Finding

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

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 1232.7 0.0 0.0979 —
logistic-4param 1237.5 4.7 0.0989 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -614.4 1,020 2 0.1955 10
logistic-4param -614.7 1,020 4 0.1986 10

Residuals

Fit parameters and provenance

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

dirty fit

criterion=-0.553 · d_prime=1.00e-6

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1242.6

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-a

dirty fit

bias=-22.000 · lower_lapse=0.500 · slope=440.000 · upper_lapse=0.091

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1257.2

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
-22.000 0.6127 142
-17.000 0.7412 85
-16.000 0.8091 110
-15.000 0.7636 110
-14.000 0.7848 79
14.000 0.7059 68
15.000 0.5143 105
16.000 0.8667 105
17.000 0.6486 74
22.000 0.6901 142

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.