knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE) library(eyeprocess)
This article adds two diagnostic layers that were present in the source research templates but should remain separate from substantive behavioral claims.
bayesian_process_diagnostics_dashboard() collects LOO, posterior convergence/effective-sample-size summaries, and optionally a Bayes factor for fitted brms process models.fit_gaze_anchored_3pl_audit() fits a standard psychometric 3PL model and descriptively aligns its item lower-asymptote parameter with gaze, pupil, response-time, or accuracy summaries.Neither function establishes a causal cognitive mechanism. In particular, a 3PL lower asymptote is an item parameter and is not a participant-level "guessing detector".
dash <- bayesian_process_diagnostics_dashboard( response_only = brms_response_model, response_plus_pupil = brms_pupil_model, compute_loo = TRUE, compute_bayes_factor = FALSE ) bayesian_process_diagnostic_flags(dash) plot(dash, type = "loo") plot(dash, type = "rhat")
Bayes factors are deliberately opt-in because they require suitable model fitting settings and answer a different evidential question than predictive LOO comparison.
three_pl <- fit_gaze_anchored_3pl_audit( response_matrix = binary_response_matrix, process_data = binary_long, item = "item_id", process_features = c("ttff_ms", "dwell_ms", "pupil_peak", "rt_ms", "accuracy") ) gaze_anchored_3pl_alignment(three_pl) audit_3pl_process_signatures(three_pl) plot(three_pl, type = "lower_asymptote") plot(three_pl, type = "process_alignment", feature = "ttff_ms")
The resulting correlations and review flags are descriptive item-level diagnostics. They require independent substantive validation before any interpretation in terms of rapid responding, guessing, effort, engagement, or strategy.
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