The Discovery and Interpretation of Evidence Accumulation Stages

Leendert Van Maanen,Jelmer P Borst,Oscar Portoles

doi:10.1007/s42113-021-00105-2

Leendert Van Maanen, Jelmer P Borst + Show 1 more

Open Access

https://doi.org/10.1007/s42113-021-00105-2

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Abstract

To improve the understanding of cognitive processing stages, we combined two prominent traditions in cognitive science: evidence accumulation models and stage discovery methods. While evidence accumulation models have been applied to a wide variety of tasks, they are limited to tasks in which decision-making effects can be attributed to a single processing stage. Here, we propose a new method that first uses machine learning to discover processing stages in EEG data and then applies evidence accumulation models to characterize the duration effects in the identified stages. To evaluate this method, we applied it to a previously published associative recognition task (Application 1) and a previously published random dot motion task with a speed-accuracy trade-off manipulation (Application 2). In both applications, the evidence accumulation models accounted better for the data when we first applied the stage-discovery method, and the resulting parameter estimates where generally in line with psychological theories. In addition, in Application 1 the results shed new light on target-foil effects in associative recognition, while in Application 2 the stage discovery method identified an additional stage in the accuracy-focused condition — challenging standard evidence accumulation accounts. We conclude that the new framework provides a powerful new tool to investigate processing stages.

Highlights

Evidence accumulation is often seen as a basic cognitive mechanism
For each dataset we describe its methodological specificities and results regarding the hidden semi-Markov models (HsMMs)-MVPA and the diffusion decision model (DDM), followed by a discussion of its results
Oscillations become briefly synchronized, leading to a similar peak in the EEG signal as predicted by the classical theory. Both theories link the onset of a cognitive process to a peak in the EEG signal; such peaks typically have low signal-to-noise and can only be observed when one averages across trials. Given such a pattern of EEG activity, the HsMM-MVPA method assumes that a processing stage starts with peaks with a consistent topology across trials which are followed by zero mean amplitude throughout the remainder of the stage

Summary

Introduction

Evidence accumulation is often seen as a basic cognitive mechanism. Evidence accumulation entails that before executing an action, cognitive agents (humans or animals) accrue evidence for the appropriateness of that action, until a certain criterion amount of evidence is collected. At that point in time, the action is initiated. This action is considered an explicit motor action — a key press — but it could be an internal decision leading to the step in a sequence of cognitive processes. The idea of evidence accumulation has been applied to explain a variety of cognitive processes. Recognition memory — that is, answering the question whether we have encountered something before — can be described as a process in which people compare a new stimulus to representations in memory.

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