An extension of the Rescorla and Wagner Simulator for context conditioning

Increased Generalization, Stronger Acquisition, or Reduced Extinction? Investigation of the Mechanisms Underlying the Acquisition-in-Multiple-Contexts Effect

Prior research has demonstrated that conducting acquisition in multiple contexts results in more responding to the point that it can even nullify the benefit of subsequent extinction in multiple contexts on reducing renewal of excitatory responding. The underlying mechanism to explain why this happens has not been systematically examined. Using self-reported expectancy of the outcome, the current study investigates three mechanisms that potentially explain why acquisition in multiple contexts results in more responding-greater generalization, stronger acquisition learning, or slower extinction learning. Participants (N = 180) received discriminative training with a conditioned stimulus (CS+) and outcome pairing and a CS- → noOutcome pairing in either one or three contexts. This was followed by either extinction treatment in a novel context or no extinction. Finally, testing occurred in the acquisition context, the extinction context, or a novel context. Stronger renewal of extinguished conditioned expectation was observed for participants who received CS+ → Outcome pairings in three contexts relative to one context. There was no effect of the number of contexts on the strength of the excitatory CS+ → Outcome association or degree of inhibitory learning that occurred during extinction. This suggests that generalization is the mechanism responsible for the adverse impact to extinction learning when acquisition is conducted in multiple contexts.

Explaining the Return of Fear with Revised Rescorla-Wagner Models

Exposure therapy - exposure to a feared stimulus without harmful consequences - can reduce fear responses in many mental disorders. However, such relief is often partial and temporary: fear can return after the therapy has ended. Conditioning research has identified three mechanisms for the return of fear, viz. change in physical context (renewal), the passage of time (spontaneous recovery), and an encounter with the fear-producing unconditioned stimulus (reinstatement). To understand why fear returns and thereby develop more effective therapies, we develop mathematical learning models based on that of Rescorla and Wagner. According to this model, context cues present during extinction become conditioned inhibitors (i.e. safety signals) which prevent total erasure of the threat association. Adding various mechanisms to the model allows it to explain different facets of the return of fear. Among these mechanisms is decay of inhibitory associations, which provides a novel explanation for spontaneous recovery. To make the benefits of exposure robust and permanent, one must minimize the degree to which the extinction context becomes inhibitory in order to maximize unlearning. We simulate several experimental paradigms that reduce the return of fear and explain them according to this principle.

Revisiting the learning curve (once again)

The vast majority of published work in the field of associative learning seeks to test the adequacy of various theoretical accounts of the learning process using average data. Of course, averaging hides important information, but individual departures from the average are usually designated "error" and largely ignored. However, from the perspective of an individual differences approach, this error is the data of interest; and when associative models are applied to individual learning curves the error is substantial. To some extent individual differences can be reasonably understood in terms of parametric variations of the underlying model. Unfortunately, in many cases, the data cannot be accomodated in this way and the applicability of the underlying model can be called into question. Indeed several authors have proposed alternatives to associative models because of the poor fits between data and associative model. In the current paper a novel associative approach to the analysis of individual learning curves is presented. The Memory Environment Cue Array Model (MECAM) is described and applied to two human predictive learning datasets. The MECAM is predicated on the assumption that participants do not parse the trial sequences to which they are exposed into independent episodes as is often assumed when learning curves are modeled. Instead, the MECAM assumes that learning and responding on a trial may also be influenced by the events of the previous trial. Incorporating non-local information the MECAM produced better approximations to individual learning curves than did the Rescorla-Wagner Model (RWM) suggesting that further exploration of the approach is warranted.