David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Cognitive Science 36 (2):333-358 (2012)
Reinforcement learning approaches to cognitive modeling represent task acquisition as learning to choose the sequence of steps that accomplishes the task while maximizing a reward. However, an apparently unrecognized problem for modelers is choosing when, what, and how much to reward; that is, when (the moment: end of trial, subtask, or some other interval of task performance), what (the objective function: e.g., performance time or performance accuracy), and how much (the magnitude: with binary, categorical, or continuous values). In this article, we explore the problem space of these three parameters in the context of a task whose completion entails some combination of 36 state–action pairs, where all intermediate states (i.e., after the initial state and prior to the end state) represent progressive but partial completion of the task. Different choices produce profoundly different learning paths and outcomes, with the strongest effect for moment. Unfortunately, there is little discussion in the literature of the effect of such choices. This absence is disappointing, as the choice of when, what, and how much needs to be made by a modeler for every learning model
|Keywords||Adaptive behavior Reinforcement learning Strategy selection Expected utility Skill acquisition and learning Choice Expected value Cognitive architecture|
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References found in this work BETA
Woo‐Young Ahn, Jerome R. Busemeyer, Eric‐Jan Wagenmakers & Julie C. Stout (2008). Comparison of Decision Learning Models Using the Generalization Criterion Method. Cognitive Science 32 (8):1376-1402.
Dana H. Ballard, Mary M. Hayhoe, Polly K. Pook & Rajesh P. N. Rao (1997). Deictic Codes for the Embodiment of Cognition. Behavioral and Brain Sciences 20 (4):723-742.
Matthew M. Botvinick, Yael Niv & Andrew C. Barto (2009). Hierarchically Organized Behavior and its Neural Foundations: A Reinforcement Learning Perspective. Cognition 113 (3):262-280.
Nathaniel D. Daw & Michael J. Frank (2009). Reinforcement Learning and Higher Level Cognition: Introduction to Special Issue. Cognition 113 (3):259-261.
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