In the first phase, we developed the Hidden Multivariate Pattern (HMP) model—a method that reliably detects cognitive events on a single-trial basis. The framework combines multivariate sensor patterns with probabilistic timing models to infer latent cognitive events. HMP was validated on both simulated and open datasets. The results were published in a peer-reviewed article (Trial-by-trial detection of cognitive events in neural time-series, Weindel, van Maanen & Borst, 2025).
The second work package focused on decomposing decision-making into its constituent cognitive processes. Two experiments were conducted with around thirty participants each. The first study was published in eLife as a reviewed preprint (Decision-making components and times revealed by the single-trial electro-encephalogram, Weindel, Borst & van Maanen). This study showed that we can decompose the reaction time in a decision-making task at the trial level into different periods of decision processing. These periods were then used to fit evidence accumulation models, improving parameter estimation beyond behavioral data alone. The second dataset, in preparation, builds on this first study to extend leading decision models, the evidence accumulation models.
The third work package aimed to integrate decision models beyond evidence accumulation models, e.g. guessing, evidence accumulation, and delayed decision,. However, the EEG data did not yield sufficiently distinct neural signatures for these strategies. As a result, the project pivoted to focus on strengthening and generalizing the HMP framework. This pivot preserved the core scientific contribution and led to a tool already being adopted by several European research groups.
