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  1. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning.Richard S. Sutton, Doina Precup & Satinder Singh - 1999 - Artificial Intelligence 112 (1-2):181-211.
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  • Goal-directed decision making as probabilistic inference: A computational framework and potential neural correlates.Alec Solway & Matthew M. Botvinick - 2012 - Psychological Review 119 (1):120-154.
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  • A model for Pavlovian learning: Variations in the effectiveness of conditioned but not of unconditioned stimuli.John M. Pearce & Geoffrey Hall - 1980 - Psychological Review 87 (6):532-552.
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  • (1 other version)The magical number seven, plus or minus two: Some limits on our capacity for processing information.George A. Miller - 1956 - Psychological Review 63 (2):81-97.
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  • Species-specific defense reactions and avoidance learning.Robert C. Bolles - 1970 - Psychological Review 77 (1):32-48.
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  • Cognitive maps in rats and men.Edward C. Tolman - 1948 - Psychological Review 55 (4):189-208.
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  • Model-Based Influences on Humans' Choices and Striatal Prediction Errors.Nathaniel D. Daw, Samuel J. Gershman, Ben Seymour, Peter Dayan & Raymond J. Dolan - 2011 - Neuron 69 (6):1204-1215.
    The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could (...)
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  • (1 other version)A learning algorithm for boltzmann machines.David H. Ackley, Geoffrey E. Hinton & Terrence J. Sejnowski - 1985 - Cognitive Science 9 (1):147-169.
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  • Vision.David Marr - 1982 - W. H. Freeman.
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  • Hopping, skipping or jumping to conclusions? Clarifying the role of the JTC bias in delusions.Cordelia Fine, Mark Gardner, Jillian Craigie & Ian Gold - 2007 - Cogn Neuropsychiatry 12 (1):46-77.
    Introduction. There is substantial evidence that patients with delusions exhibit a reasoning bias—known as the “jumping to conclusions” bias—which leads them to accept hypotheses as correct on the basis of less evidence than controls. We address three questions concerning the JTC bias that require clarification. Firstly, what is the best measure of the JTC bias? Second, is the JTC bias correlated specifically with delusions, or only with the symptomatology of schizophrenia? And third, is the bias enhanced by emotionally salient material? (...)
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  • Rational adaptation under task and processing constraints: Implications for testing theories of cognition and action.Andrew Howes, Richard L. Lewis & Alonso Vera - 2009 - Psychological Review 116 (4):717-751.
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  • (1 other version)A learning algorithm for boltzmann machines.D. H. Ackley - 1985 - Cognitive Science 9 (1):147-169.
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  • (2 other versions)Vision: Variations on Some Berkeleian Themes.Robert Schwartz & David Marr - 1985 - Philosophical Review 94 (3):411.
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  • A Bayesian formulation of behavioral control.Quentin J. M. Huys & Peter Dayan - 2009 - Cognition 113 (3):314-328.
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  • Computational psychiatry.P. Read Montague, Raymond J. Dolan, Karl J. Friston & Peter Dayan - 2012 - Trends in Cognitive Sciences 16 (1):72-80.
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  • Monte-Carlo tree search and rapid action value estimation in computer Go.Sylvain Gelly & David Silver - 2011 - Artificial Intelligence 175 (11):1856-1875.
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