Date Published: March 5, 2019
Publisher: Public Library of Science
Author(s): Gustavo Landfried, Diego Fernández Slezak, Esteban Mocskos, Gustavo Stolovitzky.
The problem of skill acquisition is ubiquitous and fundamental to life. Most tasks in modern society involve the cooperation with other subjects. Notwithstanding its fundamental importance, teammate selection is commonly overlooked when studying learning. We exploit the virtually infinite repository of human behavior available in Internet to study a relevant topic in anthropological science: how grouping strategies may affect learning. We analyze the impact of team play strategies in skill acquisition using a turn-based game where players can participate individually or in teams. We unveil a subtle but strong effect in skill acquisition based on the way teams are formed and maintained during time. “Faithfulness-boost effect” provides a skill boost during the first games that would only be acquired after thousands of games. The tendency to play games in teams is associated with a long-run skill improvement while playing loyally with the same teammate significantly accelerates short-run skill acquisition.
Skill is mainly acquired from individual experience. Humans, due to its social characteristic, also incorporate knowledge by learning from others. Social learning may affect the skill acquisition process expected from experience, and involve beneficial and risky alterations to subject abilities . In this article, we exploit the virtually infinite repository of human behavior available in Internet to study a relevant topic in anthropological science: how grouping strategies may affect skill acquisition.
Traditionally, learning is modeled as a function of experience. In this article, we focus on how the learning curve expected from practice could be altered by different grouping strategies. We exploit the virtually infinite repository of human behavior available on the Internet to study a relevant topic in anthropological science: how grouping strategies may affect skill acquisition. Our method is based on massive data which enabled conducting a longitudinal study with very high precision to detect subtle changes.
We performed a Wilcoxon rank-sum test at Figs 1, 2, 3, and at Tables A and B in S1 File. A Wilcoxon confident interval was performed at Figs 2 and 3. We performed a multiple linear regression at Table 1, and at Fig F in S1 File. We performed a general linear mixed model at Table 2. We performed a two sided Kolmogorov-Smirnov tests at Figs B and G in S1 File. Tests were performed using R version 3.0.2 stats package, and Python version 3.6 statsmodels package.