1. D ECIDING WHEN TO CUT YOUR LOSSES Matt Cieslak, Tobias Kluth, Maren Stiels & Daniel Wood

2. O UTLINE I. Introduction II. Model III. Experiment IV. Results V. Conclusion 2

3. R ESEARCH Q UESTIONS 1. Are people optimal when they decide to cut their losses? 2. Does the GSR influence the optimality? 3 ! ! ! ? ?

4. D ECISION M AKING M ODELS 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 4

5. U RGENCY G ATING M ODEL 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 5

6. S ETUP 6 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

7. D ESIGN 7 End of trial by response or Time-out after 5 sec or 8 sec Time Duration of a trial 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)

8. C ONNECTING TO THE U RGENCY -G ATING M ODEL Time out -35 Points t=0t=t t=t end t end E(t) t end E(t) t end E(t) dots no dots dots no dots dots no dots

9. C ONNECTING TO THE U RGENCY -G ATING M ODEL Correct 20 Points t=0t=t t=t end t end E(t) t end E(t) t end E(t) dots no dots dots no dots dots no dots u(t)

10. 10 t end E(t)

11. 11 8 sec 5 sec Trial length

12. R ESULTS GSR predicted the latency of their guess on no-dot trials Response-time decreased linearly by a function of time 12

13. C ONCLUSION 13 2 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

14. L ITERATURE 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.kolsterhttp://www.kolster. http://www.visualphotos.com/photo/2x2737570/businessman_guessing_cbr001146.jpg net/quatsch/bilder/computer/windows_wait.jpg 14

15. H IGHSCORE 15 Thank you! # subject5 sec Version 10430 8355 4350 1205 220 9-220 5-320 # subjects8 sec Version 1500 10290 840 220 50 9-80 4-265

16. C LASSIC M ODELS 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” 16

17. S UMMARY 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 17

18. U RGENCY G ATING M ODEL 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 18