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Journal Article

Hidden Markov models of evidence accumulation in speeded decision tasks


Tran,  N.-Han       
Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;
The Leipzig School of Human Origins (IMPRS), Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Kucharský, Š., Tran, N.-H., Veldkamp, K., Raijmakers, M., & Visser, I. (2021). Hidden Markov models of evidence accumulation in speeded decision tasks. Computational Brain & Behavior, 4, 416-441. doi:10.1007/s42113-021-00115-0.

Cite as: https://hdl.handle.net/21.11116/0000-0007-F4E8-0
Speeded decision tasks are usually modeled within the evidence accumulation framework, enabling inferences on latent cognitive parameters, and capturing dependencies between the observed response times and accuracy. An example is the speed-accuracy trade-off, where people sacrifice speed for accuracy (or vice versa). Different views on this phenomenon lead to the idea that participants may not be able to control this trade-off on a continuum, but rather switch between distinct states (Dutilh, et al., 2010).

Hidden Markov models are used to account for switching between distinct states. However, combining evidence accumulation models with a hidden Markov structure is a challenging problem, as evidence accumulation models typically come with identification and computational issues that make them challenging on their own. Thus, hidden Markov models have not used the evidence accumulation framework, giving up on the inference on the latent cognitive parameters, or capturing potential dependencies between response times and accuracy within the states.

This article presents a model that uses an evidence accumulation model as part of a hidden Markov structure. This model is considered as a proof of principle that evidence accumulation models can be combined with Markov switching models. As such, the article considers a very simple case of a simplified Linear Ballistic Accumulation. An extensive simulation study was conducted to validate the model's implementation according to principles of robust Bayesian workflow. Example reanalysis of data from Dutilh, et al. (2010) demonstrates the application of the new model. The article concludes with limitations and future extensions or alternatives to the model and its application.