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A View from the Bottom

A View from the Bottom. Peter Dayan Gatsby Computational Neuroscience Unit. Neural Decision Making. bewilderingly vast topic models playing a central role so beware of self-confirmation + battles. Ethology/Economics(?) optimality

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A View from the Bottom

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  1. A View from the Bottom Peter Dayan Gatsby Computational Neuroscience Unit

  2. Neural Decision Making • bewilderingly vast topic • models playing a central role • so beware of self-confirmation + battles

  3. Ethology/Economics(?) optimality logic of the approach Psychology economic choices instrumental/Pavlovian conditioning Computation Algorithm Neural Decision Making prediction: of important events control: in the light of those predictions • Implementation/Neurobiology neuromodulators; amygdala; prefrontal cortex nucleus accumbens; dorsal striatum

  4. Imprecision & Noise • computation • Bayesian sensory inference • Kalman filtering and optimal learning • metacognition • exploration/exploitation • game theory

  5. Imprecision & Noise • algorithm • multiple methods of choice • instrumental: model-based; model-free • (note influence on RTs) • Pavlovian: evolutionary programming • uncertainty-modulated inference and learning • DFT/drift diffusion decision-making • MCMC methods for inference

  6. Imprecision & Noise • implementation • (where does the noise come from?) • evidence accumulation • Q-learning and dopamine • metacognition and the PFC • acetylcholine/norepinephrine and uncertainty-sensitive inference and learning

  7. Diffusion to Bound Britten et al, 1992

  8. Diffusion to Bound • expected reward, priors affect starting point • some evidence for urgency signal • works for discrete evidence (WPT) • less data on >2 options • micro-stimulation works as expected • decision via striatum/superior colliculus/etc? • choice probability for single neurons Gold & Shadlen, 2007

  9. dopamine and prediction error TD error L R Vt R no prediction prediction, reward prediction, no reward

  10. Fiorillo et al, 2003 Tobler et al, 2005 Probability and Magnitude

  11. Risk Processing < 1 sec 5 sec ISI 0.5 sec 2-5sec ITI You won 40 cents 5 stimuli: 40¢ 20¢ 0/40¢ 0¢ 0¢ 19 subjects (dropped 3 non learners, N=16) 3T scanner, TR=2sec, interleaved 234 trials: 130 choice, 104 single stimulus randomly ordered and counterbalanced

  12. Neural results: Prediction errors what would a prediction error look like (in BOLD)?

  13. Neural results I: Prediction errors in NAC raw BOLD (avg over all subjects) unbiased anatomical ROI in nucleus accumbens(marked per subject*) * thanks to Laura deSouza

  14. Value Independent of Choice Roesch et al, 2007

  15. Metacognition • Fleming et al, 2010 • contrast staircase for performance; type II ROC for confidence

  16. Structural Correlate • also associated white matter (connections)

  17. Discussion • what can economics do for us? • theoretical, experimental ideas • experimental methods • like behaviorism… • what can we do for economics? • large range of constraints • objects of experimental inquiry precisely aligned with economic notions • grounding/excuse for complexity…

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