Classic motor chunking theory fails to account for behavioural diversity and speed in a complex naturalistic task
Bibliographic record
Abstract
In tasks that demand rapid performance, actions must be executed as efficiently as possible. Theories of expert motor performance such as the motor chunking framework suggest that efficiency is supported by automatization, where many serial actions are automatized into smaller chunks, or groups of commonly co-occuring actions. We use the fast-paced, professional eSport StarCraft 2 as a test case of the explanatory power of the motor chunking framework and assess the importance of chunks in explaining expert performance. To do so, we test three predictions motivated by a simple motor chunking framework. (1) StarCraft 2 players should exhibit an increasing number of chunks with expertise. (2) The proportion of actions falling within a chunk should increase with skill. (3) Chunks should be faster than non-chunks containing the same atomic behaviours. Although our findings support the existence of chunks, they also highlight two problems for existing accounts of rapid motor execution and expert performance. First, while better players do use more chunks, the proportion of actions within a chunks is stable across expertise and expert sequences are generally more varied (the diversity problem). Secondly, chunks, which are supposed to enjoy the most extreme automatization, appear to save little or no time overall (the time savings problem). Instead, the most parsimonious description of our latency analysis is that players become faster overall regardless of chunking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".