Finding

walsh-2024-prior-cue.psychometric.subject.cue-invalid.subject-g

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
sdt-2afc winner 1220.1 0.0 0.0749 —
logistic-4param 1230.3 10.2 0.0812 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -608.1 1,180 2 0.1968 10
logistic-4param -611.1 1,180 4 0.1846 10

Residuals

Fit parameters and provenance

sdt-2afc.walsh-2024-prior-cue.psychometric.subject.cue-invalid.subject-g

dirty fit

criterion=-0.803 · d_prime=1.00e-6

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1230.3

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.sdt-2afc; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

logistic-4param.walsh-2024-prior-cue.psychometric.subject.cue-invalid.subject-g

dirty fit

bias=-10.000 · lower_lapse=0.500 · slope=60.376 · upper_lapse=0.000

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1250.6

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.logistic-4param; success=True; CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL

Observed points

Signed target-distractor contrast p_right n
-10.000 0.8312 77
-7.000 0.7790 181
-6.000 0.8760 121
-4.000 0.8378 111
-3.000 0.7903 124
3.000 0.5922 103
4.000 0.7426 101
6.000 0.7800 100
7.000 0.8111 180
10.000 0.8415 82

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.