Evidence map›Paper›PMID 32964181›Full record

ArticleJournal of cognition2020

An Episodic Model of Task Switching Effects: Erasing the Homunculus from Memory.

James R Schmidt, Baptist Liefooghe, Jan De Houwer

Abstract read
In one paragraph

Article in Journal of cognition, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
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  6. Review
  7. Binding of response-independent task rules.Psychonomic bulletin & review · 2024
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Automatic effects of covert practice.Quarterly journal of experimental psychology (2006) · 2021
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Simple Control.Journal of cognition · 2020
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

James R SchmidtLEAD-CNRS UMR 5022, Université Bourgogne Franche-Comté (UBFC), FR.ORCID 0000-0002-01412-396X
Baptist LiefoogheDepartment of Experimental Clinical and Health Psychology, Ghent University, BE.ORCID 0000-0001-5896-2884
Jan De HouwerDepartment of Experimental Clinical and Health Psychology, Ghent University, BE.ORCID 0000-0003-0488-5224

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Parallel Episodic Processing (PEP) model is a neural network for simulating human performance 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.

Indexed as

bindingcomputational modellingepisodic memoryfeature integrationgoalsinstruction implementationneural networksswitch coststask-rule congruency

Identifiers

PMID32964181
PMCPMC7485406

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.