Real-life decisions often require a comparison of multi-attribute options with various benefits and costs, and the evaluation of each option depends partly on the other ones in the choice set (i.e., the choice context). Although reinforcement learning models have successfully described choice behavior, it remains unclear how multi-attribute information is taken into account for making a context-dependent decision in reinforcement learning algorithms. Here we develop a computational model of attention control that includes context effects on multi-attribute decisions, linking a context-dependent choice model with a reinforcement learning model. The overall model suggests that the distinctiveness of attributes guides an individual’s preferences among multi-attribute options via an attention-control mechanism that determines whether choices are selectively biased toward the most distinctive attribute (selective attention) or proportionally distributed based on the relative distinctiveness of attributes (divided attention). To test the model, we conducted a behavioral experiment in rhesus monkeys, in which they made simple multi-attribute decisions over three conditions that manipulated the degree of distinctiveness between alternatives: (1) four foods of different size and calorie; (2) four pieces of the same food in different colors; and (3) four identical pieces of food. The model simulation of the choice behavior captured the preference bias (i.e., overall preference structure) and the choice persistence (repeated choices) in the empirical data, providing evidence for the respective influences of attention and memory on preference bias and choice persistence. Our study provides insights into computations underlying multi-attribute decisions, linking attentional control to decision-making processes.

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