using time-varying motion stimuli to explore decision dynamics
DESCRIPTION
Using Time-Varying Motion Stimuli to Explore Decision Dynamics. Marius Usher, Juan Gao, Rebecca Tortell, and James L. McClelland. 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: - PowerPoint PPT PresentationTRANSCRIPT
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Using Time-Varying Motion Stimuli to Explore Decision
Dynamics
Marius Usher, Juan Gao, Rebecca Tortell, and James L. McClelland
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Time-accuracy curves in the time-controlled paradigm
Curve for each condition is well fit by a shiftedexponential approach to asymptote:
d’(t) = d’asy(1-e-(t-T0)/)
Hard
Easy
Medium
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Usher and McClelland (2001)Leaky Competing Accumulator Model
• Inspired by known neural mechanisms
• Addresses the process of decidingbetween two alternatives basedon external input (1 + 2 = 1) with leakage, mutual inhibition, and noise:
dx1/dt = 1-k(x1)–f(x2)+1
dx2/dt = 2-k(x2)–f(x1)+2
f(x) = [x]+
1 2
X1 X2
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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) = (1(t) – 2(t))/(t); (0) > 0
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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.
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The earlier the pulse, the more it matters(Kiani et al, 2008)
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These results rule out leak dominance
X
Still viable
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Our Preferred Model: Non-Linear LCA , with Inhibition > Leak
Final time slice
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However, there is another interpretation
> Bounded Integration
(Ratcliff 1999; Kiani et.al.2008)
t
x
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Our Questions
• Can we distinguish the models?
• Can we push around the effect?
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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?
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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…
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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.
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Our Best Participant
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Experiment 2:A Stronger Manipulation
• 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.
Stimulus Duration
1) Early
2) Late
3) Constant
4) Switch
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Results in Exp.2: Star Subject
0.2 0.4 0.6 0.8 1 1.2 1.4 1.60.4
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Time (s)
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urac
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Results in Exp.2: Star Subject
0.2 0.4 0.6 0.8 1 1.2 1.4 1.60.4
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Results in Exp.2
0.2 0.4 0.6 0.8 1 1.2 1.4 1.60.4
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Results in Exp.2
0.2 0.4 0.6 0.8 1 1.2 1.4 1.60.4
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Results in Exp.2
0.2 0.4 0.6 0.8 1 1.2 1.40.4
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Results in Exp.2
SC
0.2 0.4 0.6 0.8 1 1.2 1.40.4
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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
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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?
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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.
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Results in Exp.3, without time pressure
0.2 0.4 0.6 0.8 1 1.2 1.4 1.60.4
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Time (s)
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Results in Exp.3, without time pressure
0.2 0.4 0.6 0.8 1 1.2 1.4 1.60.4
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Results in Exp.3, without time pressure
0.2 0.4 0.6 0.8 1 1.2 1.4 1.60.4
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Results in Exp.3, without time pressure
0.2 0.4 0.6 0.8 1 1.2 1.4 1.60.4
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Results in Exp.3, without time pressure
0.2 0.4 0.6 0.8 1 1.2 1.40.4
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Results in Exp.3, without time pressure
0.2 0.4 0.6 0.8 1 1.2 1.40.4
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Our Questions
• Can we distinguish the models?– Not yet
• Can we push around the effect?– Yes
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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.
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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).
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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.
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1 2
X1 X2
The End
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Results in Exp 1. The pulse study
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