Finding

walsh-2024-prior-cue.psychometric.subject.cue-invalid.subject-d

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 923.4 0.0 0.0839 —
logistic-4param 929.5 6.1 0.0829 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -459.7 960 2 0.1353 8
logistic-4param -460.7 960 4 0.1600 8

Residuals

Fit parameters and provenance

sdt-2afc.walsh-2024-prior-cue.psychometric.subject.cue-invalid.subject-d

dirty fit

criterion=-0.895 · d_prime=0.006

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
933.1

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-invalid.subject-d

dirty fit

bias=-20.000 · lower_lapse=0.500 · slope=46.947 · upper_lapse=0.000

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
949.0

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.logistic-4param; success=True; CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL

Observed points

Signed target-distractor contrast p_right n
-20.000 0.6825 63
-18.000 0.6489 94
-16.000 0.9206 63
-14.000 0.8456 259
14.000 0.7915 259
16.000 0.8571 63
18.000 0.9158 95
20.000 0.8438 64

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.