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Chunks allow us to use long-term knowledge to efficiently represent the world in working memory. Most views of chunking assume that when we use chunks, this results in the loss of specific perceptual details, since it is presumed the contents of chunks are decoded from long-term memory rather than reflecting the exact details of the item that was presented. However, in two experiments, we find that in situations where participants make use of chunks to improve visual working memory, access to instance-specific perceptual detail (that cannot be retrieved from long-term memory) increased, rather than decreased. This supports an alternative view: that chunks facilitate the encoding and retention into memory of perceptual details as part of structured, hierarchical memories, rather than serving as mere “content-free” pointers. It also provides a strong contrast to accounts in which working memory capacity is assumed to be exhaustively described by the number of chunks remembered. 

Existing knowledge shapes and distorts our memories, serving as a prior for newly encoded information. Here, we investigate the role of stable long-term priors (e.g. categorical knowledge) in conjunction with priors arising from recently encountered information (e.g. ’serial dependence’) in visual working memory for color. We use an iterated reproduction paradigm to allow a model-free assessment of the role of such priors. In Experiment 1, we find that participants’ reports reliably converge to certain areas of color space, but that this convergence is largely distinct for different individuals, suggesting responses are biased by more than just shared category knowledge. In Experiment 2, we explicitly manipulate trial n-1 and find recent history plays a major role in participants’ reports. Thus, we find that both global prior knowledge and recent trial information have biasing influences on visual working memory, demonstrating an important role for both shortand long-term priors in actively maintained information. 

Is cognitive science interdisciplinary or multidisciplinary? We contribute to this debate by examining the authorship structure and topic similarity of contributions to the Cognitive Science Society from 2000 to 2019. Our analysis focuses on graph theoretic features of the co‐authorship network—edge density, transitivity, and maximum subgraph size—as well as clustering within the space of scientific topics. We also combine structural and semantic information with an analysis of how authors choose their collaborators based on their interests and prior collaborations. We compare findings from CogSci to abstracts from the Vision Science Society over the same time frame and validate our approach by predicting new collaborations in the 2020 CogSci proceedings. Our results suggest that collaboration across authors and topics within cognitive science has become increasingly integrated in the last 19 years. More broadly, we argue that a formal quantitative approach which combines structural co‐authorship information and semantic topic analysis provides inroads to questions about the level of interdisciplinary collaboration in a scientific community.