Finding

steinmetz-2019-distributed-coding.choice-conditional-psychometric

Steinmetz NA, Zatka-Haas P, Carandini M, Harris KD. Distributed coding of choice, action and engagement across the mouse brain. Nature 576:266-273, 2019. · Mouse unforced visual contrast wheel task · psychometric

Observed curve and fits

Fit diagnostics

Variant AIC Δ AIC RMSE Caveats
logistic-4param winner 4935.1 0.0 0.0267 —
sdt-2afc 5044.8 109.7 0.0656 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -2463.5 6,745 4 0.0542 9
sdt-2afc -2520.4 6,745 2 0.1314 9

Residuals

Fit parameters and provenance

logistic-4param.steinmetz-2019-distributed-coding.choice-conditional-psychometric

dirty fit

bias=-3.385 · lower_lapse=0.033 · slope=15.385 · upper_lapse=0.058

Method
scipy.optimize.minimize
Commit
8365eb3
Predicted points
9
BIC
4962.3

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

sdt-2afc.steinmetz-2019-distributed-coding.choice-conditional-psychometric

dirty fit

criterion=-0.011 · d_prime=0.023

Method
scipy.optimize.minimize
Commit
8365eb3
Predicted points
9
BIC
5058.4

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

Observed points

Signed contrast p_right_choice_trials n
-100.000 0.0166 722
-75.000 0.0681 587
-50.000 0.0808 891
-25.000 0.1576 476
0.000 0.5497 1,439
25.000 0.8382 544
50.000 0.8883 824
75.000 0.9174 533
100.000 0.9684 729

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.