Dataset Information

Neural representations of relevant and irrelevant features in perceptual decision making.

ABSTRACT: Although perceptual decision making activates a network of brain areas involved in sensory, integrative, and motor functions, circuit activity can clearly be modulated by factors beyond the stimulus. Of particular interest is to understand how the network is modulated by top-down factors such as attention. Here, we demonstrate in a motion coherence task that selective attention produces marked changes in the blood oxygen level-dependent (BOLD) response in a subset of regions within a human perceptual decision-making circuit. Specifically, when motion is attended, the BOLD response decreases with increasing motion coherence in many regions, including the motion-sensitive area MT+, the intraparietal sulcus, and the inferior frontal sulcus. However, when motion is ignored, the negative parametric response in a subset of this circuit becomes positive. Through both modeling and connectivity analyses, we demonstrate that this inversion both reflects a top-down influence and segregates attentional from accumulation regions, thereby permitting us to further delineate the contributions of different regions to the perceptual decision.

SUBMITTER: Kayser AS

PROVIDER: S-EPMC3020592 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Neural representations of relevant and irrelevant features in perceptual decision making.

Kayser Andrew S AS Erickson Drew T DT Buchsbaum Bradley R BR D'Esposito Mark M

The Journal of neuroscience : the official journal of the Society for Neuroscience 20101101 47

Although perceptual decision making activates a network of brain areas involved in sensory, integrative, and motor functions, circuit activity can clearly be modulated by factors beyond the stimulus. Of particular interest is to understand how the network is modulated by top-down factors such as attention. Here, we demonstrate in a motion coherence task that selective attention produces marked changes in the blood oxygen level-dependent (BOLD) response in a subset of regions within a human perce ...[more]

PMID: 21106817

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Project description:Many real-world situations require navigating decisions for both reward and threat. While there has been significant progress in understanding mechanisms of decision-making and mediating neurocircuitry separately for reward and threat, there is limited understanding of situations where reward and threat contingencies compete to create approach-avoidance conflict (AAC). Here, we leverage computational learning models, independent component analysis (ICA), and multivariate pattern analysis (MVPA) approaches to understand decision-making during a novel task that embeds concurrent reward and threat learning and manipulates congruency between reward and threat probabilities. Computational modeling supported a modified reinforcement learning model where participants integrated reward and threat value into a combined total value according to an individually varying policy parameter, which was highly predictive of decisions to approach reward vs avoid threat during trials where the highest reward option was also the highest threat option (i.e., approach-avoidance conflict). ICA analyses demonstrated unique roles for salience, frontoparietal, medial prefrontal, and inferior frontal networks in differential encoding of reward vs threat prediction error and value signals. The left frontoparietal network uniquely encoded degree of conflict between reward and threat value at the time of choice. MVPA demonstrated that delivery of reward and threat could accurately be decoded within salience and inferior frontal networks, respectively, and that decisions to approach reward vs avoid threat were predicted by the relative degree to which these reward vs threat representations were active at the time of choice. This latter result suggests that navigating AAC decisions involves generating mental representations for possible decision outcomes, and relative activation of these representations may bias subsequent decision-making towards approaching reward or avoiding threat accordingly.

Dataset Information

Neural representations of relevant and irrelevant features in perceptual decision making.

Publications

Neural representations of relevant and irrelevant features in perceptual decision making.

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