Integrated Synthetic Gazepoint Research Workflow

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Synthetic upstream outputs

This article represents prepared outputs from gp3tools, gpbiometrics, and gp3sequences. The values are synthetic and shareable. The outcome is an experimentally assigned condition; the workflow does not infer emotion, stress, cognition, health, identity, intent, or another prohibited construct.

bundle <- simulate_gazepoint_research_handoffs(
  n_participants = 18L,
  n_stimuli = 4L,
  seed = 3401L
)
validation <- validate_gazepoint_research_bundle(bundle)
validation
#>  gp3ml research bundle validation: pass
#>                                check status
#>             required_sources_present   pass
#>                    all_handoffs_pass   pass
#>                combined_rows_present   pass
#>             assigned_outcome_present   pass
#>      no_prohibited_inference_columns   pass
#>  participant_generalization_declared   pass
#>                                detail
#>  gp3tools, gpbiometrics, gp3sequences
#>                  3/3 handoffs passed.
#>                     72 combined rows.
#>                    assigned_condition
#>                        None detected.
#>                      new_participants
plot(validation)

Assemble the modelling handoff

combined <- validation$bundle
data <- as_gp3ml_data(combined)

predictors <- c(
  "valid_gaze_prop",
  "fixation_count",
  "mean_fixation_ms",
  "gaze_dispersion",
  "eda_valid_prop",
  "hr_valid_prop",
  "ibi_valid_prop",
  "sequence_length",
  "unique_state_count",
  "transition_rate"
)

Declare task and provenance

task <- declare_gazepoint_task(
  data = data,
  outcome = "assigned_condition",
  purpose = "Discriminate an experimentally assigned condition using predeclared observed non-sensitive Gazepoint-derived predictors",
  task_type = "classification",
  unit_id = "trial_id",
  participant_id = "participant_id",
  stimulus_id = "stimulus_id",
  generalization_target = "new_participants",
  positive = "B",
  observed_outcome = TRUE,
  sensitive_outcome = FALSE
)

manifest <- create_gazepoint_feature_manifest(
  features = predictors,
  scientific_source = c(
    rep("gp3tools prepared gaze/fixation summaries", 4L),
    rep("gpbiometrics prepared signal-quality summaries", 3L),
    rep("gp3sequences prepared sequence summaries", 3L)
  ),
  source_table = c(
    rep("gp3tools handoff", 4L),
    rep("gpbiometrics handoff", 3L),
    rep("gp3sequences handoff", 3L)
  ),
  transformation = "Prepared upstream summary passed through a validated interoperability handoff",
  availability_stage = "during_exposure",
  prediction_time_available = TRUE,
  outcome_derived = FALSE,
  post_outcome = FALSE,
  identifier = FALSE,
  preprocessing_scope = "none",
  fold_local_required = FALSE,
  reviewer_notes = "Synthetic shareable cross-package validation workflow."
)

validate_gazepoint_feature_manifest(manifest)
#> <gazepoint_feature_manifest_validation>
#> Overall status: PASS
#> Features: 10
#> Non-passing checks: 0
#>  status n_checks
#>  pass   110     
#>  review   0     
#>  fail     0

Participant-grouped resampling

folds <- create_gazepoint_group_folds(
  data = data,
  outcome = task$outcome,
  predictors = predictors,
  feature_manifest = manifest,
  generalization_target = task$generalization_target,
  participant_id = task$participant_id,
  trial_id = task$unit_id,
  stimulus_id = task$stimulus_id,
  v = 3L,
  repeats = 1L,
  seed = 3401L
)

validate_gazepoint_group_folds(folds)
#> <gazepoint_group_folds_validation>
#> Overall status: PASS
#> Non-passing checks: 0
#>  status n_checks
#>  pass   10      
#>  review  0      
#>  fail    0
audit_gazepoint_group_folds(folds)
#> <gazepoint_group_folds_audit>
#> Overall status: PASS
#> Audited folds: 3
#> Non-passing checks: 0

Repository-aware evaluation when available

if ("evaluate_gazepoint_group_folds" %in% getNamespaceExports("gp3ml")) {
  evaluation <- evaluate_gazepoint_group_folds(
    folds,
    task,
    predictors,
    "glm",
    seed = 3401L
  )
  validate_gazepoint_resample_evaluation(evaluation)
  summarize_gazepoint_resample_performance(evaluation)
} else {
  diagnostics <- diagnose_gazepoint_group_folds(folds)
  validate_gazepoint_fold_diagnostics(diagnostics)
}
#> <gp3ml_resample_performance_summary>
#>   Aggregation: fold_distribution
#>   Generalization target: new_participants
#>             metric direction n_folds      mean    median         sd      lower
#>           accuracy  maximize       3 0.5694444 0.5833333 0.10485881 0.46458333
#>  balanced_accuracy  maximize       3 0.5972222 0.5937500 0.06777507 0.53437500
#>        sensitivity  maximize       3 0.5763889 0.5625000 0.16710013 0.42395833
#>        specificity  maximize       3 0.6180556 0.6250000 0.30214319 0.32812500
#>          precision  maximize       3 0.6454248 0.7500000 0.25670241 0.37279412
#>             recall  maximize       3 0.5763889 0.5625000 0.16710013 0.42395833
#>                 f1  maximize       3 0.5594709 0.5555556 0.08149914 0.48377778
#>                mcc  maximize       3 0.2088324 0.1767767 0.16242982 0.07041819
#>            roc_auc  maximize       3 0.6493056 0.6562500 0.08355007 0.56718750
#>             pr_auc  maximize       3 0.7052403 0.7887512 0.14604998 0.54920647
#>              brier  minimize       3 0.2899371 0.2474772 0.09244876 0.22740236
#>           log_loss  minimize       3 0.9148567 0.6878170 0.40068648 0.67967828
#>      upper
#>  0.6625000
#>  0.6630208
#>  0.7406250
#>  0.9020833
#>  0.8291667
#>  0.7406250
#>  0.6384921
#>  0.3744940
#>  0.7255208
#>  0.7902897
#>  0.3885627
#>  1.3430188

Any reported predictive metrics are row-level outcomes under a declared participant-grouped assessment design. They are not participant-level psychological measurements and do not support causal or latent-state claims.

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.