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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.

 

The human ability to deceive others and detect deception has long been tied to theory of mind. We make a stronger argument: in order to be adept liars – to balance gain (i.e. maximizing their own reward) and plausibility (i.e. maintaining a realistic lie) – humans calibrate their lies under the assumption that their partner is a rational, utility-maximizing agent. We develop an adversarial recursive Bayesian model that aims to formalize the behaviors of liars and lie detectors. We compare this model to (1) a model that does not perform theory of mind computations and (2) a model that has perfect knowledge of the opponent’s behavior. To test these models, we introduce a novel dyadic, stochastic game, allowing for quantitative measures of lies and lie detection. In a second experiment, we vary the ground truth probability. We find that our rational models qualitatively predict human lying and lie detecting behavior better than the non-rational model. Our findings suggest that humans control for the extremeness of their lies in a manner reflective of rational social inference. These findings provide a new paradigm and formal framework for nuanced quantitative analysis of the role of rationality and theory of mind in lying and lie detecting behavior.