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Neural correlates of value-driven spatial orienting.


ABSTRACT: Reward learning has been shown to habitually guide overt spatial attention to specific regions of a scene. However, the neural mechanisms that support this bias are unknown. In the present study, participants learned to orient themselves to a particular quadrant of a scene (a high-value quadrant) to maximize monetary gains. This learning was scene-specific, with the high-value quadrant varying across different scenes. During a subsequent test phase, participants were faster at identifying a target if it appeared in the high-value quadrant (valid), and initial saccades were more likely to be made to the high-value quadrant. fMRI analyses during the test phase revealed learning-dependent priority signals in the caudate tail, superior colliculus, frontal eye field, anterior cingulate cortex, and insula, paralleling findings concerning feature-based, value-driven attention. In addition, ventral regions typically associated with scene selection and spatial information processing, including the hippocampus, parahippocampal gyrus, and temporo-occipital cortex, were also implicated. Taken together, our findings offer new insights into the neural architecture subserving value-driven attention, both extending our understanding of nodes in the attention network previously implicated in feature-based, value-driven attention and identifying a ventral network of brain regions implicated in reward's influence on scene-dependent spatial orienting.

SUBMITTER: Liao MR 

PROVIDER: S-EPMC10524674 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Neural correlates of value-driven spatial orienting.

Liao Ming-Ray MR   Kim Andy J AJ   Anderson Brian A BA  

Psychophysiology 20230512 9


Reward learning has been shown to habitually guide overt spatial attention to specific regions of a scene. However, the neural mechanisms that support this bias are unknown. In the present study, participants learned to orient themselves to a particular quadrant of a scene (a high-value quadrant) to maximize monetary gains. This learning was scene-specific, with the high-value quadrant varying across different scenes. During a subsequent test phase, participants were faster at identifying a targ  ...[more]

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