Aims:
To understand users ability to focus on a dual attention task and their visual & auditory memory, their response strategy, response inhibition, or motor impulsivity.
Groups of participants:
-Controls
-People with ADHD/ADHD traits
Task Details:
The task will include city noises, birds flying, cars moving using Artificial Intelligence, AI. Users will need to cross 5 roads only if the traffic light is green for them. If they are in the middle of the road and the traffic light turns red, cars will start to honk and only when the user crosses the road, cars will move.
During this time, users will need to go to the destination and remember missed call, answer the phone call, and remember what the text message was about. The phone for the message, the missed call and map will be shown for 3 seconds. The audio call will be 6 seconds. The task can take approximately 5 minutes, but this depends on the person. There is a time limit of 10 minutes that task will end after this time even if user hasn’t reached the destination. In this case, we would still collect data from the task, but the reaction time will be 10 minutes.
Questionnaire:
Q1: The missed call was from: (Right answer Lauren)
Q2: The call from Julie was about: (Meeting at 7pm)
Q3: I think I did very well in completing this task.
Q4: I think I didn’t do very well in completing this task.
Q5: The task was interesting and was similar to a scenario that can happen in our day-to-day life.
Q6: I felt present and involved in VR.
Data Collected:
-From the task (we save username, map pressed (how many times they pressed the map), accuracy (low, medium or high depending on the time reached to destination and map pressed), reaction time (from the first traffic light was green), timestamp)
-Eye tracker VR Add on (Pupil Labs)
-EEG VR Add on (Looxid Link)
-From the questionnaire
Hardware:
-Alienware m15 Gaming Laptop
-HTC Vive Pro
-Fovitec 2x 7'6" Light Stand VR Compatible Kit
-Pupil Labs
-Looxid Link
Software:
-Unity
-Unity Asset Store
-Daz3D
-Blender
-IBM TTS Api
Programming Languages used:
-C#
-Python using Spyder in Anaconda
-Jupyter Notebook
-MySQL
-PHP API