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Category: Project

Description: Theories of cognitive reasoning and decision processing have frequently embraced the notion of a dual or multi-processing brain. An assumption often made for this dual or multi-processing brain is that some cognitive processes might take longer, lead to different outcomes or make different trade-offs. This naturally predicts the presence of a ‘mixture’ component in response time distributions as well as stress the question on how and when we shift between cognitive processes. In the following thesis, we strive to advance the ‘methodological toolbox’ to deal with these mixtures in response time distributions and switching of cognitive processes. A combination of a Hidden Markov modeling approach and evidence accumulation model is proposed and tested by means of simulations. The new method offers a way to account for local dependencies in response times as well as incorporate switching probabilities of cognitive processes. Initial parameter recovery and model comparisons of the simulations show that the new modeling might be a promising new method for identifying mixtures and switching behavior of cognitive processes in a cognitive task.