Bridging Bayesian and representational theories of memory to predict memory bias

Understanding how people integrate gist and item-specific information is central to explaining how memory changes over time. We examined how these representations interact in visual memory by integrating a Bayesian framework with representational models of individual item and gist memory to predict gist-based distortions. Participants completed separate tasks measuring gist and item-specific memory, and we used these data with independent measures of stimulus representations to quantify memory fidelity. We substituted these parameters into our model to predict memory errors in a third task where memory for individual items was biased by category-level structure. Our model predicted entire distributions of people’s memory errors, including the magnitude and direction of memory biases, and fine-grained individual differences, such as their skew and variance, at different offsets. Our findings highlight the power of combining normative Bayesian with algorithmic representational modelling approaches to understand how people integrate noisy memory representations at different levels of abstraction.