Finding

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

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 913.4 0.0 0.0275 —
sdt-2afc 922.0 8.6 0.0603 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -452.7 795 4 0.0454 8
sdt-2afc -459.0 795 2 0.1133 8

Residuals

Fit parameters and provenance

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

dirty fit

bias=-12.940 · lower_lapse=0.138 · slope=5.531 · upper_lapse=0.191

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
932.1

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-invalid.subject-i

dirty fit

criterion=-0.289 · d_prime=0.033

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
931.4

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
-28.000 0.1765 68
-16.000 0.4286 70
-14.000 0.4091 154
-13.000 0.4902 102
13.000 0.8333 96
14.000 0.7939 165
16.000 0.8281 64
28.000 0.7763 76

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.