Information Technology Reference
In-Depth Information
Some Web Intelligence Oriented Brain
Informatics Studies
Yulin Qin
The International WIC Institute
Beijing University of Technology, China, and
Department of Psychology, Carnegie Mellon University, USA
yulinqin@gmail.com
With the advancement both in the Web (e.g., semantic Web and human-level
wisdom-Web computing) and in Brain Informatics (BI) (e.g., advanced informa-
tion technologies for brain science and non-invasive neuroimaging technologies,
such as functional magnetic resonance imaging (fMRI)), several lines of BI re-
search have been developed directly or indirectly related to Web Intelligence
(WI). Some of them can be treated as the extension to the Web research of the
tradition BI research, such as computational cognitive modeling like ACT-R.
ACT-R is a theory and model of computational cognitive architecture which
consists of functional modules, such as declarative knowledge module, procedu-
ral knowledge module, goal module and input (visual, aural), output (motor,
verbal) modules. Information can be proposed parallel inside and among the
modules, but has to be sequentially if it needs procedural module to coordinate
the behavior across modules. At the International WIC Institute (WICI), we are
trying to introduce this kind of architecture and the mechanism of activation of
the units in declarative knowledge module into our Web information system.
Based on or related to ACT-R, theories and models that are with very close re-
lation to WI have also been developed, such as threaded cognition for concurrent
multitasking, cognitive agents, human-Web interaction (e.g., SNIT-ACT (Scent-
based navigation and information foraging in the ACT cognitive architecture).
At the WICI, we are also working on the user behavior and reasoning on the
Webbyeye-trackerandfMRI.Someofother BI studies, however, have been
developed directly by the requirement of WI research. For example, to meet the
requirement of the development of Granular Reasoning (GrR) technologies in
WI research, people at the WICI have been checking how human can perceive
the real world under many levels of granularity (i.e., abstraction) and can also
easily switch among granularities. By focusing on different levels of granularity,
one can obtain different levels of knowledge, as well as in-depth understanding
of the inherent knowledge structure. The interaction between human intelligence
inspired WI methodology research and WI stimulated BI principle research will
benefit both BI and WI researches greatly.
