Adaptive-memory audit

Public R and Julia reproducibility materials for:

Auditing psychological inferences about memory from adaptive learning logs: Cognitive-model recovery, policy logging, and randomized validation

Authors: Muxue Zhou and Xiaokai Xia, School of Educational Science, Hengyang Normal University.

This repository supports reader inspection and computational reproduction of the article's simulations, empirical models, sensitivity analyses, figures, tables, and validation checks. The manuscript, supplementary document, submission correspondence, reviewer response, and document templates are not distributed here.

Scope

The project evaluates when delay effects can be interpreted in adaptive, closed-loop learning systems. Julia implements the known-truth simulations and model-recovery experiments. R analyzes the de-identified empirical transition table, summarizes simulations, builds figures and tables, and runs the machine-readable validation suite.

Included materials:

• R and Julia analysis source code;
• one minimized de-identified empirical analysis table;
• formal simulation and empirical result files;
• generated figures and tables;
• validation records and runtime information.

Excluded materials:

• the article and all submission documents;
• raw Anki exports and event-level logs;
• names, emails, telephone numbers, device identifiers, free text, source filenames, original event identifiers, and linkage keys;
• absolute dates, absolute timestamps, and exact within-day clock times;
• n-back, Stroop, FSRS-state, and participant-summary data that are outside the article's analyses.

Data privacy boundary

The public empirical workflow begins at data/analysis_transitions_deidentified.csv. It contains 2,045 cross-day transitions, 10 released fields, 12 pseudonymous participant codes, and 100 pseudonymous card codes. The codes were created during private de-identification; their crosswalk is absent from the repository and release package. No card content is included.

The private event-to-transition preparation was audited before release, but raw and event-level records are withheld to reduce longitudinal re-identification risk. Consequently, readers can reproduce the statistical analyses that begin with the released transition table, while the private de-identification and event reconstruction step is outside the public computational boundary. See data/DATA_DICTIONARY.md and data/PUBLIC_DATA_AUDIT.md.

Requirements

The formal results were produced with:

• Julia 1.11.7; Project.toml supports Julia 1.10 or later and uses no external Julia packages;
• R 4.5.3;
• R package lme4 2.0.6 for two crossed participant/card random-intercept logistic models; base and recommended R packages are used elsewhere.

The tested dependency versions are also recorded in R_DEPENDENCIES.csv.

Run commands from the repository root. Rscript is required for every mode; julia is additionally required for full and smoke modes.

Quick start

Reference reproduction

Rscript run_all.R --mode=reference

This mode starts from the included formal replication-level outputs. It regenerates summaries, five main and three supplementary figures, seven main and eight supplementary tables, runtime manifests, and the 38-check validation report. It does not rerun the longest Monte Carlo studies, empirical bootstrap, or mixed-effects models.

Full native rerun

Rscript run_all.R --mode=full

This mode reruns all Julia simulations and both R empirical analyses before rebuilding and validating the generated assets. The empirical sensitivity grid contains:

4 planned-interval thresholds x 2 outcome definitions x 3 penalties
= 24 cells x 1,200 participant-cluster bootstraps
= 28,800 estimates

The full run also fits 24 leave-one-participant-out specifications and two crossed random-intercept benchmarks. Runtime depends on available CPU resources.

Smoke test

Rscript run_all.R --mode=smoke

Smoke mode runs one replication per Julia design cell and writes only to validation/smoke_outputs/. It checks execution and output schemas without overwriting the formal results.

Repository map

Path	Contents
src/julia/	Closed-loop, cognitive-state, calibration, WCLS, and context-proxy simulations
src/R/	Empirical analyses, summaries, builders, public-data checks, and global validation
data/	Minimized de-identified transition data, dictionary, and release audit
outputs/	Formal simulation and empirical outputs plus run manifests
figures/	Generated PNG and SVG figures
tables/	Full-precision CSV and display-rounded Markdown tables
validation/	Public-data, model-extension, empirical, runtime, and 38-check records
run_all.R	Main reproduction entry point
Project.toml	Julia project metadata and compatibility declaration

Validation

The project-level gate is implemented in src/R/validate_outputs.R. The released reference results pass all 38 checks. The checks cover formal row counts, known-truth conclusions, WCLS/LPM numerical equivalence, context-proxy behavior, observation-model boundaries, calibration robustness, the minimized empirical schema, sensitivity results, generated asset inventories, and the R/Julia-only source boundary.

Scientific reproduction is assessed against the declared designs, row counts, estimands, direction, bias, coverage, recovery criteria, and validation checks. Floating-point and platform differences can prevent byte-identical Monte Carlo files across systems.

Citation

Item	Link
Repository	https://github.com/dddd1007/adaptive-memory-audit
GitHub release	https://github.com/dddd1007/adaptive-memory-audit/releases/tag/v1.0.0
Version DOI (v1.0.0)	https://doi.org/10.5281/zenodo.21850352
Concept DOI (all versions)	https://doi.org/10.5281/zenodo.21850351
Zenodo record	https://zenodo.org/records/21850352

GitHub renders citation metadata from CITATION.cff. Please cite both this software package and the accompanying article when using the materials.

Suggested software citation:

Zhou, M., & Xia, X. (2026). Adaptive-memory audit: R and Julia reproducibility
materials (Version 1.0.0) [Computer software].
https://doi.org/10.5281/zenodo.21850352

Prefer the version DOI (10.5281/zenodo.21850352) when citing this exact release. Use the concept DOI (10.5281/zenodo.21850351) only when you want to cite the software as a whole across versions. After the article is published, add the article DOI to CITATION.cff.

Licenses

Material	License
run_all.R, Project.toml, and src/	MIT License
data/analysis_transitions_deidentified.csv	Creative Commons Attribution 4.0 International
outputs/, figures/, tables/, validation records, and Markdown documentation	Creative Commons Attribution 4.0 International

See LICENSE, DATA_LICENSE.md, and LICENSES/CC-BY-4.0.txt. The software and materials are provided without warranty. Users remain responsible for ethical and lawful handling of human-participant-derived data.