A Hierarchical Bayesian Model of Adaptive Teaching.

How do teachers learn about what learners already know? How do learners aid teachers by providing them with information about their background knowledge and what they find confusing? We formalize this collaborative reasoning process using a hierarchical Bayesian model of pedagogy. We then evaluate this model in two online behavioral experiments (N = 312 adults). In Experiment 1, we show that teachers select examples that account for learners' background knowledge, and adjust their examples based on learners' feedback. In Experiment 2, we show that learners strategically provide more feedback when teachers' examples deviate from their background knowledge. These findings provide a foundation for extending computational accounts of pedagogy to richer interactive settings.

Customizing Bayesian multivariate generalizability theory to mixed-format tests.

Mixed-format tests, which typically include dichotomous items and polytomously scored tasks, are employed to assess a wider range of knowledge and skills. Recent behavioral and educational studies have highlighted their practical importance and methodological developments, particularly within the context of multivariate generalizability theory. However, the diverse response types and complex designs of these tests pose significant analytical challenges when modeling data simultaneously. Current methods often struggle to yield reliable results, either due to the inappropriate treatment of different types of response data separately or the imposition of identical covariates across various response types. Moreover, there are few software packages or programs that offer customized solutions for modeling mixed-format tests, addressing these limitations. This tutorial provides a detailed example of using a Bayesian approach to model data collected from a mixed-format test, comprising multiple-choice questions and free-response tasks. The modeling was conducted using the Stan software within the R programming system, with Stan codes tailored to the structure of the test design, following the principles of multivariate generalizability theory. By further examining the effects of prior distributions in this example, this study demonstrates how the adaptability of Bayesian models to diverse test formats, coupled with their potential for nuanced analysis, can significantly advance the field of psychometric modeling.

Bayesian Modeling with Spatial Curvature Processes.

Spatial process models are widely used for modeling point-referenced variables arising from diverse scientific domains. Analyzing the resulting random surface provides deeper insights into the nature of latent dependence within the studied response. We develop Bayesian modeling and inference for rapid changes on the response surface to assess directional curvature along a given trajectory. Such trajectories or curves of rapid change, often referred to as wombling boundaries, occur in geographic space in the form of rivers in a flood plain, roads, mountains or plateaus or other topographic features leading to high gradients on the response surface. We demonstrate fully model based Bayesian inference on directional curvature processes to analyze differential behavior in responses along wombling boundaries. We illustrate our methodology with a number of simulated experiments followed by multiple applications featuring the Boston Housing data; Meuse river data; and temperature data from the Northeastern United States.

The chronology of the human colonization of the Canary Islands.

The human colonization of the Canary Islands represents the sole known expansion of Berber communities into the Atlantic Ocean and is an example of marine dispersal carried out by an African population. While this island colonization shows similarities to the populating of other islands across the world, several questions still need to be answered before this case can be included in wider debates regarding patterns of initial colonization and human settlement, human-environment interactions, and the emergence of island identities. Specifically, the chronology of the first human settlement of the Canary Islands remains disputed due to differing estimates of the timing of its first colonization. This absence of a consensus has resulted in divergent hypotheses regarding the motivations that led early settlers to migrate to the islands, e.g., ecological or demographic. Distinct motivations would imply differences in the strategies and dynamics of colonization; thus, identifying them is crucial to understanding how these populations developed in such environments. In response, the current study assembles a comprehensive dataset of the most reliable radiocarbon dates, which were used for building Bayesian models of colonization. The findings suggest that i) the Romans most likely discovered the islands around the 1st century BCE; ii) Berber groups from western North Africa first set foot on one of the islands closest to the African mainland sometime between the 1st and 3rd centuries CE; iii) Roman and Berber societies did not live simultaneously in the Canary Islands; and iv) the Berber people rapidly spread throughout the archipelago.

EXPRESS: The Impact of Subtle Contextual Information on Remembering to Complete Academic Goals.

Remembering to complete goals-termed prospective memory (PM)-is critical for success in everyday life, yet minimal empirical work has been dedicated to examining prospective memory within an educational setting. The main goal of this study was to investigate students' ability to complete numerous future-oriented academic intentions (PM tasks) while simultaneously paying attention to a lecture and to see if working memory capacity (WM) and adding subtle contextual information would support the students' likelihood of completing their PM tasks. Participants took part in a 2-hour session of college course-like activities. Throughout the session, there was occasionally the opportunity to complete one of several naturalistic PM tasks. The following findings are based on the results of our Bayesian models. Providing subtle contextual clues about when PM tasks could be completed was found to likely increase performance. The number of PM intentions to be remembered (i.e., load) produced no discernable effect on ongoing task performance or PM performance. Furthermore, individual differences in WM capacity were likely to be predictive of a near-zero change in PM performance. The current findings hold meaningful implications for educators, wherein providing context, even at a subtle level, can enhance students' ability to remember to complete tasks, without altering their ability to focus on the tasks at hand. Moreover, it appears that asking students to remember to complete multiple, prospective in-class tasks is not likely to hinder task completion or their ability to focus on other ongoing tasks.

Bayesian transition models for ordinal longitudinal outcomes.

Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID-19 clinical trials. These outcomes are information-rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID-19 Treatment Trial (ACTT-1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time-to-event modeling.

Cognitive Phenotype Shifts in Risk Taking: Interplay of Nonsuicidal Self-Injury Behaviors and Intensified Depression.

BACKGROUND: Nonsuicidal self-injury (NSSI) behavior is significantly prevalent in both adolescents and psychiatric populations, particularly in individuals with major depressive disorder. NSSI can be considered a result of risky decision making in response to negative emotions, where individuals choose self-harm over other less harmful alternatives, suggesting a potential decision-making deficit in those engaging in NSSI. This study delves into the complex relationship between NSSI and depression severity in decision making and its cognitive underpinnings. METHODS: We assessed decision behaviors in 57 patients with major depressive disorder and NSSI, 42 patients with major depressive disorder without NSSI, and 142 healthy control participants using the Balloon Analog Risk Task, which involves risk taking, learning, and exploration in uncertain scenarios. Using computational modeling, we dissected the nuanced cognitive dimensions influencing decision behaviors. A novel statistical method was developed to elucidate interaction effects between NSSI and depression severity. RESULTS: Contrary to common perceptions, we found that individuals with NSSI behaviors were typically more risk averse. There was also a complex interaction between NSSI and depression severity in shaping risk-taking behaviors. As depressive symptoms intensified, the individuals with NSSI began to perceive less risk and behave more randomly. CONCLUSIONS: This research provides new insights into the cognitive aspects of NSSI and depression, highlighting the importance of considering the influence of comorbid mental disorders when investigating the cognitive underpinnings of such behaviors, especially in the context of prevalent cross-diagnostic phenomena such as NSSI behaviors.

Assessing tilavonemab efficacy in early Alzheimer's disease via longitudinal item response theory modeling.

INTRODUCTION: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by declines in cognitive and functional severities. This research utilized the Clinical Dementia Rating (CDR) to assess the influence of tilavonemab on these deteriorations. METHODS: Longitudinal Item Response Theory (IRT) models were employed to analyze CDR domains in early-stage AD patients. Both unidimensional and multidimensional models were contrasted to elucidate the trajectories of cognitive and functional severities. RESULTS: We observed significant temporal increases in both cognitive and functional severities, with the cognitive severity deteriorating at a quicker rate. Tilavonemab did not demonstrate a statistically significant effect on the progression in either severity. Furthermore, a significant positive association was identified between the baselines and progression rates of both severities. DISCUSSION: While tilavonemab failed to mitigate impairment progression, our multidimensional IRT analysis illuminated the interconnected progression of cognitive and functional declines in AD, suggesting a comprehensive perspective on disease trajectories. Highlights: Utilized longitudinal Item Response Theory (IRT) models to analyze the Clinical Dementia Rating (CDR) domains in early-stage Alzheimer's disease (AD) patients, comparing unidimensional and multidimensional models.Observed significant temporal increases in both cognitive and functional severities, with cognitive severity deteriorating at a faster rate, while tilavonemab showed no statistically significant effect on either domain's progression.Found a significant positive association between the baseline severities and their progression rates, indicating interconnected progression patterns of cognitive and functional declines in AD.Introduced the application of multidimensional longitudinal IRT models to provide a comprehensive perspective on the trajectories of cognitive and functional severities in early AD, suggesting new avenues for future research including the inclusion of time-dependent random effects and data-driven IRT models.

Modeling individual growth reveals decreasing gray whale body length and correlations with ocean climate indices at multiple scales.

Changes in body size have been documented across taxa in response to human activities and climate change. Body size influences many aspects of an individual's physiology, behavior, and ecology, ultimately affecting life history performance and resilience to stressors. In this study, we developed an analytical approach to model individual growth patterns using aerial imagery collected via drones, which can be used to investigate shifts in body size in a population and the associated drivers. We applied the method to a large morphological dataset of gray whales (Eschrichtius robustus) using a distinct foraging ground along the NE Pacific coast, and found that the asymptotic length of these whales has declined since around the year 2000 at an average rate of 0.05-0.12 m/y. The decline has been stronger in females, which are estimated to be now comparable in size to males, minimizing sexual dimorphism. We show that the decline in asymptotic length is correlated with two oceanographic metrics acting as proxies of habitat quality at different scales: the mean Pacific Decadal Oscillation index, and the mean ratio between upwelling intensity in a season and the number of relaxation events. These results suggest that the decline in gray whale body size may represent a plastic response to changing environmental conditions. Decreasing body size could have cascading effects on the population's demography, ability to adjust to environmental changes, and ecological influence on the structure of their community. This finding adds to the mounting evidence that body size is shrinking in several marine populations in association with climate change and other anthropogenic stressors. Our modeling approach is broadly applicable across multiple systems where morphological data on megafauna are collected using drones.

Bayesian estimation of the time-varying reproduction number for pulmonary tuberculosis in Iran: A registry-based study from 2018 to 2022 using new smear-positive cases.

Introduction: Tuberculosis (TB) is one of the most prevalent infectious diseases in the world, causing major public health problems in developing countries. The rate of TB incidence in Iran was estimated to be 13 per 100,000 in 2021. This study aimed to estimate the reproduction number and serial interval for pulmonary tuberculosis in Iran. Material and methods: The present national historical cohort study was conducted from March 2018 to March 2022 based on data from the National Tuberculosis and Leprosy Registration Center of Iran's Ministry of Health and Medical Education (MOHME). The study included 30,762 tuberculosis cases and 16,165 new smear-positive pulmonary tuberculosis patients in Iran. We estimated the reproduction number of pulmonary tuberculosis in a Bayesian framework, which can incorporate uncertainty in estimating it. Statistical analyses were accomplished in R software. Results: The mean age at diagnosis of patients was 52.3 ± 21.2 years, and most patients were in the 35-63 age group (37.1%). Among the data, 9121 (56.4%) cases were males, and 7044 (43.6%) were females. Among patients, 7459 (46.1%) had a delayed diagnosis between 1 and 3 months. Additionally, 3039 (18.8%) cases were non-Iranians, and 2978 (98%) were Afghans. The time-varying reproduction number for pulmonary tuberculosis disease was calculated at an average of 1.06 ± 0.05 (95% Crl 0.96-1.15). Conclusions: In this study, the incidence and the time-varying reproduction number of pulmonary tuberculosis showed the same pattern. The mean of the time-varying reproduction number indicated that each infected person is causing at least one new infection over time, and the chain of transmission is not being disrupted.

Invited commentary: Motivating better methods-and better data collection-for measuring prevalence of drug misuse.

The United States continues to suffer a drug overdose crisis that has resulted in over 100,000 deaths annually since 2021. Despite decades of attention, estimates of the prevalence of drug use at the spatiotemporal resolutions necessary for resource allocation and intervention evaluation are lacking. Current approaches to measure prevalence of drug use, such as population surveys, capture-recapture, and multiplier methods, have significant limitations. Santaella-Tenorio et al. (Am J Epidemiol. XXXX;XXX(XX):XXXX-XXXX)) use a novel joint Bayesian spatiotemporal modeling approach to estimate county-level opioid misuse prevalence in New York state from 2007 to 2018 and identify significant intra-state variation. By leveraging five data sources and simultaneously modeling different opioid-related outcomes - such as deaths, emergency department visits, and treatment visits - they obtain policy-relevant insights into the prevalence of opioid misuse and opioid-related outcomes at high spatiotemporal resolutions. This study provides future researchers with a sophisticated modeling approach that allows them to incorporate multiple data sources in a rigorous statistical framework. The limitations of the study reflect the constraints of the broader field and underscores the importance of enhancing current surveillance with better, newer, and more timely data that is both standardized and easily accessible to inform public health policies and interventions.

MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance.

Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG (MUlti-sample Spatial Transcriptomics data ANalysis with cross-sample transcriptional similarity Guidance), which is capable of performing multi-sample spatial transcriptomics spot cellular deconvolution by allowing both cross-sample expression-based similarity information sharing as well as spatial correlation in gene expression patterns within samples. Experiments on a semi-synthetic spatial transcriptomics dataset and three real-world spatial transcriptomics datasets demonstrate the effectiveness of MUSTANG in revealing biological insights inherent in the cellular characterization of tissue samples under study.

Early-Stage Alzheimer's Disease Affects Fast But Not Slow Adaptive Processes in Motor Learning.

Alzheimer's disease (AD) is characterized by an initial decline in declarative memory, while nondeclarative memory processing remains relatively intact. Error-based motor adaptation is traditionally seen as a form of nondeclarative memory, but recent findings suggest that it involves both fast, declarative, and slow, nondeclarative adaptive processes. If the declarative memory system shares resources with the fast process in motor adaptation, it can be hypothesized that the fast, but not the slow, process is disturbed in AD patients. To test this, we studied 20 early-stage AD patients and 21 age-matched controls of both sexes using a reach adaptation paradigm that relies on spontaneous recovery after sequential exposure to opposing force fields. Adaptation was measured using error clamps and expressed as an adaptation index (AI). Although patients with AD showed slightly lower adaptation to the force field than the controls, both groups demonstrated effects of spontaneous recovery. The time course of the AI was fitted by a hierarchical Bayesian two-state model in which each dynamic state is characterized by a retention and learning rate. Compared to controls, the retention rate of the fast process was the only parameter that was significantly different (lower) in the AD patients, confirming that the memory of the declarative, fast process is disturbed by AD. The slow adaptive process was virtually unaffected. Since the slow process learns only weakly from an error, our results provide neurocomputational evidence for the clinical practice of errorless learning of everyday tasks in people with dementia.

Response to Difficulty Drives Variation in IQ Test Performance.

In a large (N = 300), pre-registered experiment and data analysis model, we find that individual variation in overall performance on Raven's Progressive Matrices is substantially driven by differential strategizing in the face of difficulty. Some participants choose to spend more time on hard problems while others choose to spend less and these differences explain about 42% of the variance in overall performance. In a data analysis jointly predicting participants' reaction times and accuracy on each item, we find that the Raven's task captures at most half of participants' variation in time-controlled ability (48%) down to almost none (3%), depending on which notion of ability is assumed. Our results highlight the role that confounding factors such as motivation play in explaining individuals' differential performance in IQ testing.

Bayesian tensor network structure search and its application to tensor completion.

Tensor network (TN) has demonstrated remarkable efficacy in the compact representation of high-order data. In contrast to the TN methods with pre-determined structures, the recently introduced tensor network structure search (TNSS) methods automatically learn a compact TN structure from the data, gaining increasing attention. Nonetheless, TNSS requires time-consuming manual adjustments of the penalty parameters that control the model complexity to achieve better performance, especially in the presence of missing or noisy data. To provide an effective solution to this problem, in this paper, we propose a parameters tuning-free TNSS algorithm based on Bayesian modeling, aiming at conducting TNSS in a fully data-driven manner. Specifically, the uncertainty in the data corruption is well-incorporated in the prior setting of the probabilistic model. For TN structure determination, we reframe it as a rank learning problem of the fully-connected tensor network (FCTN), integrating the generalized inverse Gaussian (GIG) distribution for low-rank promotion. To eliminate the need for hyperparameter tuning, we adopt a fully Bayesian approach and propose an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior distribution sampling. Compared with the previous TNSS method, experiment results demonstrate the proposed algorithm can effectively and efficiently find the latent TN structures of the data under various missing and noise conditions and achieves the best recovery results. Furthermore, our method exhibits superior performance in tensor completion with real-world data compared to other state-of-the-art tensor-decomposition-based completion methods.

Evaluating scientific confidence in the concordance of in vitro and in vivo protective points of departure.

To fulfil the promise of reducing reliance on mammalian in vivo laboratory animal studies, new approach methods (NAMs) need to provide a confident basis for regulatory decision-making. However, previous attempts to develop in vitro NAMs-based points of departure (PODs) have yielded mixed results, with PODs from U.S. EPA's ToxCast, for instance, appearing more conservative (protective) but poorly correlated with traditional in vivo studies. Here, we aimed to address this discordance by reducing the heterogeneity of in vivo PODs, accounting for species differences, and enhancing the biological relevance of in vitro PODs. However, we only found improved in vitro-to-in vivo concordance when combining the use of Bayesian model averaging-based benchmark dose modeling for in vivo PODs, allometric scaling for interspecies adjustments, and human-relevant in vitro assays with multiple induced pluripotent stem cell-derived models. Moreover, the available sample size was only 15 chemicals, and the resulting level of concordance was only fair, with correlation coefficients <0.5 and prediction intervals spanning several orders of magnitude. Overall, while this study suggests several ways to enhance concordance and thereby increase scientific confidence in vitro NAMs-based PODs, it also highlights challenges in their predictive accuracy and precision for use in regulatory decision making.

How much multiple paternity should we expect? A study of birds and contrast with mammals.

Parentage analyses via molecular markers have revealed multiple paternity within the broods of polytocous species, reshaping our understanding of animal behavior, ecology, and evolution. In a meta-analysis of multiple paternity in bird and mammal species, we conducted a literature search and found 138 bird and 64 mammal populations with microsatellite DNA paternity results. Bird populations averaged 19.5% multiple paternity and mammals more than twice that level (46.1%). We used a Bayesian approach to construct a null model for how multiple paternity should behave at random among species, under the assumption that all mated males have equal likelihood of siring success, given mean brood size and mean number of sires. We compared the differences between the null model and the actual probabilities of multiple paternity. While a few bird populations fell close to the null model, most did not, averaging 34.0-percentage points below null model predictions; mammals had an average probability of multiple paternity 13.6-percentage points below the null model. Differences between bird and mammal species were also subjected to comparative phylogenetic analyses that generally confirmed our analyses that did not adjust for estimated historical relationships. Birds exhibited extremely low probabilities of multiple paternity, not only compared to mammals but also relative to other major animal taxa. The generally low probability of multiple paternity in birds might be produced by a variety of factors, including behaviors that reflect sexual selection (extreme mate guarding or unifocal female choice) and sperm competition (e.g., precedence effects favoring fertilization by early or late matings).

Monolingual and bilingual infants' attention to talking faces: evidence from eye-tracking and Bayesian modeling.

Introduction: A substantial amount of research from the last two decades suggests that infants' attention to the eyes and mouth regions of talking faces could be a supporting mechanism by which they acquire their native(s) language(s). Importantly, attentional strategies seem to be sensitive to three types of constraints: the properties of the stimulus, the infants' attentional control skills (which improve with age and brain maturation) and their previous linguistic and non-linguistic knowledge. The goal of the present paper is to present a probabilistic model to simulate infants' visual attention control to talking faces as a function of their language learning environment (monolingual vs. bilingual), attention maturation (i.e., age) and their increasing knowledge concerning the task at stake (detecting and learning to anticipate information displayed in the eyes or the mouth region of the speaker). Methods: To test the model, we first considered experimental eye-tracking data from monolingual and bilingual infants (aged between 12 and 18 months; in part already published) exploring a face speaking in their native language. In each of these conditions, we compared the proportion of total looking time on each of the two areas of interest (eyes vs. mouth of the speaker). Results: In line with previous studies, our experimental results show a strong bias for the mouth (over the eyes) region of the speaker, regardless of age. Furthermore, monolingual and bilingual infants appear to have different developmental trajectories, which is consistent with and extends previous results observed in the first year. Comparison of model simulations with experimental data shows that the model successfully captures patterns of visuo-attentional orientation through the three parameters that effectively modulate the simulated visuo-attentional behavior. Discussion: We interpret parameter values, and find that they adequately reflect evolution of strength and speed of anticipatory learning; we further discuss their descriptive and explanatory power.

Methodological approaches to exploring the spatial variation in social impacts of protected areas: An intercomparison of Bayesian regression modeling approaches and potential implications.

Protected Areas (PAs) are widely used to conserve biodiversity by protecting and restoring ecosystems while also contributing to socio-economic priorities. An increasing number of studies aim to examine the social impacts of PAs on aspects of people's well-being, such as, quality of life, livelihoods, and connectedness to nature. Despite the increase in literature on this topic, there are still few studies that explore possible robust methodological approaches to capturing and assessing the spatial distribution of impacts in a PA. This study aims to contribute to this research gap by comparing Bayesian spatial regression models that explore links between perceived social impacts and the relative location of local residents and communities in a PA. We use primary data collected from 227 individuals, via structured questionnaires, living in or near the Peak District National Park, United Kingdom. By comparing different models we were able to show that the location of respondents influences their perception of social impacts and that neighboring communities within the national park can have similar perceptions regarding social impacts. Simulation based on existing data using the Bootstrap sub-sampling was also conducted to validate the association between social impacts and mutual proximity of residents. Our findings suggest that this type of data is better treated, in terms of accounting for potential spatial effects, using models that allow for proximity effects to be stronger between people living nearby, e.g. between neighbors in the same community and have minimum effects otherwise. Understanding the spatial clustering of perceived social impacts in and around PA, is key to understanding their causes and to managing and mitigating them. Our findings highlight therefore the need to develop new methodological approaches to assessing and predicting accurately the spatial distribution of social impacts when designating PAs. The findings in this paper will assist practitioners in this regard by proposing approaches to the consideration of the distribution of social impacts when designing the boundaries of PAs alongside typical ecological and socio-economic criteria.

Morning administration enhances humoral response to SARS-CoV-2 vaccination in kidney transplant recipients.

Although severe acute respiratory syndrome coronavirus 2 messenger ribonucleic acid (SARS-CoV-2 mRNA) vaccines are effective in kidney transplant recipients (KTRs), their immune response to vaccination is blunted by immunosuppression. Other tools enhancing vaccination response are therefore needed. Interestingly, aligning vaccine administration with circadian rhythms (chronovaccination) has been shown to boost immune response. However, its applicability in KTRs, whose circadian rhythms are likely disrupted by immunosuppressants, remains unclear. To assess the impact of vaccination timing on seroconversion in the KTRs population, we analyzed data from 553 virus-naïve KTRs who received 2 doses of messenger ribonucleic acid (mRNA) vaccine. Bayesian logistic regression was employed, adjusting for previously identified predictors of seroconversion, including allograft function, maintenance immunosuppressants, or time since transplantation. SARS-CoV-2 immunoglobulin G (IgG) levels were measured with a median of 47 days after the second dose. The results did not reveal a reliable effect of timing of the first dose but did indicate that earlier timing for the second dose brings a notable benefit-every 1-hour delay in the application was associated with a 16% reduction in the odds of seroconversion (OR 0.84, 95% CI 0.71, 0.998). Similar results were obtained from quantile regression modeling IgG levels. In conclusion, morning vaccination is emerging as a promising and easily implementable strategy to enhance vaccine response in KTRs.