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Deciding when to cut your losses

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 ?. ?. ?. !.

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Deciding when to cut your losses

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  1. Decidingwhentocutyourlosses Matt Cieslak, Tobias Kluth, Maren Stiels & Daniel Wood

  2. Outline • Introduction • Model • Experiment • Results • Conclusion

  3. Research Questions • Are people optimal whentheydecidetocuttheirlosses? • Doesthe GSR influencetheoptimality? ? ? ! ! !

  4. Decision Making Models Classic “diffusion” model Accumulate allevidence: Compareto a constantthreshold / accuracycriterion UrgencyGating model Accumulate onlythenovelevidence: Compareto a droppingaccuracycriterion

  5. UrgencyGating 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

  6. Setup 13‘‘ at 30 Hz 7 subjects 4 ½ feet Response bythekeyboardwiththebuttons⟵ and⟶ GSR2* * GSR2: Device tomeasurethegalvanicskinresonseandsampledat 44.1 kHz

  7. Design End oftrialbyresponse or Time-out after 5 sec or 8 sec Time Duration of a trial 5 or 8 sec Duration (random): 1-5 sec (Dot-trial) 5 or 8 sec (Time-out-trial) • Random uniform distribution was usedfortheonsetofdots • Dotswerepresented on 60% ofthetrials

  8. ConnectingtotheUrgency-Gating Model Time out -35 Points t=tend t=0 t=t dots dots dots E(t) E(t) E(t) nodots nodots nodots tend tend tend

  9. ConnectingtotheUrgency-Gating Model Correct 20 Points t=tend t=0 t=t dots dots dots E(t) E(t) E(t) u(t) u(t) nodots nodots nodots tend tend tend

  10. E(t) tend

  11. Trial length 5 sec 8 sec

  12. Results • GSR predictedthelatencyoftheirguess on no-dottrials • Response-time decreasedlinearlyby a functionof time

  13. Conclusion 2 typesofsubjects: Just guess: uncertainly not handled well or time feelingverybad Wait: goodestimateof time; optimal behaviour • High GSR does not predict an earlyresponse • Insteaditappearstoincreaseasthepersonwaits •  Providesevidencefor an urgencysignal

  14. Literature • LectureSlides ‚The blurrybordersbetweendecisionanddoing‘ (Part I, Part II) of Paul CisekattheCoSMo Summer School 2011 • Cisek, PuskasandEl-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

  15. Highscore Thankyou!

  16. 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”

  17. 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

  18. UrgencyGating Model Addition 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

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