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

Glautier, Steven (2017) Preprint Glautier (2017) Revised Non-local influences on associative learning: new data and further model evaluation. (doi:10.31219/osf.io/4nx2v).

Abstract

Previous work (Glautier, 2013) showed that the responses made by humans on trial n in simple associative learning tasks were influenced by events that took place on trial n−1 and a simple extension of the Rescorla-Wagner Model (RWM Rescorla & Wagner, 1972), the Memory Environment Cue Array (MECA) model, was presented to account for those results. In the current work further evidence of non-local influences on responding during associative learning tasks is presented. The Rescorla-Wagner model and the MECA model are evaluated as models for the observed data using qualitative, näive maximum likelihood, and Akaike weight analyses. In two experiments the Akaike weight analyses strongly supported the simpler Rescorla-Wagner model over the MECA model but the qualitive and näive maximum likelihood analyses strongly supported the MECA model model over the simpler Rescorla-Wagner model. In Experiment 2 this apparent conflict was resolved using a generalisation criterion test (Ahn, Busemeyer, Wagenmakers, & Stout, 2008; Busemeyer & Wang, 2000) which gave clear support to the MECA model over the Rescorla-Wagner model. These results demonstrate the superiority of model selection using predictive validity, where possible, over selection using statistical adjustments for model complexity.

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Contributors

Author: Steven Glautier

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