Yoxas/statistical_literacy huggingface dataset:
We performed a quantitative meta-analysis of functional neuroimaging studies to identify brain areas which are commonly engaged in social and visuo-spatial perspective taking. Specifically, we compared brain activation for visual-perspective taking to activation for false belief reasoning, which requires awareness of perspective to understand someone's mistaken belief about the world which contrasts with reality. In support of a previous account by Perner and Leekam (2008), our meta-analytic conjunction analysis found common activation for false belief reasoning and visual perspective taking in the left but not the right dorsal temporo-parietal junction (TPJ). This fits with the idea that the left dorsal TPJ is responsible for representing different perspectives in a domain-general fashion. Moreover, our conjunction analysis found activation in the precuneus and the left middle occipital gyrus close to the putative Extrastriate Body Area (EBA). The precuneus is linked to mental-imagery which may aid in the construction of a different perspective. The EBA may be engaged due to imagined body-transformations when another's viewpoint is adopted.
We present a new approach for relating nucleic-acid content to fluorescence in a real-time Polymerase Chain Reaction (PCR) assay. By coupling a two-type branching process for PCR with a fluorescence analog of Beer's Law, the approach reduces bias and quantifies uncertainty in fluorescence. As the two-type branching process distinguishes between complementary strands of DNA, it allows for a stoichiometric description of reactions between fluorescent probes and DNA and can capture the initial conditions encountered in assays targeting RNA. Analysis of the expected copy-number identifies additional dynamics that occur at short times (or, equivalently, low cycle numbers), while investigation of the variance reveals the contributions from liquid volume transfer, imperfect amplification, and strand-specific amplification (i.e., if one strand is synthesized more efficiently than its complement). Linking the branching process to fluorescence by the Beer's Law analog allows for an a priori description of background fluorescence. It also enables uncertainty quantification (UQ) in fluorescence which, in turn, leads to analytical relationships between amplification efficiency (probability) and limit of detection. This work sets the stage for UQ-PCR, where both the input copy-number and its uncertainty are quantified from fluorescence kinetics.
We present a simple model of investment across a suite of different anti-predatory defences. Defences can incur an initial construction cost and and/or may be costly each time they are utilised. Our aim is to use a simple, but general, mathematical model to explore when prey that face a single predatory threat where each attack is of the same nature should invest only in a single defence, and when they should spread their investment across more than one defence. This should help to explain the observed variety of defences that a single prey individual may employ during repeated attacks of a similar nature or even at different stages during one attack. Previous verbal reasoning suggested that prey should specialise in investment in defences that can be utilised early in the predation sequence. Our quantitative model predicts that (depending of the relatively properties of different defences), there may be concentrated investment in early acting, or in late-acting defences, or a spread of investment across both defence types. This variety of predictions is in agreement with the variation in defences shown by natural organisms subjected to repeated predatory attack.
We present an extension of the Local Effect Model (LEM) to include time-dose relationships for predicting effects of protracted and split-dose ion irradiation at arbitrary LET. With this kinetic extension, the spatial and temporal induction and processing of DNA double strand breaks (DSB) in cellular nuclei can be simulated for a wide range of ion radiation qualities, doses and dose rates. The key concept of the extension is based on the joint spatial and temporal coexistence of initial DSB, leading to the formation of clustered DNA damage on the µm scale (as defined e.g., by the size scale of Mbp chromatin loops), which is considered to have an increased cellular lethality as compared to isolated, single DSB. By simulating the time dependent induction and repair of DSB and scoring of isolated and clustered DSB upon irradiation, the impact of dose rate and split dose on the cell survival probability can be computed. In a first part of this work, we systematically analyze the predicted impact of protraction in dependence of factors like dose, LET, ion species and radiosensitivity as characterized by the photon LQ-parameters. We establish links to common concepts that describe dose rate effects for low LET radiation. We also compare the model predictions to experimental data and find agreement with the general trends observed in the experiments. The relevant concepts of our approach are compared to other models suitable for predicting time effects. We investigate an apparent analogy between spatial and temporal concentration of radiation delivery, both leading to increased effectiveness, and discuss similarities and differences between the general dependencies of these clustering effects on their impacting factors. Finally, we conclude that the findings give additional support for the general concept of the LEM, i.e. the characterization of high LET radiation effects based on the distinction of just two classes of DSB (isolated DSB and clustered DSB).
We present findings from one survey and five experiments carried out in the USA, Nigeria and the Middle East showing that judgements about the use of deadly intergroup violence are strikingly insensitive to quantitative indicators of success, or to perceptions of their efficacy. By demonstrating that judgements about the use of war are bounded by rules of deontological reasoning and parochial commitment, these findings may have implications for understanding the trajectory of violent political conflicts. Further, these findings are compatible with theorizing that links the evolution of within-group altruism to intergroup violence.
We present findings from our study, which examined whether ability to identify Down syndrome and autism was linked to participants' willingness to maintain social contact with individuals with the respective conditions. Additionally, we explored whether viewers and non-viewers of Parenthood and Glee, television shows featuring a character with autism and Down syndrome respectively, differed in their awareness, beliefs regarding causes and interventions, and desire to maintain social proximity with individuals with these conditions. Participants completed an online survey, which included vignettes based on Max, the character with autism from Parenthood and Becky, the character with Down syndrome from Glee as well as the adapted Intellectual Disabilities Literacy Scale. Based on 300 responses, key differences were noted in the hypothesized direction on the assessed variables (symptom recognition, causal beliefs, and treatment beliefs) between Parenthood and Glee viewers and non-viewers. Findings are discussed in the context of practical implications and methodological limitations.
We present findings of a comparative study of numeracy and graph literacy in a representative group of 60 practicing nurses. This article focuses on a fundamental concern related to the effectiveness of numeric information displayed in various features in the electronic health record during clinical workflow. Our findings suggest the need to consider numeracy and graph literacy when presenting numerical information as well as the potential for tailoring numeric display types to an individual's cognitive strengths.
We present interoperability as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.
We present the conceptual framework, design, and study measures of Nurturing Healthy Teachers, a quasi-experimental study to examine the short- and long-term effectiveness of the Nurturing Healthy Teachers (NHT) nutrition intervention on food insecurity, dietary behaviors, mental health and cardiometabolic health among preschool and elementary school teachers. A convenience sample of 28 elementary schools with pre-kindergarten and elementary classrooms were recruited in Houston, Texas. Nurturing Healthy Teacher intervention includes Brighter Bites, an evidence-based coordinated school health program that combines access to fresh produce and nutrition education, and Create Healthy Futures, a web-based nutrition education program that targets nutrition knowledge, self-efficacy, mindfulness, and social support to create healthy habits among teachers. The primary outcome is food insecurity. Secondary outcomes include diet quality, mental health, and cardiometabolic health. Metabolic markers and skin carotenoid levels were assessed using in-person assessments, while all other measures were obtained via questionnaire. At baseline, most of the participants were female, 63 % identified as Hispanic, were highly educated, and had a mean age of 42.6 years. Overall, 50 % of teachers were classified as being obese and 20 % had high cholesterol. At baseline teachers had a mean HbA1c (%) of 5.6 %. Moderate to severe depression was experienced by 18 % of teachers and 23 % of teachers experienced moderate to severe anxiety. The results of this study will inform next steps towards future implementation and evaluation of teacher-focused interventions.
We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., , saliency maps) that assess feature importance do not explain how imaging features in important anatomical regions are relevant to the classification decision. Such reasoning is crucial for transparent decision-making in healthcare applications. Our framework explains the decision for a target class by gradually exaggerating the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision changes from its original class to its negation. Our proposed loss function preserves essential details (e.g., support devices) in the generated images. We used counterfactual explanations from our framework to audit a classifier trained on a chest X-ray dataset with multiple labels. Clinical evaluation of model explanations is a challenging task. We proposed clinically-relevant quantitative metrics such as cardiothoracic ratio and the score of a healthy costophrenic recess to evaluate our explanations. We used these metrics to quantify the counterfactual changes between the populations with negative and positive decisions for a diagnosis by the given classifier. We conducted a human-grounded experiment with diagnostic radiology residents to compare different styles of explanations (no explanation, saliency map, cycleGAN explanation, and our counterfactual explanation) by evaluating different aspects of explanations: (1) understandability, (2) classifier's decision justification, (3) visual quality, (d) identity preservation, and (5) overall helpfulness of an explanation to the users. Our results show that our counterfactual explanation was the only explanation method that significantly improved the users' understanding of the classifier's decision compared to the no-explanation baseline. Our metrics established a benchmark for evaluating model explanation methods in medical images. Our explanations revealed that the classifier relied on clinically relevant radiographic features for its diagnostic decisions, thus making its decision-making process more transparent to the end-user.
We propose a computerized framework that, given a region of interest (ROI) circumscribing a lesion, not only predicts radiological observations related to the lesion characteristics with 83.2% average prediction accuracy but also derives explicit association between low-level imaging features and high-level semantic terms by exploiting their statistical correlation. Such direct association between semantic concepts and low-level imaging features can be leveraged to build a powerful annotation system for radiological images that not only allows the computer to infer the semantics from diverse medical images and run automatic reasoning for making diagnostic decision but also provides "human-interpretable explanation" of the system output to facilitate better end user understanding of computer-based diagnostic decisions. The core component of our framework is a radiological observation detection algorithm that maximizes the low-level imaging feature relevancy for each high-level semantic term. On a liver lesion CT dataset, we have implemented our framework by incorporating a large set of state-of-the-art low-level imaging features. Additionally, we included a novel feature that quantifies lesion(s) present within the liver that have a similar appearance as the primary lesion identified by the radiologist. Our framework achieved a high prediction accuracy (83.2%), and the derived association between semantic concepts and imaging features closely correlates with human expectation. The framework has been only tested on liver lesion CT images, but it is capable of being applied to other imaging domains.
We propose a methodological framework to support the development of personalized courses that improve patients' understanding of their condition and prescribed treatment. Inspired by Intelligent Tutoring Systems (ITSs), the framework uses an eLearning ontology to express domain and learner models and to create a course. We combine the ontology with a procedural reasoning approach and precompiled plans to operationalize a design across disease conditions. The resulting courses generated by the framework are personalized across four patient axes-condition and treatment, comprehension level, learning style based on the VARK (Visual, Aural, Read/write, Kinesthetic) presentation model, and the level of understanding of specific course content according to Bloom's taxonomy. Customizing educational materials along these learning axes stimulates and sustains patients' attention when learning about their conditions or treatment options. Our proposed framework creates a personalized course that prepares patients for their meetings with specialists and educates them about their prescribed treatment. We posit that the improvement in patients' understanding of prescribed care will result in better outcomes and we validate that the constructs of our framework are appropriate for representing content and deriving personalized courses for two use cases: anticoagulation treatment of an atrial fibrillation patient and lower back pain management to treat a lumbar degenerative disc condition. We conduct a mostly qualitative study supported by a quantitative questionnaire to investigate the acceptability of the framework among the target patient population and medical practitioners.
We propose a new non-parametric conditional independence test for a scalar response and a functional covariate over a continuum of quantile levels. We build a Cramer-von Mises type test statistic based on an empirical process indexed by random projections of the functional covariate, effectively avoiding the "curse of dimensionality" under the projected hypothesis, which is almost surely equivalent to the null hypothesis. The asymptotic null distribution of the proposed test statistic is obtained under some mild assumptions. The asymptotic global and local power properties of our test statistic are then investigated. We specifically demonstrate that the statistic is able to detect a broad class of local alternatives converging to the null at the parametric rate. Additionally, we recommend a simple multiplier bootstrap approach for estimating the critical values. The finite-sample performance of our statistic is examined through several Monte Carlo simulation experiments. Finally, an analysis of an EEG data set is used to show the utility and versatility of our proposed test statistic.
We propose a new reading of the results of a solid systematic review published in 2022 in Neurology by Truong et al comparing ChEI treatment to placebo or no treatment on all-cause mortality in patients with any type of dementia. Leaving ethical considerations aside, we tested if another method of interpretation of the results of the systematic review would yield different results. We applied the "rebuild the evidence base" (REB) method to the data extracted from the meta-analysis and found that the positive result of the meta-analysis (p=0.01) and the lack of heterogeneity did not weigh enough in the balance against the strictly exploratory nature of all the results of the individual RCTs included and the suspected publication bias. There was a lack of evidence of effect of ChEI treatment on over-all mortality in patients with dementia.
We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.
We propose a novel framework based on the RuleFit method to estimate heterogeneous treatment effect in randomized clinical trials. The proposed method estimates a rule ensemble comprising a set of prognostic rules, a set of prescriptive rules, as well as the linear effects of the original predictor variables. The prescriptive rules provide an interpretable description of the heterogeneous treatment effect. By including a prognostic term in the proposed model, the selected rule is represented as an heterogeneous treatment effect that excludes other effects. We confirmed that the performance of the proposed method was equivalent to that of other ensemble learning methods through numerical simulations and demonstrated the interpretation of the proposed method using a real data application.
We propose a theory of immediate inferences from assertions containing a single quantifier, such as: All of the artists are bakers; therefore, some of the bakers are artists. The theory is based on mental models and is implemented in a computer program, mReasoner. It predicts three main levels of increasing difficulty: (a) immediate inferences in which the premise and conclusion have identical meanings; (b) those in which the initial mental model of the premise yields the correct conclusion; and (c) those in which only an alternative to the initial model establishes the correct conclusion. These levels of difficulty were corroborated for inferences to necessary conclusions (in a reanalysis of data from Newstead, S. E., & Griggs, R. A. (1983). Drawing inferences from quantified statements: A study of the square of opposition. Journal of Verbal Learning and Verbal Behavior, 22, 535-546), for inferences to modal conclusions, such as, it is possible that all of the bakers are artists (Experiment 1), for inferences with unorthodox quantifiers, such as, most of the artists (Experiment 2), and for inferences about the consistency of pairs of quantified assertions (Experiment 3). The theory also includes three parameters in a stochastic system that predicted quantitative differences in accuracy within the three main sorts of inference.
We propose and test three statistical models for the analysis of children's responses to the balance scale task, a seminal task to study proportional reasoning. We use a latent class modelling approach to formulate a rule-based latent class model (RB LCM) following from a rule-based perspective on proportional reasoning and a new statistical model, the Weighted Sum Model, following from an information-integration approach. Moreover, a hybrid LCM using item covariates is proposed, combining aspects of both a rule-based and information-integration perspective. These models are applied to two different datasets, a standard paper-and-pencil test dataset (N = 779), and a dataset collected within an online learning environment that included direct feedback, time-pressure, and a reward system (N = 808). For the paper-and-pencil dataset the RB LCM resulted in the best fit, whereas for the online dataset the hybrid LCM provided the best fit. The standard paper-and-pencil dataset yielded more evidence for distinct solution rules than the online data set in which quantitative item characteristics are more prominent in determining responses. These results shed new light on the discussion on sequential rule-based and information-integration perspectives of cognitive development.
We provide a quantitative assessment of the parallel-processing hypothesis included in various language-processing models. First, we highlight the importance of reasoning about cognitive processing at the level of single trials rather than using averages. Then, we report the results of an experiment in which the hypothesis was tested at an unprecedented level of granularity with intracerebral data recorded during a picture-naming task. We extracted patterns of significant high-gamma activity from multiple patients and combined them into a single analysis framework that identified consistent patterns. Average signals from different brain regions, presumably indexing distinct cognitive processes, revealed a large degree of concurrent activity. In comparison, at the level of single trials, the temporal overlap of detected significant activity was unexpectedly low, with the exception of activity in sensory cortices. Our novel methodology reveals some limits on the degree to which word production involves parallel processing.