A Probabilistic Approach for Detection and Analysis of Cognitive Flow

A performer may undergo a task with varying difficulty level. It is important to know the mental state in order to maintain the optimum level of performance. The mental state of an individual varies according to their IQ levels, task difficulties or other psychological or environmental reasons. We have tried to measure the cognitive state of individuals, while they are performing tasks of various complexity levels, using physiological responses like brain activation, heart rate variability and galvanic skin response. In this paper we have proposed a Bayesian network based model to probabilistically evaluate the cognitive state of an individual from the difficulty levels of the tasks, IQ level of the individual and observations made using the physiological sensing. Twenty subjects with various IQ levels are asked to play a modified Tower of London (TOL) game having three complexity levels: low, medium and high. The sensor data collected have been used to train the Bayesian model for generating the conditional probability distribution for the desired cognitive state. Results show that it can be used as a tool to determine the current cognitive state of any individual, provided we know their IQ score. In case of any contradiction between the desired cognitive state (obtained from prior knowledge) and the observed cognitive state (obtained during testing), the personal insights of a performer is