3.9 Article

LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAMD.2013.2277589

Keywords

Action-perception cycle; action selection; affordance; agent architecture; attention; autonomous agent; cognitive architecture; cognitive cycle; cognitive model; computational model; emotions; episodic learning; learning intelligent distribution agent (LIDA); neural correlates; perceptual learning; procedural learning

Funding

  1. National Science Foundation (NSF) [ITR 0325428, HCC 0834847, DRL 1235958]
  2. Institute of Education Sciences (IES) [R305A080594]
  3. Engineering and Physical Sciences Research Council (EPSRC) [EP/I028099/1]
  4. Austrian Science Fund (FWF) [P 25380] Funding Source: researchfish
  5. Austrian Science Fund (FWF) [P25380] Funding Source: Austrian Science Fund (FWF)

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We describe a cognitive architecture learning intelligent distribution agent (LIDA) that affords attention, action selection and human-like learning intended for use in controlling cognitive agents that replicate human experiments as well as performing real-world tasks. LIDA combines sophisticated action selection, motivation via emotions, a centrally important attention mechanism, and multimodal instructionalist and selectionist learning. Empirically grounded in cognitive science and cognitive neuroscience, the LIDA architecture employs a variety of modules and processes, each with its own effective representations and algorithms. LIDA has much to say about motivation, emotion, attention, and autonomous learning in cognitive agents. In this paper, we summarize the LIDA model together with its resulting agent architecture, describe its computational implementation, and discuss results of simulations that replicate known experimental data. We also discuss some of LIDA's conceptual modules, propose nonlinear dynamics as a bridge between LIDA's modules and processes and the underlying neuroscience, and point out some of the differences between LIDA and other cognitive architectures. Finally, we discuss how LIDA addresses some of the open issues in cognitive architecture research.

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