Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 118, Issue 43, Pages -Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2106237118j1of9
Keywords
risk taking; physiological foundation of behavior; neural valuation systems; neuroeconomics
Categories
Funding
- Swiss NSF [PDFMP1-123113/1]
- National Centre of Competence in Research Affective Sciences [CRSII3_141965, PP00P1_128574, PP00P1_150739, CRSII5_177277, 100014_165884, 10001C_188878]
- University of Zurich Grant Office [FK-16-016, FK-19-020]
- Swiss National Science Foundation (SNF) [10001C_188878, PDFMP1-123113, CRSII5_177277] Funding Source: Swiss National Science Foundation (SNF)
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Decisions are made based on the subjective values of choice options, which are theoretical constructs. Different theoretical models competing to explain how subjective values are assigned to choice options often make similar behavioral predictions, posing a challenge for establishing a biologically plausible explanation of decision-making based on behavior alone. Research demonstrates that model comparison at the neural level can provide insights into how cognitive processes are implemented.
Decisions are based on the subjective values of choice options. However, subjective value is a theoretical construct and not directly observable. Strikingly, distinct theoretical models competing to explain how subjective values are assigned to choice options often make very similar behavioral predictions, which poses a major difficulty for establishing a mechanistic, biologically plausible explanation of decision-making based on behavior alone. Here, we demonstrate that model comparison at the neural level provides insights into model implementation during subjective value computation even though the distinct models parametrically identify common brain regions as computing subjective value. We show that frontal cortical regions implement a model based on the statistical distributions of available rewards, whereas intraparietal cortex and striatum compute subjective value signals according to a model based on distortions in the representations of probabilities. Thus, better mechanistic understanding of how cognitive processes are implemented arises from model comparisons at the neural level, over and above the traditional approach of comparing models at the behavioral level alone.
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