---
license: cc-by-4.0
task_categories:
  - text-classification
language:
  - en
tags:
  - human-ai-interaction
  - memory-architecture
  - continuity-burden
  - affective-computing
  - longitudinal-case-study
  - CAMA
pretty_name: "CAMA Continuity Burden Dataset"
size_categories:
  - 1K<n<10K
---

# CAMA Continuity Burden Dataset

## Overview

This dataset provides aggregate research outputs from the **Circular Associative Memory Architecture (CAMA)** research program — a four-paper series investigating emotionally-indexed persistent memory for human-AI interaction.

The central finding is the introduction and preliminary quantification of **continuity burden**: the communicative effort humans expend re-establishing context when interacting with memoryless AI systems.

## Key Findings

- **Continuity reference rate**: 4.8 per 1,000 messages (320 references across 66,380 messages in 825 conversations)
- **Scaling relationship**: Continuity references significantly increase with conversation length (R² = 0.381, p < 0.001)
  - 2.1% of conversations under 10 messages contain continuity references
  - 59.3% of conversations over 200 messages contain continuity references
- **Category distribution**: Frustration markers dominate (56.6%), followed by context restoration (18.8%), re-explanation (15.9%), and temporal references (8.8%)
- **Temporal decline**: Continuity reference rate peaked at 17.7 per 1,000 in February 2025, declining to 2.7–2.9 by late 2025, consistent with user adaptation
- **CAMA coverage**: 52,602 emotionally annotated memories with 99.98% affect coverage

## Dataset Contents

| File | Description |
|------|-------------|
| `paper4_stats.json` | Complete continuity burden analysis: reference counts, category breakdown, monthly rates, regression results, sample references |
| `emergent_retention.json` | Analysis of AI behavioral retention indicators across 23,733 assistant messages |
| `phase1_summary.json` | Descriptive statistics of the CAMA memory database |
| `phase2_findings.json` | Comparative analysis between CAMA and pre-CAMA conditions |
| `phase3_findings.json` | Architectural gap analysis including temporal coverage and inference confirmation |

## Important Limitations

- **Single-participant longitudinal case study (n=1)** — findings cannot be generalized
- Participant is the system designer, creating confounds
- Continuity reference detection uses keyword matching without inter-rater reliability validation
- Pre-CAMA and post-CAMA conditions overlap temporally; the distinction is architectural
- All findings are exploratory and require multi-participant replication

## Research Papers

This dataset supports the following published preprints:

| Paper | Title | DOI |
|-------|-------|-----|
| 1 | Circular Associative Memory Architecture | [10.5281/zenodo.19051834](https://doi.org/10.5281/zenodo.19051834) |
| 2 | Implementing Emotionally-Keyed Memory Retrieval in LLM Interfaces | [10.5281/zenodo.19052129](https://doi.org/10.5281/zenodo.19052129) |
| 3 | CAMA Implementation and Functional Evaluation | [10.5281/zenodo.19192984](https://doi.org/10.5281/zenodo.19192984) |
| 4 | Continuity Burden in Longitudinal Human-AI Interaction | [10.5281/zenodo.19226509](https://doi.org/10.5281/zenodo.19226509) |

## Source Code

The CAMA architecture is open source: [github.com/LoriensLibrary/cama](https://github.com/LoriensLibrary/cama)

## Author

**Angela Reinhold**
- Lorien's Library LLC | Full Sail University
- ORCID: [0009-0005-5803-8401](https://orcid.org/0009-0005-5803-8401)

## Privacy Notice

This dataset contains **aggregate statistics only**. No personal conversation data, message content, or identifiable information is included. Raw interaction data is excluded to protect participant privacy.

## Citation

```bibtex
@misc{reinhold2026continuityburden,
  author = {Reinhold, Angela},
  title = {Continuity Burden in Longitudinal Human-AI Interaction: An Empirical Case Study of Emotionally-Indexed Persistent Memory},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.19226509}
}
```