Alignment negative controls
Negative controls ask whether apparent multimodal information depends on meaningful person-level alignment rather than only channel marginals.
library(eyeprocess)
sim <- simulate_multimodal_m2(
n_person = 80,
n_item = 10,
seed = 77
)
nc <- multimodal_m2_negative_controls(
sim,
seed = 78
)
nc
#> <eye_multimodal_m2_negative_controls>
#> controls: gaze_within_item, rt_within_item, response_within_item
#> seed: 78
#> boundary: Negative controls test whether apparent incremental process information depends on person-level channel alignment. They do not identify a causal mechanism or label participant behavior.
head(nc$provenance)
#> control changed_channel
#> 1 gaze_within_item gaze
#> 2 rt_within_item rt
#> 3 response_within_item response
#> preserved
#> 1 within-item marginal observed values and missingness pattern
#> 2 within-item marginal observed values and missingness pattern
#> 3 within-item marginal observed values and missingness pattern
#> broken
#> 1 person-level alignment for the named channel
#> 2 person-level alignment for the named channel
#> 3 person-level alignment for the named channel
#> interpretation
#> 1 falsification control; not causal and not a misconduct classifier
#> 2 falsification control; not causal and not a misconduct classifier
#> 3 falsification control; not causal and not a misconduct classifier
plot(nc)
#> Warning: Use of `d[["dataset"]]` is discouraged.
#> ℹ Use `.data[["dataset"]]` instead.
#> Warning: Use of `d[["correlation"]]` is discouraged.
#> ℹ Use `.data[["correlation"]]` instead.
#> Warning: Use of `d[["pair"]]` is discouraged.
#> ℹ Use `.data[["pair"]]` instead.
The controls permute gaze, RT, or response within item. This preserves each item’s observed marginal values and missingness pattern while breaking the named person-level alignment.
These are falsification controls, not causal interventions and not misconduct classifiers.