Finding

walsh-2024-prior-cue.psychometric.subject.cue-valid.subject-b

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
logistic-4param winner 2170.0 0.0 0.0424 —
sdt-2afc 2256.3 86.2 0.0659 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -1081.0 4,125 4 0.1070 10
sdt-2afc -1126.1 4,125 2 0.1786 10

Residuals

Fit parameters and provenance

logistic-4param.walsh-2024-prior-cue.psychometric.subject.cue-valid.subject-b

dirty fit

bias=1.072 · lower_lapse=0.122 · slope=2.488 · upper_lapse=0.014

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
2195.3

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

sdt-2afc.walsh-2024-prior-cue.psychometric.subject.cue-valid.subject-b

dirty fit

criterion=-0.360 · d_prime=0.122

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
2268.9

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.sdt-2afc; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

Observed points

Signed target-distractor contrast p_right n
-17.000 0.1020 255
-15.000 0.1138 246
-14.000 0.1124 596
-10.000 0.2387 377
-8.000 0.0904 520
8.000 0.9633 599
10.000 0.9153 425
14.000 0.9846 586
15.000 0.9885 262
17.000 0.9884 259

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.