A Metacognitive Model of Memory Encoding Modulated by Rewards

Despite robust empirical evidence supporting the role of reward in enhancing memory, the relationship between reward and memory shows complex patterns. We present a novel computational model that considers how people optimally allocate limited cognitive resources during memory encoding. Unlike previous accounts that directly link rewards with stronger memory encoding, we allow our model to adaptively decide how much to encode based on the overall reward environment and one's limited cognitive resources. Our model's predictions align closely with human behavior across three experiments. It explains why high-reward items are better remembered than low-reward items only when presented together but not separately, and how memory is modulated by rewards of both current and preceding (but not future) items. We also collected data demonstrating that this insensitivity to rewards of future items can be reversed when participants anticipate upcoming rewards. These findings provide evidence that memory encoding is an active process involving meta-level control rather than a passive response to individual reward values.