Finding

brunton-2013-rats-humans.psychometric.subject.a080

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 14020.9 0.0 0.1290
  • click-summary accumulator
click-count-logistic 14077.0 56.1 0.1261
  • click-summary baseline
logistic-4param 14078.8 57.8 0.1257 —
sdt-2afc 14204.2 183.3 0.1494 —
click-choice-rate-null 16880.0 2859.1 0.3071
  • click-summary baseline
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
click-leaky-accumulator -7004.5 12,175 6 0.8791 84
click-count-logistic -7035.5 12,175 3 0.8429 84
logistic-4param -7035.4 12,175 4 0.8396 84
sdt-2afc -7100.1 12,175 2 0.9613 84
click-choice-rate-null -8439.0 12,175 1 0.5015 84

Residuals

Fit parameters and provenance

click-leaky-accumulator.brunton-2013-rats-humans.psychometric.subject.a080

dirty fit

bias=0.003 · input_gain=3.025 · lapse=0.181 · leak=1.580 · noise_accumulator=0.028 · noise_input=9.898

Method
scipy.optimize.minimize
Commit
942a5e0
Predicted points
84
BIC
14065.4

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.a080

dirty fit

bias=0.007 · lapse=0.311 · sensitivity=0.164

Method
scipy.optimize.minimize
Commit
3ec74b4
Predicted points
84
BIC
14099.2

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

logistic-4param.brunton-2013-rats-humans.psychometric.subject.a080

dirty fit

bias=-0.167 · lower_lapse=0.152 · slope=6.116 · upper_lapse=0.159

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
84
BIC
14108.4

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

sdt-2afc.brunton-2013-rats-humans.psychometric.subject.a080

dirty fit

criterion=0.003 · d_prime=0.047

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
84
BIC
14219.0

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.a080

dirty fit

response_rate=0.498

Method
manual
Commit
3ec74b4
Predicted points
84
BIC
16887.4

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
-43.000 0.0000 1
-42.000 0.0000 1
-40.000 0.0000 2
-39.000 0.2000 5
-38.000 0.0000 5
-37.000 0.1667 6
-36.000 0.0909 11
-35.000 0.5000 4
-34.000 0.3125 16
-33.000 0.1500 20
-32.000 0.1613 31
-31.000 0.2581 31
-30.000 0.1111 36
-29.000 0.1190 42
-28.000 0.1538 52
-27.000 0.2381 63
-26.000 0.1463 82
-25.000 0.1205 83
-24.000 0.1053 76
-23.000 0.1981 106
-22.000 0.1417 120
-21.000 0.1056 142
-20.000 0.1748 143
-19.000 0.1718 163
-18.000 0.2321 168
-17.000 0.1579 171
-16.000 0.2240 183
-15.000 0.1897 195
-14.000 0.2837 208
-13.000 0.2545 220
-12.000 0.2372 215
-11.000 0.2717 254
-10.000 0.2391 276
-9.000 0.3192 260
-8.000 0.3613 274
-7.000 0.2973 296
-6.000 0.3072 319
-5.000 0.3457 376
-4.000 0.3961 308
-3.000 0.3681 345
-2.000 0.4451 337
-1.000 0.4413 315
0.000 0.5224 312
1.000 0.5687 313
2.000 0.5382 314
3.000 0.5787 356
4.000 0.6707 331
5.000 0.6062 325
6.000 0.6703 276
7.000 0.6796 309
8.000 0.7273 275
9.000 0.6990 299
10.000 0.7269 260
11.000 0.7590 249
12.000 0.7250 240
13.000 0.8207 184
14.000 0.7658 222
15.000 0.7562 201
16.000 0.7735 181
17.000 0.7987 149
18.000 0.8226 186
19.000 0.8070 171
20.000 0.7862 145
21.000 0.8120 117
22.000 0.8348 115
23.000 0.8280 93
24.000 0.8673 98
25.000 0.8354 79
26.000 0.8140 86
27.000 0.9038 52
28.000 0.8036 56
29.000 0.9091 55
30.000 0.8857 35
31.000 0.7941 34
32.000 0.7692 26
33.000 0.7917 24
34.000 0.5833 12
35.000 1.0000 12
36.000 1.0000 9
37.000 0.8000 5
38.000 0.0000 1
39.000 1.0000 4
40.000 0.5000 2
47.000 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.