Boredom as information processing: How revisiting ideas from Orin Klapp (1986) inform the psychology of boredom
Bibliographic record
Abstract
Almost forty years ago, sociologist Orin Klapp penned a treatise on boredom couched in terms of information processing. His essential claim was that boredom would arise at both low and high rates of information change. At the low end, there was too much redundancy and monotony, with any new piece of information failing to add meaning to what was already known. At the high end, noisy and chaotic barrages of information preclude meaning making and result in boredom. In essence, this can be seen as a drive to find a Goldilocks’ zone of information processing. While this theory of boredom is intriguing and clearly fits within other meaning-based accounts of the experience, there has been little direct experimental testing of the idea. This piece first characterizes Klapp’s theory before presenting what evidence there is that boredom arises at both high and low ends of various domains related to information processing (e.g., difficulty, challenge). Next, we discuss recent computational accounts that suggest a similar role for boredom in optimally processing information. We end with a call for more research to test Klapp’s model of boredom.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.037 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".