Finding

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

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 3304.1 0.0 0.0817 —
sdt-2afc 3360.2 56.1 0.0868 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -1648.1 4,110 4 0.1894 10
sdt-2afc -1678.1 4,110 2 0.1794 10

Residuals

Fit parameters and provenance

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

dirty fit

bias=-7.894 · lower_lapse=0.197 · slope=3.649 · upper_lapse=0.065

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
3329.4

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-a

dirty fit

criterion=-0.423 · d_prime=0.060

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
3372.8

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
-22.000 0.1938 547
-17.000 0.2211 285
-16.000 0.4591 416
-15.000 0.1348 423
-14.000 0.3333 303
14.000 0.9576 330
15.000 0.9200 450
16.000 0.9837 430
17.000 0.9429 315
22.000 0.8936 611

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.