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Attention can be characterized as a set of computations that optimize information gathering driven by observer uncertainty. Top-down attentional control allocates cognitive resources to optimize expected information gain (EIG). Here I develop a theory in which brain structures that respond to uncertainty, such as the anterior cingulate cortex, guide top-down selection in prefrontal cortex to modulate competitive mechanisms in visual-spatial maps. This modulation may be mediated by neurotransmitter systems such as the noradrenergic system, which originates in the locus coeruleus. Thus, a computational theory of attention can be used to make quantitative predictions about behavior and neural circuitry.

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Linking Attention with Goals: A Theory of Attentional Priority Based on Expected Information Gains

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  • 发表日期：

    2025年

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  • Jacqueline Gottlieb

摘要

Attention can be characterized as a set of computations that optimize information gathering driven by observer uncertainty. Top-down attentional control allocates cognitive resources to optimize expected information gain (EIG). Here I develop a theory in which brain structures that respond to uncertainty, such as the anterior cingulate cortex, guide top-down selection in prefrontal cortex to modulate competitive mechanisms in visual-spatial maps. This modulation may be mediated by neurotransmitter systems such as the noradrenergic system, which originates in the locus coeruleus. Thus, a computational theory of attention can be used to make quantitative predictions about behavior and neural circuitry.