Finding

walsh-2024-prior-cue.psychometric.subject.cue-neutral.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
logistic-4param winner 1155.3 0.0 0.0583 —
sdt-2afc 1155.6 0.3 0.0561 —
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -573.6 1,005 4 0.1502 10
sdt-2afc -575.8 1,005 2 0.0883 10

Residuals

Fit parameters and provenance

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

dirty fit

bias=9.486 · lower_lapse=0.494 · slope=4.159 · upper_lapse=0.000

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1174.9

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-b

dirty fit

criterion=-0.454 · d_prime=0.040

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
10
BIC
1165.4

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
-17.000 0.3443 61
-15.000 0.5082 61
-14.000 0.4701 134
-10.000 0.5204 98
-8.000 0.5704 142
8.000 0.7273 132
10.000 0.7157 102
14.000 0.8552 145
15.000 0.9394 66
17.000 0.9375 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.