Psychological models of value-based decision-making describe how subjective values are formed and mapped to single choices. Recently, additional efforts have been made to describe the temporal dynamics of these processes by adopting sequential sampling models from the perceptual decision-making tradition, such as the diffusion decision model (DDM). These models, when applied to value-based decision-making, allow mapping of subjective values not only to choices but also to response times. However, very few attempts have been made to adapt these models to situations in which decisions are followed by rewards, thereby producing learning effects. In this study, we propose a new combined reinforcement learning diffusion decision model (RLDDM) and test it on a learning task in which pairs of options differ with respect to both value difference and overall value. We found that participants became more accurate and faster with learning, responded faster and more accurately when options had more dissimilar values, and decided faster when confronted with more attractive (i.e., overall more valuable) pairs of options. We demonstrate that the suggested RLDDM can accommodate these effects and does so better than previously proposed models. To gain a better understanding of the model dynamics, we also compare it to standard DDMs and reinforcement learning models. Our work is a step forward towards bridging the gap between two traditions of decision-making research.

Violations of economic rationality principles in choices between three or more options are critical for understanding the neural and cognitive mechanisms of decision making. A recent study reported that the relative choice accuracy between two options decreases as the value of a third (distractor) option increases, and attributed this effect to divisive normalization of neural value representations. In two preregistered experiments, a direct replication and an eye-tracking experiment, we assessed the replicability of this effect and tested an alternative account that assumes value-based attention to mediate the distractor effect. Surprisingly, we could not replicate the distractor effect in our experiments. However, we found a dynamic influence of distractor value on fixations to distractors as predicted by the value-based attention theory. Computationally, we show that extending an established sequential sampling decision-making model by a value-based attention mechanism offers a comprehensive account of the interplay between value, attention, response times, and decisions.

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Although many preferential choices in everyday life require remembering relevant information, the interplay of neural systems mediating decisions and memory has rarely been studied. We addressed this question by combining a task, in which choice options had to be retrieved from memory, with cognitive modeling and fMRI. We found that memory-guided decisions are captured by established process models of choice (sequential sampling models) but constrained by forgetting. People are biased toward remembered options and reject them only if they are very unattractive. Using a Bayesian modeling approach, we determined the posterior probability that options were remembered given the observed choices. This probability correlated with hippocampal activation during encoding. During decision making, the bias toward remembered options was linked to increased connectivity between hippocampus and ventromedial prefrontal cortex. Our results provide insights into the dependency of decisions on memory constraints and show that memory-related activation can be inferred from decisions.