Prakriti Nayak

Queen Mary University of London

Prakriti is a neuroscientist passionate about pushing boundaries at the intersection of technology and biological research. Her journey began with a deep dive into neuroscience during her master’s program, where she explored large-scale imaging data and mastered statistical modelling techniques. Afterward, she pursued a career in scientific editing. She views gaming as an excellent platform to connect different fields, such as computational modelling and behaviour. Prakriti plans to develop a model of player uncertainty to enhance the gaming experience by setting difficulty levels that are enjoyable for each player, making games more accessible for people with limited cognitive capabilities. Additionally, her work has diagnostic applications.

A description of Prakriti's research:

Navigation and spatial memory are essential cognitive processes that enable individuals to orient themselves in complex environments. Amid the inherent uncertainty of environmental noise and cognitive variability, the brain employs sophisticated strategies to make navigational decisions. This project aims to elucidate the cognitive underpinnings of spatial navigation performance by leveraging gaming data to understand how individuals manage spatial uncertainty. 

The plan is to adapt a Bayesian ideal-observer model based on visual simultaneous localization and mapping. The model will fit and predict the player’s moment-by-moment movement decisions, given the first-person view and the map of the game environment. Fitting the model to the players' gameplay trajectories will yield parameters indicating each individual's levels of visual, motor, and memory noise. The combination of parameters that best differentiate between players will then be examined. This research has the potential to enhance our understanding of spatial navigation and its underlying mechanisms, as well as improve spatial navigation in games, offering an adaptive gaming experience tailored to individual spatial uncertainty levels.