TD Models of reward predictive responses in dopamine neurons
Neural Networks - Computational models of neuromodulation
Opponent interactions between serotonin and dopamine
Neural Networks - Computational models of neuromodulation
A Unified Approach to Building and Controlling Spiking Attractor Networks
Neural Computation
Emotion in the Pursuit of Understanding
International Journal of Synthetic Emotions
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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.