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Rethinking volitional control over task choice in multitask environments: use of a stimulus set selection strategy in

Catherine M Arrington1, Starla M Weaver

  • 1a Department of Psychology , Lehigh University , Bethlehem , PA , USA.

Quarterly Journal of Experimental Psychology (2006)
|October 7, 2014
PubMed
Summary

Subjects may use task-irrelevant locations to guide task selection in multitask settings. This stimulus set selection strategy was observed, particularly when location cues were highly reliable for task switching.

Keywords:
Cognitive controlTask selectionVolitional behaviourVoluntary task switching

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Area of Science:

  • Cognitive Psychology
  • Human-Computer Interaction
  • Neuroscience

Background:

  • Volitional control in multitask environments involves complex task selection strategies.
  • Current models often overlook stimulus-irrelevant dimensions in top-down task control.

Purpose of the Study:

  • To investigate if subjects utilize a "stimulus set selection strategy" by choosing task-irrelevant dimensions (e.g., location) for task guidance.
  • To determine if this strategy operates independently of explicit task intention formation.

Main Methods:

  • Employed a voluntary task switching paradigm with letter and number categorization.
  • Manipulated stimulus availability (stimulus onset asynchrony) and location repetition across 2, 4, or 8 target locations.
  • Analyzed task choice in relation to these factors to infer strategy use.

Main Results:

  • Experiment 1 indicated stimulus set selection strategy use in the 2-location condition, where location reliably predicted the target.
  • Experiment 2 confirmed these findings in a more complex visual environment.
  • Both stimulus availability and location repetition influenced task choice.

Conclusions:

  • Top-down control of task selection can occur without forming a specific task intention.
  • The stimulus set selection strategy, using task-irrelevant cues, is a viable mechanism in voluntary task switching.
  • Findings challenge existing models of task selection in dynamic environments.