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

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

3. Usher and McClelland (2001) Leaky Competing Accumulator Model
Inspired by known neural mechanisms
Addresses the process of deciding between two alternatives based on 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]+ r1 r2 X1 X2

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 interpretationx 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. Experiment 2: A Stronger Manipulation
Stimulus Duration
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

16. Results in Exp.2: Star SubjectMT
1200 trials per data point. 20 sessions.

17. Results in Exp.2: Star SubjectMT

18. Results in Exp.2 CS
600 trials per data point. 10 sessions

19. Results in Exp.2 CS

20. Results in Exp.2 SC
600 trials per data point. 10 sessions.

21. Results in Exp.2 SC
600 trials per data point. 10 sessions. 21

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 pressureMM

26. Results in Exp.3, without time pressureMM
25 session, 1500 trials per point

27. Results in Exp.3, without time pressureWW

28. Results in Exp.3, without time pressureWW
10 sessions.

29. Results in Exp.3, without time pressureDG
15 sessions, 900 trials

30. Results in Exp.3, without time pressureDG
15 sessions, 900 trials

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. r1 r2 X1 X2
The End

38. Results in Exp 1. The pulse studySC