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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-11-01
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| Series: | Frontiers in Psychology |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2016.01806/full |
| _version_ | 1811268871197294592 |
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| author | James R Schmidt |
| author_facet | James R Schmidt |
| author_sort | James R Schmidt |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T21:30:43Z |
| format | Article |
| id | doaj.art-effc2852ba9e416084a0446df56f8b42 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-12T21:30:43Z |
| publishDate | 2016-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-effc2852ba9e416084a0446df56f8b422022-12-22T03:16:03ZengFrontiers Media S.A.Frontiers in Psychology1664-10782016-11-01710.3389/fpsyg.2016.01806232201Context-specific proportion congruency effects: An episodic learning account and computational modelJames R Schmidt0Ghent UniversityIn 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.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2016.01806/fullAttentionconflict monitoringComputational modellingcontingency learningStroop tasktemporal learning |
| spellingShingle | James R Schmidt Context-specific proportion congruency effects: An episodic learning account and computational model Frontiers in Psychology Attention conflict monitoring Computational modelling contingency learning Stroop task temporal learning |
| title | Context-specific proportion congruency effects: An episodic learning account and computational model |
| title_full | Context-specific proportion congruency effects: An episodic learning account and computational model |
| title_fullStr | Context-specific proportion congruency effects: An episodic learning account and computational model |
| title_full_unstemmed | Context-specific proportion congruency effects: An episodic learning account and computational model |
| title_short | Context-specific proportion congruency effects: An episodic learning account and computational model |
| title_sort | context specific proportion congruency effects an episodic learning account and computational model |
| topic | Attention conflict monitoring Computational modelling contingency learning Stroop task temporal learning |
| url | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2016.01806/full |
| work_keys_str_mv | AT jamesrschmidt contextspecificproportioncongruencyeffectsanepisodiclearningaccountandcomputationalmodel |