Q0667—QJEP(B)SI B05/Jan 13, 03 (Mon)/ [12 pages – 1 Tables – 1 Figures – 1 Footnotes – 0 Appendices]. . KEYED THE QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2003, 56B (1), 68–79
Learned associability and associative change in human causal learning M. E. Le Pelley and I. P. L. McLaren University of Cambridge, Cambridge, UK
The Mackintosh (1975) model of associative learning specifies that processing of both the cues presented on a trial and the outcome of that trial will interact to determine the amount of associative change undergone by a given cue. Experiments looking at the distribution of associative change among the elements of a reinforced compound in animal conditioning studies indicate that processing of the outcome of a trial does indeed influence associative change. The work reported here investigates the distribution of associative change among the elements of a reinforced compound in a human causal judgement paradigm, and it indicates that processing of the cues presented on a trial also plays a role in determining associative change (in terms of changes in the associability of cues as a result of experience). Taken in combination, these results provide good support for Mackintosh (1975) and the characterizations of both cue and outcome processing that it offers.
At the core of any theory of associative learning is an expression governing the change in associative strength of each cue (or configuration of cues) as a result of experience of the relationship between that cue and an outcome. But what are the processes underlying, and factors influencing, associative change? One approach, exemplified by the Rescorla–Wagner (1972) theory, is to assume that modulation of associative change is determined solely by changes in processing of the unconditioned stimulus (US). If the US is surprising then it is able to support more learning than if it is already predicted (and therefore less surprising). The contribution of the conditioned stimulus (CS) to learning is assumed to be fixed. At the opposite extreme are theories that assume changes in associative strength to depend on changes in processing of the CS, with the contribution of the US to learning being fixed. For example, the Pearce-Hall (1980) model explains variations in associative change as resulting wholly from changes in the associability (α) of the CS. It need not be the case, however, that associative change is a wholly US-driven or wholly CS-driven phenomenon—both factors may contribute to learning. This idea is formalized, in the Mackintosh (1975) theory of associative learning. The expression governing associative change (∆V) in this model is given by: Requests for reprints should be sent to Mike Le Pelley, Department of Experimental Psychology, University of Cambridge, CB2 3EB, UK. Email:
[email protected] We thank N. J. Mackintosh for his helpful advice during the preparation of this article. 2003 The Experimental Psychology Society http://www.tandf.co.uk/journals/pp/02724995.html DOI:10.1080/02724990244000179
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∆VA = Sα Α ( λ − VA )
69 (1)
where VA is the associative strength of Cue A, S is a learning-rate parameter, αA is the associability of Cue A, and λ is the asymptote of conditioning supportable by the outcome occurring on that trial. Mackintosh allows α to change as a result of experience. Specifically, Cue A maintains a high α to the extent that it is a better predictor of the outcome of the current trial than are all other cues present. Conversely, αA will decrease if the outcome is predicted by other events at least as well as by A. The extent to which an outcome is predicted by A is represented by the absolute value of the error term (λ –VA). Hence these ideas can be encapsulated in the following rules: ∆α A > 0 if |λ − VA| ∆VA. Indeed, if Stage 1 learning proceeds near to asymptote, then VA will be approximately equal to λ, such that the error term for A will be near zero. As a result, VA cannot increase greatly over the course of AB+ trials, as it is already near its maximum value. Following this first trial, the associability of B (which incorrectly predicts no US) will fall, while that of A (which correctly predicts the US) will remain high. Over the course of Stage 2 trials, αB will tend to zero, halting any further increase in VB. Nevertheless, the effect of the early Stage 2 trials, when αB is significantly non-zero, and the error term for B is high, will ensure a larger increment in VB than in VA when assessed over all trials. To summarize, the reason that greater associative change is seen in the poorer predictor of the Stage 2 outcome is that extensive Stage 1 training ensures that the error term for the better predictor remains near zero throughout Stage 2, while that for the poorer predictor is considerably larger. The dominating effect of the error term outweighs the effect of associability modulation, which would tend to produce greater associative change in the better predictor of the Stage 2 outcome. Crucially this explanation of Rescorla’s (2000) results relies on the use of separate error terms for the different cues presented on a trial. This characterization of error means that the different associative histories of the different cues will exert a profound, and selective, influence on the amount of associative change that they undergo. An explanation of these results in terms of Mackintosh (1975), appealing to differences in the size of the error terms for the different cues making up the Stage 2 compound, implies that it should be possible to alter the distribution of associative change between the elements of the AB compound by changing the amount of training in Stage 1. Specifically, reducing the amount of Stage 1 training should increase the size of the associative change in A relative to that in B. Consider the case in which Stage 1 training is sufficient only to establish A as a weak excitor and B as a weak inhibitor. Once again αA and αB will rise over the course of Stage 1, but will not reach values as great as those obtained if training were more extensive. As before,
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during Stage 2 Cue A is a better predictor of the outcome on AB+ trials than is Cue B. Hence αA will continue to rise over these trials while αB falls. In this case, however, the reduced Stage 1 training will ensure that, at the start of Stage 2, the associative strength of A is still considerably below asymptote. As a result Cue A’s error term on Stage 2 trials will be correspondingly higher than would be the case if more extensive Stage 1 training had been provided. Thus, aided by its high associability, A will be able to undergo a greater increase in associative strength over these trials. As before, B will undergo a relatively large change on the first AB+ trial (as it has an intermediate associability and a large error), but subsequent increments in VB will rapidly decrease in magnitude as αB falls. The overall result is that the increase in the associative strength of Cue A will be larger than in the “extended training” case, and the increase in the associative strength of Cue B will be, if anything, smaller (as αB does not start Stage 2 at such a high value). Thus reducing Stage 1 training should increase the magnitude of associative change in A relative to that in B over AB+ trials, driven largely by differences in the magnitude of A’s error term during Stage 2 in the two cases. Le Pelley and Mackintosh (2002) explored this prediction, using a magazine approach paradigm with rats. The experimental design used was exactly that of Rescorla’s (2000) Experiment 1a. Each Stage 1 training session involved eight presentations of each of the Stage 1 trial types (A+, C+, X+, BX–, DX–). “Short” Stage 1 training involved two such sessions, while “long” Stage 1 training involved eight. In line with the predictions of Mackintosh (1975), varying the amount of Stage 1 training had a profound effect on the distribution of associative change among the elements of the AB compound experienced in Stage 2. Rats receiving long Stage 1 training showed the same pattern of results as those from Rescorla’s study: Conditioned responding during BC was significantly greater than that during AD on test. Rats receiving short Stage 1 training, however, showed a significantly different pattern of responding, with responding roughly equal during AD and BC. As predicted, decreasing the amount of Stage 1 training led to an increase in the size of the associative change in A relative to that in B during Stage 2. Le Pelley and Mackintosh also varied the number of Stage 2 AB+ trials received by the rats. Rats receiving short Stage 2 training experienced 4 AB+ trials, while rats receiving long Stage 2 training experienced 20 AB+ trials. The results showed the amount of Stage 2 training received to have no effect on the associative change undergone by A and B (either in relative or absolute terms), indicating that the latter compound trials have little effect on the associative strength of A and B—all of the associative change occurs within the first few trials. This result is again in line with Mackintosh (1975). After Stage 1 training followed by four AB+ trials, VA will be near asymptote (λ) and will show little additional change as a result of further reinforcement. Four reinforced AB trials alone might well be insufficient for VB to reach λ, such that B’s error term will still be large on the latter Stage 2 trials. However, the decline in αB will ensure that this error is unable to drive any significant further increase in associative strength over these trials. The results of these investigations of the effects of associative history on the distribution of associative change among the elements of a reinforced compound provide evidence supporting the use of separate error terms for the different cues presented on a trial, as assumed by Mackintosh (1975). The problem with this approach, however, is that there is considerable evidence for the phenomenon of cue competition, wherein cues presented together on a trial interact and compete with one another for associative strength. This is powerfully demonstrated in the blocking effect (Kamin, 1969). Here the gain in excitatory strength accruing to B
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following reinforcement of an AB compound is reduced if Cue A has been pretrained as a good predictor of that outcome. The two cues seem to compete for a limited amount of associative strength. Blocking is obviously problematic for any theory that treats cues independently in its determination of associative change. Mackintosh (1975) addresses this problem by specifying competition between cues in terms of associability, rather than error. Consider again the blocking paradigm. Pretraining of A will establish it as a good predictor of the outcome. The novel cue B presented on subsequent AB trials will therefore be a poorer predictor of the outcome than will A, and its associability will fall correspondingly, such that changes in VB will be smaller than those in a control group that has not had pretraining with A. Given that it is this notion of learned associability that sets Mackintosh (1975) apart from earlier theories employing independent error terms for each cue presented on a trial, we need to show that associability, as well as error, plays a part in determining the distribution of associative change among the elements of a compound. This is possible in a human causal judgement paradigm, using a modification of the procedure employed by Wills and Lochmann (2002). The design of our experiment is shown in Table 1. This study used an allergy prediction paradigm with human subjects. Cues A–Y are represented by different foods to be fed to a fictitious patient; Outcomes 1–4 are represented by different types of allergic reaction that the patient may suffer after eating those foods. This experiment used a within-subjects design, with subjects experiencing all of the different contingencies concurrently. Stage 1 of this experiment involves two simultaneous discriminations, with subjects being given information about foods and allergies for Mr. X. In this stage Cues A and D consistently indicate the occurrence of Allergy 1, Cues B and C consistently indicate the occurrence of Allergy 2, and cues V–Y provide no basis for discrimination between the two outcomes—they are paired with Allergies 1 and 2 an equal number of times. According to Mackintosh (1975), then, Cues A–D should maintain a high associability over these trials, as they are better predictors of the outcome than the cues with which they are paired on each trial. Conversely, the associability of Cues V–Y will decrease over the course of Stage 1, as they are poorer predictors of the outcome than the cues with which they are paired. In Stage 2, subjects are told that they are now to be given information about foods and allergies in a new patient, Mr. Y. On each of the first four Stage 2 trial types shown in Table 1, a “good predictor” from Stage 1 (A, B, C, or D) is paired with a “poor predictor” (V, W, X, or Y) with which it was not paired in Stage 1, and this novel food compound is paired with a novel TABLE 1 Experimental design Stage 1
Stage 2
Test
AV → 1 BV → 2 AW → 1 BW → 2 CX → 2 DX → 1 CY → 2 DY → 1
AX → 3 BY → 4 CV → 3 DW → 4 EF → 3 GH → 4 IJ → 3 KL → 4
AC BD VX WY EH FG IJ KL
A–Y = foods; 1–4 = allergic reactions.
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outcome. Thus compounds AX and CV are paired with Allergy 3, while BY and DW are paired with Allergy 4. Novel compounds were used in Stage 2 in order to reduce the direct transfer of learning about Stage 1 compounds to learning in Stage 2; that is to say, the use of novel compounds in Stage 2 will act to disrupt any configural processing of stimulus compounds employed by participants. Given that neither of the Stage 2 outcomes has been experienced before, the associative strength of all cues for these two outcomes will begin Stage 2 at zero.1 However, Stage 1 training will ensure that the associability of Cues A–D is greater than that of Cues V–Y (assuming that associability is not outcome specific—see Discussion below). As a result, on the first Stage 2 trial of each type, the change in associative strength of the good predictor (from Stage 1) should be greater than that of the poor predictor. Thus, for example, there will be a greater increment in the strength of the association between A and Allergy 3 (VA,3) than between X and Allergy 3 (VX,3) on the first AX → 3 trial. Consider now the second AX → 3 trial. Given that VA,3 > VX,3 as a result of the first trial, A will be a better predictor of Allergy 3 than will X on this second trial. Therefore the associability of A will remain high, while that of X remains low. This idea will apply equally to all subsequent Stage 2 trials, resulting in larger increases in the strength of the association between the good predictor and the Stage 2 outcome than between the poor predictor and this outcome. In other words, the distribution of associative strength between the elements of the Stage 2 compound will be determined by the difference in associability of the two cues making up this compound. As a result of these differences in learned associability, Mackintosh (1975) predicts that, at the end of Stage 2, VA,3 and VC,3 will be greater than VX,3 and VV,3, while VB,4 and VD,4 will be greater than VY,4 and VW,4. On test subjects are presented with compounds AC, BD, VX, and WY, and they are asked to provide causal judgements of the extent to which these compounds predict Allergy 3 or Allergy 4. These causal judgements provide our index of the combined associative strengths of the two foods in each compound. Given the predictions regarding the associative strengths of the various elements detailed above, Mackintosh (1975) predicts that subjects will judge AC as a strong predictor of Allergy 3, BD as a strong predictor of Allergy 4, VX as a weak predictor of Allergy 3, and WY as a weak predictor of Allergy 4. In other words, the discrimination between compounds AC and BD (made up of “good predictors” from Stage 1) should be greater than that between VX and WY (made up of “poor predictors”). In fact, Mackintosh (1975) also predicts better discrimination between the “good predictor” elements than between the “poor predictor” elements—for example, discrimination between A and D should be better than that between V and Y. Nevertheless we chose to test responding to compounds, rather than to elements. Whereas each of the good predictors is paired with only one outcome in Stage 1, each poor predictor is paired with two outcomes. We might expect Stage 1 learning to interfere proactively with retrieval of Stage 2 information when stimuli are presented on test. Retrieval of two Stage 1 outcomes by poor predictors might produce a different amount of proactive interference to retrieval of one Stage 1 outcome 1
Of course, if it is assumed that the different allergic reactions have some degree of similarity to one another, there may be some generalization of associative strength to these novel outcomes from Stage 1 learning. Given the random assignment of foods and allergies to subjects and the counterbalancing of presentation order of foods and allergies employed in this experiment, however, it is unlikely that this generalization will lead to any selective effects differentiating between Allergy 3 and Allergy 4, and hence the predictions for all theories discussed are unchanged.
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by good predictors. This potential problem is minimized by comparing responding to compounds of good predictors such as AC (whose elements were paired with different outcomes in Stage 1 and so should retrieve two Stage 1 outcomes) with compounds of poor predictors such as VX (both elements of which were paired with two outcomes in Stage 1), allowing us to equate more closely any interference from Stage 1 in the two cases. On a related matter, consider the Stage 2 compound AX. This is similar to three compounds from Stage 1 that were paired with Allergy 1 (AV, AW, and DX) and one compound paired with Allergy 2 (CX). As a result, AX may have a greater degree of “1-ness” about it than “2-ness”. This potential cause of asymmetry is again overcome by testing compounds in the test stage. Thus CV in Stage 2 is similar to three compounds paired with Allergy 2 in Stage 1 (CX, CY, and AV) and one compound paired with Allergy 1 (BV). On test we pair A (part of a “1-ish” compound in Stage 2) with C (part of a “2-ish” compound)—these influences are symmetrical and so will balance one another, leading to no differential effect on test. Theories such as Rescorla–Wagner (1972) that do not allow for changes in associability as a result of experience predict that Stage 1 training will have no influence on acquisition of associations to novel outcomes in Stage 2, such that discrimination between AC and BD should be identical to that between VX and WY. Theories of CS processing that specify a common error term governing changes in associability (e.g., Pearce & Hall, 1980) also predict no difference in discrimination. Given that all Stage 1 compounds are equally predictive of their respective outcomes, use of a common error term to determine associability on each trial will ensure that, at the end of Stage 1, all cues have equal associability. Hence there can be no difference in associative change between the “good” and “poor” predictors in each compound during Stage 2. The remaining Stage 2 trial types (EF → 3, GH → 4, IJ → 3, KL → 4) were used to verify that subjects were able to use the rating scale provided on test appropriately. On test compounds EH and FG, each containing one element predicting Allergy 3 and one predicting Allergy 4, should receive similar judgements of causal efficacy for Allergies 3 and 4. Test compounds IJ and KL, on the other hand, should produce strong, selective causal judgements for Allergy 3 and Allergy 4, respectively. These extra Stage 2 compounds also helped to ensure a large memory load, hopefully preventing subjects from basing their ratings on inferences made from explicit episodic memories of the various Stage 2 trial types. Instead subjects should have to rely on associative processes to provide a more “automatic” measure of causal efficacy for each cue.
Method Participants, apparatus, and stimuli A total of 11 University of Cambridge students between the ages of 19 and 38 years took part in the experiment. They were paid 8 pounds for their participation in this and another, unrelated, experiment. The experiment was run on a PowerPC Macintosh with a 15" monitor. The 16 foods used were: asparagus, banana, carrots, sardines, tomato, mushrooms, pasta, eggs, onion, dates, ham, lentils, garlic, rice, vinegar, yoghurt. The four types of allergic reaction were: itch, nausea, sweating, dizziness. These foods and allergies were not counterbalanced but rather randomly and independently assigned to Cues A–Y and Outcomes 1–4 in the experimental design for each participant.
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Procedure At the start of the experiment, participants read the following on-screen instructions: In this experiment you are asked to imagine that you are an allergist (someone who tries to discover the cause of allergic reactions in people). You have just been presented with a new patient, “Mr. X”, who suffers from different types of allergic reaction following different meals. In an attempt to discover which foods cause the different types of allergic reaction in Mr. X, you arrange for him to eat a number of different meals, each containing two foods, and observe the type of allergic reaction he suffers. On the following screens, you will be shown the contents of meals eaten by Mr. X, and will be asked to predict what kind of allergic reaction will result from eating each meal. You will have a choice of two allergies. Mark your prediction by clicking the option button next to one of them, and then click OK. You will then be told whether your prediction was correct or incorrect. If your prediction was incorrect, the computer will beep. You will have to guess at first, but with the aid of the feedback your predictions should soon start to become more accurate. Your reaction times are not important in this experiment: you may take as long as you like on each trial. On each Stage 1 trial, participants saw the message “Meal [meal number] contains the following foods:” followed by the two foods in the meal, one above the other. Below this were two types of allergic reaction, one above the other, with a radio button next to each. Participants were cued to enter their prediction as to which type of allergic reaction would arise as a result of eating these foods by clicking the button next to that allergy, and then clicking the “OK” button at the bottom of the screen. The screen then cleared, and immediate feedback was provided: If participants had made the correct decision for that meal, the word “Correct” appeared in a green box; if they had made the incorrect decision, the word “Wrong” appeared in a red box, and the computer beeped. Stage 1 comprised 14 blocks (with no breaks between blocks), with each of the eight trial types occurring once per block. The order of trials within a block was randomized, with the constraint that there could be no immediate repetitions across blocks. For each trial type the order of presentation of the foods on the screen (top/bottom) was counterbalanced across blocks. For example, for trial type AV → 1, there would be seven presentations with Food A above Food V, and seven presentations with V above A (the order of these presentations was randomized). The two allergy options presented on each Stage 1 trial were always Allergy 1 and Allergy 2. For each trial type, the order of presentation of these allergies on the screen (top/bottom) was counterbalanced across blocks. So for trial type AV → 1, there would be seven presentations with Allergy 1 above Allergy 2, and seven presentations with Allergy 2 above Allergy 1 (the order of these presentations was again randomized). After the 112 trials of Stage 1, the following message appeared on the screen: Treatment of Mr. X is now finished. In the next stage, you will be studying a new patient, Mr. Y. Mr. Y suffers from different allergic reactions to Mr. X. Some of the foods given to Mr. Y are the same as those given to Mr. X, some are not. Continue to enter your predictions as in the previous stage: Click the option button next to one of them, and then click OK. The form of each Stage 2 trial was exactly the same as that for Stage 1, except that now the two allergy options were always Allergy 3 and Allergy 4. There were four blocks in Stage 2, with each of the eight trial types appearing once per block. The randomization took the same form as that for Stage 1. At the completion of Stage 2, the following message appeared on the screen:
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You will now be shown a number of meals to be eaten by Mr. Y. On the basis of the contents of these meals, you are asked to rate how likely Mr. Y is to suffer from each type of allergic reaction that he is prone to. Rate the likelihood of each allergy occurring on a scale from 0–10. A rating of 0 means that eating the meal is very unlikely to cause that type of allergic reaction, whilst a rating of 10 means that eating the meal is very likely to cause that type of allergic reaction. To enter your rating, click on the appropriate option button. Once you have rated the meal with respect to both allergic reactions, click OK. Remember, you will have to rate each meal twice: once for each type of allergic reaction. For clarification, participants also had access to a card on which instructions on how to use the rating scale were printed. Each of the eight test compounds was presented in random order for rating. On each test trial, the message “Meal [meal number] contains the following foods:” appeared, followed by the two foods, and below them the message “How likely is it that Mr. Y will suffer from the following allergic reactions after eating this meal?”. Below that were two boxes placed side by side, with the title of Allergy 3 (e.g., “Nausea”) at the top of one and the title of Allergy 4 (e.g., “Itch”) at the top of the other. In each box were 11 radio buttons labelled 0–10, one above the other, with 0 at the bottom and 10 at the top. Participants were cued to enter a rating for each allergy by clicking the appropriate radio button in each box. They then clicked the “OK” button at the bottom of the screen to progress to the next trial. Participants could not move to the next trial until they had provided a rating for the current meal for each of the two allergies. The order of presentation of the two foods in the meal (top/bottom) was randomized, and the order of presentation of the two allergy scales (left/right) was counterbalanced over participants (half of the participants had the rating scale for Allergy 3 on the left and Allergy 4 on the right; for the other half these were reversed—this counterbalancing was complete given the exclusion of participants as detailed in the Results section).
Results and discussion We could only hope to see any effect of differential associability on Stage 2 learning if participants had actually learnt the discriminations in Stage 1, otherwise there would be no basis on which the associability of A–D could rise above that of V–Y. Therefore we imposed an arbitrary selection criterion of 60% correct or above over all the trials of Stage 1 (chance = 50% correct). Three participants failed to meet this criterion, and their results were therefore excluded from all further analyses. In the last block of Stage 1, all participants gave the “Allergy 1” response to all trial types predicting Allergy 1 (AV, AW, DX, DY; all of which are equivalent to one another), and the “Allergy 2” response to all trial types predicting Allergy 2 (BV, BW, CX, CY; again all of which are equivalent). During Stage 2, responding again adapted appropriately to the contingencies. In the final block, participants gave the “Allergy 3” response to compounds AX and CV (which are equivalent and paired with Allergy 3 in Stage 2) on 94% of trials, to compounds EF and IJ (equivalent and paired with Allergy 3) on 88% of trials, to BY and DW (equivalent, paired with Allergy 4) on 38% of trials, and to GH and KL (equivalent, paired with Allergy 4) on 19% of trials. Given that only four blocks were experienced, it is not surprising that the discrimination is not as pronounced as that in the Stage 1 data. The results of main interest are the causal judgement ratings given for each of the test compounds. In this analysis, we are interested in the differential predictiveness of each compound for the two allergies—that is, the extent to which a compound predicted Allergy 3 more (or less) than it did Allergy 4. Therefore for each compound, the rating on the Allergy 4 scale has
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Figure 1. Mean difference scores (Allergy 3 rating – Allergy 4 rating) for each of the eight test compounds. A positive score indicates greater perceived likelihood of Allergy 3; a negative score indicates greater perceived likelihood of Allergy 4; a score of zero indicates no differential predictiveness.
been subtracted from the rating on the Allergy 3 scale to yield a single variable—this difference score is shown for each test compound in Figure 1. On this scale positive values indicate that the compound is perceived as a stronger predictor of Allergy 3 than of Allergy 4, and negative values indicate that it is perceived as a stronger predictor of Allergy 4 than Allergy 3. First we verified that subjects were able to learn from the contingencies in Stage 2 and were able to apply the knowledge learnt appropriately to the rating scales used in the test stage. Compounds IJ and KL were explicitly trained as predictors of Allergies 3 and 4, respectively, during Stage 2. If subjects had learnt this information and could apply it to the rating scales used in the test stage, we would expect to see a higher difference score for IJ than KL. Planned comparisons reveal this to be the case, F(1, 7) = 27.56, p < 01. Compounds EH and FG, on the other hand, are both made up of one element previously paired with Allergy 3 and one element previously paired with Allergy 4. Thus there should be no significant difference in the difference scores recorded for these compounds. Moreover, if subjects were able to successfully combine the information learnt in Stage 2 about the elements making up these compounds, and to apply this knowledge appropriately to the rating scales, then the ratings received by the compounds should not discriminate between Allergy 3 and Allergy 4—that is, these compounds should elicit difference scores near zero. This predicted pattern of results was again confirmed statistically: Planned comparisons reveal no difference in the difference scores recorded for compounds EH and FG, F < 1, and one-sample t tests indicate that neither set of scores differed significantly from zero, tmax(7) = 1.07, p < .1. This demonstration that subjects did indeed learn from Stage 2, and could apply the knowledge gained to the rating scales provided on test paves the way for an analysis of the results of main interest—the difference scores for compounds AC, BD, VX, and WY. These scores were analysed using a repeated measures analysis of variance, with factors of predictiveness (good predictors from Stage 1, AC and BD, versus poor predictors from Stage 1, VX and WY), and outcome (compounds composed of elements paired with Allergy 3 in Stage 2, AC and VX, versus compounds composed of elements paired with Allergy 4 in Stage 2, BD and WY). This analysis reveals a significant main effect of outcome, with compounds composed of elements previously paired with Allergy 3 receiving higher difference scores than those
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composed of elements previously paired with Allergy 4, F(1, 7) = 14.71, p < .01, but no main effect of predictiveness, F < 1. Most important, the interaction between predictiveness and outcome is significant, F(1, 7) = 6.82, p < .05. In line with the predictions of Mackintosh (1975), the discrimination between AC and BD is significantly better than that between VX and WY. Moreover, analysis of simple effects reveals that the discrimination between AC and BD is significant, with AC eliciting higher difference scores than BD, F(1, 7) = 30.55, p < .01, whereas there is no statistical evidence for discrimination between VX and WY, F(1, 7) = 2.92, p > .1. These results are incompatible with theories of associative learning that do not allow for changes in stimulus associability with experience, or that specify associability changes to be governed by a common error term. These results provide clear evidence for an effect of Stage 1 training on learning in Stage 2. The pattern of results fits well with the Mackintosh (1975) theory, which proposes that the better predictors of the various trial outcomes in Stage 1 maintain higher associability than the poorer predictors. When, in Stage 2, compounds of “good” and “poor” predictors are paired with novel outcomes, learning proceeds more rapidly to the “good” predictors as a result of this higher associability. This experiment therefore provides strong evidence for a role of associability in determining the distribution of associative change undergone by the elements of a reinforced compound. Note that, if Mackintosh (1975) is to provide an explanation of the effect of Stage 1 predictiveness on Stage 2 associative change, it must be assumed that associability is a cue-specific, but not an entirely outcome-specific, property. In other words, in our experiment it would seem that a cue does not have a separate associability for each outcome (as this would prevent transfer of associability from Stage 1 learning to the new cue–outcome relationships learnt in Stage 2); rather, associability would seem to be a general property of a cue, depending on how good that cue is at predicting allergic reactions in general. This is not the first paper to claim that learned associability plays a role in human associative learning. For example, Kruschke and Blair (2000) obtained evidence supporting a view of blocking as resulting from learned “inattention” (in terms of Mackintosh, 1975, a reduction in associability). Our experiment was designed to be more analogous to Rescorla’s (2000) animal studies, demonstrating a difference in the magnitude of associative change undergone by two elements of the same compound as a result of reinforcement of that compound, unlike Kruschke and Blair’s study, which involved a comparison between elements trained in different compounds. In addition, it might be possible to appeal to proactive interference as an explanation of Kruschke and Blair’s results—it is harder to formulate an explanation of the current results along similar lines. It should also be noted that Mackintosh (1975) is not the only theory able to account for the pattern of results seen in the current study. Kruschke (2001) introduced two connectionist models—EXIT, and a variant of the “mixture of experts” class of model—both of which allow for changes in attention to cues in much the same way as Mackintosh (1975) allows for changes in associability. As a result, both of these models could account for the effect of learned associability demonstrated in the present experiment. However, there are small but important differences between Kruschke’s models and the Mackintosh theory. The EXIT model, for instance, uses a common error term in its calculation of associative change and hence is unable to explain the results of Rescorla (2000) and Le Pelley and Mackintosh (2002). Moreover, Kruschke specifies that in both EXIT and the mixture of experts model, attention influences both learning and performance, whereas in the Mackintosh theory, associability influences only learning (with performance simply given by
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ΣV). This difference has important implications for the study by Rescorla (2000). In Stage 2, attention to the poorer predictor of the US on AB+ trials, B, will be drastically reduced, while attention to the better predictor, A, remains strong. If attention plays a role in determining performance, then this decline in attention to B relative to A over Stage 2 trials will result in a corresponding decline in responding to B relative to A, leading to the prediction that responding to AD will be greater than to BC on test—the opposite to the empirical result. In summary, then, the present experiment with human participants provides evidence for a role of learned associability in determining the relative magnitude of associative change undergone by elements of the same compound as a result of reinforcement of that compound. Specifically, the results suggest that good predictors of the outcome of a trial maintain a higher associability than poorer predictors. The pattern of results from related animal experiments indicates that the separate error term for each cue can also influence the distribution of associative change undergone by the elements of a reinforced compound. The characterizations of learned associability and error suggested by these results are exactly those implemented by Mackintosh (1975). More generally, these results suggest that it is insufficient to view associative change as being modulated either by processing of the US or by processing of the CS— instead both processes seem to play an important role in determining the amount of associative change undergone by a cue on a given trial.
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