A Markov Model of Conditional Associative Learning in a Cognitive Behavioural Scenario
In conditional learning, one investigates the computational principles by which the human brain solves challenging recognition problems. The role of temporal context in the learning of arbitrary visuo-motor associations has so far been studied mostly in primates. We model the explicit learning task where a sequence of visual objects is presented to human subjects. The computational modelling of the algorithms that appear to underlie human performance shall capture the effects of confusion in ordered and random presentation of objects. We present a Markov model where the learning history of a subject on a certain object is represented by the states of the model. The analysis of the resulting Markov chain makes it possible to judge the influence of two model parameters without the simulation of a specific learning scenario. As the model is able to reproduce the learning behaviour of human subjects it might be useful in the development of future experiments.
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