Key Research Topics
- Knowledge Representation. Representing and using
knowledge about the environment is a crucial task facing any organism.
One line of research in our unit investigates how people represent
information, and how these representations can be learned (e.g., Lee,
2002; Navarro & Lee, 2003, 2004). This basic research has been
applied to develop novel approaches to data visualisation, particularly
in collaboration with the Defence Science and Technology Organisation
(Lee, Butavicius, & Reilly, 2003).
- Language. Language research in our unit has
focused on sentence parsing processes and the extraction of
propositional information from text (e.g., Dennis, 2004). Not only does
this work provide basic insights into fundamental cognitive processes,
but it has been applied in Defence and Education contexts. For example,
systems for assisting vocabulary learning and for automated essay
marking have been developed for deployment in Colorado schools. In
addition, a prototype question-answering system has been developed,
based on new theoretical models of the relational and semantic
structure in language.
- Decision-Making. Viewing human decision-making as
a process of evidence-accumulation provides an elegant theoretical
account for a range of phenomena. A basic theoretical contribution (Lee
& Cummins 2004) showed how this approach can unify Bayesian and
heuristic approaches to understanding human decision-making. We have
applied evidence-accumulation models to develop novel and effective
algorithms for text classification (Lee & Corlett 2003), and
adaptive and scalable algorithms for prioritising e-mail (Lee,
Chandrasena, & Navarro 2002).
- Categorisation. Past theoretical work developed
category-learning models able to accommodate sophisticated knowledge
structures (Lee & Navarro, 2002; Navarro, in press). Our current
research considers how human categorisation might approximate rational
statistical ideals, and how different models of categorisation mimic
one another.
- Memory. Investigations of the structure and
function of human memory span work on short-term memory, episodic
memory, and semantic memory (e.g., Dennis & Humphreys, 2001).
Applications include knowledge management systems and adapting user
profiles to changing environments. For example, a recent collaborative
research project with a telecommunications service provider aims to use
models of human memory to understand people’s use of mobile phone
applications.
- Statistical Methods. Cognitive science progresses
through the development of computational models of human cognitive
processes. Accordingly, it is important to use modern statistical
methods (e.g., Bayesian and information theoretic methods) for
evaluating and comparing competing models. We do both basic research
developing new model evaluation methods, such as “landscaping” and
“parameter space partitioning” (Navarro, Pitt & Myung, 2004), and
apply these sorts of methods to evaluate cognitive models in laboratory
and applied contexts (e.g., Navarro & Lee, 2003, in press; Navarro,
2004).
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