There are many applications of multilayer neural networks to pattern classification problems in the engineering field. Recently, it has been shown that Bayes a posteriori probability can be estimated by feedforward neural networks through computer simulation. In this paper, Bayes decision theory is
Neural affective decision theory: Choices, brains, and emotions
โ Scribed by Abninder Litt; Chris Eliasmith; Paul Thagard
- Publisher
- Elsevier Science
- Year
- 2008
- Tongue
- English
- Weight
- 725 KB
- Volume
- 9
- Category
- Article
- ISSN
- 1389-0417
No coin nor oath required. For personal study only.
โฆ Synopsis
We present a theory and neurocomputational model of how specific brain operations produce complex decision and preference phenomena, including those explored in prospect theory and decision affect theory. We propose that valuation and decision making are emotional processes, involving interacting brain areas that include two expectation-discrepancy subsystems: a dopamine-encoded system for positive events and a serotonin-encoded system for negative ones. The model provides a rigorous account of loss aversion and the shape of the value function from prospect theory. It also suggests multiple distinct neurological mechanisms by which information framing may affect choices, including ones involving anticipated pleasure. It further offers a neural basis for the interactions among affect, prior expectations and counterfactual comparisons explored in decision affect theory. Along with predicting the effects of particular brain disturbances and damage, the model suggests specific neurological explanations for individual differences observed in choice and valuation behaviors.
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