Abstract
Cybernetics provides a framework for understanding the behavior of closed-loop systems, including the feedback control intrinsic to cognitive systems (Smith and Smith in continuing the conversation: a newsletter of ideas in cybernetics. Greg and Pat Williams, Gravel Switch, KY, [1]). We propose adopting our interpretation of the cybernetics concept of feedback control of cognition by integrating across metacognition, performance, computational cognitive modeling, and physiological levels of analysis. To accomplish this objective, we tie cognitive variables to each level of analysis, including: (1) metacognition—self-evaluation of cognition; (2) performance—objective measures of progress toward a goal state; (3) physiology—indications of cognitive function (e.g., heart rate variability as an index of the level of task engagement); and (4) cognitive models—prediction and understanding of empirical results using sequences of cognitive steps. We call this integrative approach, Multi-Level Cognitive Cybernetics (MLCC). In this paper, we define the MLCC framework, discuss how MLCC can inform the design of adaptive automation technologies, and discuss the benefits of the MLCC approach in human factors.
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Cassenti, D.N., Gamble, K.R., Bakdash, J.Z. (2017). Multi-level Cognitive Cybernetics in Human Factors. In: Hale, K., Stanney, K. (eds) Advances in Neuroergonomics and Cognitive Engineering. Advances in Intelligent Systems and Computing, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-41691-5_27
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DOI: https://doi.org/10.1007/978-3-319-41691-5_27
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