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The central M2 question is not whether RT and gaze have their own likelihood information. It is whether they improve measurement of the scored-response trait under a joint model.
multimodal_m2_ablation() therefore fits a compatible
sequence:
multimodal_m2_process_information() compares all three
models on the same response observations.
library(eyeprocess)
sim <- simulate_multimodal_m2(
n_person = 150,
n_item = 15,
seed = 220
)
abl <- multimodal_m2_ablation(
sim,
chains = 4,
parallel_chains = 4,
iter_warmup = 1000,
iter_sampling = 1000,
seed = 221
)
info <- multimodal_m2_process_information(abl)
info
plot(info, type = "response_elpd")
plot(info, type = "theta_variance")The evidence object reports:
This LOO target is conditional on the study’s observed person/item population. It is not a transport estimate for entirely new persons or new items.
A useful process channel may improve response-target held-out prediction, reduce posterior uncertainty, both, or neither. A destabilizing channel may worsen predictive performance or inflate uncertainty.
The package does not sum channel Fisher information by default. Under correlated latent and item structures, information is generally not additive in the simple independent-channel sense. The ablation sequence preserves this point explicitly.
An incremental response-target gain supports a model-based measurement claim under the fitted assumptions. It does not by itself establish that gaze measures attention, that RT measures strategy, or that either channel is causally informative.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.