Neural Dynamics of Decision Making Under Risk: Affective Balance and Cognitive-Emotional Interactions

摘要

A real-time neural network model, called affective balance theory, is developed to explain many properties of decision making under risk that heretofore have been analyzed using formal algebraic models, notably prospect theory. The model describes cognitive-emotional interactions that are designed to ensure adaptive responses to environmental demands but whose emergent properties nonetheless can lead to paradoxical and even irrational decisions in risky environments. Emotional processing in the model is carried out by an opponent processing network called a gated dipole. Learning enables cognitive representations to generate affective adaptation level, or context, against which later events are evaluated. Neutral events can become affectively charged either through direct activations or antagonistic rebounds within a previously habituated dipole. The theory describes the affective consequences of strategies in which an individual compares pairs of events or statements that are not necessarily explicitly grouped within the stimuli.