Revisiting the learning curve (once again)

Abstract

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.

Computational modeling of epiphany learning

“…This behavior is typically overlooked when examining aggregate behavior because different subjects have epiphanies at different points in time and so averaging over them one is left with a picture of a gradual increase in optimal behavior. This phenomenon underscores the importance of examining individual-level behavior (39)(40)(41).…”

Preprint Glautier (2017) Revised Non-local influences on associative learning: new data and further model evaluation

“…The primary purpose of the current paper is to extend the work presented by Glautier (2013) by further applications of the MECA model and comparisons with the Rescorla-Wagner model. Two experiments are reported.…”

“…For example, Glautier (2013) studied acquisition of predictive judgements in humans and found that in many cases the Rescorla-Wagner model provided only poor approximations to individual learning curves. Individual learning curves frequently contain large trial-to-trial changes in ratings, including regressions, which cannot be captured in standard applications of the Rescorla-Wagner model.…”

Individual differences in associative learning

“…Models that have been developed to account for associative learning (e.g., Rescorla and Wagner, 1972;Sutton and Barto, 1981) provide explanations and descriptions for the development of acquired associations and as such these models make concrete the parameters upon which individuals might vary. Variation conceived here might be at the level of behavior as described in the papers of this volume, but the models are also used to identify neural circuits and substrates which must be the bases for the differences described.The first three papers in this volume outline approaches to the computational analysis of individuals and provides a powerful case for how and why individual differences should be studied (Sauce and Matzel, 2013), and how the acquisition of a response or association might vary within an individual across a particular training experience (Glautier, 2013) or between individuals across the same experience (Byrom, 2013). These three contributions describe some of the elements underlying the complexity in thinking and quantitative modeling of the computational approach.…”

“…The first three papers in this volume outline approaches to the computational analysis of individuals and provides a powerful case for how and why individual differences should be studied (Sauce and Matzel, 2013), and how the acquisition of a response or association might vary within an individual across a particular training experience (Glautier, 2013) or between individuals across the same experience (Byrom, 2013). These three contributions describe some of the elements underlying the complexity in thinking and quantitative modeling of the computational approach.…”