Human social learning biases in virtual environments

This dataset is from a study on human social learning biases conducted by C.Easter (University of Leeds) as part of her PhD thesis.<br>This data was collected using a novel research tool, "Virtual Environments for Research into Social Evolution" (VERSE), which uses gaming technology (Unity3D) to study human social learning behaviour within realistic, open world environments. VERSE aims to tackle some of the limitations of previous lab-based experiments, which are restricted by the use of abstract tasks, unrealistic social information sources and extremely localised spatial scales.<br>In this study, 143 undergraduate students from the University of Leeds were asked to solve a series of novel tasks within a set of virtual environments. Participants were divided into two groups:-- "Same Rewards": Rewards equal in the environment.<br>-- "Different Rewards": Rewards vary in the environment. One demonstrator in each demonstrator condition always displays a more profitable option than the alternative demonstrator.<br><br>The tasks were as follows: "Container" task, deposit a token into one of two containers over ten rounds. "Route Choice" task, find the shortest route to a fixed end point. "Foraging" task, navigate a large, open environment to collect food items. <br>For each task, participants were subjected to 6 demonstrator conditions:-- "Asocial": No demonstrators present, participant plays alone.-- "SocVsAsoc": One demonstrator present, all other options are undemonstrated.-- "Dominance": Two demonstrators present, a dominant AI and a subordinate AI, distinguished by physical appearance and behavioural differences. Dominant AI displays one option (in the 'Different Rewards group, always the more profitable option) while one AI displays an alternative.-- "Frequency": Four demonstrators present, three AIs display one option (in the 'Different Rewards group, always the more profitable option) while one AI displays an alternative.-- "Gender": Two demonstrators present, a male and a female. The male AI displays one option (in the 'Different Rewards group, always the more profitable option) while the female AI displays an alternative.-- "Size": Two demonstrators present, a large AI and a small AI. The large AI displays one option (in the 'Different Rewards group, always the more profitable option) while the small AI displays an alternative.<br><br>The data is arranged as follows.<br>In the root of the "HumanLearningVERSE" folder:Three R code files:-- "_Dataset_Generation_Code": Generates the 'Diff_' and 'Same_' datasets in the root folder.-- "_GLM_Analysis_Code": Conducts the glm analyses in the main paper.-- "_Graphs_Additional_Analyses_Code": Creates the graphs in the main manuscript and in the supplementary material. Also conducts some additional analyses, e.g. correlations in social information usage.Datasets:-- A dataset called "ParticipantData", which gives each participant's answers to a series of questions asked after the study. These answers are used as individual variables for each participant during the analysis. These include: gender, age, a series of answers to Bryant and Smith’s (2001), how often they play video games and how easy they found it to follow the instructions given / play the game during the experiment.-- A series of datasets beginning with "Same_" and "Diff_". These datasets give the proportion of times each demonstrator or no demonstrator were copied by each participant, during each demonstrator condition, for each task. Files are labelled with the task type (Container, Route, Foraging) and the reward group (Different Rewards, Same Rewards) the participant was placed in. Files ending in "ILV" are the main datasets, giving a summary of all the choices made by each participant. Files ending in "InitialChoice" give only the initial choices made by each participant, at the beginning of each demonstrator condition.<br>The HumanLearningVERSE folder contains two additional folders, "DiffRewards" and "SameRewards", which contain the raw data collected from VERSE during the experiment. "DiffRewards" contains data for participants in the Different Rewards group and "SameRewards" for the Same Rewards group. <br>In these folders are a series of folders, named with the participant's reference number (these numbers match the data in the ParticipantData csv file). In each participant's folders are the data for each of the three tasks, again placed into their individual folders. The name of each data file is descriptive and gives details of the replicate in question like so: "Ref_<b>participantReferenceNumber</b>_Data<b>TaskName</b>_<b>NumberOfGameLevel/Replicate</b>_<b>SceneName(IncludingRewardGroupAndDemonstratorCondition)</b>_<b>DataType</b>.csv"<br>For the Container task, there are two types of data per participant:-- "InteractionsData": All interactions with 'interactable objects' including which character interacted (participant = "player", demonstrators are labelled by their names, e.g. "AI (Large)", which object they interacted with, and when it occurred. 'ContainerY' and 'ContainerB' refer to the yellow and blue containers.-- "FoodCollectionScore": The final value for the for the player's food collection score and the potential amount they could have collected.<br>For the Foraging task, four data types are collected:-- "FoodPatchVisits": Reports which character visited which food patch and when.-- "PlayerFoodEatenData": Reports which food items were collected by the player and when, plus the nutritional value of each food item and a cumulative nutrition score. -- "FoodCollected": The final value for the for the player's food collection score and the potential amount they could have collected.<br>-- A "PositionData" dataset for each character: The x,y,z coordinates for a particular character each timestep, for . The character is stated in the filename.<br>For the Route Choice task, two data types were collected:-- A "PositionData" dataset for each character: The x,y,z coordinates for a particular character each timestep, for . The character is stated in the filename.<br>-- "RemainingEnergy": The final energy value of the player at the end of the 'level'/replicate.

本数据集源自利兹大学C·伊斯特（C.Easter）的博士论文中一项关于人类社会学习偏差的研究。<br>本研究的数据依托一款名为「社会进化研究虚拟环境（Virtual Environments for Research into Social Evolution, VERSE）」的新型研究工具采集，该工具借助Unity3D游戏引擎，可在逼真的开放世界环境中开展人类社会学习行为研究，旨在解决过往实验室实验的诸多局限：过往实验受限于抽象任务范式、非现实的社会信息来源以及极为狭窄的空间尺度。<br>本次研究共招募143名利兹大学本科生，要求其在预设虚拟环境中完成一系列新颖任务。参与者被分为两组：<br>-- 「相同奖励组（Same Rewards）」：环境内奖励值统一<br>-- 「不同奖励组（Different Rewards）」：环境内奖励值存在差异。所有示范者条件下，均存在一名始终提供更优收益选项的示范者，相较于另一示范者。<br><br>本次实验包含三类任务：<br>1. 容器任务（Container task）：在10轮实验中，将代币（Token）投入两个容器之一。<br>2. 路径选择任务（Route Choice task）：寻找通往固定终点的最短路径。<br>3. 觅食任务（Foraging task）：在大型开放环境中导航并收集食物道具。<br><br>针对每类任务，参与者共接受6种示范者条件：<br>-- 「非社会学习组（Asocial）」：无示范者，参与者独立完成任务<br>-- 「社会vs非社会组（SocVsAsoc）」：仅存在一名示范者，其余选项无示范行为展示<br>-- 「支配地位组（Dominance）」：两名示范者，分别为显性AI（Dominant AI）与从属AI（Subordinate AI），二者通过外观与行为模式区分。显性AI会展示某一选项（在不同奖励组中，该选项始终为更优收益选项），从属AI则展示另一选项。<br>-- 「频率组（Frequency）」：四名示范者，其中三名AI展示同一选项（不同奖励组中该选项始终为更优收益选项），剩余一名AI展示另一选项。<br>-- 「性别组（Gender）」：两名示范者，分别为男性AI与女性AI。男性AI展示某一选项（不同奖励组中该选项始终为更优收益选项），女性AI则展示另一选项。<br>-- 「体型组（Size）」：两名示范者，分别为大型AI与小型AI。大型AI展示某一选项（不同奖励组中该选项始终为更优收益选项），小型AI则展示另一选项。<br><br>数据集的组织形式如下：<br>在「HumanLearningVERSE」根目录下，包含三类R代码文件：<br>-- `_Dataset_Generation_Code`：用于生成根目录下的「Diff_」与「Same_」系列数据集<br>-- `_GLM_Analysis_Code`：用于执行主论文中的广义线性模型（Generalized Linear Model, GLM）分析<br>-- `_Graphs_Additional_Analyses_Code`：用于绘制主论文及补充材料中的图表，同时可执行部分额外分析，例如社会信息使用频率的相关性分析。<br><br>根目录下同时包含两类数据集：<br>1. `ParticipantData`：收录所有参与者在实验结束后填写的一系列问卷答案，可作为分析中各参与者的个体变量，具体包括：性别、年龄、Bryant与Smith（2001）量表的作答结果、日常游戏时长，以及对实验指导说明的理解难度与游戏操作体验评分。<br>2. 以「Same_」和「Diff_」开头的系列数据集：记录了每名参与者在每种示范者条件、每类任务中，选择各示范者选项或无示范者选项的比例。文件命名规则包含任务类型（容器、路径选择、觅食）与参与者所属奖励组别（不同奖励组、相同奖励组）。其中后缀为「ILV」的文件为核心数据集，汇总了每名参与者的所有选择结果；后缀为「InitialChoice」的文件仅收录参与者在每种示范者条件初始阶段的选择数据。<br><br>此外，「HumanLearningVERSE」根目录还包含两个子文件夹：`DiffRewards`与`SameRewards`，分别存储不同奖励组与相同奖励组参与者的原始实验数据。<br>在这两个子文件夹中，存在若干以参与者编号命名的文件夹（编号与`ParticipantData.csv`中的数据一一对应）。每个参与者文件夹内，又包含三类任务的独立子文件夹。数据文件命名规则清晰，包含以下关键信息：`Ref_<参与者编号>_Data<任务名称>_<游戏关卡/重复次数>_<场景名称（包含奖励组别与示范者条件）>_<数据类型>.csv`<br><br>针对容器任务，每名参与者包含两类数据：<br>1. `InteractionsData`：记录所有与「可交互对象」的交互行为，包括交互主体（参与者标记为「player」，示范者以名称标注，例如「AI (Large)」）、交互对象以及交互发生时间。其中「ContainerY」与「ContainerB」分别代表黄色容器与蓝色容器。<br>2. `FoodCollectionScore`：记录参与者最终的食物收集得分与理论最大可收集食物量。<br><br>针对觅食任务，共采集四类数据：<br>1. `FoodPatchVisits`：记录各角色访问各食物斑块的时间与信息<br>2. `PlayerFoodEatenData`：记录参与者收集的所有食物道具的时间、种类、每份食物的营养价值，以及累计营养得分<br>3. `FoodCollected`：记录参与者最终的食物收集得分与理论最大可收集食物量<br>4. `PositionData`：每名角色的位置数据集，记录特定角色在每个时间步的x、y、z三维坐标，文件名中会标注角色身份。<br><br>针对路径选择任务，共采集两类数据：<br>1. `PositionData`：每名角色的位置数据集，记录特定角色在每个时间步的x、y、z三维坐标，文件名中会标注角色身份。<br>2. `RemainingEnergy`：记录参与者在关卡/重复实验结束时的剩余能量值。