DECIDING WHEN TO CUT YOUR LOSSESMatt Cieslak, Tobias Kluth, Maren Stiels & Daniel Wood

2

OUTLINE

I. Introduction

II. Model

III. Experiment

IV. Results

V. Conclusion

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RESEARCH QUESTIONS

1. Are people optimal when they decide to cut their losses?

2. Does the GSR influence the optimality?

! !! ??

4

DECISION MAKING MODELS

Classic “diffusion” model

Accumulate all evidence:

Compare to a constant threshold / accuracy criterion

Urgency Gating model

Accumulate only the novel evidence:

Compare to a dropping accuracy criterion

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URGENCY GATING MODEL

Compute estimate of evidence- summation (≈ integration!) of new information- low-pass filtering (to deal with noise)- “temporal filter model” (Ludwig et al. 2005 J. Neurosci 25:9907-9912)

Multiply by growing function of time and compare to a threshold

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SETUP

4 ½ feet

GSR2*

13‘‘ at 30 Hz

* GSR2:Device to measure the galvanic skin resonse and sampled at 44.1 kHz

Response by the keyboard with the buttons ⟵ and ⟶7 subjects

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DESIGN

End of trial by response or Time-out after 5 sec or 8 sec

Time

Duratio

n of a tr

ial 5 or

8 sec

• Random uniform distribution was used for the onset

of dots

• Dots were presented on 60% of the trials

Duration (random): 1-5 sec (Dot-trial) 5 or 8 sec (Time-out-trial)

CONNECTING TO THE URGENCY-GATING MODEL

Time out-35 Points

t=0 t=t t=tend

tend

E(t)tend

E(t)tend

E(t)dots

no dots

dots

no dots

dots

no dots

CONNECTING TO THE URGENCY-GATING MODEL

Correct20 Points

t=0 t=t t=tend

tend

E(t)tend

E(t)tend

E(t)dots

no dots

dots

no dots

dots

no dotsu(t)u(t)

10

tend

E(t)

11

8 sec5 secTrial length

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RESULTS GSR predicted the latency of their guess on no-dot trials Response-time decreased linearly by a function of time

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CONCLUSION2 types of subjects:

Just guess: uncertainly not handled well

or time feeling very bad

Wait: good estimate of time; optimal behaviour

High GSR does not predict an early response

Instead it appears to increase as the person waits

Provides evidence for an urgency signal

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LITERATURE Lecture Slides ‚The blurry borders between decision and doing‘ (Part I, Part II) of Paul

Cisek at the CoSMo Summer School 2011 Cisek, Puskas and El-Murr

Pictures http://static.fjcdn.com/pictures/Hope_03ca1c_2759561.jpg http://www.oodora.com/life-stories/why-did-the-duck-cross-the-road.html/ducks-

crossing-road/ http://odyniec.net/projects/imgareaselect/duck.jpg http://www.flickr.com/photos/islandboy/3120743762/ http://www.ergo-online.de/uploads/ergo-online-tipps/tft-tief-nah-.jpg http://medpazar.com/content_files/prd_images/GSR2.1.jpg http://

www.beneaththecover.com/wp-content/uploads/2011/01/AGarcia-010511-monkey-thinker1.jpeg

http://www.kolster. http://www.visualphotos.com/photo/2x2737570/businessman_guessing_cbr001146.jpg

net/quatsch/bilder/computer/windows_wait.jpg

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HIGHSCORE

Thank you!

# subject

5 sec Version

10 430

8 355

4 350

1 205

2 20

9 -220

5 -320

# subjects

8 sec Version

1 500

10 290

8 40

2 20

5 0

9 -80

4 -265

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CLASSIC MODELS

Well-supported by data like - behavioral data (error rates, reaction time distributions) - neural activity

Similar to the sequential probability ratio test (SPRT)- optimal for requiring the fewest samples to reach a given

criterion of accuracy

Widely accepted conclusion: “Diffusion model explains decisions”

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SUMMARY

Serial model: When Cognition is done, action can begin i.e. “decision threshold”

But what controls growth toward the threshold is an urgency signal

i.e. a signal related to motor initiation

When reaching a motor initiation threshold, we commit to our current best guess

Cognition and Action are not so separate

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URGENCY GATING MODELAddition of a criterion of confidence that drops over time

Results confirm urgency-gating model over integrator models - Cisek, Puskas and El-Murr, 2009

Previous results with constant-evidence tasks compatible with both models- Error rates- Reaction time distributions- Neural activity in LIP, SC, PFC, etc.

Optimization of reward rate, and redundancy between samples

Proposed to be responsible for observed neural activity growth/distributions of RTs