We present a multi-attribute incentive salience (MAIS) model as a computational account of incentive salience in model-based Pavlovian learning. A model of incentive salience as a joint function of reward value and physiological state has been previously proposed by Zhang et al. (2009). In that model, the function takes additive or multiplicative forms depending on whether a preference shifts from positive to negative or vice versa. We demonstrate that arbitrarily varying this function is unnecessary to explain observed data. A multiplicative function is sufficient if one takes into account empirical data suggesting the incentive salience function for an incentive is comprised of multiple physiological signals. We compare our model to the previously proposed model on two datasets. We find the MAIS model predicts the outcomes equally well, fits empirical data describing multiple sensory representations of a single stimulus, better approximates the dual-structure appetitive-aversive nature of the reward system, is compatible with existing knowledge about incentive salience in Pavlovian learning, and better describes revaluation in Pavlovian learning. This model addresses a call (Dayan & Berridge, 2014) for algorithmic and computational models of model-based Pavlovian learning that consistently and fully explain empirical observations. Because a multi-attribute model is relevant even for simple Pavlovian associations, it should be useful in a wide variety of decision-making contexts, including agent modeling and addiction research.
Keywords: Incentive salience; Multi-attribute utility; Pavlovian learning; Reinforcement learning; Revaluation; Reward.
© 2021. The Psychonomic Society, Inc.