Finding

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

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 3065.6 0.0 0.0255 —
sdt-2afc 3068.4 2.8 0.0327 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -1528.8 3,800 4 0.0449 8
sdt-2afc -1532.2 3,800 2 0.0463 8

Residuals

Fit parameters and provenance

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

dirty fit

bias=-12.574 · lower_lapse=0.000 · slope=6.446 · upper_lapse=0.026

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
3090.6

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-d

dirty fit

criterion=-0.727 · d_prime=0.067

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
8
BIC
3080.9

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
-20.000 0.2292 253
-18.000 0.2674 374
-16.000 0.4055 254
-14.000 0.4341 1,009
14.000 0.9462 1,022
16.000 1.0000 255
18.000 0.9867 377
20.000 0.9453 256

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.