Daniel Schloesser's research has been focused on investigating the dynamics of interpersonal coordination over the last five years. Specifically, he has been interested in how initial coordination dynamics develop into increasingly more stable dynamics when groups (human/non-human) are provided additional sensory information to coordinate with another agent during a task.
His research interests are in the areas of joint action, self-organization, entropy, machine learning, dynamic systems theory, and embedded/embodied cognition. His past research projects investigated the impact of differing sensory modalities during a cooperative joint action task in virtual environments. During one such project, He investigates the extent to which additional sensory information in a virtual environment influences group performance with and without access to this additional information. Additionally, investigating the point at which the additional information ceases to be sufficient for dyadic partners to sustain optimal performance strategies within the task space as task difficulty increased. He is interested in how initial performance strategies develop into increasingly more optimal performance strategies when groups (broadly speaking) are provided additional information to anticipate the other agent’s actions. Implications of this line of research are applicable to social behaviors in virtual environments (e.g., online cooperative gaming) and human/AI interactions."
Advisor: Chris Kello