Shi Cao, PhD, Assistant Professor, University of Waterloo
时间： 2015-04-13 15:00 - 17:00
地点： 王克桢楼1113
Cognitive modeling is a valuable method for both cognitive psychology research and human factors applications. Cognitive models can help psychologists examine theories and test hypotheses; models can also support the evaluation of operators/users' performance and workload while interacting with a system and compare different design options to determine the optimal design choice. Traditionally, such evaluation and tests mainly rely on empirical human studies, which add a heavy cost to the design process. In addition, it is difficult to conduct comprehensive user tests at early design stages when no physical interfaces have been implemented. To address these issues, I develop computational human performance modeling techniques that can simulate human-computer interaction (currently focusing on cognitive aspects). In this talk, I will introduce the recent progress on an integrated cognitive architecture that has the capability to model human performance and mental workload in complex cognitive multi-task scenarios. This QN-ACTR architecture integrates two previously isolated but complementary cognitive architectures – Queueing Network (QN) and Adaptive Control of Thought-Rational (ACT-R). It uses a general cognitive architecture to computationally represent human cognitive capabilities and constraints. Task-specific models can be built with the specifications of user knowledge, user strategies, and user group differences. The simulation results include performance measures such as task completion time and error rate as well as mental workload measures. Completed studies have modeled multitasking scenarios in a wide range of domains, including transportation, healthcare, and human-computer interaction. Software tools have been designed to simplify the model building process. I have also designed the connection protocol between QN-ACTR and an open-source driving simulation program so that models can drive simulated vehicles as a way to simulate and evaluate human driving performance. Implications and future research will be discussed.2015-04-13
2015-04-13