Finding

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

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 2070.1 0.0 0.0712 —
sdt-2afc 2839.5 769.5 0.1502 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -1031.0 4,135 4 0.1823 8
sdt-2afc -1417.8 4,135 2 0.2596 8

Residuals

Fit parameters and provenance

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

dirty fit

bias=2.455 · lower_lapse=0.179 · slope=1.00e-6 · upper_lapse=0.006

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
2095.4

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-h

dirty fit

criterion=-0.505 · d_prime=0.140

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
2852.2

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
-22.000 0.1946 257
-10.000 0.3617 376
-7.000 0.1334 1,027
-6.000 0.1102 363
6.000 0.9944 359
7.000 0.9972 1,072
10.000 0.9924 397
22.000 0.9824 284

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.