Research

RESEARCH STATEMENT
I am deeply fascinated by how people learn and perform complex and dynamic tasks. Examples of such tasks include driving a car, piloting a plane, and controlling air-traffic. I empirically investigate and develop computational cognitive models of people performing complex and dynamic tasks to understand the cognitive processes underlying the skills necessary for performing such tasks.

RESEARCH PROJECTS

Synthetic Characters for Games and Virtual Worlds
The goal of this work is to develop realistic (i.e. humanistic) synthetic characters for computer games and virtual worlds. We explore ways to develop synthetic characters with beliefs, goals, and emotions, that are able to think and act in virtual worlds and are indistinguishable from human-controlled characters.

Cognitive Softbot (Cogbot) Project
The purpose of of the Cogbot project is to develop a human-like bot for Unreal Tournament (c) game. This project uses open software tool called Gamebots to interface with the ACT-R cognitive architecture to control a bot in UT. We are also exploring an extension to the Cogbot project to develop an autonomous Mobile Robot (Cogmobot) using ACT-R as a control architecture.

Prospective Memory
Prospective memory refers to remembering future events, whereas retrospective memory refers to remembering past events (Neisser, 1982). Examples of people using prospective memory abound in everyday life, from a child intending to meet her friends after school to a parent intending to stop by the store to pick up milk after work. While the use of prospective memory is a crucial aspect of human cognition, memory researchers have mostly focused on retrospective memory. Unfortunately, while interest in prospective memory has been increasing, the progress in understanding prospective memory has been rather slow. One of the main reasons for this has been the lack of “integrative theoretical frameworks for organizing the empirical findings” (Goschke and Kuhl, 1996, p.53). We are working to develop such an integrative computational framework to help us understand the nature and the structure of prospective memory in human cognition.

Multitasking
From the most extreme situations to the most mundane, tasks in the real world require multitasking — prioritizing, managing, and integrating multiple subtasks for the successful execution of the desired goal(s). For instance, fighter pilots, commanding officers, and air-traffic controllers must all perform many subtasks at once, often in situations in which several subtasks are time- and safety-critical. Multitasking is a fundamental component of successful real-world behavior: even the greatest skill for particular subtasks becomes useless without proper integration with other interdependent tasks (e.g., an expert flyer that cannot simultaneously monitor his environment and make sound tactical decisions). Thus, rigorous understanding of human multitasking is critical to the design of complex systems involving interacting humans and machines. We are working to develop a rigorous computational model of human multitasking in a cognitive architecture.

Psychological Time
Time is an integral aspect of human problem solving and learning in most real world tasks. For example, time is a critical factor in behavior when people rush to meet deadlines or when they perform under a dynamically changing environment. While the role of time, especially that of time pressure, on human behavior has been examined and documented by researchers in many task domains, ranging from simple reaction-time tasks to more complex decision-making and problem solving tasks, these results have not had a significant impact in the development of unified theories of cognition (UTC). However, in order for UTCs to truly reflect the depth and the breadth of human behavior, they must provide an account of psychological time. The purpose of this research is to provide such an account by developing a rigorous computational theory of psychological time through both empirical investigation and development of computational cognitive models.