A two-hour tutorial session on the CLARION cognitive architecture will
take place on June 14th 2009 at the International Joint Conference on
Neural Networks in Atlanta, GA (http://www.ijcnn2009.com/).
This tutorial introduces CLARION, a dual-process/dual-representation
cognitive architecture that centers on the distinction between
explicit and implicit cognitive processes. CLARION is neural-network-
based, and composed of four main subsystems: the Action-Centered
Subsystem (ACS), the Non-Action-Centered Subsystem (NACS), the Meta-
Cognitive Subsystem (MCS), and the Motivational Subsystem (MS). The
ACS is mainly for action decision-making. The NACS is a slave system
to the ACS and is used to store declarative and episodic knowledge. It
is also responsible for reasoning in CLARION. The MS is responsible
for determining motivational drive levels. The MCS is responsible for
cognitive monitoring and parameter setting in both the ACS and NACS,
and makes the goal setting determinations based on drive levels from
the MS.
In addition, CLARION is based on two other basic theoretical
assumptions: representational differences and learning differences of
two types of knowledge: implicit versus explicit. The main difference
between these two types of knowledge is accessibility. In each
subsystem, the top level contains explicit knowledge (easily
accessible) whereas the bottom level contains implicit knowledge
(harder to access).
Explicit knowledge is represented using symbolic, localist
representations; implicit knowledge is represented using distributed
representations. The inaccessible nature of implicit knowledge is
captured by distributed representations (in the bottom level), because
representational units in a distributed representation are capable of
accomplishing tasks but are less individually meaningful. This accords
well with the relative inaccessibility of implicit knowledge (as shown
by psychology). In contrast, explicit knowledge may be better captured
in computational modeling by localist representations (in the top
level), in which each unit has a clearer conceptual meaning. This
captures the property of explicit knowledge being more accessible.
The second theoretical assumption concerns the different learning
processes in the two levels. In the bottom level, implicit
associations are learned through gradual trial-and-error learning. In
contrast, learning of explicit knowledge is often one-shot and
represents the abrupt availability of knowledge . The inclusion and
emphasis on bottom-up learning (i.e., the transformation of implicit
knowledge into explicit knowledge) is, in part, what distinguishes
CLARION from other cognitive architectures. Nevertheless, top-down
learning is also carried out in CLARION: Knowledge that is initially
explicit can be assimilated into implicit knowledge (to capture
proceduralization and automatization found in human psychological data).
CLARION is capable of capturing a wide range of cognitive processes,
as well as providing theoretical integration and interpretations of
many psychological functions and processes. It has been used to
capture numerous tasks. CLARION may also be a useful tool for building
cognitively-oriented intelligent systems.
This tutorial presents a detailed description, along with many
cognitive simulations, and formal results. Prior exposure to
artificial neural networks can be helpful, but prior understanding of
cognitive architecture/psychological modeling is not required. This
tutorial will enable participants to apply the basic concepts,
theories, and computational models of CLARION to their own
(cognitively-oriented) work.
For registration, go to: http://www.ijcnn2009.com/
For more information, see the previous abstracts, the CLARION project
website, or contact Sebastien Helie (he...@psych.ucsb.edu)
-----------------------------------
Sebastien Helie, Ph.D.
Postdoctoral researcher
Department of Psychology
University of California, Santa Barbara
Santa Barbara, CA 93106-9660
--
Office: Bldg. 551, Rm 1504/3227
Phone: (805) 893-7909
Fax: (805) 893-4303
E-mail: he...@psych.ucsb.edu
Website: http://www.psych.ucsb.edu/~helie
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