Finding

walsh-2024-prior-cue.psychometric.subject.cue-neutral.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 934.1 0.0 0.0812 —
logistic-4param 936.4 2.3 0.0729 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -465.0 940 2 0.1698 8
logistic-4param -464.2 940 4 0.1673 8

Residuals

Fit parameters and provenance

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

dirty fit

criterion=-0.797 · d_prime=0.030

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
943.8

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-d

dirty fit

bias=-20.000 · lower_lapse=0.136 · slope=6.544 · upper_lapse=0.102

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
955.8

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
-20.000 0.5000 62
-18.000 0.5319 94
-16.000 0.7969 64
-14.000 0.6586 249
14.000 0.8617 253
16.000 0.9688 64
18.000 0.9565 92
20.000 0.8548 62

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.