Bottom-up learning of explicit knowledge using a Bayesian algorithm and a new Hebbian learning rule

Authors:
Sébastien Hélie;Robert Proulx;Bernard Lefebvre
Affiliations:
Department of Psychology, University of California, Santa Barbara, CA 93106-9660, United States;Université du Québec í Montréal, Département de psychologie, C.P. 8888, Succ. Centre-Ville, Montréal (Québec), Canada H3C 3P8;Université du Québec í Montréal, Département d'informatique, C.P. 8888, Succ. Centre-Ville, Montréal (Québec), Canada H3C 3P8
Venue:
Neural Networks
Year:
2011

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Abstract

The goal of this article is to propose a new cognitive model that focuses on bottom-up learning of explicit knowledge (i.e., the transformation of implicit knowledge into explicit knowledge). This phenomenon has recently received much attention in empirical research that was not accompanied by a corresponding work effort in cognitive modeling. The new model is called TEnsor LEarning of CAusal STructure (TELECAST). In TELECAST, implicit processing is modeled using an unsupervised connectionist network (the Joint Probability EXtractor: JPEX) while explicit (causal) knowledge is implemented using a Bayesian belief network (which is built online using JPEX). Every task is simultaneously processed explicitly and implicitly and the results are integrated to provide the model output. Here, TELECAST is used to simulate a causal inference task and two serial reaction time experiments.