The Properties of Personal Semantics
Bibliographic record
Abstract
Recent cognitive neuroscience research has uncovered some similarities and some differences between general semantic memory (GS; e.g., knowledge about family in general) and personal semantic memory (PS; e.g., knowledge about my family in particular). To better understand the representational content and cognitive processes of PS and their relation to general semantics, we adapted a staple of General semantic memory research, the Property Generation task. In a first study, we randomly assigned 240 adult participants to a traditional General semantics perspective (e.g., listing the properties of families and bedrooms in general) or to a PS perspective (e.g., my family, my bedroom) in a between-subjects design. In a second, replication study, 124 participants completed the task in a within-subject design, taking each perspective for different concepts. Relative to the General semantics condition, the PS condition was associated with more features; these were more semantically distant from each other, and included adjectives more frequently and nouns less frequently. However, the two conditions had substantial (~46%) overlap in the frequency of their features. The findings contribute to the identification of the cognitive differences (e.g., semantic richness) and similarities (e.g., correspondence in semantic features) between personal and general semantic memory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".