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.
Highlights
Performance timings of motor behaviour are suggestive of higher level processes that control entire sequences (i.e., ‘chunks’, e.g., [1,2,3])
We can be more specific about what the motor-chunking framework (MCF) would predict regarding the prevalence and time-savings of chunks in StarCraft 2, if it were a complete account of performance improvements
The MCF predicts a net decrease in the diversity of expert sequences
Summary
Performance timings of motor behaviour are suggestive of higher level processes that control entire sequences (i.e., ‘chunks’, e.g., [1,2,3]). These chunks have often be used as an explanation for performance improvements during learning. For example learning curves have been explained in terms of the acquisition of these chunks by several researchers [4,5,6,7]. Chunked sequences are advantageous for performance because chunks are executed quickly [8], and because automatization frees up cognitive resources for higher-level processing [9, 10].
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