Finding

walsh-2024-prior-cue.psychometric.subject.cue-neutral.subject-f

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 963.1 0.0 0.0944 —
sdt-2afc 1022.3 59.2 0.1387 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -477.5 950 4 0.2306 12
sdt-2afc -509.1 950 2 0.2955 12

Residuals

Fit parameters and provenance

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

dirty fit

bias=-2.477 · lower_lapse=0.169 · slope=3.365 · upper_lapse=0.154

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
12
BIC
982.5

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-neutral.subject-f

dirty fit

criterion=-0.109 · d_prime=0.048

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
12
BIC
1032.0

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
-26.000 0.2500 64
-25.000 0.1429 63
-20.000 0.1270 63
-10.000 0.1875 96
-7.000 0.4149 94
-6.000 0.2826 92
6.000 0.7579 95
7.000 0.8105 95
10.000 0.8830 94
20.000 0.9846 65
25.000 0.9063 64
26.000 0.6154 65

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.