Neural dynamics of decision making under risk: Affective balance and cognitive-emotional interactions

Author(s): Grossberg, S. | Gutowski, W.E. |

Year: 1987

Citation: Psychological Review, 94, 300-318

Abstract: 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 mergent 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 reactions of the dipole. Habituating chemical transmitters within a gated dipole determine an affective adaptation level, or context, against which later events are evaluated. Neutral events can become effectively 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. The same preference orders may sometimes, but not always, emerge from different sequences of pair-wise alternatives. The role of shortterm memory updating and expeCtancy-modulatedm atching processesin regulating affective reactions is described. The formal axioms of prospect theory are dynamically explicated through this analysis. Analyses of judgments of the utility of a single alternative, choices between pairs of regular alternatives, choices between riskless and risky alternatives, and choices between pairs of risky alternatives lead to explanations of such phenomena as preference reversals,t he gambler s fallacy, the framing effect, and the tendency toward risk aversion when gains are involved but risk taking when losses are involved. These explanations illustrate that data concerning decision making under risk may now be related to data concerning the dynamics of conditioning, cognition, and emotion as consequences of a single psychophysiological theory.

Topics: Biological Learning, Mathematical Foundations of Neural Networks, Models: Other,

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