Finding

walsh-2024-prior-cue.psychometric.subject.cue-neutral.subject-j

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
sdt-2afc winner 518.7 0.0 1.17e-8 —
logistic-4param 522.7 4.0 1.52e-8 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -257.4 705 2 1.60e-8 2
logistic-4param -257.4 705 4 1.94e-8 2

Residuals

Fit parameters and provenance

sdt-2afc.walsh-2024-prior-cue.psychometric.subject.cue-neutral.subject-j

dirty fit

criterion=-1.190 · d_prime=0.069

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
2
BIC
527.8

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

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

dirty fit

bias=-9.531 · lower_lapse=0.410 · slope=6.122 · upper_lapse=0.007

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
2
BIC
541.0

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

Observed points

Signed target-distractor contrast p_right n
-10.000 0.6905 336
10.000 0.9702 369

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.