1. Dynamics of Reward and Stimulus Information in Human Decision Making Juan Gao, Rebecca Tortell & James L. McClelland With inspiration from Bill Newsome and Phil Holmes

2. Questions Can we track the time course of reward bias as stimulus information is accumulated over time? How well can human participants adjust their bias to optimize reward when optimal bias varies over time? How do humans achieve the observed bias effects? Can we distinguish between alternative accounts of the data?

3. Design follows Rorie et al. but there is a variable delay between stimulus onset and go cue. Reward cue signals which alternative is worth 2 points 750 msec before Stimulus onset. Stimulus is a rectangle 1,3, or 5 pixels longer to the Left or Right. Participant must respond within 250 msec of go cue.

4. 4 or 5 Participants Show Reward Bias Effect

5. Accuracy Analysis

6. Individual Differences in Accuracy and Time Parameters

7. Leak and Inhibition Dominant LCA: Both can fit the d’ data

8. 2-D inhibition-dominant LCA can fit the data too Final time slice

9. Optimal Criterion Placement

10. Optimal vs. Observed Bias Effects Reward harvest rates For short lags:

11. Reward bias in leak-dominant LCA Reward as input to the accumulators Reward as offset to initial conditions Reward as constant shift or shifted criterion Like the data!

12. Excellent fits are obtained under leak dominance with reward as a constant offset

13. But there are drawbacks to the leak-dominant model Leak-dominance produces equivocal decision states, while inhibition dominance produces more categorical activations. –These states may leave the participant better prepared to respond when the signal comes. Evidence Juan will present later favors inhibition dominance in similar paradigms

14. Reward Bias in Inhibition-dominant LCA ( < 0) Reward as input to the accumulators Reward as offset to initial conditions Reward as constant shift or shifted criterion Like the data!

15. Simulation of Inhibition-Dominant LCA using Parameters Derived from 1-D Reduction

16. Relationship between response speed and choice accuracy

17. Different levels of activation of correct and incorrect responses in Inhibition-dominant LCA Final time slice correct errors

19. Preliminary Simulation of High- Threshold LCA

20. Conclusion and Future Directions Two viable models remain, though we favor the leak-dominant LCA model. –Juan Gao will present other evidence relevant to this later. There is evidence that the decision state remains continuous until the response is made, consistent with the high-threshold model –Further tests of the details of this model are necessary. We plan to examine whether the same approach can fit the primate physiology data We look forward to seeing the activations of the accumulators in Human MEG/EEG data