The Discovery and Interpretation of Evidence Accumulation Stages

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.

Uncovering the Cognitive Mechanisms Underlying the Gaze Cueing Effect

“…Additionally, the processes described in these models map onto neural activity observed during decision-making tasks (e.g. Brosnan et al, 2020;Gold & Shadlen, 2007;Liu & Pleskac, 2011;van Maanen et al, 2021), acting as convincing convergent evidence supporting the theoretical assumptions of these models and their explanations of decision-making processes. Given that these models provide the dominant theoretical account of decision-making to date, it is important that we understand the gaze cueing effect through the lens of formal evidence accumulation theories of decision-making.…”

Trial-by-trial detection of cognitive events in neural time-series

“…Simulations (see Section 3.1) and application to real data (see Section 2.4) show that this sequential estimation procedure performs well in recovering the exact number, location, and magnitudes of underlying events. If necessary, this can be followed by an LOOCV procedure, for example to test which events generalize over subjects (Anderson et al, 2016;Van Maanen et al, 2021).…”

“…By explicitly assuming a linking function (Schall, 2019) between the detected EEG/MEG events and cognitive processes, researchers can then interpret the resulting HsMM-MVPA solution as the sequence of cognitive operations that happened on each trial. This method has been successfully applied to a number of tasks, ranging from simple perceptual (Van Maanen et al, 2021) or lexical decision tasks (Berberyan et al, 2021) to more complex tasks, such as visual working memory (Zhang et al, 2018), associative recognition (Anderson et al, 2016;Portoles et al, 2022), and mathematical problem solving (Anderson et al, 2018;Groeneweg et al, 2021). Each of those studies provided new insights on the nature and latencies of cognitive events of the analyzed tasks.…”

Individual differences in working memory impact the trajectory of non-native speech category learning

“…These results are in line with prior work that demonstrates that evidence accumulation rates are linked to individual differences in WM (Ester et al, 2014;Schmeidek et al, 2007). Faster, more efficient evidence accumulation rates reflect higher motivation (Hübner & Schlösser, 2010), faster mobilization of attentional resources (Sarter et al, 2006), and lower task difficulty (Gold & Shadlen, 2007;Mulder et al, 2014;O'Connell et al, 2012;van Maanen et al, 2016van Maanen et al, , 2021.…”