Finding

walsh-2024-prior-cue.psychometric.subject.cue-valid.subject-k

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 2746.1 0.0 0.0062 —
sdt-2afc 2870.9 124.9 0.0850 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -1369.0 2,855 4 0.0097 6
sdt-2afc -1433.5 2,855 2 0.1439 6

Residuals

Fit parameters and provenance

logistic-4param.walsh-2024-prior-cue.psychometric.subject.cue-valid.subject-k

dirty fit

bias=-7.497 · lower_lapse=0.277 · slope=0.278 · upper_lapse=0.105

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
6
BIC
2769.9

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-valid.subject-k

dirty fit

criterion=-0.383 · d_prime=0.066

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
6
BIC
2882.9

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 0.2863 234
-12.000 0.2715 361
-8.000 0.3642 810
8.000 0.8994 835
12.000 0.8855 358
22.000 0.8949 257

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.