Finding

walsh-2024-prior-cue.psychometric.subject.cue-invalid.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 1237.0 0.0 0.1291 —
sdt-2afc 1249.5 12.5 0.1417 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -614.5 955 4 0.2792 12
sdt-2afc -622.7 955 2 0.2644 12

Residuals

Fit parameters and provenance

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

dirty fit

bias=-7.784 · lower_lapse=0.400 · slope=1.00e-6 · upper_lapse=0.325

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
12
BIC
1256.4

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-invalid.subject-f

dirty fit

criterion=-0.242 · d_prime=0.017

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
12
BIC
1259.2

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 0.3651 63
-25.000 0.3231 65
-20.000 0.4127 63
-10.000 0.4681 94
-7.000 0.8125 96
-6.000 0.6915 94
6.000 0.5895 95
7.000 0.4316 95
10.000 0.6429 98
20.000 0.9538 65
25.000 0.7937 63
26.000 0.5781 64

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.