M3 device transport, falsification controls, and sensor value

The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

More sensors can create more failure modes

Pupil measurements can vary with device, sampling rate, gaze geometry, missingness and preprocessing. A four-channel model therefore needs falsification and transport diagnostics in addition to a richer likelihood. M3 exposes those concerns without asserting that a device label itself explains measurement differences.

Device-dependent stress

sim <- simulate_multimodal_m3(
  n_person = 80,
  n_item = 10,
  pupil_missingness = "device",
  seed = 20260815
)

audit <- audit_multimodal_m3_identifiability(sim)
audit$device
#>            device   n pupil_missing pupil_mean
#> device_A device_A 400        0.0775 0.10029058
#> device_B device_B 400        0.2825 0.07845766

plot(sim, type = "device")
#> Warning: Removed 144 rows containing non-finite outside the scale range
#> (`stat_boxplot()`).

plot(audit, type = "device")

These outputs identify device-specific shifts or availability patterns in the simulation. They do not establish measurement invariance. Empirical device transport requires repeated-device or appropriately linked data, explicit equivalence/invariance analysis and validation of the underlying pupil units and preprocessing semantics.

Falsification controls

neg <- multimodal_m3_negative_controls(sim, seed = 20260816)
neg$provenance
#>                  control
#> 1       gaze_within_item
#> 2         rt_within_item
#> 3      pupil_within_item
#> 4    pupil_within_person
#> 5 pupil_phase_randomized
#> 6   luminance_only_pupil
#> 7       irrelevant_pupil
#>                                                                                                 purpose
#> 1                                                               break gaze-person alignment within item
#> 2                                                                 break RT-person alignment within item
#> 3                                                              break pupil-person alignment within item
#> 4                                                              break pupil-item alignment within person
#> 5 destroy within-person ordered pupil-series phase structure while retaining its spectrum approximately
#> 6                                                 test measurement artefact masquerading as pupil value
#> 7                                                          test irrelevant channel false-positive value
#>                                                              interpretation
#> 1 falsification diagnostic only; not causal and not a behavioral classifier
#> 2 falsification diagnostic only; not causal and not a behavioral classifier
#> 3 falsification diagnostic only; not causal and not a behavioral classifier
#> 4 falsification diagnostic only; not causal and not a behavioral classifier
#> 5 falsification diagnostic only; not causal and not a behavioral classifier
#> 6 falsification diagnostic only; not causal and not a behavioral classifier
#> 7 falsification diagnostic only; not causal and not a behavioral classifier

M3 includes pupil permutations within item and within person, phase randomization, a luminance-only pseudo-pupil, and an irrelevant synthetic pupil channel. These deliberately break different aspects of person/item alignment. They are tests of whether the analysis pipeline is too willing to manufacture process value; they are not causal interventions and not participant-behavior or misconduct detectors.

plot(neg, type = "pupil_alignment")

Sensor value is conditional

After an ablation lattice has been fitted, multimodal_m3_process_information() reports the response-target pupil gain per usable pupil observation and per analyst-supplied relative sensor cost. This can support design discussions about whether a pupil channel is worth collecting under a specific model and target.

ab <- multimodal_m3_ablation(sim, chains = 4, parallel_chains = 4)
info <- multimodal_m3_process_information(ab, pupil_cost = 1.5)
info$sensor_value
plot(info, type = "sensor_value")

The calculation is intentionally labeled a sensor value-of-information screen, not an economic cost-effectiveness analysis. It does not account automatically for equipment depreciation, staff time, participant burden, calibration failures, or the value of non-response outcomes.

Channel conflict

The same information object places response ELPD beside convergence diagnostics. This makes a useful failure mode visible: a channel can look incrementally predictive while substantially worsening computational stability, or can sharpen latent estimates without improving held-out response prediction. M3 keeps those dimensions separate so that a single improvement cannot hide a meaningful trade-off.

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.
Health stats visible at Monitor.