Finding

walsh-2024-prior-cue.psychometric.subject.cue-valid.subject-i

Walsh K, McGovern DP, Dully J, Kelly SP, O'Connell RG. Prior probability cues bias sensory encoding with increasing task exposure. eLife 12:RP91135, 2024. · Human visual contrast 2AFC keyboard task · psychometric

Observed curve and fits

Fit diagnostics

Variant AIC Δ AIC RMSE Caveats
logistic-4param winner 2252.7 0.0 0.0455 —
sdt-2afc 2528.3 275.5 0.0977 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -1122.4 3,200 4 0.1166 8
sdt-2afc -1262.1 3,200 2 0.1667 8

Residuals

Fit parameters and provenance

logistic-4param.walsh-2024-prior-cue.psychometric.subject.cue-valid.subject-i

dirty fit

bias=-3.338 · lower_lapse=0.168 · slope=0.560 · upper_lapse=0.070

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
2277.0

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

sdt-2afc.walsh-2024-prior-cue.psychometric.subject.cue-valid.subject-i

dirty fit

criterion=-0.216 · d_prime=0.066

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
2540.4

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

Observed points

Signed target-distractor contrast p_right n
-28.000 0.1579 266
-16.000 0.1711 263
-14.000 0.1839 647
-13.000 0.1458 391
13.000 0.9610 410
14.000 0.9478 670
16.000 0.9599 274
28.000 0.8136 279

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.