Fast-forward to boredom: How switching behaviour on digital media makes people more bored
Bibliographic record
Abstract
Boredom is unpleasant, with people going to great lengths to avoid it. One way to escape boredom and increase stimulation is to consume digital media, for example watching short videos on YouTube or TikTok. One common way that people watch these videos is to switch between videos and fast-forward through them, a form of viewing we call digital switching. Here, we hypothesize that people consume media this way to avoid boredom, but this behaviour paradoxically intensifies boredom. Across seven experiments (total N = 1,223; six pre-registered), we found a bidirectional, causal relationship between boredom and digital switching. When participants were bored, they switched (Study 1); and they believed that switching would help them avoid boredom (Study 2). Switching between videos (Study 3) and within video (Study 4), however led not to less boredom, but more boredom; it also reduced satisfaction, reduced attention, and lowered meaning. Even when participants had the freedom to watch videos of personal choice and interest on YouTube, digital switching still intensified boredom (Study 5). However, when examining digital switching with online articles and with non-university samples, the findings were less conclusive (Study 6), potentially due to factors such as opportunity cost (Study 7). Overall, our findings suggest that attempts to avoid boredom through digital switching may sometimes inadvertently exacerbate it. When watching videos, enjoyment likely comes from immersing oneself in the videos rather than swiping through them.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".