| Paper: | PS-2A.39 | ||
| Session: | Poster Session 2A | ||
| Location: | H Lichthof | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | Prospective planning and retrospective learning in a large-scale combinatorial game | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
||
| DOI: | https://doi.org/10.32470/CCN.2019.1164-0 | ||
| Authors: | Ionatan Kuperwajs, New York University, United States; Bas van Opheusden, Princeton University, United States; Wei Ji Ma, New York University, United States | ||
| Abstract: | What algorithms do people use to make decisions with future consequences in complex environments? In order to investigate the cognitive processes underlying sequential planning, we collected large-scale behavioral data in a challenging variant of tic-tac-toe. This task is at an intermediate level of complexity, providing rich behavior for which modeling is still tractable. We argue that a data set of this nature is necessary for distinguishing theoretical frameworks for integration between prospective and retrospective decision-making, and show preliminary evidence for the existence of both systems in our task. We outline a computational model based on an intuitive value function and decision tree search to demonstrate that people engage in prospective planning. We then explain discrepancies between the model’s predictions and observed data in early game choices, finding behavioral patterns consistent with retrospective learning. | ||