Finding

walsh-2024-prior-cue.psychometric.subject.cue-invalid.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 838.0 0.0 0.0724 —
logistic-4param 840.9 2.9 0.0697 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -417.0 700 2 0.1549 8
logistic-4param -416.4 700 4 0.1236 8

Residuals

Fit parameters and provenance

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

dirty fit

criterion=-0.145 · d_prime=0.034

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
847.1

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-c

dirty fit

bias=2.116 · lower_lapse=0.337 · slope=2.717 · upper_lapse=0.236

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
859.1

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 0.2903 62
-18.000 0.3061 98
-15.000 0.3368 95
-14.000 0.4043 94
14.000 0.6915 94
15.000 0.8600 100
18.000 0.8021 96
20.000 0.6393 61

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.