Finding

walsh-2024-prior-cue.psychometric.subject.cue-invalid.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
sdt-2afc winner 1376.1 0.0 0.2375 —
logistic-4param 1382.8 6.7 0.2387 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -686.1 1,020 2 0.4404 10
logistic-4param -687.4 1,020 4 0.4381 10

Residuals

Fit parameters and provenance

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

dirty fit

criterion=-0.256 · d_prime=1.00e-6

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1386.0

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-b

dirty fit

bias=-17.000 · lower_lapse=0.500 · slope=340.000 · upper_lapse=0.310

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1402.5

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
-17.000 0.7460 63
-15.000 0.5079 63
-14.000 0.8264 144
-10.000 0.8440 109
-8.000 0.9549 133
8.000 0.1606 137
10.000 0.4118 102
14.000 0.3706 143
15.000 0.7143 63
17.000 0.5397 63

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.