title: Context-Specific Proportion Congruency Effects: An Episodic Learning Account and Computational Model.
abstract: In the Stroop task, participants identify the print color of color words. The congruency effect is the observation that response times and errors are increased when the word and color are incongruent (e.g., the word "red" in green ink) relative to when they are congruent (e.g., "red" in red). The proportion congruent (PC) effect is the finding that congruency effects are reduced when trials are mostly incongruent rather than mostly congruent. This PC 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, PC 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 (CSPC) 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 model is presented. This model successfully simulates CSPC 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.
authors.affiliation: Department of Experimental Clinical and Health Psychology, Ghent University Gent, Belgium.
authors.forename: James R
authors.initials: JR
authors.lastname: Schmidt
keywords: Stroop task; attention; computational modeling; conflict monitoring; context-specificity; contingency learning; proportion congruent effect; temporal learning