Unified theories of cognition
The Architecture of Cognition
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
FEATURE: Neuroscience and the future of human-computer interaction
interactions - The Counterfeit You
Toward a Unified Catalog of Implemented Cognitive Architectures
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
A cognitive approach to web-based intelligent agents: The TRIPLE architecture
Web Intelligence and Agent Systems
How to build bridges between intelligent tutoring system subfields of research
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Rethinking cognitive architecture via graphical models
Cognitive Systems Research
Conflict resolution and learning probability matching in a neural cell-assembly architecture
Cognitive Systems Research
Perception processing for general intelligence: bridging the symbolic/subsymbolic gap
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
Strategic cognitive sequencing: a computational cognitive neuroscience approach
Computational Intelligence and Neuroscience - Special issue on Neurocognitive Models of Sense Making
A functional model of sensemaking in a neurocognitive architecture
Computational Intelligence and Neuroscience - Special issue on Neurocognitive Models of Sense Making
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The SAL cognitive architecture is a synthesis of two well-established constituents: ACT-R, a hybrid symbolic-subsymbolic cognitive architecture, and Leabra, a neural architecture. These component architectures have vastly different origins yet suggest a surprisingly convergent view of the brain, the mind and behaviour. Furthermore, both of these architectures are internally pluralistic, recognising that models at a single level of abstraction cannot capture the required richness of behaviour. In this article, we offer a brief principled defence of epistemological pluralism in cognitive science and artificial intelligence, and elaborate on the SAL architecture as an example of how pluralism can be highly effective as an approach to research in cognitive science.