One or two minds? Neural network modeling of decision making by the unified self. (December 2019)

• Record Type: Journal Article
• Authors: Levine, Daniel S.
• Is Part Of: Neural networks. Volume 120(2019)
• Page Start: 74
• Page End: 85
• Publication Date: 2019-12
• Subjects: Decision making -- Adaptive resonance theory -- Fuzzy trace theory -- Amygdala -- Prefrontal cortex -- Anterior cingulate; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2019.08.008
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Ever since the seminal work of Tversky and Kahneman starting in the late 1960s, it has generally been accepted that many characteristic human decision patterns do not follow the norms of economic theories based on rational utility maximization and consistency across frames. Yet people do often make decisions and numerical judgments that are mathematically consistent. In order to account for the range of rational, intuitive, and emotional influences on decision making, prominent psychologists have developed a number of dual-process or dual-system theories of decision processes. Among these theories are System 1 and System 2, Cognitive-Experiential Self-theory, and Fuzzy Trace Theory. Fuzzy Trace Theory (FTT) is the most amenable of these theories to a unified biologically based neural network approach. This article reviews several years of research on a decision theory that integrates the gist and verbatim representations of FTT into a network model. The model is based on several constructs by Grossberg and his colleagues including Adaptive Resonance Theory and Gated Dipole Theory, combined with selective attention and probabilistic distribution of some parameters representing individual differences in decision style. It incorporates data on the functions of several brain regions including sensory cortex, amygdala, orbitofrontal cortex, ventral striatum, thalamus, and anterior cingulate, and premotor cortex.