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Multimodal Process IRT: Responses, Time, Gaze, and Missingness

Measurement channels, not feature dumping

The 0.7 architecture treats response-process observations as explicit measurement channels. A process variable is not automatically useful merely because it predicts an outcome. It should have a declared role, latent target, family, provenance, and validation programme.

spec <- irt_model_spec(
  id = "accuracy_time_gaze",
  latent = c("ability", "speed", "engagement"),
  channels = list(
    response = irt_response_channel("2pl"),
    rt       = irt_rt_channel("lognormal"),
    gaze     = irt_count_channel("negative_binomial")
  ),
  status = "experimental"
)
spec
#> <eye_irt_model_spec> accuracy_time_gaze 
#>  status: experimental 
#>  latent: ability, speed, engagement 
#>  channels: response, rt, gaze

Other channels include nominal choices, survival/event time, compositional AOI measurements, process sequences, functional trajectories, and bounded continuous process measures.

irt_continuous_channel("censored_normal", value = "evidence_dwell_proportion")
#> $type
#> [1] "continuous"
#> 
#> $family
#> [1] "censored_normal"
#> 
#> $role
#> [1] "process"
#> 
#> $link
#> NULL
#> 
#> $variables
#> [1] "evidence_dwell_proportion"
#> 
#> $latent
#> [1] "process"
#> 
#> $options
#> $options$lower
#> [1] 0
#> 
#> $options$upper
#> [1] 1
#> 
#> 
#> attr(,"class")
#> [1] "eye_irt_continuous_channel" "eye_irt_channel"
irt_sequence_channel("scanpath", family = "hmm")
#> $type
#> [1] "sequence"
#> 
#> $family
#> [1] "hmm"
#> 
#> $role
#> [1] "process"
#> 
#> $link
#> NULL
#> 
#> $variables
#> [1] "scanpath"
#> 
#> $latent
#> [1] "strategy"
#> 
#> $options
#> list()
#> 
#> attr(,"class")
#> [1] "eye_irt_sequence_channel" "eye_irt_channel"

Registry

list_irt_models()
#>                            id       status                           latent
#> 1  bounded_continuous_process experimental                    process_trait
#> 2                   flow_mirt        gated             ability_1, ability_2
#> 3           gpirt_shape_audit        gated                          ability
#> 4           graded_rt_process experimental          ability, speed, process
#> 5               joint_gaze_rt    reference       ability, speed, engagement
#> 6        latent_space_process experimental       ability, interaction_space
#> 7           manyfacet_process    reference            person, item, process
#> 8   multiple_response_process experimental          ability, option_process
#> 9                nominal_gaze experimental          ability, option_process
#> 10          omission_survival experimental ability, speed, omission_process
#> 11                process_hmm experimental           ability, process_state
#>                 channels requirements
#> 1                process             
#> 2               response             
#> 3               response             
#> 4  response, rt, process             
#> 5     response, rt, gaze             
#> 6               response       LSMjml
#> 7         response, gaze             
#> 8      response, process             
#> 9         response, gaze             
#> 10        response, time             
#> 11     response, process             
#>                                                                                                 description
#> 1                                 Conditional censored-normal calibration for bounded process measurements.
#> 2   Normalizing-flow MIRT research gate; no production claim without external engine and recovery evidence.
#> 3                        Flexible item-response-curve audit; exact GP engine requires an external callback.
#> 4                                  Mixed/graded response extension with response-time and process channels.
#> 5                                                Response + RT + gaze-count joint measurement architecture.
#> 6                                      Person-item latent-space adapter for residual interaction structure.
#> 7                                                 Crossed person/item/device/session/algorithm facet model.
#> 8  Multiple-response option/process reference model; exact MRM/MRM-LD requires a validated external engine.
#> 9                                      Nominal response choices integrated with option-level gaze evidence.
#> 10                                   Separates observed responses, omissions, and not-reached observations.
#> 11                                  Two-stage process-state HMM plus response measurement reference engine.

Models can be registered and later promoted only after their validation evidence passes an explicit gate.

register_irt_model(spec)
validate_irt_model("accuracy_time_gaze", validation_data)
promote_irt_model("accuracy_time_gaze", evidence = evidence_object)

Response + response time + gaze

fit_joint_gaze_rt_irt() supports two roles:

engine = "reference" gives a transparent crossed-effects decomposition for development and validation;
engine = "brms" builds a multivariate Bayesian model with shared grouping identifiers, which is the preferred route when a full Bayesian joint model is scientifically required.

fit <- fit_joint_gaze_rt_irt(
  data = trials,
  response = "correct",
  rt = "rt_ms",
  gaze = "fixation_count",
  person = "person_id",
  item = "item_id",
  gaze_family = "negative_binomial",
  engine = "brms"
)
plot(fit)

The function does not claim that a convenient reference engine is identical to the published three-way Bayesian model. That distinction is kept in the fit metadata.

Graded responses

The same idea extends to ordinal/graded outcomes:

fit_joint_graded_rt_process_irt(
  data = trials,
  response = "rating",
  rt = "rt_ms",
  process = "fixation_count",
  person = "person_id",
  item = "item_id",
  engine = "brms"
)

This is experimental until parameter recovery and external validation are completed.

Nominal distractors + option gaze

Binary correct/incorrect scoring discards which alternative was selected. A nominal process model can retain both the selected option and visual consideration of each option.

fit <- fit_nominal_gaze_irt(
  data = option_trials,
  response_option = "choice",
  option_gaze = c("dwell_A", "dwell_B", "dwell_C", "dwell_D"),
  item = "item_id",
  person = "person_id"
)

option_process_information(fit)
distractor_process_map(fit)
audit_distractor_attention(fit)
plot(fit)

Interpretation should stay process-based: an option attracted or retained more visual processing. This does not establish why.

Missingness as a process

missing <- classify_item_missingness(
  trials,
  response = "response",
  reached = "reached",
  inspected = "inspected_response_region",
  started = "started_response"
)

audit <- fit_omission_survival_irt(
  data = missing,
  response = "correct",
  response_time = "rt",
  omission_time = "elapsed",
  reached = "reached",
  person = "person_id",
  item = "item_id"
)
plot(audit)

The classification separates not reached, reached but not inspected, inspected omission, and started-but-unanswered cases instead of converting them all to NA.

Device and algorithm facets

facet_fit <- fit_manyfacet_process_irt(
  data = trials,
  response = "correct",
  process = "fixation_count",
  person = "person_id",
  item = "item_id",
  device = "device",
  session = "session",
  algorithm = "fixation_algorithm"
)

facet_effects(facet_fit)
audit_process_measurement_invariance(facet_fit)
plot(facet_fit)

A complementary generalizability_process_study() decomposes variance before a full measurement model is attempted.

Bounded gaze measures

AOI proportions and similar process quantities often have real mass at 0 and 1. The conditional censored-normal calibration helper respects those bounds rather than silently applying ordinary Gaussian regression.

cn <- fit_censored_normal_process_irt(
  response_matrix = aoi_proportion_matrix,
  theta = calibration_theta,
  lower = 0,
  upper = 1
)
predict(cn, theta = seq(-2, 2, length.out = 9))

This is conditional calibration given supplied theta; it is not labelled as the full marginal EM estimator from the 2026 CNRM paper.

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.