Neural Correlates of Mental State Decoding in Dysphoria: An Event‐Related Potential Study
Bibliographic record
Abstract
Depression is often associated with profound social and interpersonal functioning impairments. Negative interpersonal experiences may lead depressed individuals to withdraw from social interaction, which may in turn exacerbate the depression state (Rippere, 1980). As a result, it is of theoretical and clinical importance to understand the mechanisms underlying these social deficits. Researchers have applied the theory‐of‐mind framework to better understand the impaired social functioning in depressed individuals. Theory of mind refers to the everyday ability to attribute mental states (i.e., beliefs, desires, emotions) to others to both understand and predict their behaviour (Wellman, 1990). Research has found that individuindividuals with dysphoria (i.e. elevated scores on a measure of depression symptoms, but not necessarily a diagnosis of clinical depression) demonstrate enhanced mental state judgments (Harkness, Sabbagh, Jacobson, Chowdrey, & Chen, 2005). This study will determine neural mechanisms that may underlie this phenomenon by examining whether differences in brain activity exist between dysphoric and nondysphoric groups during mental states decoding. I will record electrophysiological data while participants are judging the mental states from pictures of eyes. Based on previous research (Sabbagh, Moulson, & Harkness, 2004), I anticipate that mental state decoding will be associated with the right inferior frontal and right anterior temporal regions of the brain. Furthermore, I hypothesize that dysphoric individuals will have greater activations in these brain regions and make significantly more accurate judgments than nondysphoric individuals when making mental state judgments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".