Finding

walsh-2024-prior-cue.psychometric.subject.cue-valid.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
logistic-4param winner 2073.7 0.0 0.0308 —
sdt-2afc 2129.9 56.2 0.0502 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -1032.9 3,250 4 0.0636 10
sdt-2afc -1063.0 3,250 2 0.0853 10

Residuals

Fit parameters and provenance

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

dirty fit

bias=-8.997 · lower_lapse=0.141 · slope=0.438 · upper_lapse=0.053

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
2098.1

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-e

dirty fit

criterion=-0.296 · d_prime=0.105

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
2142.1

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
-15.000 0.1612 273
-14.000 0.2045 264
-12.000 0.0828 471
-11.000 0.1709 275
-10.000 0.2138 318
10.000 0.9596 322
11.000 0.9659 293
12.000 0.9461 464
14.000 0.9375 288
15.000 0.9255 282

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.