Finding

walsh-2024-prior-cue.psychometric.cue-invalid

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
sdt-2afc winner 13313.4 0.0 0.1753 —
logistic-4param 13317.6 4.2 0.1755 —
ddm-starting-point-bias 13336.5 23.1 0.1753
  • aggregate DDM RT approximation
ddm-drift-bias 13336.6 23.2 0.1753
  • aggregate DDM RT approximation
ddm-vanilla 15301.0 1987.6 0.2413
  • aggregate DDM RT approximation
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
sdt-2afc -6654.7 10,985 2 0.5083 38
logistic-4param -6654.8 10,985 4 0.5092 38
ddm-starting-point-bias -6664.2 10,985 4 0.5087 38
ddm-drift-bias -6664.3 10,985 4 0.5085 38
ddm-vanilla -7647.5 10,985 3 0.3901 38

Residuals

Fit parameters and provenance

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

dirty fit

criterion=-0.541 · d_prime=0.002

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
38
BIC
13328.0

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

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

dirty fit

bias=-28.000 · lower_lapse=0.484 · slope=140.362 · upper_lapse=0.112

Method
scipy.optimize.minimize
Commit
24fd416
Predicted points
38
BIC
13346.8

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

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

dirty fit

boundary=0.764 · drift_per_unit_evidence=0.005 · non_decision_time=0.649 · starting_point=0.706

Method
scipy.optimize.minimize
Commit
10e1c18
Predicted points
38
BIC
13365.7

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

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

dirty fit

boundary=0.703 · drift_bias=1.244 · drift_per_unit_evidence=0.005 · non_decision_time=0.745

Method
scipy.optimize.minimize
Commit
10e1c18
Predicted points
38
BIC
13365.8

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

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

dirty fit

boundary=0.001 · drift_per_unit_evidence=1.00e-6 · non_decision_time=0.745

Method
scipy.optimize.minimize
Commit
10e1c18
Predicted points
38
BIC
15322.9

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.ddm-vanilla; success=True; CONVERGENCE: NORM OF PROJECTED GRADIENT <= PGTOL

Observed points

Signed target-distractor contrast p_right n
-28.000 0.1765 68
-26.000 0.3651 63
-25.000 0.3231 65
-22.000 0.5316 269
-20.000 0.4628 188
-18.000 0.4740 192
-17.000 0.7432 148
-16.000 0.7284 243
-15.000 0.6053 337
-14.000 0.6873 806
-13.000 0.4902 102
-12.000 0.8200 661
-11.000 0.8592 71
-10.000 0.8574 954
-8.000 0.8382 340
-7.000 0.8901 546
-6.000 0.8539 308
-4.000 0.8378 111
-3.000 0.7903 124
3.000 0.5922 103
4.000 0.7426 101
6.000 0.5583 283
7.000 0.5422 533
8.000 0.4386 342
10.000 0.7430 895
11.000 0.4638 69
12.000 0.6420 609
13.000 0.8333 96
14.000 0.6910 793
15.000 0.6518 336
16.000 0.8534 232
17.000 0.5985 137
18.000 0.8586 191
20.000 0.8158 190
22.000 0.7971 276
25.000 0.7937 63
26.000 0.5781 64
28.000 0.7763 76

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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.