Bayesian models of cognition assume that prior knowledge about the world influences judgments. Recent approaches have suggested that the loss of fidelity from working to long-term (LT) memory is simply due to an increased rate of guessing (e.g. Brady, Konkle, Gill, Oliva, & Alvarez, 2013). That is, recall is the result of either remembering (with some noise) or guessing. This stands in contrast to Bayesian models of cognition while assume that prior knowledge about the world influences judgments, and that recall is a combination of expectations learned from the environment and noisy memory representations. Here, we evaluate the time course of fidelity in LT episodic memory, and the relative contribution of prior category knowledge and guessing, using a continuous recall paradigm. At an aggregate level, performance reflects a high rate of guessing. However, when aggregate data is partitioned by lag (i.e., the number of presentations from study to test), or is un-aggregated, performance appears to be more complex than just remembering with some noise and guessing. We implemented three models: the standard remember-guess model, a three-component remember-guess model, and a Bayesian mixture model and evaluated these models against the data. The results emphasize the importance of taking into account the influence of prior category knowledge on memory.

Categorical knowledge and episodic memory have traditionally been viewed as separate lines of inquiry. Here, we present a perspective on the interrelatedness of categorical knowledge and reconstruction from memory. We address three underlying questions: what knowledge do people bring to the task of remembering? How do people integrate that knowledge with episodic memory? Is this the optimal way for the memory system to work? In the review of five studies spanning four category domains (discrete, continuous, temporal, and linguistic), we evaluate the relative contribution and the structure of influence of categorical knowledge on long-term episodic memory. These studies suggest a robustness of peoples’ knowledge of the statistical regularities of the environment, and provide converging evidence of the quality and influence of category knowledge on reconstructive memory. Lastly, we argue that combining categorical knowledge and episodic memory is an efficient strategy of the memory system.

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Expectations learned from our perceptual experiences, culture, and language can shape how we perceive, interact with, and remember features of the past. Here, we questioned whether environment also plays a role. We tested recognition memory for color in Bolivia’s indigenous Tsimanè people, who experience a different color environment than standard U.S. populations. We found that memory regressed differently between the groups, lending credence to the idea that environmental variations engender differences in expectations, and in turn perceptual memory for color.

Lieder and Griffiths present the computational framework “resource-rational analysis” to address the reverse-engineering problem in cognition. Here we discuss how developmental psychology affords a unique and critical opportunity to employ this framework, but which is overlooked in this piece. We describe how developmental change provides an avenue for ongoing work as well as inspiration for expansion of the resource-rational approach.