6. Mental State Decoding in Dysphoric Individuals: The Role of Motivation
Bibliographic record
Abstract
Depression is associated with pervasive impairments in social and interpersonal functioning. Research demonstrates that individuals with depression have difficulty interacting with peers and show lower levels of social activity than do nondepressed individuals (Levendosky, Okun, & Parker, 1995). In addition, depressed individuals report that their social interactions are less supportive and less rewarding than those of non‐depressed individuals (Nezlek, Hamptom, & Shean, 2000). This reduced social competence may cause depressed individuals to disengage from social interaction, which may in turn exacerbate their state of depression (Rippere, 1980). It is thus important to understand and identify the mechanisms beneath these deficits. Researchers commonly use the theory of mind framework to understand impaired social functioning in clinical conditions. Theory of mind refers to the ability to make judgments about others’ mental states to understand and predict their social behaviour. Research has found a relationship between theory of mind and dysphoria (i.e., elevated scores on a measure of depression symptoms, but not necessarily a diagnosis of clinical depression). Specifically, dysphoric individuals demonstrate enhanced mental state judgments (Harkness, Sabbagh, Jacobson, Chowdrey, & Chen, 2005). My research investigates social motivation as an underlying mechanism for dysphoric individuals’ enhanced decoding ability. A sample of undergraduates will participate in a theory of mind decoding task following social, monetary or no motivation. I hypothesize that dysphoric individuals will make significantly more accurate judgments than non‐dysphoric individuals. Further, I predict that social motivation will enhance non‐dysphoric individuals’ sensitivity to others’ mental states.
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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 | 0.001 | 0.004 |
| 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) | 0.003 | 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".