BrainCallus Gaming Project

Games to Quantify Symptoms of Psychiatric Disorders

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Project Overview

Motivation Figure


The BrainCallus Gaming Project is a volunteer-based effort seeking to improve psychiatric decision-making by leveraging a combination of mobile gaming, machine learning and computational cognitive science.

Project Motivation

Cognitive deficits are a hallmark of many psychiatric disorders but they are difficult to evaluate outside the clinic. This is because evaluating a patient’s cognitive state is currently done by the physician, leveraging psychometric inventories that are expensive in both time and money.

The data obtained through traditional psychometric inventories provide temporal snapshots of the patient’s symptoms, with little insight into how symptoms dynamically change across patient context (e.g., time and location). Moreover, these data rely on the patient to consciously report on symptoms, which can be problematic in cases when the patient is unable or unwilling to self-report.

Our Solution

The BrainCallus Project seeks to overcome these limitations by developing mobile games that are useful for evaluating patient symptoms outside the clinic. The proposed computational games are compelling to play, and produce data used to derive metrics of the patient’s perceptual, cognitive and motor performance. The metrics can be implicitly captured, allowing clinicians insight into symptoms the patient is not able to self-report.

Finally, patients of all ages find these games enjoyable, allowing for large amounts of data to be captured. These data are useful for deriving metrics to inform the physician’s clinical decisions, in addition to developing machine learning models that provide diagnosis and treatment recommendations.

Project Approach


Computational Games

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 Metrics

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.

Decision Metrics

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.

Motor Metrics

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.

Project Benefits


Continuous Measurements

By leveraging mobile games, it allows the patient to participate in measurement activities while at home. This enables powerful time series methods (e.g., LSTM RNNs) to be used to predict when the patient may realize an increase in negative symptoms between clinical visits. The physician can use this information to provide the patient with extra tools during periods that have a high-probability of a crisis, in addition to attempting to explore reasons for the increase in symptoms across time.

Implicit (Unconscious) Measures

Many times, patients are unwilling or unable to report when they are experiencing symptoms related to their disorder. Computational games allow for implicit measurements of patient symptoms to be obtained, providing the physician with otherwise unavailable metrics that are useful to inform diagnosis or treatment decisions.

Compelling Data Elicitation

The use of mobile games to quantify patient symptoms provides an approachable and enjoyable measurement activity. Games will be developed so patients in all age groups (including children) find data elicitation compelling. This will likely result in larger amounts of high-quality data to be produced, compared to traditional psychometric approaches. The increase in high-quality data will allow for researchers to leverage data-driven approaches, such as AI and machine learning, to improve the diagnosis and treatment of psychiatric disorders across age groups.

Get Involved



We strongly encourage collaboration opportunities from researchers and developers in academia and industry. We are especially keen on securing volunteers who have expertise in one of the following domains:

  • Mobile Game Developers: volunteers with the ability to convert technical game specs into robust and scalable code that leverages secure cloud data storage.
  • Clinical Psychologists and Psychiatrists: volunteers will act as subject-matter-experts who assure the game is developed in a manner that provides information useful to practicing clinicians.
  • Business Development: volunteers will raise awareness of the project among potential investors.

Contact our team to explore how you may be able to help contribute to this project.


If you are considering investing in this project, contact our team and we will be happy to answer questions to allow you to make an informed decision.

Project Leadership


Erik J. Schlicht, PhD (Project Lead, Machine Learning Algorithms, and Game Design)

Erik Schlicht is the founder of the Computational Cognition Group, LLC. He conducted research at Harvard University, MIT, Caltech and the University of Minnesota, where he used machine learning and AI to quantify human performance under uncertainty and risk. At MIT Lincoln Laboratory, he was a core member of a team who developed methods utilizing Serious Games to quantify operational decision-making under risk. While a postdocoral researcher between Harvard University and Caltech, Dr. Schlicht created a simplified poker task to quantify decision-making in a competitive (zero-sum) task. This effort resulted in a publication that ranks in the top 5% of all research output, according to metrics by Altmetric.