The Subjective Consequences of Experiencing Random Events
Bibliographic record
Abstract
In everyday life, one’s experience is usually highly structured, coherent, and predictable, a regularity stemming from the many constraints (e.g., cultural and physical constraints) operating upon the natural and social worlds. Consider that events that are experienced in an office meeting are usually not experienced in the great outdoors, and vice versa. This predictability of the outside world is capitalized upon by the brain, which is highly prospective and incessantly extracts meaningful patterns from event sequences. Despite these considerations, to our knowledge there have been no investigations into the ways that the brain copes with experiences that violate this structured regularity. Here we demonstrate a novel paradigm designed to tax this prospective system (by presenting the brain with a rapid series of random events) and show that such exposure reliably induces negative affect. Participants are exposed to Rapid, Random Semantic Activation (RRSA) prior to completing a mood scale; compared to a mood baseline, RRSA yields a consistent pattern of negative affect. This pattern did not emerge in a control group that completed a task with identical stimuli. While previous research has focused on randomness in terms of humans’ ability to produce and detect random sequences, our paradigm explores this issue as it relates to human experience. Our findings are consistent with the idea that, due to the prospective nature of the brain and one’s “epistemic needs” (Kruglanski, 1980), gross violations in the regularity of experience produce some form of negative subjective experience.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".