Finding

brunton-2013-rats-humans.psychometric.subject.b075

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 12175.8 0.0 0.1276
  • click-summary accumulator
logistic-4param 12256.9 81.0 0.1251
click-count-logistic 12259.7 83.9 0.1276
  • click-summary baseline
sdt-2afc 12375.3 199.5 0.1428
click-choice-rate-null 15646.3 3470.5 0.3330
  • click-summary baseline
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
click-leaky-accumulator -6081.9 11,285 6 0.8873 66
logistic-4param -6124.4 11,285 4 0.8413 66
click-count-logistic -6126.9 11,285 3 0.8572 66
sdt-2afc -6185.7 11,285 2 0.9785 66
click-choice-rate-null -7822.2 11,285 1 0.5000 66

Residuals

Fit parameters and provenance

click-leaky-accumulator.brunton-2013-rats-humans.psychometric.subject.b075

dirty fit

bias=2.235 · input_gain=3.978 · lapse=0.204 · leak=0.000 · noise_accumulator=0.000 · noise_input=10.000

Method
scipy.optimize.minimize
Commit
942a5e0
Predicted points
66
BIC
12219.8

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.b075

dirty fit

bias=0.146 · lower_lapse=0.130 · slope=4.567 · upper_lapse=0.158

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
66
BIC
12286.2

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.b075

dirty fit

bias=0.747 · lapse=0.284 · sensitivity=0.216

Method
scipy.optimize.minimize
Commit
3ec74b4
Predicted points
66
BIC
12281.7

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.b075

dirty fit

criterion=0.055 · d_prime=0.065

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
66
BIC
12390.0

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.b075

dirty fit

response_rate=0.500

Method
manual
Commit
3ec74b4
Predicted points
66
BIC
15653.7

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
-32.000 0.0000 1
-30.000 0.0000 3
-29.000 0.0000 6
-28.000 0.0000 6
-27.000 0.0000 7
-26.000 0.0625 16
-25.000 0.1333 15
-24.000 0.0690 29
-23.000 0.1613 31
-22.000 0.1471 68
-21.000 0.1429 84
-20.000 0.1538 104
-19.000 0.1351 111
-18.000 0.1484 128
-17.000 0.1618 173
-16.000 0.1481 189
-15.000 0.1452 186
-14.000 0.1277 235
-13.000 0.1674 239
-12.000 0.1928 306
-11.000 0.2305 334
-10.000 0.2287 293
-9.000 0.2209 344
-8.000 0.2170 341
-7.000 0.2248 347
-6.000 0.2775 364
-5.000 0.2533 300
-4.000 0.2929 297
-3.000 0.3630 281
-2.000 0.3623 207
-1.000 0.5278 180
0.000 0.4911 169
1.000 0.5497 191
2.000 0.5973 221
3.000 0.6408 284
4.000 0.6510 341
5.000 0.6331 357
6.000 0.7113 381
7.000 0.6855 372
8.000 0.7222 360
9.000 0.7598 358
10.000 0.7186 334
11.000 0.7568 333
12.000 0.7827 336
13.000 0.7928 304
14.000 0.8289 263
15.000 0.8174 241
16.000 0.8346 254
17.000 0.8580 176
18.000 0.8333 180
19.000 0.8438 160
20.000 0.8304 112
21.000 0.9239 92
22.000 0.7123 73
23.000 0.8657 67
24.000 0.8000 35
25.000 0.9167 24
26.000 0.8235 17
27.000 0.9091 11
28.000 0.6667 3
29.000 1.0000 3
30.000 0.6667 3
32.000 0.0000 1
33.000 1.0000 2
35.000 1.0000 1
37.000 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.