using time-varying motion stimuli to explore decision dynamics

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

Curve for each condition is well fit by a shiftedexponential approach to asymptote:

d’(t) = d’asy(1-e-(t-T0)/)

Hard

Easy

Medium

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]+

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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

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.

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

These results rule out leak dominance

X

Still viable

Our Preferred Model: Non-Linear LCA , with Inhibition > Leak

Final time slice

However, there is another interpretation

> Bounded Integration

(Ratcliff 1999; Kiani et.al.2008)

t

x

Our Questions

• Can we distinguish the models?

• Can we push around the effect?

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?

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…

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

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

Results in Exp.2: Star Subject

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Results in Exp.2: Star Subject

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Results in Exp.2

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Results in Exp.2

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Results in Exp.2

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Results in Exp.2

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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

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?

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.

Results in Exp.3, without time pressure

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Results in Exp.3, without time pressure

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Results in Exp.3, without time pressure

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Results in Exp.3, without time pressure

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Results in Exp.3, without time pressure

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Results in Exp.3, without time pressure

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Our Questions

• Can we distinguish the models?– Not yet

• Can we push around the effect?– Yes

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.

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).

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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The End

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Results in Exp 1. The pulse study

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