Dataset for: Response Selection Can Feed Back on Task Selection Through Episodic Retrieval

DOI

This is the project folder of the manuscript Response Selection Can Feed Back on Task Selection Through Episodic Retrieval. Abstract: Goal-directed behaviour is thought to subsume integration, or binding, of perceptual and action features. In task-switching settings, this entails forming a task–response binding in each trial that can then be retrieved in the following trial. Accordingly, repeating the same response in a trial supposedly retrieves the previously relevant task (the N−1 task). In task switches, the retrieved task mismatches with the current task, which causes costs for response repetitions in task-switch trials (RR costs). In the present study (two re-analyses of published data: N = 255, N = 39, and two new experiments: Ns = 96 each), we tested such a binding and retrieval account of the RR costs by isolating specific task confusion errors, namely the erroneous re-application of the N−1 task. Coupled with the use of Multinomial Processing Tree (MPT) models, we could test the prediction, unique to the binding account of RR costs, that selecting a repeating response triggers retrieval of the N−1 task. Coherent with this prediction, the MPT model results showed a larger probability of selecting the N−1 task when the response should be repeated compared to switched. These results challenge strict feedforward processing flowing from task selection to response selection. In fact, selecting a repeating response may divert task selection from the N task towards the N−1 task, via retrieval of the bound N−1 task. Taken together, this study provides novel evidence for episodic retrieval in task switching while specifying the interplay of task and response selection.

Identifier
DOI https://doi.org/10.23668/psycharchives.21164
Metadata Access https://api.datacite.org/dois/10.23668/psycharchives.21164
Provenance
Creator Benini, Elena
Publisher PsychArchives
Contributor Leibniz Institut für Psychologie (ZPID)
Publication Year 2025
OpenAccess true
Representation
Language English
Resource Type Dataset; researchData
Discipline Social Sciences