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

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

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

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

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 J. Neurosci 25: ) Multiply by growing function of time and compare to a threshold 5

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

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)

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

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 t end E(t)

11 8 sec 5 sec Trial length

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

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

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 road/ thinker1.jpeg net/quatsch/bilder/computer/windows_wait.jpg 14

H IGHSCORE 15 Thank you! # subject5 sec Version # subjects8 sec Version

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

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

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