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Revised and Neuroimaging-Compatible Versions of the Dual Task Screen

Revised and Neuroimaging-Compatible Versions of the Dual Task Screen

Published on: October 5, 2020

Modeling test learning and dual-task dissociations.

Tobias Johansson¹

¹Department of Psychology, Kristianstad University, 291 88, Kristianstad, Sweden. Tobias.Johansson@hkr.se.

Psychonomic Bulletin & Review

|June 17, 2020

Summary

This summary is machine-generated.

Cognitive psychology often distinguishes automatic and intentional processes. This study proposes test learning and knowledge updates explain task performance, challenging the necessity of this distinction for artificial grammar learning.

Keywords:

Dual task Exemplar model Memory Test learning

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Area of Science:

Cognitive Psychology
Learning and Memory

Background:

The distinction between automatic and intentional processes is central to cognitive psychology but often lacks clear operational definitions.
Previous research, such as Hendricks et al. (2013), used dual-task paradigms to infer these underlying processes in artificial grammar learning.

Purpose of the Study:

To propose and evaluate an alternative explanation for performance dissociations in artificial grammar learning tasks.
To investigate the role of test learning and knowledge updating as mechanisms underlying observed performance differences.

Main Methods:

Formal memory models were implemented to simulate learning and performance.
Models focused on test-phase learning and item similarity, contrasting with automatic/intentional process accounts.

Main Results:

The implemented memory models successfully accounted for the dissociations observed in dual-task studies.
Performance differences were explained by participants updating their knowledge during the test phase, rather than by distinct automatic and intentional systems.

Conclusions:

The findings suggest that performance dissociations in artificial grammar learning can be explained by test learning and similarity.
This provides an alternative to the automatic versus intentional process distinction, offering a more parsimonious explanation for empirical observations.