Exploring Genre-Specific Thought Patterns Through Real-Time Experience Sampling of Movie Clips
Bibliographic record
Abstract
Understanding cognition during media consumption is crucial to understanding how individuals process complex stimuli in everyday life. This study employed multi-dimensional Experience Sampling (mDES) to capture real-time thoughts as participants viewed 2-3 minute movie clips across various genres. By utilizing movie clips, this study bridges real-world scenarios with the control of a laboratory setting; revealing how different movie genres uniquely shape thought patterns. I conducted Principal Component Analysis to identify the underlying structure of participants' thoughts. The analysis revealed four distinct thought components, named based on their loading structure: (1) Narrative Comprehension, (2) Episodic Knowledge, (3) Intrusive Distraction, and (4) Sensory Engagement, replicating components found in earlier mDES movie-watching studies (Konu et al., 2021; Wallace et al., 2024). To examine how subjects’ thoughts differed based on the genre of the movie clip, I conducted a series of linear mixed models comparing component scores for each component across genres. The analysis indicated certain thought patterns differed by genre. In Intrusive Distraction, Comedy clips scored significantly lower than Drama clips (p <.001), Romantic Comedy clips (p = .007), and clips of an unspecified genre (other) (p = .010). In Episodic Knowledge, Thriller clips scored significantly lower than Action clips (p = .002), Comedy clips (p <.001), Drama clips (p <.001), Family clips (p <.001), and Romantic Comedy clips (p <.001). Altogether, this study replicates the component structure found in earlier movie watching mDES studies, showing consistency across samples. Additionally, this study makes a significant contribution to the field of ongoing thought research by being the first to empirically establish that individuals’ internal experiences systematically differ based on movie genre. These findings suggest researchers should consider genre-specific influences when designing future movie-based tasks in psychology and neuroimaging studies. However, the small sample size (N=15) warrants caution; larger studies are needed for improved reliability and validity.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".