Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration

By J. Cohen, Samuel M. McClure, Angela J. Yu. Philosophical Transactions of the Royal Society B: Biological Sciences, 2007

1,104 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/532551ab6713a9fdab7c517401dd6d81b102c3dd

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In Cognitive Neuroscience

Citations in the corpus

Environmental challenge and animal agency. (2011, background): “For instance, Cohen et al. (2007) argue that the trade-off between the”
Asymptotic non-learnability of universal agents with computable horizon functions (2013, background): “In the remainder, without loss of generality with respect to any bounded interval, we always consider that r t ∈ [0] [1].”
Hierarchical Active Inference: A Theory of Motivated Control (2018, methodology): “Active inference has been applied to cognitive control phenomena, such as the strategic decisions to execute, defer or stop an impending action or exploration – exploitation dynamics [90 – 92].”
Pupil Diameter Predicts Changes in the Exploration–Exploitation Trade-off: Evidence for the Adaptive Gain Theory (2011, background): “Although there has been a recent rise in studies investigating the strategies that are used to handle this trade-off and the neural mechanisms involved (for a review, see Cohen, McClure, & Yu, 2007 ), these issues are still poorly understood.”
Mushroom bodies regulate habit formation in Drosophila. (2008, positive): “The success of an animal thus depends on finding the right balance between efficient exploitation of known resources through routine behavior and flexible exploration of possible new resources through novel behaviors (the exploitation-exploration dilemma [20, 21]).”
Human volition: towards a neuroscience of will (2008, background): “Third, a general drive to perform occasional voluntary actions would allow exploration of the behavioural landscap”
Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function (2010, result): “The neural and computational basis for behavior in such paradigms has already begun to be explored by others (Daw, O’Doherty, Dayan, Seymour, & Dolan, 2006), and the findings seem largely consistent with AGT (Cohen et al., 2007).”
On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning (2017, background): “For additional discussions on the connections between softmax selection rule and the neurophysiology of decision-making, see [30], [31], [32], [33].”
Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model (2011, background): “NEUROROBOTICS ORIGINAL RESEARCH ARTICLE published: 1 July 2011 doi: 10.3389/fnbot.2011.00001 Robot cognitive control with a neurophysiologically inspired reinforcement learning model”
An Introduction to Deep Reinforcement Learning (2018, positive): “For instance, computational models have been an inspiration to explain cognitive phenomena such as exploration (Cohen et al., 2007) and temporal discounting of rewards (Story et al.”
Free Energy, Value, and Attractors (2011, methodology): “This is not dissimilar to Win-Stay, Lose-Shift strategies used to model optimal decisions in game theory [74].”