Finding

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

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

Joint-fit companions

Other findings jointly fit by the same model fits.

Fit diagnostics

Caveats on these fits (1 term)

Each tag below is attached to one or more candidate fits for this finding. The chip column in the table flags which row carries which tag.

aggregate DDM RT approximation
DDM likelihood uses aggregate chronometric constraints rather than a full trial-level response-time likelihood.
Variant AIC Δ AIC RMSE Caveats
logistic-4param winner 11393.3 0.0 0.0708 —
ddm-drift-bias 11523.4 130.1 0.0836
  • aggregate DDM RT approximation
sdt-2afc 11525.4 132.1 0.0873 —
ddm-starting-point-bias 11569.4 176.1 0.0904
  • aggregate DDM RT approximation
ddm-vanilla 13667.9 2274.7 0.1974
  • aggregate DDM RT approximation
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
logistic-4param -5692.6 10,960 4 0.2725 38
ddm-drift-bias -5757.7 10,960 4 0.3344 38
sdt-2afc -5760.7 10,960 2 0.3406 38
ddm-starting-point-bias -5780.7 10,960 4 0.3400 38
ddm-vanilla -6831.0 10,960 3 0.3339 38

Residuals

Fit parameters and provenance

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

dirty fit

bias=-11.449 · lower_lapse=0.211 · slope=6.092 · upper_lapse=0.111

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
38
BIC
11422.5

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.logistic-4param; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

ddm-drift-bias.walsh-2024-prior-cue.psychometric.cue-neutral

dirty fit

boundary=0.835 · drift_bias=1.245 · drift_per_unit_evidence=0.088 · non_decision_time=0.705

Method
scipy.optimize.minimize
Commit
10e1c18
Predicted points
38
BIC
11552.6

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.ddm-drift-bias; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

sdt-2afc.walsh-2024-prior-cue.psychometric.cue-neutral

dirty fit

criterion=-0.609 · d_prime=0.042

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
38
BIC
11540.0

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.sdt-2afc; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

ddm-starting-point-bias.walsh-2024-prior-cue.psychometric.cue-neutral

dirty fit

boundary=0.668 · drift_per_unit_evidence=0.107 · non_decision_time=0.705 · starting_point=0.723

Method
scipy.optimize.minimize
Commit
10e1c18
Predicted points
38
BIC
11598.6

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.ddm-starting-point-bias; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

ddm-vanilla.walsh-2024-prior-cue.psychometric.cue-neutral

dirty fit

boundary=0.656 · drift_per_unit_evidence=0.090 · non_decision_time=0.705

Method
scipy.optimize.minimize
Commit
10e1c18
Predicted points
38
BIC
13689.8

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.ddm-vanilla; success=True; CONVERGENCE: RELATIVE REDUCTION OF F <= FACTR*EPSMCH

Observed points

Signed target-distractor contrast p_right n
-28.000 0.3000 70
-26.000 0.2500 64
-25.000 0.1429 63
-22.000 0.3518 253
-20.000 0.2513 191
-18.000 0.3938 193
-17.000 0.4328 134
-16.000 0.5880 233
-15.000 0.3922 334
-14.000 0.4692 778
-13.000 0.4242 99
-12.000 0.5424 625
-11.000 0.5753 73
-10.000 0.6362 885
-8.000 0.6278 352
-7.000 0.6572 528
-6.000 0.5945 291
-4.000 0.6757 111
-3.000 0.7009 117
3.000 0.8780 123
4.000 0.8333 114
6.000 0.8658 313
7.000 0.8830 547
8.000 0.7847 339
10.000 0.9258 944
11.000 0.8243 74
12.000 0.8990 624
13.000 0.8911 101
14.000 0.8501 814
15.000 0.8142 339
16.000 0.9540 239
17.000 0.8707 147
18.000 0.9312 189
20.000 0.8836 189
22.000 0.8718 273
25.000 0.9063 64
26.000 0.6154 65
28.000 0.8529 68

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.