Mind Metrics

Note that there are issues with this app if you are not using iOS 8.

This app provides researchers, scientists, and interested individuals with a toolbox of cognitive tasks that are easy to administer, modify, analyze, and export. The tasks included in the application can be used to measure your arousal level, memory capacity, and multitasking abilities. Customize the difficulty level of the task, the number of trials administered, and the layout of the stimuli presented. Get individual response times by case and export the data collected to a comma-delimited format.

Tasks Included in the App:

1) The Psychomotor Vigilance Task (PVT) has been used for more than 40 years by sleep and alertness researchers. It is proven sensitive to the major components of alertness. Additionally, the modified Spatial Vigilance Task can be used to evaluate your alertness level in a shorter amount of time.

2) N-Back tasks measure your attention and memory capabilities. These tasks are good for measuring your higher level cognitive abilities. The color N-Back requires you to remember the color of objects that appeared 1, 2, 3, or 4 trials back. You have the option of selecting how many trials back you have to remember, with a 4-Back being a very challenging task.

3) The combination N-Back and vigilance tasks are a measure of attention, memory capabilities, and multitasking ability. This task combines the spatial 2-Back working memory task with a spatial discrimination task. This is the most difficult task in the app because it requires performing two tasks that utilize the same spatial cognitive resources. It is sensitive to the components of sleep and higher level cognitive processes.

Features:

- View individual reaction times and response accuracy
- Get the average reaction time for a game and a score that combines accuracy and reaction time
- Get feedback on your alertness level based on your unique individual performances
- Manage and delete your game performances
- Export all of your data so that you can do your own analysis

A special thanks to Professor Parasuraman and the ARCH Lab for providing your helpful feedback.