Finding

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

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 2617.7 0.0 0.0416 —
sdt-2afc 2648.4 30.7 0.0591 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -1304.9 2,860 4 0.0681 8
sdt-2afc -1322.2 2,860 2 0.0984 8

Residuals

Fit parameters and provenance

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

dirty fit

bias=13.358 · lower_lapse=0.181 · slope=0.426 · upper_lapse=0.129

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
2641.5

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-c

dirty fit

criterion=-0.031 · d_prime=0.057

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
2660.3

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
-20.000 0.1873 251
-18.000 0.1722 395
-15.000 0.2435 382
-14.000 0.1237 388
14.000 0.7461 386
15.000 0.8568 405
18.000 0.9148 399
20.000 0.8031 254

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.