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

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. 9 dopamine and prediction error no predictionprediction, rewardprediction, no reward TD error VtVt R RL

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

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

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

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

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…