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.
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.
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.
-- "Different Rewards": Rewards vary in the environment. One demonstrator in each demonstrator condition always displays a more profitable option than the alternative demonstrator.

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.
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.

The data is arranged as follows.
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.
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.
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_participantReferenceNumber_DataTaskName_NumberOfGameLevel/Replicate_SceneName(IncludingRewardGroupAndDemonstratorCondition)_DataType.csv"
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.
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.
-- 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.
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.
-- "RemainingEnergy": The final energy value of the player at the end of the 'level'/replicate.