Finding

zatka-haas-2021-sensory-coding-causal-impact.choice-conditional-psychometric

Zatka-Haas P, Steinmetz NA, Carandini M, Harris KD. Sensory coding and the causal impact of mouse cortex in a visual decision. eLife 10:e63163, 2021. · Mouse unforced visual contrast wheel task · psychometric

Observed curve and fits

Fit diagnostics

Variant AIC Δ AIC RMSE Caveats
logistic-4param winner 7828.9 0.0 0.0881 —
sdt-2afc 7881.2 52.3 0.1111 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -3910.5 7,547 4 0.1975 13
sdt-2afc -3938.6 7,547 2 0.1966 13

Residuals

Fit parameters and provenance

logistic-4param.zatka-haas-2021-sensory-coding-causal-impact.choice-conditional-psychometric

dirty fit

bias=0.612 · lower_lapse=0.106 · slope=7.956 · upper_lapse=0.137

Method
scipy.optimize.minimize
Commit
8365eb3
Predicted points
13
BIC
7856.6

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

sdt-2afc.zatka-haas-2021-sensory-coding-causal-impact.choice-conditional-psychometric

dirty fit

criterion=0.075 · d_prime=0.030

Method
scipy.optimize.minimize
Commit
8365eb3
Predicted points
13
BIC
7895.1

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
-54.000 0.0501 579
-44.000 0.1289 357
-30.000 0.2632 418
-24.000 0.0893 549
-14.000 0.2288 389
-10.000 0.1590 283
0.000 0.4739 2,357
10.000 0.7867 286
14.000 0.6997 393
24.000 0.8827 554
30.000 0.6469 388
44.000 0.8329 419
54.000 0.9513 575

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.