Finding

walsh-2024-prior-cue.psychometric.subject.cue-neutral.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 1108.4 0.0 0.0960 —
logistic-4param 1112.3 3.8 0.0962 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -552.2 975 2 0.1731 10
logistic-4param -552.1 975 4 0.1713 10

Residuals

Fit parameters and provenance

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

dirty fit

criterion=-0.437 · d_prime=0.031

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1118.2

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-a

dirty fit

bias=-16.165 · lower_lapse=0.000 · slope=17.231 · upper_lapse=0.046

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1131.8

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 0.3730 126
-17.000 0.5068 73
-16.000 0.6505 103
-15.000 0.3551 107
-14.000 0.4638 69
14.000 0.7500 76
15.000 0.7273 99
16.000 0.9907 107
17.000 0.8193 83
22.000 0.8409 132

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.