Finding

brunton-2013-rats-humans.psychometric.subject.t074

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
click-leaky-accumulator winner 13163.3 0.0 0.1457
  • click-summary accumulator
logistic-4param 13163.6 0.3 0.1303 —
click-count-logistic 13211.3 48.0 0.1450
  • click-summary baseline
sdt-2afc 13405.6 242.3 0.1696 —
click-choice-rate-null 15650.8 2487.5 0.3070
  • click-summary baseline
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
click-leaky-accumulator -6575.6 11,290 6 0.8275 82
logistic-4param -6577.8 11,290 4 0.7700 82
click-count-logistic -6602.7 11,290 3 0.8005 82
sdt-2afc -6700.8 11,290 2 0.9678 82
click-choice-rate-null -7824.4 11,290 1 0.5074 82

Residuals

Fit parameters and provenance

click-leaky-accumulator.brunton-2013-rats-humans.psychometric.subject.t074

dirty fit

bias=-2.735 · input_gain=4.317 · lapse=0.340 · leak=0.000 · noise_accumulator=10.370 · noise_input=10.000

Method
scipy.optimize.minimize
Commit
942a5e0
Predicted points
82
BIC
13207.3

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

logistic-4param.brunton-2013-rats-humans.psychometric.subject.t074

dirty fit

bias=-2.902 · lower_lapse=0.137 · slope=4.864 · upper_lapse=0.230

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
82
BIC
13192.9

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

click-count-logistic.brunton-2013-rats-humans.psychometric.subject.t074

dirty fit

bias=-0.847 · lapse=0.399 · sensitivity=0.225

Method
scipy.optimize.minimize
Commit
3ec74b4
Predicted points
82
BIC
13233.3

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.t074

dirty fit

criterion=0.013 · d_prime=0.044

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
82
BIC
13420.3

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.t074

dirty fit

response_rate=0.507

Method
manual
Commit
3ec74b4
Predicted points
82
BIC
15658.1

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
-41.000 0.0000 3
-39.000 0.0000 2
-37.000 0.0000 2
-36.000 0.0000 8
-35.000 0.1111 9
-34.000 0.0000 8
-33.000 0.0000 13
-32.000 0.0370 27
-31.000 0.0952 21
-30.000 0.0690 29
-29.000 0.0571 35
-28.000 0.0789 38
-27.000 0.1923 52
-26.000 0.0746 67
-25.000 0.1519 79
-24.000 0.1818 88
-23.000 0.1205 83
-22.000 0.1000 100
-21.000 0.2054 112
-20.000 0.1709 117
-19.000 0.1826 115
-18.000 0.1701 147
-17.000 0.1790 162
-16.000 0.1879 165
-15.000 0.1960 199
-14.000 0.2134 164
-13.000 0.2344 209
-12.000 0.2096 229
-11.000 0.2804 214
-10.000 0.3128 243
-9.000 0.2881 243
-8.000 0.2275 233
-7.000 0.3434 265
-6.000 0.3564 275
-5.000 0.3750 280
-4.000 0.3889 288
-3.000 0.4250 320
-2.000 0.4421 328
-1.000 0.4746 276
0.000 0.5331 287
1.000 0.6232 276
2.000 0.6317 315
3.000 0.6452 341
4.000 0.6413 329
5.000 0.6424 330
6.000 0.7161 317
7.000 0.6821 324
8.000 0.7709 275
9.000 0.7099 262
10.000 0.7143 238
11.000 0.8059 237
12.000 0.7662 231
13.000 0.7163 215
14.000 0.7500 216
15.000 0.7273 187
16.000 0.7239 163
17.000 0.7708 144
18.000 0.7622 164
19.000 0.7770 139
20.000 0.7863 131
21.000 0.7537 134
22.000 0.7434 113
23.000 0.7395 119
24.000 0.7549 102
25.000 0.8194 72
26.000 0.7471 87
27.000 0.6977 43
28.000 0.6667 57
29.000 0.8947 38
30.000 0.9000 40
31.000 0.7778 27
32.000 0.5263 19
33.000 0.7143 14
34.000 0.7059 17
35.000 0.6667 12
36.000 1.0000 7
37.000 0.4286 7
38.000 0.6667 6
40.000 0.3333 3
41.000 1.0000 1
42.000 0.0000 2
44.000 1.0000 1

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.