Finding

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

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 1219.6 0.0 0.2686 —
logistic-4param 1226.4 6.8 0.2689 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -607.8 1,040 2 0.4561 8
logistic-4param -609.2 1,040 4 0.4558 8

Residuals

Fit parameters and provenance

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

dirty fit

criterion=-0.609 · d_prime=1.00e-6

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
1229.5

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-h

dirty fit

bias=-22.000 · lower_lapse=0.500 · slope=440.000 · upper_lapse=0.057

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
1246.2

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.logistic-4param; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

Observed points

Signed target-distractor contrast p_right n
-22.000 0.5373 67
-10.000 0.9505 101
-7.000 0.9926 269
-6.000 0.9892 93
6.000 0.2727 88
7.000 0.3953 258
10.000 0.8557 97
22.000 0.8657 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.