Author: Perlovsky L1, Schoeller F2
Affiliation: <sup>1</sup>Northeastern University, USA. Electronic address: lperl@rcn.com.
<sup>2</sup>Fluid Interfaces Group, Media Lab, Massachusetts Institute of Technology, Cambridge, USA. Electronic address: felixsch@mit.edu.
Conference/Journal: Phys Life Rev.
Date published: 2019 Oct 24
Other:
Pages: S1571-0645(19)30158-7 , Special Notes: doi: 10.1016/j.plrev.2019.10.007. [Epub ahead of print] , Word Count: 150
Brain and behavioral data have provided ample evidence that the largest part of emotion processes occur below the threshold of conscious awareness. In this article, we present computational models of the relation between emotion and cognition describing emotions as homeostatic signals critical to need regulation. These models suggest that an innate drive to regulate information and accompany the genesis of meaning evolved over the history of life. Most emotions underlying this innate mechanism of knowledge-acquisition occur below the threshold of consciousness. We review empirical data on the emotions of deep learning in humans, and suggest three families of unconscious emotions regulating learning. Methods for their measurement are proposed and we suggest that these unconscious emotions are crucial to the well-functioning of cognition, language comprehension, and decision-making.
Copyright © 2019. Published by Elsevier B.V.
KEYWORDS: Aesthetic emotions; Cognitive hierarchy; Consciousness; Feelings; Interoception; Knowledge instinct; Language; Learning; Unconscious emotions
PMID: 31759873 DOI: 10.1016/j.plrev.2019.10.007