Finding

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

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 992.8 0.0 0.0779 —
logistic-4param 996.8 3.9 0.0783 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -494.4 840 2 0.1115 10
logistic-4param -494.4 840 4 0.1149 10

Residuals

Fit parameters and provenance

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

dirty fit

criterion=-0.499 · d_prime=0.029

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1002.3

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-e

dirty fit

bias=-3.129 · lower_lapse=0.405 · slope=11.079 · upper_lapse=0.102

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1015.7

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
-15.000 0.5278 72
-14.000 0.6000 70
-12.000 0.4488 127
-11.000 0.5753 73
-10.000 0.6892 74
10.000 0.8974 78
11.000 0.8243 74
12.000 0.7087 127
14.000 0.9028 72
15.000 0.7671 73

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.