Understanding autobiographical memory content using computational text analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although research on autobiographical memory (AM) continues to grow, there remain few methods to analyze AM content. Past approaches are typically manual, and prohibitively time- and labour-intensive. These methodological limitations are concerning because content may provide insights into the nature and functions of AM. In particular, analyzing content in recurrent involuntary autobiographical memories (IAMs; those that spring to mind unintentionally and repetitively) could resolve controversies about whether these memories typically involve mundane or distressing events. Here, we present computational methods that can analyze content in thousands of participants' AMs, without needing to hand-code each memory. A sample of 6,187 undergraduates completed surveys about recurrent IAMs, resulting in 3,624 text descriptions. Using frequency analyses, we identified common (e.g., "time", "friend") and distinctive words in recurrent IAMs (e.g., "argument" as distinctive to negative recurrent IAMs). Using structural topic modelling, we identified coherent topics (e.g., "Negative past relationships", "Conversations", "Experiences with family members") within recurrent IAMs and found that topic use significantly differed depending on the valence of these memories. Computational methods allowed us to analyze large quantities of AM content with enhanced granularity and reproducibility. We present the means to enable future research on AM content at an unprecedented scope and scale.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it