Finding

brunton-2013-rats-humans.psychometric.subject.t014

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 14339.3 0.0 0.0500 —
click-leaky-accumulator 14371.1 31.8 0.0439
  • click-summary accumulator
click-count-logistic 14414.0 74.7 0.0539
  • click-summary baseline
sdt-2afc 14502.6 163.3 0.0606 —
click-choice-rate-null 18785.1 4445.8 0.3655
  • click-summary baseline
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -7165.7 13,617 4 0.1511 85
click-leaky-accumulator -7179.6 13,617 6 0.1861 85
click-count-logistic -7204.0 13,617 3 0.1809 85
sdt-2afc -7249.3 13,617 2 0.2313 85
click-choice-rate-null -9391.6 13,617 1 0.5415 85

Residuals

Fit parameters and provenance

logistic-4param.brunton-2013-rats-humans.psychometric.subject.t014

dirty fit

bias=2.317 · lower_lapse=0.045 · slope=5.568 · upper_lapse=0.150

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
85
BIC
14369.4

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.t014

dirty fit

bias=9.416 · input_gain=3.327 · lapse=0.005 · leak=1.545 · noise_accumulator=28.356 · noise_input=10.000

Method
scipy.optimize.minimize
Commit
942a5e0
Predicted points
85
BIC
14416.3

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.t014

dirty fit

bias=4.542 · lapse=0.125 · sensitivity=0.151

Method
scipy.optimize.minimize
Commit
3ec74b4
Predicted points
85
BIC
14436.6

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.t014

dirty fit

criterion=0.312 · d_prime=0.065

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
85
BIC
14517.6

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.t014

dirty fit

response_rate=0.458

Method
manual
Commit
3ec74b4
Predicted points
85
BIC
18792.6

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
-44.000 0.0000 1
-43.000 0.0000 1
-41.000 0.0000 2
-38.000 0.0000 3
-37.000 0.0000 2
-36.000 0.0000 1
-35.000 0.0000 6
-34.000 0.0000 7
-33.000 0.0000 12
-32.000 0.0000 10
-31.000 0.0500 20
-30.000 0.0909 22
-29.000 0.0811 37
-28.000 0.0455 44
-27.000 0.0943 53
-26.000 0.0233 43
-25.000 0.0421 95
-24.000 0.0563 71
-23.000 0.0698 86
-22.000 0.0280 107
-21.000 0.0426 94
-20.000 0.0604 149
-19.000 0.1138 123
-18.000 0.1061 132
-17.000 0.0440 159
-16.000 0.0861 151
-15.000 0.0566 159
-14.000 0.0793 164
-13.000 0.1094 192
-12.000 0.1307 199
-11.000 0.0838 179
-10.000 0.1018 226
-9.000 0.1239 218
-8.000 0.1462 253
-7.000 0.1589 258
-6.000 0.1705 264
-5.000 0.2013 313
-4.000 0.2318 302
-3.000 0.2718 309
-2.000 0.3213 333
-1.000 0.3209 349
0.000 0.3915 378
1.000 0.3599 439
2.000 0.4563 469
3.000 0.4989 461
4.000 0.5562 489
5.000 0.5670 448
6.000 0.5772 492
7.000 0.6025 473
8.000 0.6214 420
9.000 0.6486 407
10.000 0.6718 387
11.000 0.6697 333
12.000 0.7492 295
13.000 0.7205 297
14.000 0.7695 295
15.000 0.7403 258
16.000 0.7854 261
17.000 0.8028 213
18.000 0.7752 218
19.000 0.8010 191
20.000 0.8430 172
21.000 0.8471 157
22.000 0.8222 135
23.000 0.8102 137
24.000 0.8652 89
25.000 0.8800 100
26.000 0.8526 95
27.000 0.8750 72
28.000 0.8269 52
29.000 0.9111 45
30.000 0.8438 32
31.000 0.8571 35
32.000 0.8095 21
33.000 0.7500 28
34.000 0.9091 22
35.000 0.9286 14
36.000 0.9167 12
37.000 0.7500 8
38.000 0.8000 10
39.000 1.0000 4
40.000 1.0000 1
42.000 1.0000 1
43.000 1.0000 1
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.