READ ME File For ‘Egocentric visual distance perception in vista space in normal and virtual environments’

Dataset DOI: 10.5258/SOTON/D1110

ReadMe Author: Matthew Blyth, University of Southampton ORCID ID: 0000-0002-1584-3946

This dataset supports the thesis entitled: “Auditory fitness for duty: Acoustic stealth awareness”
AWARDED BY: University of Southampton
DATE OF AWARD: 2019

DESCRIPTION OF THE DATA
Participants had to estimate the distance between themselves and a human target at distances of 25, 50, 75, 100 and 125 m, on Southampton Common (real environment) and in a virtual model of Southampton Common (virtual environment, viewed via Oculus Rift virtual reality headset). Each participant (n = 23) viewed the target at three different azimuths: -10, 0 and 10 degrees relative to facing forward (in the dataset this is referred to as L/C/R). They estimated the egocentric distance at each target location 3 times. The procedure was identical in both real and virtual environments. Participants gave their estimates verbally.
This dataset contains the verbal estimates for each environment, at each distance, at each azimuth, for each repeat. Each row represents a different participant. Each column represents a different condition. The dataset should be opened using Microsoft Excel.
This experiment was approved by the University Ethics board (ERGO ID: 26625).

Date of data collection: June 2017 - September 2018

Legal: 
Copyright - CC BY

This project was funded by the Royal Centre of Defence Medicine.
Copyright 

Date that the file was created: September, 2019