Description:	Includes data taken from two datasets. The first dataset is using a cross-sectional methodology to collect replay files from the game StarCraft 2 where each participant contributes a single game. This data was initially collected with the goal of using video game data analogously to how games like chess have previously been used in Cognitive Science. We first determined which variables were related to the development of skill since StarCraft 2 provides a ranking system which allows skill level to be measured. Reaction Times were found to be a high predictor of skill. We also studied the relationship between age and skill and found that cognitive decline, as measured by increasing reaction times, began in the mid 20s. We also studied such things as motor chunking and carried out sentiment analysis on chat data. The second dataset is longitudinal in nature where several replays from each participant are collected. So far we have used this dataset to study learning transfer. The Dynamics of Reaction Time Distributions project examined reaction time distributions using both of these datasets. The data included here are subsets of these two datasets selected for the Dynamics of Reaction Time Distributions project and include a limited subset of both of these datasets. Other parts of these datasets are available, linked to below, the corresponding author can also be contacted if further information is desired. The folders are set up so as to be prepared for the data analysis, this includes two empty folders called "OutputData" where data from running the data processing gets stored in preparation for running the final analysis scripts. The code necessary for the data processing and analysis are included here in a subdirectory. Running the code runs MLE on reaction times for individual games of StarCraft 2 for the Generalized Beta 2 (GB2) and ex-Gaussian distributions, parameters for the distributions are collected and the AICs are collected alongside some descriptive statistics. These distributions are compared and ranked in relation to each other and the AIC weight is taken, this allows us to classify the distribution best fits into three categories 1) GB2, 2) ex-Gaussian, and 3) Inconclusive. Then the ratios of distribution best fits to total games can be taken and the proportions of each distribution for both the cross-sectional and longitudinal datasets can be compared to each other. Further analyses is carried out for the cross-sectional data, ANOVA and Tukey HSD are run on the distribution parameters relative to skill. For the longitudinal data linear mixed effects models are run on the parameters relative to experience, correlations between the parameters and median reaction time are also taken alongside other related correlations. Lastly an individual differences analysis is carried out through data visualization. Several plots are produced which show individual players change over time relative to each other on several variables relative to skill.
Notes:	Cross Sectional Data (SkillCraft1) collected as part of: Thompson, J. J., Blair, M. R., Chen, L., & Henrey, A. J. (2013). Video game telemetry as a critical tool in the study of complex skill learning. PloS One, 8(9). https://doi.org/10.1371/journal.pone.0075129 Longitudinal Data (BFL) collected as part of: Thompson, J., O’Camb, J. W., Barrett, R. C., Harrison, S., & Blair, M. R. (2023). Robustness of performance during domain change in an esport: A study of within-expertise transfer. Plos one, 18(12), e0295037. https://doi.org/10.1371/journal.pone.0295037 Item exited embargo and became publicly available on 2024-12-26
Authors:	Blair, Mark; Simon Fraser University
Keywords:	reaction time distributions learning power law ex-Gaussian video game
Field of Research:	Psychology and cognitive sciences > Cognitive sciences > Sensory processes, perception and performance
Publication Date:	2024-12-26
Publisher:	Federated Research Data Repository / dépôt fédéré de données de recherche
URI:	https://doi.org/10.20383/103.01000
Related Identifiers:	This dataset is cited by https://doi.org/10.1371/journal.pone.0075129 This dataset is cited by https://doi.org/10.1371/journal.pone.0295037 This dataset is cited by https://doi.org/10.1371/journal.pone.0075129 This dataset is cited by https://doi.org10.1371/journal.pone.0094215 This dataset is cited by https://doi.org/10.1111/tops.12254 This dataset is cited by https://doi.org/10.1016/j.knosys.2017.09.022 This dataset is cited by https://doi.org/10.1371/journal.pone.0218251 This dataset is cited by https://doi.org/10.3758/s13414-020-02019-w This dataset is cited by https://doi.org/10.1371/journal.pone.0295037 This dataset is part/subset of https://summit.sfu.ca/item/13328 This dataset has part/subset https://github.com/SFU-Cognitive-Science-Lab/StarTransfer Show More
Appears in Collections:	SFU Research Data