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Deciding when to cut your losses. Matt Cieslak , Tobias Kluth, Maren Stiels & Daniel Wood. Outline. Introduction Model Experiment Results Conclusion. Research Questions. Are people optimal when they decide to cut their losses ? Does the GSR influence the optimality ?. ?. ?. !. - PowerPoint PPT Presentation
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DECIDING WHEN TO CUT YOUR LOSSESMatt Cieslak, Tobias Kluth, Maren Stiels & Daniel Wood
2
OUTLINE
I. IntroductionII. ModelIII. ExperimentIV. ResultsV. Conclusion
3
RESEARCH QUESTIONS
1. Are people optimal when they decide to cut their losses?
2. Does the GSR influence the optimality?
! !! ??
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DECISION MAKING MODELSClassic “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
Duration of a tri
al 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)
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tend
E(t)
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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 4308 3554 3501 2052 209 -2205 -320
# subjects
8 sec Version
1 50010 2908 402 205 09 -804 -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