4.7 Article

BOLD Subjective Value Signals Exhibit Robust Range Adaptation

期刊

JOURNAL OF NEUROSCIENCE
卷 34, 期 49, 页码 16533-16543

出版社

SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.3927-14.2014

关键词

adaptive coding; decision making; fMRI; value

资金

  1. National Institutes of Health/National Institute on Drug Abuse [R01 DA029149]

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Many theories of decision making assume that choice options are assessed along a common subjective value (SV) scale. The neural correlates of SV are widespread and reliable, despite the wide variation in the range of values over which decisions are made (e.g., between goods worth a few dollars, in some cases, or hundreds of dollars, in others). According to adaptive coding theories (Barlow, 1961), an efficient value signal should exhibit range adaptation, such that neural activity maintains a fixed dynamic range, and the slope of the value response varies inversely with the range of values within the local context. Although monkey data have demonstrated range adaptation in single-unit correlates of value (Padoa-Schioppa, 2009; Kobayashi et al., 2010), whether BOLD value signals exhibit similar range adaptation is unknown. To test for this possibility, we presented human participants with choices between a fixed immediate and variable delayed payment options. Across two conditions, the delayed options' SVs spanned either a narrow or wide range. SV-tracking activity emerged in the posterior cingulate, ventral striatum, anterior cingulate, and ventromedial prefrontal cortex. Throughout this network, we observed evidence consistent with the predictions of range adaptation: the SV response slope increased in the narrow versus wide range, with statistically significant slope changes confirmed for the posterior cingulate and ventral striatum. No regions exhibited a reliably increased BOLD activity range in the wide versus narrow condition. Our observations of range adaptation present implications for the interpretation of BOLD SV responses that are measured across different contexts or individuals.

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