Finding

walsh-2024-prior-cue.psychometric.subject.cue-neutral.subject-c

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 714.6 0.0 0.0503 —
logistic-4param 718.6 4.0 0.0506 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -355.3 710 2 0.0704 8
logistic-4param -355.3 710 4 0.0706 8

Residuals

Fit parameters and provenance

sdt-2afc.walsh-2024-prior-cue.psychometric.subject.cue-neutral.subject-c

dirty fit

criterion=-0.102 · d_prime=0.051

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
723.7

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-neutral.subject-c

dirty fit

bias=0.529 · lower_lapse=0.070 · slope=10.816 · upper_lapse=0.000

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
736.9

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
-20.000 0.1364 66
-18.000 0.2626 99
-15.000 0.2553 94
-14.000 0.2447 94
14.000 0.7216 97
15.000 0.8515 101
18.000 0.9072 97
20.000 0.8065 62

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.