Agency, frustration, and the experience of boredom
Bibliographic record
Abstract
Prior work shows that highly boredom prone individuals report feeling diminished levels of agency. The current study investigated the possibility that the highly boredom prone would be more sensitive (and less tolerant) to disruptions to their own agency. Participants played the video game Pong, with delays gradually introduced between their initiation of movements of the paddle and actual movements on the screen as a means of disrupting agency. In addition, participants had the option to reset the game (which also reset delays to zero) as often as they liked. State boredom ratings were negatively associated with subjective ratings of control, a proxy for agency, during game play. Frustration ratings were shown to mediate the association between state boredom and control ratings. For participants who made a minimum of two resets during game play, boredom proneness was predictive of the total number of resets, such that those higher in boredom proneness tended to reset the game more frequently. Further work is needed to determine how the relation between boredom and agency might influence the failure to launch into action that is characteristic of boredom proneness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".