Development of a computer game-based framework for cognitive behaviour identification by using Bayesian inference methods

In this work a novel technique for cognitive behavioural data acquisition via computer/console games is introduced by which the user feels more relax than s/he is in a formal environment (e.g., labs and clinics) and has less disruption as s/he provides cognitive data sequence by playing a game. The method can be adapted into any game and is based on the assumption that in this way more efficient analysis of mind can be made to unveil the cognitive or mental characteristics of an individual. In experiments of the proposed work a commercial console game was utilised by different users to complete the tasks in which each game player followed his/her own optional scenarios of the game for a certain period of time. The attributes were then extracted from the behavioural video data sequence by visual inspection where each one corresponds to user's behavioural characteristics spotted throughout the game and then analysed by the Bayesian network utility. At the end of all the experiments, two types of results were obtained: semantic representation of behavioural attributes and classification of personal behavioural characteristics. The approach is proved to be a unique way and helped identify general and specific behavioural characteristics of the individuals and is likely to open new areas of applications.