Finding

walsh-2024-prior-cue.chronometric.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 · chronometric

Observed curve and fits

Joint-fit companions

Other findings jointly fit by the same model fits.

Fit diagnostics

Caveats on these fits (2 terms)

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.

chronometric-summary fit
Descriptive median-RT-by-strength fit over summary chronometric points, not a full reaction-time likelihood or process model.
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
chronometric-constant-rt winner 15.6 0.0 0.1264
  • chronometric-summary fit
chronometric-hyperbolic-rt 19.6 4.0 0.1264
  • chronometric-summary fit
ddm-starting-point-bias 13336.5 13320.9 -
  • aggregate DDM RT approximation
ddm-drift-bias 13336.6 13321.0 -
  • aggregate DDM RT approximation
ddm-vanilla 15301.0 15285.4 -
  • aggregate DDM RT approximation
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
chronometric-constant-rt -6.8 10,985 1 0.3373 19
chronometric-hyperbolic-rt -6.8 10,985 3 0.3373 19
ddm-starting-point-bias -6664.2 10,985 4 - 0
ddm-drift-bias -6664.3 10,985 4 - 0
ddm-vanilla -7647.5 10,985 3 - 0

Residuals

Fit parameters and provenance

chronometric-constant-rt.walsh-2024-prior-cue.chronometric.cue-invalid

dirty fit

rt_level=0.913

Method
manual
Commit
3ec74b4
Predicted points
19
BIC
16.6

Fitted with manual via behavtaskatlas.model_fits.chronometric-constant-rt; success=True; closed-form weighted constant RT estimate

chronometric-hyperbolic-rt.walsh-2024-prior-cue.chronometric.cue-invalid

dirty fit

half_saturation_strength=13.812 · rt_floor=0.913 · rt_span=0.000

Method
scipy.optimize.minimize
Commit
3ec74b4
Predicted points
19
BIC
22.5

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

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
0
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
0
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
0
BIC
15322.9

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

Observed points

Absolute target-distractor contrast median_rt_s n
3.000 0.9299 227
4.000 0.9199 212
6.000 0.9399 591
7.000 0.9199 1,079
8.000 0.7446 682
10.000 0.8999 1,849
11.000 0.8949 140
12.000 0.7597 1,270
13.000 0.9449 198
14.000 0.9999 1,599
15.000 0.8899 673
16.000 0.8899 475
17.000 0.8799 285
18.000 1.0999 383
20.000 1.1699 378
22.000 0.8795 545
25.000 1.2499 128
26.000 1.0299 127
28.000 0.8149 144

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.