Horizon CDT Research Highlights

Research Highlights

Fitbit for the Brain

  Serena Midha (2017 cohort)   www.nottingham.ac.uk/~psxsm19

We can now view our physical lives, such as the number of steps walked or heart rate, in data effectively; the next progression is to track our mental lives. Therefore, this project is concerned with the objective viewing of cognitive activity as personal data.

Mental workload can be described as the amount of mental effort (involving psychological processes such as sustained attention and working memory; Wickens & Hollands, 2000) required to complete a task (Maior, Pike, Sharples & Wilson, 2015), and can be physiologically measured using Functional Near-infrared Spectroscopy (fNIRS). fNIRS is a brain imaging device which is now considered to be a robust and popular mental workload measure because is portable and considerably less affected by motion than other scanners (Pike, Maior, Porcheron, Sharples & Wilson, 2014), such as functional Magnetic Resonance Imaging (fMRI); fNIRS works by measuring the oxygenation in the brain which is dependent upon brain activity.

Up until now, mental workload research using physiological measures has mainly been conducted in laboratory settings and almost always has involved the careful manipulation of mental workload levels. Thus, this project proposes to investigate mental workload in the wild; this means the study of mental workload over an extended period of time and in a more natural setting. Regarding this, the following research questions have been identified:

  • Can we still read mental workload from the noisy data collected in an uncontrolled environment (which includes other mental processes not associated with mental workload, such as emotional processing)?

  • What other factors are influencing the fNIRS data to make reading/measuring mental workload difficult (can we solve the problems?)?

  • How do people understand, interpret and reflect on their mental workload in daily life?

To address this, an initial exploratory study will be conducted where a large amount of longitudinal data will be collected, including fNIRS, Apple Watch data (steps, heart rate, sleep quality), subjective ratings, video footage and galvanic skin response, from a small number of participants in their daily lives. The results will be used to point us in the direction of further research which aims to address the research questions. Investigating mental workload in this natural context will require a new approach as it is considered from a prolonged perspective rather than a short-term, one-off task point of view, which current theories are based upon. Therefore, this research might be significant in enabling people to track, understand and reflect back on their mental workload data.

References

  1. Maior, Horia A. and Pike, Matthew and Sharples, Sarah and Wilson, Max L. (2015) Examining the reliability of using fNIRS in realistic HCI settings for spatial and verbal tasks. In: CHI 2015: Crossings, 18-23 April 2015, Seoul, South Korea.
  2. Pike, M. F., Maior, H. A., Porcheron, M., Sharples, S. C., and Wilson, M. L. Measuring the effect of think aloud protocols on workload using fnirs. In Proc. SIGCHI, ACM (2014), 3807–3816.
  3. Wickens, C.D., Hollands, J.G. (2000). Attention, timesharing, and workload. Engineering Psychology and Human Performance. NY: Prentice Hall.

Publications

  1. My Blog Post on Example
  2. Full citation of a journal article

This author is supported by the Horizon Centre for Doctoral Training at the University of Nottingham (RCUK Grant No. EP/L015463/1) and Brain+.