Abstract
A cognitive architecture is the essential structures and processes of a domain-generic computational cognitive model used for a broad, multiple-level, multiple domain analysis of cognition and behavior. This chapter reviews some of the most popular psychologically-oriented cognitive architectures, namely adaptive control of thought-rational (GlossaryTerm
ACT-R
), Soar, and CLARION. For each cognitive architecture, an overview of the model, some key equations, and a detailed simulation example are presented. The example simulation with GlossaryTermACT-R
is the initial learning of the past tense of irregular verbs in English (developmental psychology), the example simulation with Soar is the well-known missionaries and cannibals problem (problem solving), and the example simulation with CLARION is a complex mine field navigation task (autonomous learning). This presentation is followed by a discussion of how cognitive architectures can be used in multi-agent social simulations. A detailed cognitive social simulation with CLARION is presented to reproduce results from organizational decision-making. The chapter concludes with a discussion of the impact of neural network modeling on cognitive architectures and a comparison of the different models.Access this chapter
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Abbreviations
- ACS:
-
action-centered subsystem
- ACT-R:
-
adaptive control of thought-rational
- ANOVA:
-
analysis of variance
- DLPFC:
-
dorsolateral prefrontal cortex
- MCS:
-
meta-cognitive subsystem
- MS:
-
motivational subsystem
- NACS:
-
non-action-centered subsystem
- PSCM:
-
problem-space computational model
- RL:
-
reinforcement learning
- VLPFC:
-
ventrolateral prefrontal cortex
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Hélie, S., Sun, R. (2015). Cognitive Architectures and Agents. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_36
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