Associations of state and chronic loneliness with interpretation bias: The role of internalizing symptoms
Bibliographic record
Abstract
Loneliness is common and, while generally transient, persists in up to 22% of the population. The rising prevalence and adverse impacts of chronic loneliness highlight the need to understand its underlying mechanisms. Evolutionary models of loneliness suggest that chronically lonely individuals demonstrate negative interpretation biases towards social information. It may also be that such biases are exacerbated by momentary increases in state loneliness, or elevated anxiety or depression. Yet, little research has tested these possibilities. The current study aimed to advance understandings of loneliness by examining associations of chronic loneliness with individual differences in negative interpretation bias for social (relative to non-social) stimuli, and testing whether these associations change in the context of increased state loneliness and current levels of anxiety and depressive symptoms. These aims were explored in 591 participants who completed an interpretation bias task before and after undergoing a state loneliness induction. Participants also self-reported chronic loneliness, anxiety, and depression. Linear mixed models indicated that only state (but not chronic) loneliness was associated with more positive interpretations of non-social stimuli, with greater anxiety and depressive symptoms predicting more negative interpretations. Implications of these findings for present theoretical models of loneliness are discussed.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".