Finding

walsh-2024-prior-cue.chronometric.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 · 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 13.9 0.0 0.1343
  • chronometric-summary fit
chronometric-hyperbolic-rt 17.9 4.0 0.1343
  • chronometric-summary fit
ddm-drift-bias 11523.4 11509.5 -
  • aggregate DDM RT approximation
ddm-starting-point-bias 11569.4 11555.5 -
  • aggregate DDM RT approximation
ddm-vanilla 13667.9 13654.0 -
  • aggregate DDM RT approximation
Full diagnostics (logL · n · params · max abs error)
Variant logL n Free params Max |error| Predicted points
chronometric-constant-rt -5.9 10,960 1 0.2750 19
chronometric-hyperbolic-rt -5.9 10,960 3 0.2750 19
ddm-drift-bias -5757.7 10,960 4 - 0
ddm-starting-point-bias -5780.7 10,960 4 - 0
ddm-vanilla -6831.0 10,960 3 - 0

Residuals

Fit parameters and provenance

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

dirty fit

rt_level=0.885

Method
manual
Commit
3ec74b4
Predicted points
19
BIC
14.8

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-neutral

dirty fit

half_saturation_strength=13.938 · rt_floor=0.885 · rt_span=0.000

Method
scipy.optimize.minimize
Commit
3ec74b4
Predicted points
19
BIC
20.7

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

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
0
BIC
11552.6

Fitted with scipy.optimize.minimize via behavtaskatlas.model_fits.ddm-drift-bias; 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
0
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
0
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

Absolute target-distractor contrast median_rt_s n
3.000 0.7049 240
4.000 0.8499 225
6.000 0.9199 604
7.000 0.9099 1,075
8.000 0.7093 691
10.000 0.8799 1,829
11.000 0.7799 147
12.000 0.7699 1,249
13.000 0.8599 200
14.000 0.9799 1,592
15.000 0.8299 673
16.000 0.8899 472
17.000 0.8399 281
18.000 1.0799 382
20.000 1.1399 380
22.000 0.8499 526
25.000 1.1599 127
26.000 1.0800 129
28.000 0.7449 138

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.