Evaluating and Shaping Cognitive Training with Artificial Intelligence Agents

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Virtual reality (VR) has emerged as a promising tool for cognitive training for several neurological conditions (ie. mild cognitive impairment, acquired brain injury) as well as for enhancing healthy ageing and reducing the impact of mental health conditions (ie. anxiety and fear). Cognitive training refers to behavioural training that results in enhancement of specific cognitive abilities such as visuospatial attention and working memory. Using VR for such training offers several advantages towards achieving improvements, including its high level of versatility and its ability to dynamically adjust difficulty in real-time.

Furthermore, it is an immersive technology and thus has great potential to increase motivation and compliance in subjects. Currently, VR and serious video games come in a wide variety of shapes and forms and the emerging data are difficult to quantify and compare in a meaningful way (Sokolov 2020).

This project aims to exploit machine learning to develop intuitive measures of cognitive training in a platform independent way. The project is challenging as there is great variability in cognitive measures even in well controlled/designed lab experiments (Learmonth et al., 2017; Benwell et al., 2014). So the objectives of the projects are:

Predict psychological dimensions (ie. enjoyment, anxiety, valence and arousal) based on performance and neurophysiological data.
Relate performance improvements (ie. learning rate) to psychological dimensions and physiological data (ie. EEG and eye-tracking).
Develop artificial intelligence approaches that are able to modulate the VR world to control learning rate and participant satisfaction.

VR is a promising new technology that provides new means of building frameworks that will help to improve socio-cognitive processes. Machine learning methods that dynamically control aspects of the VR games are critical to enhanced engagement and learning rates (Darzi et al. 2019, Freer et al. 2020). Developing continuous measures of spatial attention, cognitive workload and overall satisfaction would provide intuitive ways for users to interact with the VR technology and allow the development of a personalised experience. Furthermore, these measures will play a significant role in objectively evaluating and shaping new emerging VR platforms and this approach will thus generate significant industrial interest.

Related Publications

Benwell, C.S.Y, Thut, G., Grant, A. and Harvey, M. (2014). A rightward shift in the visuospatial attention vector with healthy aging. Frontiers in Aging Neuroscience, 6, article 113, 1-11.
A. Darzi, T. Wondra, S. McCrea and D. Novak (2019). Classification of Multiple Psychological Dimensions in Computer Game Players Using Physiology, Performance, and Personality Characteristics. Frontiers in Neuroscience, 2019.
D. Freer, Y. Guo, F. Deligianni and G-Z. Yang (2020). On-Orbit Operations Simulator for Workload Measurement during Telerobotic Training. IEEE RA-L, https:arxiv.orgabs2002.10594.
Learmonth, G., Benwell, C. S.Y., Thut, G. and Harvey, M. (2017). Age-related reduction of hemispheric lateralization for spatial attention: an EEG study. Neuro-Image, 153, 139-151.
A. Sokolov, A. Collignon and M. Bieler-Aeschlimann (2020). Serious video games and virtual reality for prevention and neurorehabilitation of cognitive decline because of aging and neurodegeneration. Current Opinion in Neurology, 33(2), 239-248.