Finding

brunton-2013-rats-humans.psychometric.subject.b127

Brunton BW, Botvinick MM, Brody CD. Rats and humans can optimally accumulate evidence for decision-making. Science, 2013, 340(6128):95-98. · Rat auditory clicks nose-poke task · psychometric

Observed curve and fits

Fit diagnostics

Caveats on these fits (2 terms)

Each tag below is attached to one or more candidate fits for this finding. The chip column in the table flags which row carries which tag.

click-summary accumulator
Click accumulator fit is tied to the curated click summary fields rather than a full behavioral model audit against raw event streams.
click-summary baseline
Same-scope baseline for the clicks task using per-subject choice-rate or signed click-count summaries.
Variant AIC Δ AIC RMSE Caveats
logistic-4param winner 13765.6 0.0 0.1167 —
click-leaky-accumulator 13801.1 35.6 0.1287
  • click-summary accumulator
click-count-logistic 13864.1 98.5 0.1294
  • click-summary baseline
sdt-2afc 14307.6 542.1 0.1566 —
click-choice-rate-null 17819.2 4053.7 0.3310
  • click-summary baseline
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -6878.8 12,855 4 0.8822 103
click-leaky-accumulator -6894.6 12,855 6 0.8364 103
click-count-logistic -6929.0 12,855 3 0.8161 103
sdt-2afc -7151.8 12,855 2 0.9862 103
click-choice-rate-null -8908.6 12,855 1 0.5084 103

Residuals

Fit parameters and provenance

logistic-4param.brunton-2013-rats-humans.psychometric.subject.b127

dirty fit

bias=-3.822 · lower_lapse=0.118 · slope=4.318 · upper_lapse=0.218

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
103
BIC
13795.4

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.logistic-4param; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

click-leaky-accumulator.brunton-2013-rats-humans.psychometric.subject.b127

dirty fit

bias=-2.175 · input_gain=1.969 · lapse=0.326 · leak=0.000 · noise_accumulator=1.702 · noise_input=4.229

Method
scipy.optimize.minimize
Commit
942a5e0
Predicted points
103
BIC
13845.9

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.click-leaky-accumulator; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

click-count-logistic.brunton-2013-rats-humans.psychometric.subject.b127

dirty fit

bias=-1.614 · lapse=0.368 · sensitivity=0.247

Method
scipy.optimize.minimize
Commit
3ec74b4
Predicted points
103
BIC
13886.4

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.click-count-logistic; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

sdt-2afc.brunton-2013-rats-humans.psychometric.subject.b127

dirty fit

criterion=0.030 · d_prime=0.043

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
103
BIC
14322.5

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.sdt-2afc; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

click-choice-rate-null.brunton-2013-rats-humans.psychometric.subject.b127

dirty fit

response_rate=0.508

Method
manual
Commit
3ec74b4
Predicted points
103
BIC
17826.7

Fitted with manual via behavtaskatlas.model_fits.click-choice-rate-null; success=True; closed-form per-subject Bernoulli MLE

Observed points

Right minus left clicks p_right n
-53.000 0.0000 1
-52.000 0.0000 1
-51.000 0.0000 1
-50.000 1.0000 1
-49.000 0.0000 3
-48.000 0.0000 3
-47.000 0.0000 3
-46.000 0.0000 2
-45.000 0.0000 4
-44.000 0.0000 3
-43.000 0.0000 5
-42.000 0.0000 8
-41.000 0.1429 7
-40.000 0.0769 13
-39.000 0.0714 14
-38.000 0.0370 27
-37.000 0.0500 20
-36.000 0.0417 24
-35.000 0.1724 29
-34.000 0.1667 24
-33.000 0.1087 46
-32.000 0.0976 41
-31.000 0.0377 53
-30.000 0.1579 57
-29.000 0.1507 73
-28.000 0.0800 75
-27.000 0.1094 64
-26.000 0.1200 75
-25.000 0.1081 74
-24.000 0.1354 96
-23.000 0.1889 90
-22.000 0.1600 150
-21.000 0.1417 127
-20.000 0.1218 156
-19.000 0.1436 188
-18.000 0.1309 191
-17.000 0.1545 246
-16.000 0.1179 280
-15.000 0.1554 296
-14.000 0.2057 282
-13.000 0.2179 280
-12.000 0.2204 313
-11.000 0.2337 291
-10.000 0.2131 305
-9.000 0.3118 279
-8.000 0.3297 279
-7.000 0.2896 259
-6.000 0.3615 260
-5.000 0.3750 232
-4.000 0.4460 213
-3.000 0.4082 196
-2.000 0.4795 146
-1.000 0.5800 150
0.000 0.6547 139
1.000 0.6269 134
2.000 0.6510 149
3.000 0.6940 183
4.000 0.7033 209
5.000 0.7137 262
6.000 0.7189 281
7.000 0.6975 281
8.000 0.7138 304
9.000 0.7614 306
10.000 0.7570 284
11.000 0.7915 331
12.000 0.7930 343
13.000 0.7645 327
14.000 0.7855 317
15.000 0.7833 263
16.000 0.8089 293
17.000 0.8123 261
18.000 0.8066 212
19.000 0.7799 209
20.000 0.7308 182
21.000 0.6918 159
22.000 0.7808 146
23.000 0.7481 131
24.000 0.7545 110
25.000 0.8200 100
26.000 0.7342 79
27.000 0.7945 73
28.000 0.7765 85
29.000 0.7875 80
30.000 0.7442 86
31.000 0.7237 76
32.000 0.7460 63
33.000 0.7872 47
34.000 0.8085 47
35.000 0.8049 41
36.000 0.6944 36
37.000 0.7667 30
38.000 0.7813 32
39.000 0.8000 30
40.000 0.6111 18
41.000 0.6842 19
42.000 0.7778 9
43.000 0.9091 11
44.000 0.6667 6
45.000 0.8000 5
46.000 0.7500 4
47.000 1.0000 2
48.000 1.0000 1
49.000 0.3333 3

Take it with you

Cover sheet

Self-contained Markdown for citation, slides, or notebooks

The cover sheet pins the finding to the atlas commit and includes the observed points, fit ranking, caveats, and provenance — everything needed to drop into a paper or notebook without losing the trail back to the deploy.