Finding

walsh-2024-prior-cue.psychometric.subject.cue-neutral.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 899.4 0.0 0.0493 —
sdt-2afc 904.8 5.4 0.0682 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -445.7 1,035 4 0.0916 8
sdt-2afc -450.4 1,035 2 0.1192 8

Residuals

Fit parameters and provenance

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

dirty fit

bias=-20.103 · lower_lapse=0.000 · slope=8.462 · upper_lapse=0.061

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
919.2

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-neutral.subject-h

dirty fit

criterion=-0.973 · d_prime=0.041

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
914.7

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 0.4091 66
-10.000 0.8125 96
-7.000 0.7432 257
-6.000 0.7701 87
6.000 0.8791 91
7.000 0.8951 267
10.000 1.0000 97
22.000 0.8919 74

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.