Although current state-of-the-art evidence accumulation models are excellent predictors of behavior that is determined by single decisions, they cannot be used to investigate multiple sequential decisions. It therefore remains unclear how latent decision processes influence subsequent cognitive processes and decisions, and ultimately overt behavior. This has resulted in a lack of understanding of behavior that involves a sequence of decisions – which is imperative, as this is a situation that occurs almost immediately when addressing slightly more complex laboratory tasks, let alone when leaving the lab to investigate real-life situations.

To address this issue, the lab has joined forces with the University of Groningen to develop a novel event detection method, that uses multivariate neural time series to identify the most likely points in time for a cognitively salient event. 

The Python package can be found here; a preprint of the paper that introduces the method can be found here.

Involved researchers and collaborators

Leendert van Maanen

Jelmer Borst (University of Groningen)

Mael Lebreton (Paris School of Economics)

Gabriel Weindel

Joaquina Couto

Rick den Otter