Editors’ Introduction and Review: An Appraisal of Surprise: Tracing the Threads That Stitch It Together
Bibliographic record
Abstract
Though the scientific study of surprise dates back to Darwin (), there was an upsurge in interest beginning in the 1960s and 70s, and this has continued to the present. Recent developments have shed much light on the cognitive mechanisms and consequences of surprise, but research has often been siloed within sub-areas of Cognitive Science. A central challenge for research on surprise is, therefore, to connect various research programs around their overlapping foci. This issue has its roots in a symposium on surprise, entitled "Triangulating Surprise: Expectations, Uncertainty, and Making Sense," at the 36th Annual Conference of the Cognitive Science Society (Quebec City, July 2014). Building on the interdisciplinary conversations that started at the symposium, this issue aims to draw attention to some promising empirical and modeling results and their theoretical implications. The present paper sets the stage for the issue by presenting a historical summary, discussing contrasting definitions of surprise, and then by tracing major threads that run through both this issue and the larger literature on surprise. Our aim is to develop broader, shared understandings of the main insights, theories, and findings regarding surprise, with a view to supporting future integration and progress.
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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 | 0.009 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".