Formal requirements of Markov state models for paired associate learning

Discrete stale learning models that make Markov assumptions are a powerful tool for the analysis and optimization of performance in pail cd associate tasks. We seek here to derive bounds on the complexity needed by such model s in order to account for the critical effects of lag and retention interva ls on paired associate learning, More specifically, after establishing that two different Markov chains are needed tone for describing the effects of trials where a paired associate is presented and one for describing the eff ects of trials where the paired associate is not presented), we determine t he minimum number of states required in a Markov model with two chains. It is shown formally that, under certain psychologically plausible assumptions , more than three states are required. A model with two chains and four sta tes is presented and it is shown empirically that it can account For the la g and retention effects in paired associate learning. (C) 2001 Academic Pre ss.