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Is Task-Irrelevant Learning Really Task-Irrelevant?

Overview of attention for article published in PLOS ONE, November 2008
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Title
Is Task-Irrelevant Learning Really Task-Irrelevant?
Published in
PLOS ONE, November 2008
DOI 10.1371/journal.pone.0003792
Pubmed ID
Authors

Aaron R. Seitz, Takeo Watanabe

Abstract

In the present study we address the question of whether the learning of task-irrelevant stimuli found in the paradigm of task-irrelevant learning (TIPL) [1]-[9] is truly task irrelevant. To test the hypothesis that associations that are beneficial to task-performance may develop between the task-relevant and task-irrelevant stimuli, or the task-responses and the task-irrelevant stimuli, we designed a new procedure in which correlations between the presentation of task-irrelevant motion stimuli and the identity of task-targets or task-responses were manipulated. We found no evidence for associations developing between the learned (task-irrelevant) motion stimuli and the targets or responses to the letter identification task used during training. Furthermore, the conditions that had the greatest correlations between stimulus and response showed the least amount of TIPL. On the other hand, TIPL was found in conditions of greatest response uncertainty and with the greatest processing requirements for the task-relevant stimuli. This is in line with our previously published model that suggests that task-irrelevant stimuli benefit from the spill-over of learning signals that are released due to processing of task-relevant stimuli.

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Geographical breakdown

Country Count As %
United States 3 4%
United Kingdom 2 2%
China 2 2%
Switzerland 1 1%
Canada 1 1%
Unknown 72 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 26%
Researcher 16 20%
Student > Master 9 11%
Lecturer 7 9%
Professor > Associate Professor 5 6%
Other 15 19%
Unknown 8 10%
Readers by discipline Count As %
Psychology 42 52%
Agricultural and Biological Sciences 8 10%
Neuroscience 7 9%
Medicine and Dentistry 5 6%
Computer Science 2 2%
Other 8 10%
Unknown 9 11%