Learning the opportunity cost of time in a patch-foraging task
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[1] D. Bernoulli. Exposition of a New Theory on the Measurement of Risk , 1954 .
[2] R J HERRNSTEIN,et al. Relative and absolute strength of response as a function of frequency of reinforcement. , 1961, Journal of the experimental analysis of behavior.
[3] David Elkind,et al. Learning: An Introduction , 1968 .
[4] J. McCall. Economics of Information and Job Search , 1970 .
[5] W. Baum. Choice in Free-Ranging Wild Pigeons , 1974, Science.
[6] E. Charnov. Optimal foraging, the marginal value theorem. , 1976, Theoretical population biology.
[7] J. T. Erichsen,et al. Optimal prey selection in the great tit (Parus major) , 1977, Animal Behaviour.
[8] J. Ollason. Learning to forage--optimally? , 1980, Theoretical population biology.
[9] A. Kacelnik. CENTRAL PLACE FORAGING IN STARLINGS (STURNUS-VULGARIS) .1. PATCH RESIDENCE TIME , 1984 .
[10] Clayton M. Hodges. Bumble Bee Foraging: Energetic Consequences of Using a Threshold Departure Rule , 1985 .
[11] A. Houston,et al. Optimal foraging and learning , 1985 .
[12] J. Salamone. Dopaminergic involvement in activational aspects of motivation: Effects of haloperidol on schedule-induced activity, feeding, and foraging in rats , 1988, Psychobiology.
[13] J. Krebs,et al. INDIVIDUAL DECISIONS AND THE DISTRIBUTION OF PREDATORS IN A PATCHY ENVIRONMENT , 1988 .
[14] C. Watkins. Learning from delayed rewards , 1989 .
[15] J. Krebs,et al. Starlings exploiting patches: the effect of recent experience on foraging decisions , 1990, Animal Behaviour.
[16] R. Herrnstein. Experiments on Stable Suboptimality in Individual Behavior , 1991 .
[17] J. Krebs,et al. Learning and Foraging: Individuals, Groups, and Populations , 1992, The American Naturalist.
[18] E. Smith,et al. Evolutionary Ecology and Human Behavior , 1992 .
[19] Anton Schwartz,et al. A Reinforcement Learning Method for Maximizing Undiscounted Rewards , 1993, ICML.
[20] Joel L. Davis,et al. A Model of How the Basal Ganglia Generate and Use Neural Signals That Predict Reinforcement , 1994 .
[21] Martin L. Puterman,et al. Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .
[22] Joel L. Davis,et al. Adaptive Critics and the Basal Ganglia , 1995 .
[23] P. Dayan,et al. A framework for mesencephalic dopamine systems based on predictive Hebbian learning , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[24] E. A. Jacobs,et al. Humans' choices in situations of time-based diminishing returns: effects of fixed-interval duration and progressive-interval step size. , 1996, Journal of the experimental analysis of behavior.
[25] Peter Dayan,et al. A Neural Substrate of Prediction and Reward , 1997, Science.
[26] A. Kacelnik. Normative and descriptive models of decision making: time discounting and risk sensitivity. , 2007, Ciba Foundation symposium.
[27] Kenji Doya,et al. What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? , 1999, Neural Networks.
[28] C. Gallistel,et al. Time, rate, and conditioning. , 2000, Psychological review.
[29] David S. Touretzky,et al. Long-Term Reward Prediction in TD Models of the Dopamine System , 2002, Neural Computation.
[30] James Bernard Murphy,et al. Tug of War. , 2003 .
[31] W. Newsome,et al. Matching Behavior and the Representation of Value in the Parietal Cortex , 2004, Science.
[32] Michael J. Frank,et al. By Carrot or by Stick: Cognitive Reinforcement Learning in Parkinsonism , 2004, Science.
[33] D. Stephens,et al. Impulsiveness without discounting: the ecological rationality hypothesis , 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[34] D. Barraclough,et al. Prefrontal cortex and decision making in a mixed-strategy game , 2004, Nature Neuroscience.
[35] Jonathan D. Cohen,et al. An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. , 2005, Annual review of neuroscience.
[36] P. Dayan,et al. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control , 2005, Nature Neuroscience.
[37] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 2005, IEEE Transactions on Neural Networks.
[38] Richard S. Sutton,et al. Learning to predict by the methods of temporal differences , 1988, Machine Learning.
[39] Peter Dayan,et al. How fast to work: Response vigor, motivation and tonic dopamine , 2005, NIPS.
[40] R. Hertwig,et al. The priority heuristic: making choices without trade-offs. , 2006, Psychological review.
[41] P. Dayan,et al. Cortical substrates for exploratory decisions in humans , 2006, Nature.
[42] J. O'Doherty,et al. The Role of the Ventromedial Prefrontal Cortex in Abstract State-Based Inference during Decision Making in Humans , 2006, The Journal of Neuroscience.
[43] P. Dayan,et al. Tonic dopamine: opportunity costs and the control of response vigor , 2007, Psychopharmacology.
[44] T. Robbins,et al. A role for mesencephalic dopamine in activation: commentary on Berridge (2006) , 2007, Psychopharmacology.
[45] Timothy E. J. Behrens,et al. Learning the value of information in an uncertain world , 2007, Nature Neuroscience.
[46] Sabrina M. Tom,et al. The Neural Basis of Loss Aversion in Decision-Making Under Risk , 2007, Science.
[47] H. Seo,et al. Dynamic signals related to choices and outcomes in the dorsolateral prefrontal cortex. , 2007, Cerebral cortex.
[48] Colin Camerer,et al. A framework for studying the neurobiology of value-based decision making , 2008, Nature Reviews Neuroscience.
[49] Aaron P. Wagner,et al. Spatial grouping in behaviourally solitary striped hyaenas, Hyaena hyaena , 2008, Animal Behaviour.
[50] A. Rustichini. Neuroeconomics: Formal models of decision making and cognitive neuroscience , 2008 .
[51] Wolfgang Hauber,et al. Dopamine D1 and D2 receptors in the nucleus accumbens core and shell mediate Pavlovian-instrumental transfer. , 2008, Learning & memory.
[52] P. Todd,et al. Patch leaving in humans: can a generalist adapt its rules to dispersal of items across patches? , 2008, Animal Behaviour.
[53] Colin Camerer,et al. Neuroeconomics: decision making and the brain , 2008 .
[54] Roshan Cools,et al. Role of Dopamine in the Motivational and Cognitive Control of Behavior , 2008, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.
[55] M. Walton,et al. Comparing the role of the anterior cingulate cortex and 6‐hydroxydopamine nucleus accumbens lesions on operant effort‐based decision making , 2009, The European journal of neuroscience.
[56] Karl J. Friston,et al. Bayesian model selection for group studies , 2009, NeuroImage.
[57] K. Doya,et al. Validation of Decision-Making Models and Analysis of Decision Variables in the Rat Basal Ganglia , 2009, The Journal of Neuroscience.
[58] G. McNickle,et al. Plant root growth and the marginal value theorem , 2009, Proceedings of the National Academy of Sciences.
[59] Mark S. Gilzenrat,et al. Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function , 2010, Cognitive, affective & behavioral neuroscience.
[60] M. Walton,et al. Dissociable cost and benefit encoding of future rewards by mesolimbic dopamine , 2009, Nature Neuroscience.
[61] Daeyeol Lee,et al. Beyond working memory: the role of persistent activity in decision making , 2010, Trends in Cognitive Sciences.
[62] Alex Kacelnik,et al. Darwin’s “tug-of-war” vs. starlings’ “horse-racing”: how adaptations for sequential encounters drive simultaneous choice , 2010, Behavioral Ecology and Sociobiology.
[63] Thomas T. Hills,et al. Information Search in Decisions From Experience , 2010, Psychological science.
[64] A. Rangel,et al. Visual fixations and the computation and comparison of value in simple choice. , 2010, Nature neuroscience.
[65] N. Daw,et al. Serotonin and Dopamine: Unifying Affective, Activational, and Decision Functions , 2011, Neuropsychopharmacology.
[66] Peter Dayan,et al. Vigor in the Face of Fluctuating Rates of Reward: An Experimental Examination , 2011, Journal of Cognitive Neuroscience.
[67] H. Seo,et al. A reservoir of time constants for memory traces in cortical neurons , 2011, Nature Neuroscience.
[68] Alex Kacelnik,et al. Rational Choice, Context Dependence, and the Value of Information in European Starlings (Sturnus vulgaris) , 2011, Science.
[69] P. Dayan,et al. Model-based influences on humans’ choices and striatal prediction errors , 2011, Neuron.
[70] Colin Camerer,et al. Transformation of stimulus value signals into motor commands during simple choice , 2011, Proceedings of the National Academy of Sciences.
[71] John M. Pearson,et al. Neuronal basis of sequential foraging decisions in a patchy environment , 2011, Nature Neuroscience.
[72] Amir Dezfouli,et al. Speed/Accuracy Trade-Off between the Habitual and the Goal-Directed Processes , 2011, PLoS Comput. Biol..
[73] J. O'Doherty,et al. Annals of the New York Academy of Sciences Contributions of the Ventromedial Prefrontal Cortex to Goal-directed Action Selection , 2022 .
[74] N. Daw,et al. Signals in Human Striatum Are Appropriate for Policy Update Rather than Value Prediction , 2011, The Journal of Neuroscience.
[75] M. Rushworth,et al. Valuation and decision-making in frontal cortex: one or many serial or parallel systems? , 2012, Current Opinion in Neurobiology.
[76] Timothy E. J. Behrens,et al. Neural Mechanisms of Foraging , 2012, Science.
[77] Edward Vul,et al. A Bayesian Optimal Foraging Model of Human Visual Search , 2012, Psychological science.
[78] Thomas T. Hills,et al. Optimal foraging in semantic memory. , 2012, Psychological review.
[79] Andrew M. Wikenheiser,et al. Subjective costs drive overly patient foraging strategies in rats on an intertemporal foraging task , 2013, Proceedings of the National Academy of Sciences.
[80] Angela L. Duckworth,et al. An opportunity cost model of subjective effort and task performance. , 2013, The Behavioral and brain sciences.
[81] M. Botvinick,et al. The intrinsic cost of cognitive control. , 2013, The Behavioral and brain sciences.
[82] Jonathan D. Cohen,et al. The Expected Value of Control: An Integrative Theory of Anterior Cingulate Cortex Function , 2013, Neuron.
[83] Timothy Edward John Behrens,et al. Ventromedial Prefrontal and Anterior Cingulate Cortex Adopt Choice and Default Reference Frames during Sequential Multi-Alternative Choice , 2013, The Journal of Neuroscience.
[84] Ulrik R Beierholm,et al. Dopamine Modulates Reward-Related Vigor , 2013, Neuropsychopharmacology.
[85] Peter Dayan,et al. Optimal indolence: a normative microscopic approach to work and leisure , 2014, Journal of The Royal Society Interface.
[86] Mark A. Straccia,et al. Anterior Cingulate Engagement in a Foraging Context Reflects Choice Difficulty, Not Foraging Value , 2014, Nature Neuroscience.
[87] R. K. Simpson. Nature Neuroscience , 2022 .