Bibliographic record
Abstract
Theory of Mind (ToM) is the ability to attribute mental states (e.g., beliefs, desires) to others in order to understand and predict their social behavior. Decoding others’ mental states based on immediately perceptible social information (e.g., facial expressions) forms the foundation of this ability and is significantly impaired in individuals with depression. Interestingly, those who possess various risk factors, such as sub-threshold symptoms or a past or maternal history, but who are not currently depressed, show enhanced ToM decoding accuracy. Although having a mother with depression is one of the most powerful risk factors for adulthood depression, the mechanisms by which it might result in superior ToM have not been examined. Additionally, the relation between a genetic risk factor, the serotonin transporter-linked polymorphic region, to ToM, has not yet been studied. I will be the first to examine this relation as well as its role as a moderator in the relation of maternal depressive history to superior ToM, using a cross-sectional design and an archival sample of depressed and non-depressed young adults. I hypothesize that both maternal depressive history and possession of at least one s-allele will be associated with enhanced ToM ability, as well as an interaction whereby individuals with both a maternal history and at least one s-allele outperform those with a maternal history but who are l-allele homozygous. Since superior ToM decoding is associated with social dysfunction, the results may contribute to the identification of at-risk individuals and have implications for the prevention and treatment of depression.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".