Using Time-Varying Motion Stimuli to Explore Decision Dynamics

1. Using Time-Varying Motion Stimuli to Explore Decision Dynamics Marius Usher, Juan Gao, Rebecca Tortell, andJames L. McClelland

2. Time-accuracy curves in the time-controlled paradigm Easy Medium Hard Curve for each condition is well fit by a shiftedexponential approach to asymptote: d’(t) = d’asy(1-e-(t-T0)/t)

3. X2 X1 r1 r2 Usher and McClelland (2001)Leaky Competing Accumulator Model • Inspired by known neural mechanisms • Addresses the process of decidingbetween two alternatives basedon external input (r1 + r2 = 1) with leakage, mutual inhibition, and noise: dx1/dt = r1-k(x1)–bf(x2)+x1 dx2/dt = r2-k(x2)–bf(x1)+x2 f(x) = [x]+

4. Leak and Inhibition Dominant LCA:Both can fit the d’ data • Participant chooses the most active accumulator when the go cue occurs • This is equivalent to choosing response 1 iff x1-x2 > 0 • Non-linearity at 0 is neglected for analytic tractability • Graphs track this difference variable for a single difficulty level when the motion is to the left (Red) or to the right (Blue) • d’(t) = (m1(t) – m2(t))/s(t); s(0) > 0

5. Kiani, Tanks 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 ear reward.

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

7. These results rule out leak dominance Still viable X

8. Our Preferred Model: Non-Linear LCA , with Inhibition > Leak Final time slice

9. However, there is another interpretation x t > Bounded Integration (Ratcliff 1999; Kiani et.al.2008)

10. Our Questions • Can we distinguish the models? • Can we push around the effect?

11. Our Experiments Repeat Kiani 2008 with human subjects. The effect was small...Let’s try a stronger manipulation. Now we have a big effect:Can we reverse or eliminate it?

12. Ongoing Investigations • Random dot motion stimuli, like those used by Shadlen and Newsome, Kiani et al, and many others. • Multiple coherences: 6.4%, 12.8%, 25.6%, 51.2% • Three participants per experiment, each run for up to 25 sessions. • Data shown are after performance stabilizes, after varying numbers of sessions. • Ongoing recruitment, Ongoing analysis…

13. Kiani Replication • Exponential distribution of trial durations • Go cue when motion stops • Participant must response within 300 msec of go cue and must be correct to earn a point • Pulse occurs on a subset of trials, at a random time within the trial: • Motion increment of +/-2% for 200 msec.

14. Our Best Participant mt

15. Three motion conditions crossed with 8 coherences. LCALD and BI both predict Early > Late Data shown are percent correct, averaged across coherences We include a switch condition with 6.4% and 12.8% coherences only (no right answer). LCALD and BI both predict %Early Choices > 50% Each participant has at least 600 trials per data point over at least 10 sessions. 1) Early 2) Late 3) Constant 4) Switch Experiment 2:A Stronger Manipulation Stimulus Duration

16. Results in Exp.2: Star Subject MT

17. Results in Exp.2: Star Subject MT

18. Results in Exp.2 CS

19. Results in Exp.2 CS

20. Results in Exp.2 SC

21. Results in Exp.2 SC

22. Take home message • Yes, it seems earlier > later in all three subjects with this time pressure. • But 2 of 3 participants show some sensitivity to late information even at longer durations, while one does not. • Model accounts for individual differences: • BI: Low vs. high bound • LCALD: strong vs. weak inhibition dominance

23. Our Experiments Repeat Kiani 2008 with human subjects. The effect was small...Let’s try a stronger manipulation. Now we have a big effect:Can we reverse or eliminate it?

24. Experiment 3: Time-limited integration without time pressure to respond • Same stimulus conditions as before. • New participants. • Only two procedural changes: • Uniform vs. exponential distribution of stimulus durations • Participants have a full second after the end of the stimulus to respond.

25. Results in Exp.3, without time pressure MM

26. Results in Exp.3, without time pressure MM

27. Results in Exp.3, without time pressure WW

28. Results in Exp.3, without time pressure WW

29. Results in Exp.3, without time pressure DG

30. Results in Exp.3, without time pressure DG

31. Our Questions • Can we distinguish the models? • Not yet • Can we push around the effect? • Yes

32. How do the models explain the data? • BI: participants can perform unbounded integration if there is no time pressure • LCALD: participants can balance leak and inhibition if there is no time pressure • In both cases, it appears that we have balanced, unbounded integration.

33. Two remaining questions • Can we create a situation in which we will observe leaky integration? • Very long trials? • Detect motion pulse in otherwise 0% background? • Why does accuracy level off with long integration times if there is perfect integration? • Between trial drift variance?? (Ratcliff, 1978).

34. The Bottom Line • The dynamics of information integration might not be fixed characteristics of the decision making mechanism • Instead, they may be tunable in response to task demands: • Leak vs. competition • Presence of a bound on integration • Etc.

35. X2 X1 r1 r2 The End

38. Results in Exp 1. The pulse study SC