Quasi-Continuous Decision States in the Leaky Competing Accumulator Model

1. Quasi-Continuous Decision Statesin the Leaky Competing Accumulator Model Jay McClellandStanford University With Joel Lachter, Greg Corrado, and Jim Johnstonas well as Juan Gao and Marius Usher

2. Is the rectangle longer toward the northwest or longer toward the northeast?

3. Longer toward the Northeast! 1.99” 2.00”

4. A Classical Model of Decision Making:The Drift Diffusion Model of Choice Between Two Alternative Decisions • At each time step a small sample of noisy information is obtained; each sample adds to a cumulative relative evidence variable. • Mean of the noisy samples is +m for one alternative, –m for the other, with standard deviation s. • When a bound is reached, the corresponding choice is made. • Alternatively, in ‘time controlled’ or ‘interrogation’ tasks, respond when signal is given, based on value of the relative evidence variable.

5. The DDM is an optimal model, and it is consistent with neurophysiology • It achieves the fastest possible decision on average for a given level of accuracy • It can be tuned to optimize performance under different kinds of task conditions • Different prior probabilities • Different costs and payoffs • Variation in the time between trials… • The activity of neurons in a brain area associated with decision making seems to reflect the DD process

6. Neural Basis of Decision Making in Monkeys (Shadlen & Newsome; Roitman & Shadlen, 2002) RT task paradigm of R&T. Motion coherence anddirection is varied fromtrial to trial.

7. Neural Basis of Decision Making in Monkeys: Results Data are averaged over many different neurons that areassociated with intended eye movements to the locationof target.

8. Easy Prob. Correct Hard A Problem with the DDM • Accuracy should gradually improve toward ceiling levels as more time is allowed, even for very hard discriminations, but this is not what is observed in human data. • Two possible fixes: • Trial-to-trial variance in the direction of drift • Evidence accumulation may reach a bound and stop, even if more time is available

9. Usher and McClelland (2001)Leaky Competing Accumulator Model y2 y1 I1 I2 • Addresses the process of decidingbetween two alternatives basedon external input, with leakage, mutual inhibition, and noise: dy1/dt = I1-gy1–bf(y2)+x1 dy2/dt = I2-gy2–bf(y1)+x2 f(y) = [y]+ • Participant chooses the most active accumulator when the go cue occurs • This is equivalent to choosing response 1 iff y1-y2 > 0 • Let y = (y1-y2). While y1 and y2 are positive, the model reduces to: dy/dt = I-ly+x [I=I1-I2; l = g-b; x=x1-x2]

10. Wong & Wang (2006) ~Usher & McClelland (2001)

12. Time-accuracy curves for different |k-b| or |l| |k-b| = 0 |k-b| = .2 |k-b| = .4

13. Prob. Correct

14. Kiani, Hanks and Shadlen 2008 Random motion stimuli of different coherences. Stimulus duration follows an exponential distribution. ‘go’ cue can occur at stimulus offset; response must occur within 500 msec to earn reward.

15. The earlier the pulse, the more it matters(Kiani et al, 2008)

16. These results rule out leak dominance Still viable X

17. The Full Non-Linear LCAi Model y1 y2 Although the value of the differencevariable is not well-captured by thelinear approximation, the sign of thedifference is approximated very closely.

18. Three Studies Related to these Issues • Integration of reward and payoff information under time controlled conditions • Gao, Tortell & McClelland • Investigations of decision making with non-stationary stimulus information • Usher, Tsetsos & McClelland • Does the confidence of a final decision state vary continuously with the strength of the evidence? • Lachter, Corrado, Johnston & McClelland

19. Timeline of the Experiment

20. Proportion of Choices toward Higher Reward

21. Sensitivity varies with time

22. Three Hypotheses • Reward acts as an input from reward cue onset til the end of the integration period • Reward influences the state of the accumulators before the onset of the stimulus • Reward introduces an offset into the decision

23. Fits Based on Linear Model

24. Fits based on full LCAi

25. Relationship between response speed and choice accuracy

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

27. High-Threshold LCAi

28. Preliminary Simulation Results

29. Three Studies Related to these Issues • Integration of reward and payoff information under time controlled conditions • Gao, Tortell & McClelland • Investigations of decision making with non-stationary stimulus information • Usher, Tsetsos & McClelland • Does the confidence of a final decision state vary continuously with the strength of the evidence? • Lachter, Corrado, Johnston & McClelland

30. Continuous Report of ConfidenceLachter, Corrado, Johnston & McClelland (in progress) Expt 1: Probability bias included; trial could end at any time Expt 2: No probability bias; observers terminate trial when they have determined their best answer

32. Trial Duration Protocol and performance as a functionof time for participants groupedby performance

35. Results and Descriptive Model of Data from 1 Participant