. Experimental design. The experimental conditions in Experiment 2 describe a 3 x 9 factorial design, with the following factors: three outcome conditions (gain money, no money, lose money); and nine levels of response-outcome contingency ( + .80, + 60, + 40, + .20, 0, - .20, - .40, - .60, - .80). The contingency factor was varied within subjects; the

20

CHATLOSH,

NELJNABER,

AND WASSERMAN

outcome condition factor was varied between subjects. Seven females and six males comprised each of the three experimental subgroups. Because no main effects or salient interactions involving sex were obtained in Experiment 1, it was not included as a factor in the present experiment. The five noncontingent problems, which varied in terms of overall outcome probability, were analyzed within a 3 x 5 factorial design. As stated above, the three outcome conditions were manipulated between subjects; the five levels of outcome probability (.lO, .30, SO, .70, .90) were manipulated within subjects. The 20 noncontingent problem was included in the analyses of both contingency and outcome probability. Statistical analyses. As in Experiment 1, analyses of variance, including tests of trend, were conducted on both contingency ratings and response probability. Contingency ratings were made on the scale of - 100 to + 100, and were again divided by 100. Response probability was defined as the proportion of l-s sampling intervals containing at least one telegraph key response. Overall tests were conducted with (Y set equal to .05; (Y was adjusted to .Ol for all follow-up comparisons. Results and Discussion Contingency Ratings The left panel of Fig. 6 shows subjects’ mean scaled rating scores in the three experimental conditions at each of the nine levels of responseoutcome contingency. In all three experimental conditions, ratings rose as a function of increases in response-outcome contingency and were symmetrical about zero. As in Experiment 1, the linear trend of the statistically significant main effect of contingency was reliable, F(1, 36)

L 0.5v) P ;: 0.0.

2 f o.o-

pL+$@y

ii

t: -0.5-

8 -0.5.

0 RESPDNSE-OUTCOf'E

CONTIiiENCY

0 OUTCOME PROBABILITY

FIG. 6. Left panel: Mean scaled contingency ratings (divided by 100) for gain money, no money, and lose money conditions at each of the nine levels of response-outcome contingency. Right panel: Mean scaled contingency ratings (divided by 100) on noncontingent problems for gain money, no money, and lose money conditions at each of the five levels of outcome probability.

CONTINGENCY

AND

BEHAVIOR

21

= 160.86, p < 801. However, unlike Experiment 1, the cubic component of the main effect of contingency was not significant, F < 1. Thus, subjects’ estimates of response-outcome contingency in the present experiment were best described as strong linear functions of the programmed contingencies. No reliable main effect for outcome condition was found; however, the linear trend of the statistically significant contingency x outcome condition interaction was reliable, F(2, 36) = 3.41, p < .05. Pairwise follow-up analyses of variance revealed no significant differences at the .Ol level, although comparisons between the contingency-rating functions of the gain money and no money conditions and between the gain money and lose money conditions approached significance (contingency x outcome condition interactions: F(1, 24) = 7.04, p < .025 and F( 1, 24) = 5.30, p < .05, respectively). The contingency-rating functions of the lose money and no money conditions were not reliably different from each other (contingency x outcome condition interaction: F < 1). As shown in the left panel of Fig. 6, the contingency-rating function was somewhat steeper for the gain money condition (slope = I .04) than for the no money (slope = 0.73) and lose money (slope = 0.66) conditions. Thus, the use of money as the reinforcer in this experiment produced slightly more extreme contingency ratings than the no money control, but only when subjects were instructed to earn money by maximizing light onset. This finding is consistent with Alloy and Abramson’s (1979) suggestion that favorable outcomes may support higher judgments of control than unfavorable outcomes. Indeed, Alloy and Abramson might interpret the tendencies of subjects in the gain money condition to rate positive contingencies as more positive, and negative contingencies as more negative than subjects in the lose money condition as reflecting a self-serving bias. That is, perhaps individuals, in an attempt to maintain self-esteem, are willing to assume personal control for good events, but are reluctant to assume control for bad events. While this explanation is plausible, it does not address the judgments of subjects in the no money condition. Furthermore, it is important to note that the finding of somewhat more extreme contingency ratings in the gain money condition does not necessarily indicate that these ratings were less accurate than those in the lose money and no money conditions. Indeed, the contingency-rating function of the gain money condition most closely approximated the ideal function depicted by the positive diagonal in Fig. 6 (left panel). In contrast, the contingency-rating functions of the lose money and no money conditions were shallower than the ideal function. The finding that the accumulation of money influenced contingency ratings, whereas the accumulation of points (Experiment 1) did not, confirms our prediction that a reinforcer must be particularly potent to measurably influence subjects’ perceptions of contingency. It would be interesting to systematically examine the

22

CHATLOSH,

NEUNABER,

AND

WASSERMAN

effects of differing amounts of monetary reinforcement on contingency perception. The right panel of Fig. 6 shows subjects’ mean scaled ratings of noncontingent problems in the three experimental conditions at each of the five levels of outcome probability. Subjects’ ratings were generally very close to zero. Analysis of ratings in the five noncontingent problems, which varied in outcome probability from .I0 to 90, revealed no significant effects due either to outcome probability or to outcome condition. Thus, a variable frequently reported to produce biased contingency ratings, overall probability of the outcome, p(O), had no measurable effect on subjects’ ratings. This result, coupled with the finding of only small bias for-p(O) in Experiment 1, and no measurable bias for p(O) in our previous investigation (Wasserman et al., 1983), provide further support for the use of the free-operant paradigm in the measurement of human contingency perception. Telegraph

Key Responding

Analysis of the probability of a recorded response revealed a significant main effect of blocks, F(29, 1044) = 3.92, p < .OOl, a reliable blocks x outcome condition interaction, F(58, 1044) = 1.81, p < ,001, reliable linear, F(2, 36) = 139.06, p < .OOl, and quadratic, F(2, 36) = 6.87, p < .Ol, trends of the statistically significant contingency x outcome condition interaction and a reliable linear trend of the statistically significant contingency x blocks x outcome condition interaction, F(58, 8352) = 5.30, p < .OOl. Because the main effect and double interactions were all subsumed by the significant contingency x blocks x outcome condition interaction, follow-up tests were aimed primarily at elucidating this interaction. The left panel of Fig. 7 shows the contingency-response probability function in the first 5-s block for each of the three outcome groups at each of the nine levels of response-outcome contingency. Response probability across all levels of contingency was higher in the gain money condition than in the no money and lose money conditions. A followup analysis of variance for the first 5-s block of training revealed a significant main effect for outcome condition, F(2, 36) = 11.18, p < .OOl. Pairwise analyses of variance indicated that the gain money condition produced a significantly higher response probability than either the no money (F(1, 24) = 11.82, p < .Ol) or the lose money (F(1, 24) = 17.85, p < .OOl) conditions; response probability was not significantly different between the no money and lose money conditions, F < 1. The analysis of variance indicated no significant effects for response-outcome contingency. Thus, relative to the other two groups, subjects in the gain money condition showed a greater initial tendency to respond than subjects in

CONTINGENCY

0.9.

CONDITION

0.8.

0 GAIN MINEY + NO MONEY

5 0.7.

23

AND BEHAVIOR

0.8.

$ 0.6. g 0.5. 6 0.4t+ u 0.3. %J 0.20.1.

o.ol -1.0

-0.5

0.0

0.5

1

RESPONSE-OUTCOME CONTINGENCY

FIG. 7. Left panel: Mean probability of making a recorded response in the first five cycles of training for gain money, no money, and lose money conditions at each of the nine levels of response-outcome contingency. Right panel: Mean probability of making a recorded response during the last five cycles of training for gain money, no money, and lose money conditions at each of the nine levels of response-outcome contingency.

the lose money and no money conditions. However, the greater initial responsiveness produced by associating experimental outcomes with the accumulation of money need not be interpreted as due to an error or distortion in contingency perception. Indeed, one might expect that, in the early phases of each problem, responding on half or close to half of the sampling intervals would be an optimal strategy for learning the scheduled contingency. In fact, in our previous report, we found that subjects’ ratings conformed more closely to the A P metric as overall response probability approached SO. Thus, the initial responding of subjects in the gain money condition of the present experiment-which was closer to SO than in the lose money and no money conditions-reflected possibly the best strategy for learning the contingencies. It is not clear why the lose money and no money conditions failed to support similar responding. Perhaps these initial differences are a reflection of our subjects’ prior expectations about the relationship between responses and outcomes. In a recent review, Ahoy and Tabachnik (1984) promote an interactional view of contingency perception. In addition to utilizing the objective information about response-outcome contingencies obtained during the experimental session, subjects are thought to draw upon their prior expectations about the relationship between events in making their judgments. It may be possible to interpret the performance described here in terms of this interactional approach. One could hypothesize that the initial between-group differences in response probability, across all levels of contingency, are attributable to strong preexperimental biases which are manifested in performance when an insufficient amount of objective in-

24

CHATLOSH,

NEUNABER,

AND

WASSERMAN

formation is available (cf. Alloy & Tabachnik’s “Cell 2”). For example, subjects might expect good outcomes to be the consequence of their own actions, but bad outcomes to be the consequence of others’ actions. It would be instructive to determine if initial biases also occur with contingency judgments. The right panel of Fig. 7 shows the contingency-response probability function for each of the three outcome conditions at each of the nine levels of response-outcome contingency during the last 5-s block of training. Response probability increased with increases in the scheduled contingency in the gain money condition, and decreased with increases in the scheduled contingency in the lose money condition. Such systematic changes in response probability were not found in the no money condition. A follow-up analysis of variance for the last 5-s block of training revealed a reliable linear trend of the significant contingency x outcome condition interaction, F(16, 288) = 65.90, p < .OOl. Pair-wise analyses of variance indicated statistically significant differences between the functions of the gain money and no money conditions (contingency x outcome condition interaction: F(8, 192) = 6.31, p < .OOl), between the gain money and lose money conditions (contingency x outcome condition interaction: F(8, 192) = 28.10, p < JOI), and between the lose money and no money conditions (contingency x outcome condition interaction: F(8, 192) = 6.26, p < 401). There were no significant main effects for contingency or outcome condition. The lack of a main effect for outcome condition indicated that the tendency for subjects in the gain money condition to show a higher initial level of responding than the other two conditions disappeared as subjects’ responding came into close accord with the scheduled contingencies. The analysis of response probability in the last 5-s block indicated that under instructions to maximize (gain money condition) or to minimize (lose money condition) light onset, the scheduled contingencies had opposite and predictable effects on telegraph key responding. When subjects were instructed to maximize light onset, responding increased with increases in the scheduled contingency; when instructed to minimize light onset, responding decreased with increases in the scheduled contingency. This finding raises an additional question: Are the effects of instructing subjects to maximize or to minimize light onset equal as well as opposite? That is, are the effects of the scheduled contingencies on response probability in the gain money and lose money conditions completely symmetrical? To address this question, an analysis of variance of response probability was conducted in which the ordering of the nine problems in the lose money condition was reversed. Thus, the + .80 problem in the gain money condition was matched with the - .80 problem in the lose money condition, the + 60 problem was matched with the - .60 problem, and so on. Figure 8 shows the problem-response probability functions for

CONTINGENCY

AND BEHAVIOR

2.5

CONDITION 0

GAIN NONEY

0

LOSE MONEY

PROBLEN

8. Mean probability of making a recorded response during the last five cycles of training for gain money and lose money conditions as a function of problem ordering (problem ordering was reversed for the lose money condition). FIG.

the gain money and lose money conditions. These functions are highly similar to one another; both show increases in response probability with increases in problem ordering. It was expected that if the effects of contingency on telegraph key responding in these experimental conditions were symmetrical, then a significant main effect for problem ordering should be obtained, but significant effects for outcome condition or for the problem ordering x outcome condition interaction should not. Consistent with this reasoning, only the linear (F( 1, 24) = 13 I .09, p < .OOl) and quadratic (F( 1, 24) = 8.04, p < .Ol) trends of the reliable main effect of problem ordering were significant. Finally, an analysis of variance of response probability in the five noncontingent problems revealed a significant main effect for blocks, F(29, 1044) = 2.56, p < .OOl, a reliable linear trend of the significant outcome probability x outcome condition interaction, F(2, 36) = 6.95, p < .Ol, and a significant blocks x outcome condition interaction, F(58. 1044) = 1.64, p < .Ol. The left panel of Fig. 9 portrays the outcome probability x outcome condition interaction. Contrary to predictions derived from analysis of superstitious behavior, increases in outcome probability were associated with (a) a general decrease in overall response probability in the gain money condition and (b) a steady increase in overall response probability in the lose money condition. There were no changes in overall response probability in the no money condition. Pairwise follow-up analyses of variance indicated statistically significant differences between the outcome

CHATLOSH,

26

NEUNABER,

1.0,

AND WASSERMAN l,O-

CONDITION L=

=; 0.7. g 0.6 ;::

&

~~~~~

;:::w.m#

0.1.

0.1. o.o* 0.0

0.5 OUTCORE PROBABILITY

3 1,O

0.0.

r

LOSE MONEY

NO MONEY OUTCOME GROUP

GAIN MONEY

FIG. 9. Left panel: Mean overall probability of making a recorded response on noncontingent problems for gain money, no money, and lose money conditions at each of the five levels of outcome probability. Right panel: Mean probability of making a recorded response on noncontingent problems during the first and the last five cycles of training for gain money, no money, and lose money conditions.

probability-response probability functions of the gain money and lose money conditions (outcome probability x outcome condition interaction: F(4, 96) = 4.86, p < .OOl). No significant differences between the iose money and no money conditions or between the gain money and no money conditions were found at the .Ol level; however, both of these comparisons approached significance (outcome probability x outcome condition interactions: F(4, 96) = 2.58, p < .05 and F(4, 96) = 2.46, p < .06, respectively). In order to follow up the significant blocks x outcome condition interaction obtained from the overall analysis of response probability in the five noncontingent problems, separate analyses of variance were conducted for the first and last 5-s blocks of training. The right panel of Fig. 9 shows response probability for the gain money, no money, and lose money conditions in the first and last 5-s blocks. Response probability in the first 5-s block was highest in the gain money condition; however, this tendency dissipated by the time the last 5-s block was reached. The analysis of variance for the first 5-s block of training revealed a significant main effect for outcome condition, F(2, 36) = 13.61, p < .OOl. Pairwise analyses of variance indicated that mean response probability in the first block was significantly higher in the gain money condition than in the no money condition, F(2, 36) = 14.33, p < .OOl, or in the lose money condition, F(2, 36) = 20.84, p < .OOl; response probability was not significantly different between the lose money and no money conditions, F < 1. As was the case with the nine contingent problems (see Fig. 7), the tendency for subjects in the gain money condition to respond more in the first 5-s block was no longer present in the last block of training.

CONTINGENCY

AND

BEHAVIOR

27

An analysis of variance of response probability in the five noncontingent problems for the last block yielded no significant effects for outcome probability or outcome condition. This finding parallels the absence of significant terminal performance differences with the noncontingent problems in Experiment 1 (see Fig. 5, right panel). GENERAL

DISCUSSION

The present findings replicate those of Wasserman et al. (1983) by providing additional support for the conclusion that college students are indeed capable of making sensitive assessments of cause-effect relationships, and extend this conclusion to situations with appetitive and aversive outcomes. In both Experiments 1 and 2, and in all experimental groups, subjects’ mean scaled rating scores were near approximations to the experimenter-programmed contingencies (Fig. 1, upper panels, and Fig. 6, left panel). Furthermore, the symmetry of the rating functions around zero and the lack of significant quadratic trends suggest that positive and negative contingencies were equally discriminable (although such symmetry need not always occur with other kinds of tasks; see Wasserman & Shaklee, 1984). In Experiment 1, making outcomes favorable did not bias subjects’ ratings, and in Experiment 2, more potent positive and negative outcomes also failed to influence ratings adversely. These findings are quite heartening in light of the equivocal results obtained from previous work in this area (Cracker, 1981), as well as from the fact that presumably less intelligent laboratory animals have demonstrated the ability to discriminate between response-produced and responseindependent outcomes (Killeen, 1978). Several possible reasons for the discrepancy between these and prior results present themselves. There is some evidence that the nature of the response options influences contingency judgments. Allan and Jenkins (1980) found judgments of connection or influence to be more accurate when the response alternatives were to respond or not to respond than when two active choices were designated. The authors hypothesized that the availability of two active responses predisposed subjects erroneously to assume that an outcome would not occur in the absence of a response (see Allan & Jenkins, 1983, for another interpretation of this result in terms of the differential treatment of types of input-output pairings). Also, previous investigators who have included negative contingencies in their problem sets have ignored the valence of the contingency and instructed subjects to rate only the magnitude of their control over outcome occurrence (Allan & Jenkins, 1983; Alloy & Abramson, 1979). Using this control scale, it is possible that increases in the variability of ratings could be mistakenly interpreted as judgmental biases (see Shaklee, 1983). Taking advantage of these two factors, our subjects’ behavioral options were whether or not to respond, and subjects were asked to rate both

28

CHATLOSH,

NEUNABER,

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the magnitude and the direction of the relationship between responses and outcomes. Finally, procedural differences may account for the disparity between experimental conclusions. The free-operant technique has been demonstrated to produce relatively sensitive and unbiased contingency estimates (Wasserman et al., 1983). Although the reasons for its possible superiority over discrete-trial and continuous-trial procedures are not clear, our decision here was to opt for the most sensitive method to investigate conditions that may influence causal perception. When light onsets were associated with the gaining of points (Experiment 1) or the gaining or losing of money (Experiment 2), variations in contingency governed subjects’ operant performance as well as their causal ratings. Consideration of several response measures provides evidence of this behavioral sensitivity. First, overall response probabilities increased as a linear function of A P when outcomes were points (Experiment 1, condition P, Fig. 2, upper panels). Second, there was a generally ordered divergence of acquisition functions for the various contingencies in the points condition (Experiment 1, Figs. 3 and 4). Third, when points were earned for light onsets in Experiment 1, terminal response probabilities progressively rose with increases in A P (Fig. 5, left panel); in Experiment 2, more potent favorable outcomes (money) produced the same effect, while unfavorable monetary outcomes produced opposite and symmetrical terminal response tendencies (Fig. 7, right panel, and Fig. 8). Analysis of terminal operant performance in Experiment 1 raises an interesting question concerning the appropriateness of the A P metric for representing our subjects’ behavior. While the significant linear trend accounted for 86.64% of the contingency variance, an additional 5.14% was accounted for by the reliable quadratic component. Can a plausible metric other than A P be found to account for more of the variability in terminal response levels? If this were the case, we would be obliged to consider different models to describe subjects’ perceptions of responseoutcome contingencies and the behavioral effects of those contingencies. Such a finding would have important implications for psychological theories, like learned helplessness, which maintain that cog&ions mediate behavior. One study, in particular, gives us reason to suspect the A P rule in guiding behavior. Tomie and Loukas (1983) investigated the adequacy of several metrics in accounting for rats’ performance on a time-allocation task, when various degrees of response-reinforcer contingency were implemented. A comparison of the proportions of variance accounted for by each metric, including A P, revealed that (X - Y)/X was the best predictor of asymptotic performance, where X = p(O/R) and Y = p(O/no R). Although this metric assumes that subjects use p(O/R) as a baseline by which to determine contingencies, it is also conceivable that subjects may use p(O/no R) as a baseline, implying an (X - Y)/Y metric. In

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addition, Church (1969) suggested the use of an (X - Y)I(X + Y) metric, which takes into account overall outcome probability, to describe responseoutcome contingencies. Therefore, we compared the amounts of variance explained by these three metrics, as well as A P (i.e., X - Y), using both the terminal operant performance data (i.e., response probabilities from the last block of six cycles) and rating scores for Experiment 1, collapsed across sexes and information groups (because the terminal performance analysis involved only condition P data, rating scores for condition P were also analyzed separately for this comparison). Table 1 gives the proportion of the contingency variance accounted for by each of the four metrics (r2 = ratio of the linear sum of squares for the contingency main effect over the total contingency sum of squares). Unlike the Tomie and Loukas results, comparison of these proportions reveals that A P best predicted terminal operant performance and rating scores; in fact, of the four metrics, (X - Y)/X was the worst predictor in both cases. To evaluate the reliability of these results, an analogous comparison was made for both ratings and terminal performance in Experiment 2. Since the differences found to exist between the rating functions of the experimental groups were restricted to linear trends, rating scores from the gain money and lose money conditions were combined. Similarly, terminal response probabilities (i.e., from the last block of five cycles) for these two groups were combined, with the problem ordering reversed for the lose money condition. As can be seen from Table 1, once again A P was the best predictor and (X - Y)/X the worst predictor, in both cases. (Note that, for the contingencies studied in Experiment 2, A P and (X - Y)l(X + Y) are necessarily equivalent.) TABLE 1 3 for Contingency Main Effect Metric X-Y (X - v/x (X - WY (X - Y)/(X + n Probability X-Y (X - n/x (X - WY (X - n/v

.___

Expt I

+ Y)

Mean scaled rating score .9X6 .7089 .7509 9078 of making a response during the last block of training .8664 .5176 .8567 I535

__Expt 2 .9650 .5716 .6740 .9650 .9195 .4860 .6707 ,919s

Nore. Experiment 1 data are from the points condition; Experiment 2 data are from the gain and lose money conditions (see expIanation in text).

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The fact that A P proved superior for both ratings and operant behavior suggests that one need not reject parsimony in favor of separate models for each aspect of behavior. However, we must be quite careful in interpreting the general correspondence between our subjects’ verbal reports of response-outcome contingency and their operant response to it. If, for example, subjective estimates of contingency precisely mediated between the objective contingencies and operant behavior, we would have expected telegraph key responding to be in complete accord with rating scores. However, as discussed in the first experiment, the terminal performance analysis revealed a reliable quadratic trend (Fig. 5, left panel) unlike the reliable cubic trend found with rating scores (Fig. 1, upper panels). In the second experiment, we would have expected a generally smaller behavioral effect in the lose money condition than in the gain money condition, because its contingency-rating function was somewhat shallower (Fig. 6, left panel). However, the actual behavioral effects of the different response-outcome contingencies were of equal size in these groups (Fig. 8). Just why such differences between subjective estimates of contingency and the behavioral effects of contingencies exist is not clear. It would be interesting to track subjects’ ratings of contingency continuously within each problem to determine how closely contingency ratings mirror momentary changes in operant performance. The fact that increases in response-outcome contingency produced graded increases in terminal response probability (Fig. 5, left panel; Fig. 8) indicates that subjects were not behaving optimally in terms of maximizing reinforcement. A maximization rule predicts categorical responding to contingencies as determined by the valence of A P (i.e., a step-function where, in an appetitive situation, p(R) = 0 for all negative contingencies and p(R) = 1 for all positive contingencies). Thus, graded differences in response probability as a function of contingency suggest the operation of some other response rule. Conditional matching represents an alternative that might better describe our subjects’ response patterns. It predicts that, for any given reinforcement contingency, response probability should match the relative conditional reinforcement probability in the following manner for appetitive and aversive situations: p(R) = p(O/R)/lp(O/R) + p(O/no R)] and p(R) = P(O/no R)/[p(O/R) + p (O/no R)], respectively (Gibbon et al., 1974). In keeping with our previous definitions, this rule is tantamount to an X/(X + Y) metric in the appetitive case and to a Y/(X + Y) metric in the aversive case. Unlike the other metrics discussed, however, conditional matching specifically pertains to operant performance. For none of the metrics in Table 1 does substituting conditional probabilities for X and Y produce values confined to the range of possible response probabilities. The conditional matching rule, on the other hand, provides point estimates of operant response probabilities, ranging from 0 to I. Table 2 gives the expected p(R) for each of the problems of Experiment

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TABLE 2 Matching to Relative Conditional Reinforcement Probabilities -_____ Expected p(R) Mean obtained p(R) p(OIR) - p(Olno R) --..Expt 1 !.875 - .12S) ,875 ,673 (SO0 - .125) ,800 ,512 c.875 - ,500) ,636 ,463 (.I25 - ,125) ,500 ,386 (.500 - ,500) ,500 .392 f.875 - .875) ,500 ,272 t.500 - ,875) ,364 ,321 t.125 - ,500) .200 ,275 t.125 - ,875) ,125 ,204 Expt 2

-

,800 .600 .400 ,200 .ooo .200 .400 ,600 .800

(900 (.800 c.700 (.600 (.500 (.400 (.300 (.200 (.lOo

-

.lOO) ,200) ,300) ,400) .500) .600) .700) .800) .900)

,900 ,800 ,700 .600 ,500 ,400 .300 ,200 .loo

,808 ,777 .715 ,377 ,369 ,169 ,185 .I08 ,069

Note. Obtained p(R)s are from the last block of training. Experiment 1 data are from the points condition; Experiment 2 data are from the gain and lose money conditions (see explanation in text).

1, as well as the means of the obtained terminal response probabilities (from the last block of six cycles, collapsed across sexes and information groups) from condition P. Inspection reveals that the expected and the obtained probabilities do not closely concur; the range of the obtained means is restricted, and response probabilities for zero contingencies are consistently lower than predicted. Notice, however, that the conditional matching rule does succeed in ranking the means obtained from the nonzero contingencies; in particular, it correctly orders the obtained probabilities for equivalent A P’s that were derived from different pairs of conditional probabilities (i.e., the .375 and the - .375 problems). Comparable data for each of the problems used in the contingency analyses of Experiment 2 are also presented in Table 2. Here, the obtained terminal response probabilities (from the last block of five cycles) represent means calculated from the gain money and lose money conditions combined, with the problem ordering reversed for the lose money condition. Note that, with monetary reinforcers, the range of the obtained values more closely approximates that of the expected values than with presumably less potent point reinforcers. In Experiment 1, conditional matching ac-

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counted for 85.35% of the contingency variance in terminal response probability; in Experiment 2, 91.95% of the variance was explained by conditional matching. (Because the rank orderings of the matching predictions and the A P’s in Experiment 2 are the same, and because both sets of values are equally spaced, the proportions of the variance explained by each are necessarily equivalent.) Therefore, in terms of accountable variance, conditional matching does no better than A P. However, it has the distinct advantage of predicting response probabilities, and may help to explain why our subjects failed to maximize reinforcement. The issue of superstitious behavior was addressed by looking at subjects’ reactions to response-independent events. Along with the nonzero contingencies, subjects were given either three (Experiment 1) or five (Experiment 2) response-independent schedules, where p(O) was varied among problems. We expected increasing inaccuracy of contingency ratings as p(O) rose, increased operant responding as the probability of an appetitive outcome rose, and decreased operant responding as the probability of an aversive outcome rose. Although there was some evidence from the first experiment that subjects’ judgments of zero contingencies were influenced by p(O), mean rating scores tended to converge upon zero as p(0) increased, clearly lacking the expected bias (i.e., increasingly inaccurate ratings)-(see Fig. 1, lower panels). In the second experiment, mean rating scores were not consistently affected by p(O)-(Fig. 6, right panel). Moreover, the obtained operant performance data directly contradict our predictions. Unlike the no money condition, in which subjects responded similarly at all levels of p(O), response probabilities in the gain money and lose money conditions were reliably and oppositely affected by p(0); increases in p(O) brought about decreased responding in the gain money condition, but increased responding in the lose money condition (Fig. 9, left panel). It is most interesting that increases in the occurrence of an appetitivem event decreased operant responding, whereas increases in the occurrence of an aversive event increased operant responding. This pattern of results is clearly counter to expectations regarding superstitious behavior. One could, however, speculate that in noncontingent problems, subjects’ telegraph key responding represents the perceived success of their behavior. As favorable outcomes decrease in frequency and unfavorable outcomes increase in frequency, subjects increase their telegraph key responding in the hope of happening upon a more successful mode of behavior: When all else fails, do something rather than nothing. A similar behavioral pattern was exhibited by subjects in two of the information groups under the response-independent problems in Experiment 1 (see Fig. 2, lower panels). In both groups receiving 4-min problems (groups 4-s (60) and l-s (240)), increases in the likelihood of an outcome associated with point gain decreased telegraph key responding; increases

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33

in the probability of neutral outcomes had little effect on operant responding. Subjects in the third information group (group l-s (fjo)), however, did not respond in this fashion. A possible reason for this discrepancy is suggested by the fact that this group received less training per problem, in terms of total trial time (i.e., 1 min), than any other experimental group (i.e., 4 min in Experiment 1 or 2.5 min in Experiment 2). It is conceivable that the very short problems this group received precluded the development of response tendencies like those of the other groups. Finally, note that analyses of these subsets of noncontingent problems once again indicated that ratings of contingency were not always completely congruent with operant responding. Although overall outcome probability had little effect on subjects’ assessments of contingency, reliable operant performance differences were obtained when favorable or unfavorable outcomes varied in overall probability. The failure for subjects’ verbal reports of response-outcome dependency to jibe completely with their operant behavior introduces an important complexity into the psychology of causation, a complexity which requires further investigation. Referring back to Lloyd Morgan’s (1894) distinction, we are now in a position to conclude that, yes, human subjects do demonstrate both responsivity to and accurate perception of response-outcome contingencies. Hammond’s probabilistic reinforcement procedure has served us well in this respect, and it is now possible to proceed with this free-operant method to attempt to define the temporal boundaries of response-outcome contingencies as well as to answer new questions. For example, does our subjects’ remarkably rapid responsivity to contingencies also occur at the cognitive level, as reflected in their estimates of contingency? Do contingency ratings exhibit any initial biases such as those discovered with operant performance? Are there metrics which are more successful than A P in predicting behavior and judgments? These and other questions appear to be eminently answerable, now that we have available reliable methods for simultaneously studying contingency judgments and operant behavior. REFERENCES Allan, L. G. (1980). A note on measurements of contingency between two binary variables in judgment tasks. Bulletin of the Psychonomic Society, 415, 147-149. Allan, L. G., & Jenkins, H. M. (1980). The judgment of contingency and the nature of the response. Canadian Journal of Psychology, 34, l-11. Allan, L. G., & Jenkins, H. M. (1983). The effect of representations of binary variables on judgment of influence. Learning and Motivation, 14, 381-405. Alloy, L. B., & Abramson, L. Y. (1979). Judgment of contingency in depressed and nondepressed students: Sadder but wiser? Journal of Experimental Psychology: General, 108, 441-485. Alloy, L. B., & Tabachnik, N. (1984). Assessment of covariation by humans and animals: The joint influence of prior expectations and current situational information. Psycholopiral Review. 91, 112-149.

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Wasserman, E. A., & Shaklee, H. (1984). Judging response-outcome relations: The role of response-outcome contingency, outcome probability, and method of information presentation. Memory & Cognition, 12, 270-286. Received June 4, 1984 Revised November 16. 1984