Finding

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

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 3835.7 0.0 0.0273 —
sdt-2afc 4055.8 220.1 0.1193 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -1913.8 4,645 4 0.0693 10
sdt-2afc -2025.9 4,645 2 0.2453 10

Residuals

Fit parameters and provenance

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

dirty fit

bias=1.037 · lower_lapse=0.406 · slope=0.890 · upper_lapse=0.030

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
3861.5

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-g

dirty fit

criterion=-0.712 · d_prime=0.145

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
4068.7

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
-10.000 0.4750 320
-7.000 0.3991 664
-6.000 0.3965 401
-4.000 0.4077 390
-3.000 0.3756 402
3.000 0.9215 484
4.000 0.9389 442
6.000 0.9766 471
7.000 0.9544 746
10.000 0.9969 325

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.