Context-specific proportion congruency effects: An episodic learning account and computational model

In the Stroop task, participants identify the print colour of colour words. The congruency effect is the observation that response times and errors are increased when the word and colour are incongruent (e.g., the word red in green ink) relative to when they are congruent (e.g., red in red). The pro...

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Bibliographic Details
Main Author: James R Schmidt
Format: Article
Language:English
Published: Frontiers Media S.A. 2016-11-01
Series:Frontiers in Psychology
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fpsyg.2016.01806/full
Description
Summary:In the Stroop task, participants identify the print colour of colour words. The congruency effect is the observation that response times and errors are increased when the word and colour are incongruent (e.g., the word red in green ink) relative to when they are congruent (e.g., red in red). The proportion congruent effect is the finding that congruency effects are reduced when trials are mostly incongruent rather than mostly congruent. This proportion congruent effect can be context-specific. For instance, if trials are mostly incongruent when presented in one location and mostly congruent when presented in another location, the congruency effect is smaller for the former location. Typically, proportion congruent effects are interpreted in terms of strategic control of attention in response to conflict, termed conflict adaptation or conflict monitoring. In the present manuscript, however, an episodic learning account is presented for context-specific proportion congruent effects. In particular, it is argued that context-specific contingency learning can explain part of the effect, and context-specific rhythmic responding can explain the rest. Both contingency-based and temporal-based learning can parsimoniously be conceptualized within an episodic learning framework. An adaptation of the Parallel Episodic Processing (PEP) model is presented. This model successfully simulates context-specific proportion congruent effects, both for contingency-biased and contingency-unbiased (transfer) items. The same fixed-parameter model can explain a range of other findings from the learning, timing, binding, practice, and attentional control domains.
ISSN:1664-1078