The structure of prior knowledge enhances memory in experts by reducing interference

Abstract

The influence of prior knowledge on memory is ubiquitous, making the specific mechanisms of this relationship difficult to disentangle. Here, we show that expert knowledge produces a fundamental shift in the way that interitem similarity (i.e., the perceived resemblance between items in a set) biases episodic recognition. Within a group of expert birdwatchers and matched controls, we characterized the psychological similarity space for a set of well-known local species and a set of less familiar, nonlocal species. In experts, interitem similarity was influenced most strongly by taxonomic features, whereas in controls, similarity judgments reflected bird color. In controls, perceived episodic oldness during a recognition memory task increased along with measures of global similarity between items, consistent with classic models of episodic recognition. Surprisingly, for experts, high global similarity did not drive oldness signals. Instead, for local birds memory tracked the availability of species-level name knowledge, whereas for nonlocal birds, it was mediated by the organization of generalized conceptual space. These findings demonstrate that episodic memory in experts can benefit from detailed subcategory knowledge, or, lacking that, from the overall relational structure of concepts. Expertise reshapes psychological similarity space, helping to resolve mnemonic separation challenges arising from high interitem overlap. Thus, even in the absence of knowledge about item-specific details or labels, the presence of generalized knowledge appears to support episodic recognition in domains of expertise by altering the typical relationship between psychological similarity and memory.

Neurocognitive Model of Schema-Congruent and -Incongruent Learning in Clinical Disorders: Application to Social Anxiety and Beyond

“…As noted above, there are bidirectional connections between the vmPFC and hippocampus that are critical for mental simulation, and these pathways activate important subcortical areas relevant for the appraisal aspect of the simulation, including the amygdala that supports emotional appraisals (Bechara & Damasio, 2005; Rangel et al, 2008). Critically, the nature of the interaction between the vmPFC and the hippocampus during mental simulation—and the activity of these intervening regions—is thought to depend on the content included in the simulation (Greve et al, 2019; Ritchey & Cooper, 2020; Sheldon et al, 2019; van Kesteren et al, 2012; Wing et al, 2022).…”

Conceptualizing Landscapes Through Language: The Role of Native Language and Expertise in the Representation of Waterbody Related Terms

“…Strategic policies around landscape involve experts communicating their knowledge to people with different backgrounds, but expert conceptual representations may differ from lay people in important ways. Previous studies in other domains, such as folk biology, have shown that experts have more alternative knowledge structures to draw upon when making judgments about the structure of a domain (Boster & Johnson, 1989; Wing, Burles, Ryan, & Gilboa, 2022). For example, rather than relying primarily on surface‐level perceptual features, experts also use functional information to make judgments.…”

Episodic learning-induced representational change is both influenced by and re-sculpts semantic memory

“…Neuroimaging experiments have demonstrated shared neural activation 5 and functional connectivity 6,7 during episodic and semantic memory processes, and preexisting semantic knowledge can act as a scaffold to facilitate the acquisition of new episodic memories [8][9][10] . Moreover, semantic relatedness can help facilitate memory in the face of increased interference [11][12][13] and promote integration of to-be-learned materials to later improve memory [14][15][16] . Conversely, episodic learning can selectively reshape pre-existing semantic information, for instance by increasing discriminability of within-category items by expanding representations of to-be-learned information and minimizing those of irrelevant information 17,18 .…”