An Episodic Model of Task Switching Effects: Erasing the Homunculus from Memory
The Parallel Episodic Processing (PEP) model is a neural network for simulating human performance<br />in speeded response time tasks. It learns with an exemplar-based memory store and it is capable of modelling findings from various subdomains of cognition. In this paper, we show how the PEP...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ubiquity Press
2020-09-01
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| Series: | Journal of Cognition |
| Subjects: | |
| Online Access: | https://www.journalofcognition.org/articles/97 |
| _version_ | 1818139822460502016 |
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| author | James R. Schmidt Baptist Liefooghe Jan De Houwer |
| author_facet | James R. Schmidt Baptist Liefooghe Jan De Houwer |
| author_sort | James R. Schmidt |
| collection | DOAJ |
| description | The Parallel Episodic Processing (PEP) model is a neural network for simulating human performance<br />in speeded response time tasks. It learns with an exemplar-based memory store and it is capable of modelling findings from various subdomains of cognition. In this paper, we show how the PEP model can be designed to follow instructions (e.g., task rules and goals). The extended PEP model is then used to simulate a number of key findings from the task switching domain. These include the switch cost, task-rule congruency effects, response repetition asymmetries, cue repetition benefits, and the full pattern of means from a recent feature integration decomposition of cued task switching (Schmidt & Liefooghe, 2016). We demonstrate that the PEP model fits the participant data well, that the model does not possess the flexibility to match any pattern of results, and that a number of competing task switching models fail to account for key observations that the PEP model produces naturally. Given the parsimony and unique explanatory power of the episodic account presented here, our results suggest that feature-integration biases have a far greater power in explaining task-switching performance than previously assumed. |
| first_indexed | 2024-12-11T10:34:12Z |
| format | Article |
| id | doaj.art-d4b0f0e2a4664b268c4b2e82e8bb2cad |
| institution | Directory Open Access Journal |
| issn | 2514-4820 |
| language | English |
| last_indexed | 2024-12-11T10:34:12Z |
| publishDate | 2020-09-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | Journal of Cognition |
| spelling | doaj.art-d4b0f0e2a4664b268c4b2e82e8bb2cad2022-12-22T01:10:46ZengUbiquity PressJournal of Cognition2514-48202020-09-013110.5334/joc.97131An Episodic Model of Task Switching Effects: Erasing the Homunculus from MemoryJames R. Schmidt0Baptist Liefooghe1Jan De Houwer2LEAD-CNRS UMR 5022, Université Bourgogne Franche-Comté (UBFC), FR; Department of Experimental Clinical and Health Psychology, Ghent UniversityDepartment of Experimental Clinical and Health Psychology, Ghent University, BE; Department of Social, Health and Organizational Psychology, Utrecht UniversityDepartment of Experimental Clinical and Health Psychology, Ghent UniversityThe Parallel Episodic Processing (PEP) model is a neural network for simulating human performance<br />in speeded response time tasks. It learns with an exemplar-based memory store and it is capable of modelling findings from various subdomains of cognition. In this paper, we show how the PEP model can be designed to follow instructions (e.g., task rules and goals). The extended PEP model is then used to simulate a number of key findings from the task switching domain. These include the switch cost, task-rule congruency effects, response repetition asymmetries, cue repetition benefits, and the full pattern of means from a recent feature integration decomposition of cued task switching (Schmidt & Liefooghe, 2016). We demonstrate that the PEP model fits the participant data well, that the model does not possess the flexibility to match any pattern of results, and that a number of competing task switching models fail to account for key observations that the PEP model produces naturally. Given the parsimony and unique explanatory power of the episodic account presented here, our results suggest that feature-integration biases have a far greater power in explaining task-switching performance than previously assumed.https://www.journalofcognition.org/articles/97computational modellingneural networksepisodic memorybindingswitch costsfeature integrationtask-rule congruencyinstruction implementationgoals |
| spellingShingle | James R. Schmidt Baptist Liefooghe Jan De Houwer An Episodic Model of Task Switching Effects: Erasing the Homunculus from Memory Journal of Cognition computational modelling neural networks episodic memory binding switch costs feature integration task-rule congruency instruction implementation goals |
| title | An Episodic Model of Task Switching Effects: Erasing the Homunculus from Memory |
| title_full | An Episodic Model of Task Switching Effects: Erasing the Homunculus from Memory |
| title_fullStr | An Episodic Model of Task Switching Effects: Erasing the Homunculus from Memory |
| title_full_unstemmed | An Episodic Model of Task Switching Effects: Erasing the Homunculus from Memory |
| title_short | An Episodic Model of Task Switching Effects: Erasing the Homunculus from Memory |
| title_sort | episodic model of task switching effects erasing the homunculus from memory |
| topic | computational modelling neural networks episodic memory binding switch costs feature integration task-rule congruency instruction implementation goals |
| url | https://www.journalofcognition.org/articles/97 |
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