Finding

walsh-2024-prior-cue.psychometric.subject.cue-neutral.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 760.6 0.0 0.0300 —
sdt-2afc 773.2 12.6 0.0813 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -376.3 725 4 0.0564 6
sdt-2afc -384.6 725 2 0.1568 6

Residuals

Fit parameters and provenance

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

dirty fit

bias=-9.226 · lower_lapse=0.246 · slope=1.515 · upper_lapse=0.146

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
6
BIC
778.9

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-neutral.subject-k

dirty fit

criterion=-0.556 · d_prime=0.049

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
6
BIC
782.4

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 0.2459 61
-12.000 0.3297 91
-8.000 0.6667 210
8.000 0.8213 207
12.000 0.8876 89
22.000 0.9104 67

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.