Association Between Passion and Optimal Functioning in Autistic Individuals: The Dualistic Model of Passion
Bibliographic record
Abstract
Background: Autistic individuals have intense interests in which they invest a significant amount of time and energy. Intense interests (i.e., passions) and their impact on optimal functioning were investigated for the first time using the Dualistic Model of Passion (DMP). The DMP posits that harmonious (HP) and obsessive passions (OP) can predict optimal functioning (well-being, contribution to society, and performance). Whereas HP is described as a balanced and flexible form of engagement toward a topic or an activity, OP is defined as a rigid form of engagement that negatively impacts optimal functioning. Methods: Autistic individuals aged 14–33 ( n = 108) participated in an online quantitative study and completed self-report measures relating to their favorite interest of the moment (i.e., HP, OP, emotions, flow, conflict, rumination, and optimal functioning). Aims were to characterize passion and to determine whether HP and OP predicted emotions, flow, conflict, rumination, and optimal functioning. Descriptive analyses (means, standard deviations, and pairwise correlations) and a path analysis model were performed to answer these aims. Results: Results revealed that participants were highly passionate for their favorite interest, showing relatively high levels of HP and OP for activities such as video games, knowledge acquisition, and creative arts. Structural equation modeling showed that, as predicted by the DMP, HP was associated with positive emotions and flow. In turn, OP was positively associated with conflict, rumination, and negative emotions. Finally, HP and OP were positively and negatively associated with optimal functioning respectively. Conclusion: Findings suggest that intense interests can be defined as passion using the DMP. The DMP offers a theoretical framework that can account for the duality of intense interest and predict psychological and functional outcomes. Learning to foster higher levels of HP for intense interests can improve well-being and promote positive psychological experiences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".