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Parsing Heuristic and Forward Search in First‐Graders' Game‐Play Behavior
Author(s) -
Paz Luciano,
Goldin Andrea P.,
Diuk Carlos,
Sigman Mariano
Publication year - 2015
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12181
Subject(s) - heuristics , monte carlo tree search , computer science , markov decision process , parsing , heuristic , tree (set theory) , artificial intelligence , domain (mathematical analysis) , function (biology) , machine learning , markov process , monte carlo method , mathematics , statistics , mathematical analysis , evolutionary biology , biology , operating system
Seventy‐three children between 6 and 7 years of age were presented with a problem having ambiguous subgoal ordering. Performance in this task showed reliable fingerprints: (a) a non‐monotonic dependence of performance as a function of the distance between the beginning and the end‐states of the problem, (b) very high levels of performance when the first move was correct, and (c) states in which accuracy of the first move was significantly below chance. These features are consistent with a non‐Markov planning agent, with an inherently inertial decision process, and that uses heuristics and partial problem knowledge to plan its actions. We applied a statistical framework to fit and test the quality of a proposed planning model (Monte Carlo Tree Search). Our framework allows us to parse out independent contributions to problem‐solving based on the construction of the value function and on general mechanisms of the search process in the tree of solutions. We show that the latter are correlated with children's performance on an independent measure of planning, while the former is highly domain specific.