Periodic Tapping Mechanisms of Skill Learning in a Fast-Paced Video Game
收藏DataCite Commons2023-07-07 更新2024-07-13 收录
下载链接：
https://kilthub.cmu.edu/articles/dataset/Periodic_Tapping_Mechanisms_of_Skill_Learning_in_a_Fast-Paced_Video_Game/21599775/2
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资源简介：
Dataset, software, and computational models used for a project entitled "Periodic Tapping Mechanisms of Skill Learning in a Fast-Paced Video Game". <br> This folder includes the 3 ACT-R models that are referenced in the paper as well as all the code and data that were used to preprocess and analyze the data. We are leveraging ACT-R's periodic tapping motor extension to simulate human learning at very fast speed (<= 500 ms) in the Auto Orbit video game. <br> Folder organization: 1) Periodic Tapping Skill Learning - Code: This includes the ACT-R models, demographic information, Human & model measure files, and all the scripts that were used to generate the figures and statistics <br> 2) Periodic Tapping Skill Learning - Data: This includes the game scores and event files (log files) for both humans and ACT-R models
本数据集、软件及计算模型均服务于题为《快速节奏电子游戏中技能学习的周期性敲击机制》的研究项目。
本文件夹包含论文中提及的3个ACT-R模型，以及所有用于数据预处理与分析的代码与数据。本研究借助ACT-R的周期性敲击运动扩展模块，在《Auto Orbit》电子游戏中模拟人类在≤500毫秒的极快节奏下的学习过程。
文件夹组织如下：1) 「周期性敲击技能学习-代码」：内含ACT-R模型、人口统计学信息、人类与模型行为测量文件，以及所有用于生成图表与统计数据的脚本
2) 「周期性敲击技能学习-数据」：内含人类参与者与ACT-R模型的游戏得分数据及事件文件（日志文件）
提供机构：
Carnegie Mellon University
创建时间：
2023-07-07
