Transcription of Thinking fast or slow? A reinforcement learning approach
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Thinking fast or slow? A reinforcement learning approach
P < and detailsConclusionCost-benefit arbitration between multiple RL systemsFlexible adapation based on reward-advantageIn progress: w s relationship with outgroup bias and psychiatric symptomsExperiment 4. New paradigm with stakes1x50%5x50%Prediction:w5x > w1xExperiment Parameter r pExp 1. w1x .54 < .001 w5x .32 < .001 Exp. 2 w1x .92 w5x .81 Stakes manipulationResultsCorrelations1x5x1x5xE xp. 1 Exp. 2** = 94 Accuracy-demand tradeoff in novel 2-step rateExperiment simulationsDoes w predict reward?r = **n = 184 Novel paradigmRL modelQnet = w QMB + (1 - w) QMFQMF( ) = QMF( ) + RPEStay probabilityw = 0 SameDifferentPrevious start statew = 1 SameDifferentPrevious start statePrevious outcomeWinLossQMB( ) = Q( ) = QMB( )model-freemodel-basedBehavioral predictionsTaskDrift rate = 2chance of winning pieces of space treasure:Range = [ ],No accuracy-demand tradeoff in Daw 2-step taskRL rateExperiment 2 Does w predict reward?
wkool@fas.harvard.edu wouterkool.com *** p < 0.001 Contact and details Conclusion Cost-benefit arbitration between multiple RL systems Flexible adapation based on reward-advantage
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KAHNEMAN’S THINKING FAST AND SLOW, Thinking, Fast and Slow, Dee Wilson June 2012, Thinking Fast and Slow, Cognitive Model Fleshes out Kahneman, Fast and Slow, Fast, Thinking Fast and Slow with Deep Learning and Tree Search, Thinking Fast, Thinking Slow! Combining Knowledge Graphs, Fast thinking, Thinking fast, Thinking slow, Fast and slow thinking, Thinking, Fast and Slow D.Kahneman February 12-13, 2014