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 |
| 2 | Implementing Emotionally-Keyed Memory Retrieval in LLM Interfaces | 10.5281/zenodo.19052129 |
| 3 | CAMA Implementation and Functional Evaluation | 10.5281/zenodo.19192984 |
| 4 | Continuity Burden in Longitudinal Human-AI Interaction | 10.5281/zenodo.19226509 |
Source Code
The CAMA architecture is open source: github.com/LoriensLibrary/cama
Author
Angela Reinhold
- Lorien's Library LLC | Full Sail University
- ORCID: 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
@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}
}