2018 Conference on Cognitive Computational Neuroscience

5-8 September 2018    Philadelphia, Pennsylvania

Technical Program

Paper Detail

Paper: PS-1B.20
Session: Poster Session 1B
Location: Symphony/Overture
Session Time: Thursday, September 6, 18:45 - 20:45
Presentation Time:Thursday, September 6, 18:45 - 20:45
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: Memory mechanisms predict sampling biases in sequential decision tasks
Manuscript:  Click here to view manuscript
DOI: https://doi.org/10.32470/CCN.2018.1164-0
Authors: Marcelo Mattar, Princeton University, United States; Deborah Talmi, University of Manchester, United Kingdom; Nathaniel Daw, Princeton University, United States
Abstract: Good decisions are informed by past experience. Accordingly, models of memory encoding and retrieval can shed light on the evaluation processes underlying choice. In one classic memory model aimed at explaining biases in free recall, known as the temporal context model (TCM), a drifting temporal context serves as a cue for retrieving previously encoded items. The associations built by this model share a number of similarities to the successor representation (SR) -- a particular type of world model used in reinforcement learning to capture the long-run consequences of actions. Here, we show how decision variables may be constructed by retrieval in the TCM, corresponding to drawing samples from the SR. Since the SR and TCM encode long-term sequential relationships, this provides a mechanistic, process level model for evaluating candidate actions in sequential, multi-step tasks, connecting them to the details of memory encoding and retrieval. This framework reveals three ways in which the phenomenology of memory predict novel choice biases that are counterintuitive from a decision perspective: the effects of emotion, of sequential retrieval, and of backward reactivation. The suggestion that the brain employs an efficient sampling algorithm to rapidly compute decision variables offers a normative view on decision biases, explains patterns of memory retrieval during deliberation, and may shed light on psychiatric disorders such as rumination and craving.