Video game telemetry as a critical tool in the study of complex skill learning

Abstract

Cognitive science has long shown interest in expertise, in part because prediction and control of expert development would have immense practical value. Most studies in this area investigate expertise by comparing experts with novices. The reliance on contrastive samples in studies of human expertise only yields deep insight into development where differences are important throughout skill acquisition. This reliance may be pernicious where the predictive importance of variables is not constant across levels of expertise. Before the development of sophisticated machine learning tools for data mining larger samples, and indeed, before such samples were available, it was difficult to test the implicit assumption of static variable importance in expertise development. To investigate if this reliance may have imposed critical restrictions on the understanding of complex skill development, we adopted an alternative method, the online acquisition of telemetry data from a common daily activity for many: video gaming. Using measures of cognitive-motor, attentional, and perceptual processing extracted from game data from 3360 Real-Time Strategy players at 7 different levels of expertise, we identified 12 variables relevant to expertise. We show that the static variable importance assumption is false--the predictive importance of these variables shifted as the levels of expertise increased--and, at least in our dataset, that a contrastive approach would have been misleading. The finding that variable importance is not static across levels of expertise suggests that large, diverse datasets of sustained cognitive-motor performance are crucial for an understanding of expertise in real-world contexts. We also identify plausible cognitive markers of expertise.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Variable Importance.

The rank of the permutation importance for each variable. Each column refers to a random forrest classification. Grid colors and numbers reflect ranks. White numbers indicate that the variable is above the cuttoff defined by the control variable (see Supplementary Materials and methods in Materials S1).

Figure 2. Bronze-Gold Permutation Importance.

Histograms for permuted importance values from 25 conditional inference forests for each of the 16 variables used in the Bronze-Gold classifier. Classification rate: 82.32%. Baseline accuracy of classifier from choosing more common class: 77.81%.

Figure 3. Silver-Platinum Permutation Importance.

Histograms for permuted importance values from 25 conditional inference forests for each of the 16 variables used in the Silver-Platinum classifier. Classification rate: 78.27%. Baseline accuracy of classifier from choosing more common class: 70.03%.

Figure 4. Gold-Diamond Permutation Importance.

Histograms for permuted importance values from 25 conditional inference forests for each of the 16 variables used in the Gold-Diamond classifier. Classification rate: 79.01%. Baseline accuracy of classifier from choosing more common class: 59.31%.

Figure 5. Platinum-Masters Permutation Importance.

Histograms for permuted importance values from 25 conditional inference forests for each of the 16 variables used in the Platinum-Master classifier. Classification rate: 80.70%. Baseline accuracy of classifier from choosing more common class: 56.63%.

Figure 6. Diamond-Professional Permutation Importance.

Histograms for permuted importance values from 25 conditional inference forests for each of the 16 variables used in the Diamond-Professional classifier. Classification rate: 96.75%. Baseline accuracy of classifier from choosing more common class: 93.61%.

Figure 7. Bronze-Professional Permutation Importance.

Histograms for permuted importance values from 25 conditional inference forests for each of the 16 variables used in the Bronze-Professional classifier. Classification rate: 98.59%. Baseline accuracy of classifier from choosing more common class: 75.23%.

Figure 8. Perception Action Cycles (PACs).

Actions and attention shifts for a typical StarCraft 2 player over 15 seconds. Each vertical line tic represents a single action. Notice that most aspects of the PAC become faster with an increase in League.