Finding

walsh-2024-prior-cue.psychometric.subject.cue-neutral.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 762.2 0.0 0.0233 —
sdt-2afc 797.4 35.2 0.0846 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -377.1 805 4 0.0441 8
sdt-2afc -396.7 805 2 0.1391 8

Residuals

Fit parameters and provenance

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

dirty fit

bias=-12.929 · lower_lapse=0.272 · slope=0.062 · upper_lapse=0.103

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
780.9

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-i

dirty fit

criterion=-0.369 · d_prime=0.049

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
806.8

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 0.3000 70
-16.000 0.2879 66
-14.000 0.2531 162
-13.000 0.4242 99
13.000 0.8911 101
14.000 0.9240 171
16.000 0.8824 68
28.000 0.8529 68

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.