Computational games are designed to produce data that is useful for quantifying player performance. More specifically, they contain gaming modules that are created to quantify each component of the human perception-action cycle.
Perception-action cycles are robust representations that are used to understand human performance across disparate operational settings. Intuitively, perception-action cycles reflect that humans perceive the state of
the world and use this information to decide about the best course of action to accomplish their goals. Then, they act on the world based on their decision, which changes the state of the world and the cycle is repeated.
Perceptual deficits are prevalent in several psychiatric disorders, such as schizophrenia. In order to quantify these deficits within the game, modules will be developed that are inspired from computational experiments in perceptual science.
This will allow for metrics to be produced that reflect high-level perceptual performance, such as perceptual and emotional inference, in addition to low-level metrics
that evaluate attention and perceptual memory.
The decision-making modules are motivated by experiments in Behavioral Economics. As a result, metrics such as risk and loss-aversion can be computed, in addition to metrics that reflect the ability of the player to delay reward.
Together, these metrics will reflect the player’s impulsivity, which is known to be associated with several psychiatric disorders.
Psychogenic movement disorders are a hallmark of some psychiatric disorders. The computational games produced in this effort will contain modules that are inspired from computational
sensorimotor control experiments. The modules will produce metrics that evaluate changes in motor variability and optimality of action selection across time, providing insight into motor performance.