Task specificity of attention training: the case of probability cuing

Yuhong V. Jiang, Khena M. Swallow, Bo Yeong Won, Julia D. Cistera, Gail M. Rosenbaum

    Research output: Contribution to journalArticlepeer-review

    32 Scopus citations


    Statistical regularities in our environment enhance perception and modulate the allocation of spatial attention. Surprisingly little is known about how learning-induced changes in spatial attention transfer across tasks. In this study, we investigated whether a spatial attentional bias learned in one task transfers to another. Most of the experiments began with a training phase in which a search target was more likely to be located in one quadrant of the screen than in the other quadrants. An attentional bias toward the high-probability quadrant developed during training (probability cuing). In a subsequent, testing phase, the target’s location distribution became random. In addition, the training and testing phases were based on different tasks. Probability cuing did not transfer between visual search and a foraging-like task. However, it did transfer between various types of visual search tasks that differed in stimuli and difficulty. These data suggest that different visual search tasks share a common and transferrable learned attentional bias. However, this bias is not shared by high-level, decision-making tasks such as foraging.

    Original languageEnglish (US)
    Pages (from-to)50-66
    Number of pages17
    JournalAttention, Perception, and Psychophysics
    Issue number1
    StatePublished - 2014

    Bibliographical note

    Funding Information:
    This study was supported in part by NIH Grant No. MH102586. We thank Jeremy Wolfe and Roger Remington for discussions, and Anthony Asaad, Lily Berrin, Christian Capistrano, Youngki Hong, Hyejin Lee, Jie Hua Ong, and Heather Sigstad for help with data collection.

    Publisher Copyright:
    © 2014, The Psychonomic Society, Inc.


    • Incidental learning
    • Probability cuing
    • Spatial attention
    • Visual search


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