Situational familiarity improves decision quality in dynamic environments
Bibliographic record
Abstract
We employed a framework of field-based observation, utilizing helmet-mounted camera technology, to investigate pattern-matching aspects of decision-making by experts and non-experts during competitive play in ice hockey. We examined both the frequency with which experts and non-experts reported a decision-making scenario as familiar, or typical to them, and the resulting quality of those decisions. Expert (n=23) and non-expert (n=14) ice hockey players were videotaped from egocentric and exocentric positions during competitive game situations. Decision points (n=118) were isolated from the game tapes and player decision quality resulting from each situation was scored independently by two expert coaches. Retrospective interviews were conducted using a cognitive task analysis methodology (Hoffman et al., 1998). Prompted by the game videos, players were queried about the role that situational familiarity played in their decision-making process. Experts described decision-making situations as 'familiar' more often than non-experts ( p =.021). Further, the decisions that were judged to be familiar were also judged by coaches to be of superior quality to those decisions that were based on situations that were perceived as 'unfamiliar' ( p =.01). This pattern-matching aspect of the decision-making process, which isolates decision quality as a function of familiarity, has not previously been examined in dynamic, time-sensitive environments. In light of our findings, we discuss the processes which underpin the decisions of highly skilled athletes under time constrained situations, in view of models of decision making based on pattern-matching theories of expertise.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".