A Bayesian framework for group testing under dilution effects has been developed, using lattice-based models. This work has particular relevance given the pressing public health need to enhance testing capacity for coronavirus disease 2019 and future pandemics, and the need for wide-scale and repeated testing for surveillance under constantly varying conditions. The proposed Bayesian approach allows for dilution effects in group testing and for general test response distributions beyond just binary outcomes. It is shown that even under strong dilution effects, an intuitive group testing selection rule that relies on the model order structure, referred to as the Bayesian halving algorithm, has attractive optimal convergence properties. Analogous look-ahead rules that can reduce the number of stages in classification by selecting several pooled tests at a time are proposed and evaluated as well. Group testing is demonstrated to provide great savings over individual testing in the number of tests needed, even for moderately high prevalence levels. However, there is a trade-off with higher number of testing stages, and increased variability. A web-based calculator is introduced to assist in weighing these factors and to guide decisions on when and how to pool under various conditions. High-performance distributed computing methods have also been implemented for considering larger pool sizes, when savings from group testing can be even more dramatic.

The ability to associate different types of number representations referring to the same quantity (symbolic Arabic numerals, signed/spoken number words, and nonsymbolic quantities), is an important predictor of overall mathematical success. This foundational skill-mapping-has not been examined in deaf and hard-of-hearing (DHH) children. To address this gap, we studied 188 4 1/2 to 9-year-old DHH and hearing children and systematically examined the relationship between their language experiences and mapping skills. We asked whether the timing of children's language exposure (early vs. later), the modality of their language (signed vs. spoken), and their rote counting abilities related to mapping performance. We found that language modality did not significantly relate to mapping performance, but timing of language exposure and counting skills did. These findings suggest that early access to language, whether spoken or signed, supports the development of age-typical mapping skills and that knowledge of number words is critical for this development.

Complex cognitive processes, like creative thinking, rely on interactions among multiple neurocognitive processes to generate effective and innovative behaviors on demand, for which the brain’s connector hubs play a crucial role. However, the unique contribution of specific hub sets to creative thinking is unknown. Employing three functional magnetic resonance imaging datasets (total N= 1,911), we demonstrate that connector hub sets are organized in a hierarchical manner based on diversity, with “control-default hubs”—which combine regions from the frontoparietal control and default mode networks—positioned at the apex. Specifically, control-default hubs exhibit the most diverse resting-state connectivity profiles and play the most substantial role in facilitating interactions between regions with dissimilar neurocognitive functions, a phenomenon we refer to as “diverse functional interaction”. Critically, we found that the involvement of control-default hubs in facilitating diverse functional interaction robustly relates to creativity, explaining both task-induced functional connectivity changes and individual creative performance. Our findings suggest that control-default hubs drive diverse functional interaction in the brain, enabling complex cognition, including creative thinking. We thus uncover a biologically plausible explanation that further elucidates the widely reported contributions of certain frontoparietal control and default mode network regions in creativity studies. © The Author(s) 2023. Published by Oxford University Press. All rights reserved.

This study examines high school preparation measures [ACT/SAT scores, high school grade point average (HSGPA), and conceptual physics pretest scores], in-class behavior measures (homework submission rates and lecture attendance rates), and in-class achievement measures (homework and test averages) for the last two fully face-to-face prepandemic and the first two fully face-to-face postpandemic semesters of an introductory calculus-based electricity and magnetism class. This class was offered at a large eastern land grant university in the United States. The total number of students for the four semesters was 1033. While some significant differences were measured (higher postpandemic HSGPA, lower postpandemic conceptual pretest scores, higher postpandemic homework average (fall semesters only), and lower postpandemic lecture attendance (spring semesters only), none were larger than a small effect. As such, student achievement, attendance rates, and assignment completion rates were largely unchanged after the pandemic.

Number line estimation tasks are frequently used to study numerical cognition skills. In a typical version, the bounded number line task, target numerals must be placed on a bounded line labeled only at its endpoints (e.g., with 0 and 100). Placements by adults, while highly accurate, reveal a cyclical pattern of over-and underestimation of target numerals. The pattern suggests use of proportion judgment strategies and is well -captured by cyclical power models. Another systematic number line bias that has recently been observed, but has not yet been considered in modeling efforts, is the left digit effect. Numerals with different leftmost digits (e.g., 39 and 41) are placed farther apart on a line than is warranted. In the current study (N = 60), adult estimates were obtained for all numerals on a 0-100 number line estimation task, and fit of the standard cyclical power model was compared with two modified versions of the model. One modified version included a parameter that underweights the rightward digit's place value (e.g., the ones digit here), and the other used the same parameter to underweight all digits' place values. We found that both modifications provided a considerably better fit for individual and median data than the standard model, and we discuss their relative merits and cognitive interpretations. The data and models suggest how a left digit bias might impact estimates across the number line.

Human languages can express an infinite number of thoughts despite having a finite set of words and rules. This is due, in part, to recursive structures, which allow us to embed one instance of a rule inside another. We investigated the origins of recursion by studying the development of Nicaraguan Sign Language (LSN), which emerged in the last 40 years and is not derived from any existing language. Before this, deaf individuals in Nicaragua lacked access to language models and each individual created their own gestural system, called homesign. We tested four groups: homesigners, who represent the point of origin, and the first three generations of LSN signers, who represent consecutive stages in the language's development. We used a task that was designed to elicit sentences with relative clauses, a device that allows for the recursive embedding of sentences inside of sentences (e.g., [the girl [who was drawing] removed the picture]). Signers in all three LSN cohorts consistently produced utterances that appeared to have embedded predicates (girl draw remove picture) which served the function of a relative clause (picking out the correct member of a set, based on previously mentioned information). Furthermore, in these utterances, the first verb was shorter than the second and shorter than the same verb in parallel unembedded structures. In contrast, homesigners produced similar utterances in embedded and unembedded contexts. They did not reintroduce previously mentioned information or produce reduced verb forms in the embedded context. These results demonstrate that syntactic embedding that is potentially recursive can emerge very early in a language. These embedded predicates, however, may not be widespread, or sys-tematically marked, in homesign systems. This raises the possibility that the emergence of recursive linguistic structure is a consequence of interaction within a language community. These findings pave the way for future work which investigates the syntactic form of these embedded predicates and explores whether multiple levels of embedding are possible.

Music is a complex system consisting of many dimensions and hierarchically organized information-the or-ganization of which, to date, we do not fully understand. Network science provides a powerful approach to representing such complex systems, from the social networks of people to modelling the underlying network structures of different cognitive mechanisms. In the present research, we explored whether network science methodology can be extended to model the melodic patterns underlying expert improvised music. Using a large corpus of transcribed improvisations, we constructed a network model in which 5-pitch sequences were linked depending on consecutive occurrences, constituting 116,403 nodes (sequences) and 157,429 edges connecting them. We then investigated whether mathematical graph modelling relates to musical characteristics in real -world listening situations via a behavioral experiment paralleling those used to examine language. We found that as melodic distance within the network increased, participants judged melodic sequences as less related. Moreover, the relationship between distance and reaction time (RT) judgements was quadratic: participants slowed in RT up to distance four, then accelerated; a parallel finding to research in language networks. This study offers insights into the hidden network structure of improvised tonal music and suggests that humans are sen-sitive to the property of melodic distance in this network. More generally, our work demonstrates the similarity between music and language as complex systems, and how network science methods can be used to quantify different aspects of its complexity.

How people reason about disease transmission is central to their commonsense theories, scientific literacy, and adherence to public health guidelines. This study provided an in-depth assessment of U.S. children's (ages 5–12, N = 180) and their parents' (N = 125) understanding of viral transmission of COVID-19 and the common cold, during the first year of the COVID-19 pandemic. The primary aim was to discover children's causal models of viral transmission, by asking them to predict and explain counter-intuitive outcomes (e.g., asymptomatic disease, symptom delay) and processes that cannot be directly observed (e.g., viral replication, how vaccines work). A secondary aim was to explore parental factors that might contribute to children's understanding. Although even the youngest children understood germs as disease-causing and were highly knowledgeable about certain behaviors that transmit or block viral disease (e.g., sneezing, mask-wearing), they generally failed to appreciate the processes that play out over time within the body. Overall, children appeared to rely on two competing mental models of viruses: one in which viruses operate strictly via mechanical processes (movement through space), and one in which viruses are small living creatures, able to grow in size and to move by themselves. These results suggest that distinct causal frameworks co-exist in children's understanding. A challenge for the future is how to teach children about illness as a biological process without also fostering inappropriate animism or anthropomorphism of viruses. © 2023 Elsevier B.V.

Compelling evidence, from multiple levels of schooling, suggests that teachers' knowledge and beliefs about knowledge, knowing, and learning (i.e., epistemologies) play a strong role in shaping their approaches to teaching and learning. Given the importance of epistemologies in science teaching, we as researchers must pay careful attention to how we model them in our work. That is, we must work to explicitly and cogently develop theoretical models of epistemology that account for the learning phenomena we observe in classrooms and other settings. Here, we use interpretation of instructor interview data to explore the constraints and affordances of two models of epistemology common in chemistry and science education scholarship: epistemological beliefs and epistemological resources. Epistemological beliefs are typically assumed to be stable across time and place and to lie somewhere on a continuum from instructor-centered (worse) to student-centered (better). By contrast, a resources model of epistemology contends that one's view on knowledge and knowing is compiled in-the-moment from small-grain units of cognition called resources. Thus, one's epistemology may change one moment to the next. Further, the resources model explicitly rejects the notion that there is one best epistemology, instead positing that different epistemologies are useful in different contexts. Using both epistemological models to infer instructors' epistemologies from dialogue about their approaches to teaching and learning, we demonstrate that how one models epistemology impacts the kind of analyses possible as well as reasonable implications for supporting instructor learning. Adoption of a beliefs model enables claims about which instructors have better or worse beliefs and suggests the value of interventions aimed at shifting toward better beliefs. By contrast, modeling epistemology as in situ activation of resources enables us to explain observed instability in instructors' views on knowing and learning, surface and describe potentially productive epistemological resources, and consider instructor learning as refining valuable intuition rather than fixing wrong beliefs.

What factors influence predictions of creative performance? Prior work indicates that images can skew predictions in the contexts of learning, memory, and decision making, but little work has devoted attention to the metacognitive effects of images in creative thinking. Metacognitive frameworks indicate that people often base predictions of performance on the subjective ease with which related information comes to mind. The present experiments tested whether the presence of object photographs in the alternate uses task (AUT) inflates predictions of creativity. In Experiments 1-4, participants made ratings about their predictions of creative performance for various objects in the AUT either with or without photographs of the object. Participants provided higher ratings and were faster to make ratings in the image than no-image condition. In Experiment 5, participants actually attempted to generate creative uses for the objects, half of which were accompanied by object photographs. Creativity scores for these responses were lower in the image condition than no-image condition, but participants' retrospective judgments indicated the opposite. These results provide a novel extension of metacognitive work showing that images inflate predictions of performance and fit with prior research showing that images can limit creativity.