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  1. Rationalizable Irrationalities of Choice.Peter Dayan - 2014 - Topics in Cognitive Science 6 (2):204-228.
    Although seemingly irrational choice abounds, the rules governing these mis‐steps that might provide hints about the factors limiting normative behavior are unclear. We consider three experimental tasks, which probe different aspects of non‐normative choice under uncertainty. We argue for systematic statistical, algorithmic, and implementational sources of irrationality, including incomplete evaluation of long‐run future utilities, Pavlovian actions, and habits, together with computational and statistical noise and uncertainty. We suggest structural and functional adaptations that minimize their maladaptive effects.
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  • Algorithms for computing strategies in two-player simultaneous move games.Branislav Bošanský, Viliam Lisý, Marc Lanctot, Jiří Čermák & Mark H. M. Winands - 2016 - Artificial Intelligence 237:1-40.
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  • Information capture and reuse strategies in Monte Carlo Tree Search, with applications to games of hidden information.Edward J. Powley, Peter I. Cowling & Daniel Whitehouse - 2014 - Artificial Intelligence 217 (C):92-116.
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  • People's thinking plans adapt to the problem they're trying to solve.Joan Danielle K. Ongchoco, Joshua Knobe & Julian Jara-Ettinger - 2024 - Cognition 243 (C):105669.
    Much of our thinking focuses on deciding what to do in situations where the space of possible options is too large to evaluate exhaustively. Previous work has found that people do this by learning the general value of different behaviors, and prioritizing thinking about high-value options in new situations. Is this good-action bias always the best strategy, or can thinking about low-value options sometimes become more beneficial? Can people adapt their thinking accordingly based on the situation? And how do we (...)
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  • Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation.Kevin Lloyd, Adam Sanborn, David Leslie & Stephan Lewandowsky - 2019 - Cognitive Science 43 (12):e12805.
    Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct (...)
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  • Building machines that learn and think like people.Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum & Samuel J. Gershman - 2017 - Behavioral and Brain Sciences 40.
    Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...)
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