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.
Highlights
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
The question addressed in this paper is whether or not we can improve on the standard Rescorla–Wagner Model (RWM) to obtain a better model for individual learning curves by using the Memory Environment Cue Array Model (MECAM)’s extended description of the stimulus environment
The main purpose of the current paper is to explore a development in the application of the RWM with the aim of trying to obtain a better approximation to individual acquisition data within a simple associative framework
Summary
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. The modified approach, which I have named the Memory Environment Cue Array Model (MECAM), works algorithmically in the same way as the RWM but incorporates memory buffers to hold representations of the previous trial’s events These memory representations are processed alongside representations of the current trial. It is competitive in the sense that the updates applied to the associative strength of a stimulus depend not just on the strength of that stimulus and on the strength of all the other stimuli that are present on the trial— V is used in the error term rather than V alone This competitive error driven formulation is a defining feature of the RWM and has been adopted in many neural network models of learning This competitive error driven formulation is a defining feature of the RWM and has been adopted in many neural network models of learning (c.f. Sutton and Barto, 1981)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.