Cognitive Science Lab

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Video Game Telemetry as a Critical Tool in the Study of Complex Skill Learning

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2013
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.

Document type: 
Article
File(s): 

StarCraft 2 Replay Analysis

Peer reviewed: 
Yes, item is peer reviewed.
Date created: 
2013-09-18
Abstract: 

This data was used in Thompson et al. (2013).

Document type: 
Dataset

Category learning with imperfect feedback

Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2011-01-01
Document type: 
Dataset

Speed-Accuracy Trade-offs in Category Learning

Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2011-01-01
Document type: 
Dataset

Probability gain versus information gain in category learning: eye movements.

Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2011-03-01
Document type: 
Dataset

Stimulus specific association with learning dependent feedback

Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2010-01-01
Document type: 
Dataset

Probability gain versus information gain in category learning: hand movements.

Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2011-03-01
Document type: 
Dataset
File(s): 

4 category continuous dimension learning

Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2009-06
Document type: 
Dataset