Robots in Behaverse are artificial agents capable of interacting with the Behaverse Activities (e.g, the training game, surveys) and which may mimic human behavior. Prototypes of dummy robots (acting randomly) have been implemented to generate data for subsequent data quality and system integrity checks. In the future we will interface with state-of-the-art RL agents libraries (e.g., stable-baselines), as comparing the performance of those robots with humans may provide invaluable insights into how the human mind works.
Computational models of cognition
Currently at the concept stage, Computational models of cognition aims to provide a framework and software tools to support integrative and cumulative efforts in modeling cognition.
It will contain a library of models implemented in various programming languages but following the same API. These models will be available on Docker and Singularity so they can run on the High Performance Computing clusters of the University of Luxembourg.
The models will be tested and compared using common metrics and datasets, which will be collected within Behaverse. Following the principles of continuous integration, each time new datasets are collected, all applicable models will be tested on those new datasets. Similarly as new models are introduced, they will be tested on all available relevant datasets and compared to the other models in the library.
This setting allows modelers to develop their models in their tools of choice, to more objectively and easily test the performance and scope of their models, while offering the possibility to share their models with the community (either with or without access to the source code).
Further tools will be needed for example, to explore results (e.g., dashboards) and tinker with the various models (e.g., notebooks). We believe this type of system is necessary both to promote modeling and to get a clearer sense of where we currently stand as a field.
The ultimate goal of Behaverse is to construct a “Psych Engine” (in analogy to the physics engine in video games) which will integrate all of our knowledge of the human cognitive system. The Psych Engine should be able to use a person’s historic data to simulate that person’s cognitive system and make non-trivial predictions about how that specific person would perform in various cognitive tests or games. In other words, the Psych Engine would instantiate a “digital twin” of a person’s cognitive system.
In addition to the scientific value of creating such a system, the Psych Engine may be used both to guide experiments to further our understanding of cognition (e.g., by pointing out predictions with high uncertainty), suggest interventions to help people improve their cognitive abilities and for diagnostics (e.g., unexpected performance drops in specific cognitive abilities may be indicative of a specific neurological issue).