Finding

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

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 2329.6 0.0 5.09e-4 —
sdt-2afc 2339.6 10.0 0.0331 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -1160.8 4,630 4 9.61e-4 4
sdt-2afc -1167.8 4,630 2 0.0643 4

Residuals

Fit parameters and provenance

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

dirty fit

bias=-6.430 · lower_lapse=0.216 · slope=0.868 · upper_lapse=0.005

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
4
BIC
2355.4

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-l

dirty fit

criterion=-0.884 · d_prime=0.143

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
4
BIC
2352.5

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
-12.000 0.2173 1,620
-10.000 0.2286 420
10.000 0.9956 675
12.000 0.9943 1,915

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.