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{ "pk": 29222, "title": "Predicting human decisions in a sequential planning puzzle with a large state space", "subtitle": null, "abstract": "We study human sequential decision-making in large state spaces using a puzzle game called Rush Hour. A puzzle consistsof a dense configuration of rectangular cars on a 6x6 grid. Each car moves only horizontally or vertically. The goal isto move a target car to an exit. In a given state (board position), a subject (n=86) could move a car, restart the puzzle,or surrender. A move is correct if it reduces the distance (number of moves) to the goal. Using mixed-effects logisticregression modeling, we find that the probabilities of an error, a restart, and a surrender are higher with a longer distanceto goal, higher mobility, and when the previous move was an error. The effects of distance to goal and mobility areconsistent with tree search. As a next step, we plan to investigate the heuristics that people might use for such tree search.", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [], "section": "Member Abstracts", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/2nx113qk", "frozenauthors": [ { "first_name": "Yichen", "middle_name": "", "last_name": "Li", "name_suffix": "", "institution": "New York University", "department": "" }, { "first_name": "Zahy", "middle_name": "", "last_name": "Bnaya", "name_suffix": "", "institution": "New York University", "department": "" }, { "first_name": "Wei", "middle_name": "Ji", "last_name": "Ma", "name_suffix": "", "institution": "New York University", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2019-01-01T13:00:00-05:00", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/29222/galley/19093/download/" } ] }