Finding

walsh-2024-prior-cue.psychometric.subject.cue-neutral.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 1144.5 0.0 0.0484 —
sdt-2afc 1144.6 0.0 0.0553 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -568.3 1,230 4 0.1153 10
sdt-2afc -570.3 1,230 2 0.1289 10

Residuals

Fit parameters and provenance

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

dirty fit

bias=-2.703 · lower_lapse=0.500 · slope=5.934 · upper_lapse=0.000

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1165.0

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-neutral.subject-g

dirty fit

criterion=-0.903 · d_prime=0.065

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1154.8

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 0.7284 81
-7.000 0.6610 177
-6.000 0.7143 112
-4.000 0.6757 111
-3.000 0.7009 117
3.000 0.8780 123
4.000 0.8333 114
6.000 0.9370 127
7.000 0.9027 185
10.000 0.9880 83

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.