Aural detectability judgements in normal-hearing civilians

This dataset supports the thesis entitled: “Auditory fitness for duty: Acoustic stealth awareness” AWARDED BY: University of Southampton DATE OF AWARD: 2019DESCRIPTION OF THE DATA This dataset contains the aural detectability judgements of normal-hearing civilians which underpin experiment 2 of the thesis “Auditory fitness for duty: Acoustic stealth awareness”. The participant was stood in an anechoic chamber wearing a virtual reality headset, and was viewing a virtual environment resembling Southampton Common. The participant viewed a human target located 25, 50 or 100 m away in the distance, then listened to a sound presented at their feet or 1 m behind them (at head height), and had to respond “Yes” or “No” (via an xbox controller) whether they judged that sound to be aurally detectable by the target in the distance (Yes = the sound was audible to the target; No = the sound was not audible to the target). Three sounds were used (whispered digits, white noise and pine cone crunching sounds). There were 9 conditions in total. The method of constants was used in each condition: - Sounds were presented at six different levels. - Each level had 10 repeats, in order to calculate the proportion of “Yes” responds at each level so the psychometric function could be obtained. - Levels for each condition were selected based on a practice period of 108 trials prior to the main data collection period. - All trials for all conditions were presented in a random order. - The subject’s view of the target was impeded between each trial.This dataset contains a MATLAB .mat file containing the structure ‘PFs’. Within this structure there are two nested structures for each participant (n = 28). Each structure contains data for each session (2 sessions in total). Each session structure (for each participant) contains: - condition identifier (note, each row represents a different condition) - the sound levels presented in that condition (expressed as: 1. sensation level (dB SL) at the target, predicted by a sound propagation and loudness model; 2. The RMS level (dB SPL) at the subject; 3. The peak level (dB SPL) at the subject) - the number of repeats - the number of ‘yes’ responses at each level - the psychometric function parameters and fit, as fitted by the Palamedes toolbox. Note, this dataset can only be opened using MATLAB. This experiment was approved by the University of Southampton ethics board (ERGO ID: 23783).Date of data collection: October 2018 - December 2018Legal: Copyright: CC BY This doctoral project was funded by the Royal Centre for Defence Medicine.