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A model-based explanation of performance related changes in abstract stimulus-response learning

2019

Conference Paper

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Stimulus-response learning constitutes an important part of human experience over the life course. Independent of the domain, it is characterized by changes in performance with increasing task progress. But what cognitive mechanisms are responsible for these changes and how do additional task requirements affect the related dynamics? To inspect that in more detail, we introduce a computational modeling approach that investigates performance-related changes in learning situations with reference to chunk activation patterns. It leverages the cognitive architecture ACT-R to model learner behavior in abstract stimulus-response learning in two conditions of task complexity. Additional situational demands are reflected in embedded secondary tasks that interrupt participants during the learning process. Our models apply an activation equation that also takes into account the association between related nodes of information and the similarity between potential responses. Model comparisons with two human datasets (N = 116 and N = 123 participants) indicate a good fit in terms of both accuracy and reaction times. Based on the existing neurophysiological mapping of ACT-R modules on defined human brain areas, we convolve recorded module activity into simulated BOLD responses to investigate underlying cognitive mechanisms in more detail. The resulting evidence supports the connection of learning effects in both task conditions with activation-related patterns to explain changes in performance.

Author(s): Wirzberger, Maria and Borst, Jelmer P. and Krems, Josef F. and Rey, Günter Daniel
Year: 2019
Month: July

Department(s): Rationality Enhancement
Bibtex Type: Conference Paper (conference)
Paper Type: Abstract

Event Name: 52nd Annual Meeting of the Society for Mathematical Psychology
Event Place: Montréal

BibTex

@conference{Wirzberger2019MathPsych,
  title = {A model-based explanation of performance related changes in abstract stimulus-response learning},
  author = {Wirzberger, Maria and Borst, Jelmer P. and Krems, Josef F. and Rey, G{\"u}nter Daniel},
  month = jul,
  year = {2019},
  month_numeric = {7}
}