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During compound conditioning in which two or more cues are paired with an unconditioned stimulus (US), animals form associations between each cue and the US and associations between the cues (the latter of which are called within-compound associations). Most contemporary theories of associative learning assert that summation of cue–US associations drives negative mediation (e.g., blocking, overshadowing, and conditioned inhibition) because of their effects on the processing of the US representation. Using a computational modeling approach, we reviewed and simulated experiments that suggest that within-compound associations are necessary for cue interactions. A mathematical model that attributes all cue interactions to within-compound associations provided a better fit than a model that attributes negative mediation effects to variations in processing of the US. Overall, the results of this analysis suggest that within-compound associations are important for all cue interactions, including cue competition, conditioned inhibition, counteraction effects, retrospective revaluation, and second-order conditioning.
Within-compound associations: models and data
Pavlov (1927) discovered that both positive and negative mediation effects can occur when a target cue (X) is presented during training in conjunction with a nontarget cue (A). Positive mediation effects refer to situations in which the presence of A during training results in more excitatory behavioral control by X than if X was trained elementally. An example of positive mediation is second-order conditioning, which occurs when A–unconditioned stimulus (A–US) pairings (Phase 1) precede X–A pairings (Phase 2, which presumably establishes an X–A within-compound association), resulting in more excitatory conditioned responding to X than in a control condition lacking one or the other phase (Pavlov, 1927).
What one knows and what one shows: acquisition vs. performance
Since the time of Aristotle, it has been commonly assumed that two events occurring in close proximity become associated to each other. This learning by contiguity is a central determinant of the phenomenon of classical conditioning, in which a neutral target stimulus (X) is repeatedly presented in close proximity to a stimulus that unconditionally produces a response (i.e., an unconditioned stimulus, US), with the consequence that X comes to elicit a response appropriate to the US. That is, X becomes a conditioned stimulus (CS) that produces a conditioned response (CR). However, there have been reports of various conditions under which CS–US pairings fail to result in an acquired response to the CS. For example, even though pairings of X and the US ordinarily result in a robust CR (i.e., acquisition), X will fail to gain response potential if it is trained in the presence of another, more salient CS, A (i.e., overshadowing; Pavlov,1927). Thus, even though the contiguity between CS X and the US is the same in the X–US acquisition and AX–US overshadowing conditions, the resulting behavioral control by CS X is not. An easy explanation for this difference is that subjects did not acquire an X–US association in the overshadowing condition.
We argue that the propositional and link-based approaches to human contingency learning represent different levels of analysis because propositional reasoning requires a basis, which is plausibly provided by a link-based architecture. Moreover, in their attempt to compare two general classes of models (link-based and propositional), Mitchell et al. refer to only two generic models and ignore the large variety of different models within each class.
Behavioral momentum theory has evolved within the realm of operant conditioning. The thought-provoking momentum metaphor equates the strength of an operant response with its resistance to change and preference (i.e., choice) for that response over other available responses. Whereas baseline response rate (velocity in the metaphor) is assumed to be largely influenced by the response-reinforcer contingency, resistance to change and preference are assumed to reflect an intervening variable called behavioral mass, which is determined primarily by the stimulus-reinforcer relationship. This invites the question of how well the momentum metaphor applies to the stimulus-reinforcer relationships of traditional Pavlovian paradigms. Presumably, a correspondence exists between behavioral mass and the notion of associative strength in the associative learning literature. Although response rate has little meaning in the trialwise structure of classical (i.e., Pavlovian) conditioning, response probability or magnitude might be regarded metaphorically as velocity. Momentum theory suggests that resistance to change (e.g., extinction) is a better indicator of associative strength than is response probability or magnitude. Therefore, variables that strengthen Pavlovian learning should influence resistance to extinction of conditioned responding in a similar manner. Moreover, it is important to assess momentum theory outside of strictly operant paradigms, particularly because in clinical settings many common disorders (e.g., phobias) and their therapies (e.g., cue exposure) are thought to be classically conditioned.
Rachlin's substantive points about the relationship between altruism and self-control are obscured by simplistic and outdated portrayals of evolutionary psychology in relation to learning theory.
We emphasize the feature of Webb's presentation that bears most directly on contemporary research with real animals. Many neuroscience modelers erroneously conclude that a model that performs like an animal must have achieved this goal through processes analogous with those used by the animal. A simulation failure justifies rejecting a model, but success does not justify acceptance. However, an important benefit of models, successful or otherwise, is to stimulate new research.
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