Finding

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

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 1056.2 0.0 0.2105 —
logistic-4param 1063.3 7.1 0.2127 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -526.1 800 2 0.4746 10
logistic-4param -527.7 800 4 0.4731 10

Residuals

Fit parameters and provenance

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

dirty fit

criterion=-0.338 · d_prime=1.00e-6

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1065.6

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.sdt-2afc; success=True; CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL

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

dirty fit

bias=-15.000 · lower_lapse=0.500 · slope=300.000 · upper_lapse=0.249

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1082.1

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
-15.000 0.8116 69
-14.000 0.6974 76
-12.000 0.8596 114
-11.000 0.8592 71
-10.000 0.7895 76
10.000 0.6076 79
11.000 0.4638 69
12.000 0.1579 114
14.000 0.7188 64
15.000 0.5000 68

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.