Technical Program

Paper Detail

Paper: PS-2A.20
Session: Poster Session 2A
Location: Symphony/Overture
Session Time: Friday, September 7, 17:15 - 19:15
Presentation Time:Friday, September 7, 17:15 - 19:15
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: Decision by Sampling Implements Efficient Coding of Psychoeconomic Functions
Manuscript:  Click here to view manuscript
Authors: Rahul Bhui, Samuel J Gershman, Harvard University, United States
Abstract: The theory of decision by sampling (DbS) proposes that an attribute's subjective value is its rank within a sample of attribute values retrieved from memory. This can account for behavioral and neural data demonstrating context dependence beyond classic theories of decision making which assume stable preferences. In this paper, we provide a normative justification for DbS that is based on the principle of efficient coding. The efficient representation of information in a noiseless communication channel is characterized by a uniform response distribution, which the rank transformation implements. However, cognitive limitations imply that decision samples are finite, introducing noise. Efficient coding in a noisy channel requires smoothing of the signal, a principle that leads to a new generalization of DbS. This generalization helps descriptively account for a wider set of behavioral and neural observations, such as linearity in neural tuning curves, and variation in sensitivity to attribute range.