Predictive posture stabilization before contact with moving objects: equivalence of smooth pursuit tracking and peripheral vision

Postural stabilization is essential to effectively interact with our environment. Humans preemptively adjust their posture to counteract impending disturbances, such as those encountered during interactions with moving objects, a phenomenon known as anticipatory postural adjustments (APAs). APAs are thought to be influenced by predictive models that incorporate object motion via retinal motion and extraretinal signals. Building on our previous work that examined APAs in relation to the perceived momentum of moving objects, here we explored the impact of object motion within different visual field sectors on the human capacity to anticipate motion and prepare APAs for contact between virtual moving objects and the limb. Participants interacted with objects moving toward them under different gaze conditions. In one condition, participants fixated on either a central point (central fixation) or left-right of the moving object (peripheral fixation), whereas in another, they followed the moving object with smooth pursuit eye movements (SPEMs). We found that APAs had the smallest magnitude in the central fixation condition and that no notable differences in APAs were apparent between the SPEM and peripheral fixation conditions. This suggests that the visual system can accurately perceive motion of objects in peripheral vision for posture stabilization. Using Bayesian model averaging, we also evaluated the contribution of different gaze variables, such as eye velocity and gain (ratio of eye and object velocity) and showed that both eye velocity and gain signals were significant predictors of APAs. Taken together, our study underscores the roles of oculomotor signals in the modulation of APAs.NEW & NOTEWORTHY We show that the human visuomotor system can detect motion in peripheral vision and make anticipatory adjustments to posture before contact with moving objects, just as effectively as when the eye movement system tracks those objects with smooth pursuit eye movements. These findings pave the way for research into how age-induced changes in spatial vision, eye movements, and motion perception could affect the control of limb movements and postural stability during motion-mediated interactions with objects.

Automated facial expression measurement in a longitudinal sample of 4- and 8-month-olds: Baby FaceReader 9 and manual coding of affective expressions

Facial expressions are among the earliest behaviors infants use to express emotional states, and are crucial to preverbal social interaction. Manual coding of infant facial expressions, however, is laborious and poses limitations to replicability. Recent developments in computer vision have advanced automated facial expression analyses in adults, providing reproducible results at lower time investment. Baby FaceReader 9 is commercially available software for automated measurement of infant facial expressions, but has received little validation. We compared Baby FaceReader 9 output to manual micro-coding of positive, negative, or neutral facial expressions in a longitudinal dataset of 58 infants at 4 and 8 months of age during naturalistic face-to-face interactions with the mother, father, and an unfamiliar adult. Baby FaceReader 9's global emotional valence formula yielded reasonable classification accuracy (AUC = .81) for discriminating manually coded positive from negative/neutral facial expressions; however, the discrimination of negative from neutral facial expressions was not reliable (AUC = .58). Automatically detected a priori action unit (AU) configurations for distinguishing positive from negative facial expressions based on existing literature were also not reliable. A parsimonious approach using only automatically detected smiling (AU12) yielded good performance for discriminating positive from negative/neutral facial expressions (AUC = .86). Likewise, automatically detected brow lowering (AU3+AU4) reliably distinguished neutral from negative facial expressions (AUC = .79). These results provide initial support for the use of selected automatically detected individual facial actions to index positive and negative affect in young infants, but shed doubt on the accuracy of complex a priori formulas.

Dynamic causal modelling highlights the importance of decreased self-inhibition of the sensorimotor cortex in motor fatigability

Motor fatigability emerges when challenging motor tasks must be maintained over an extended period of time. It is frequently observed in everyday life and affects patients as well as healthy individuals. Motor fatigability can be measured using simple tasks like finger tapping at maximum speed for 30 s. This typically results in a rapid decrease of tapping frequency, a phenomenon called motor slowing. In a previous study (Bächinger et al, eLife, 8 (September), https://doi.org/10.7554/eLife.46750 , 2019), we showed that motor slowing goes hand in hand with a gradual increase in blood oxygen level dependent signal in the primary sensorimotor cortex (SM1), supplementary motor area (SMA), and dorsal premotor cortex (PMd). It is unclear what drives the activity increase in SM1 caused by motor slowing and whether motor fatigability affects the dynamic interactions between SM1, SMA, and PMd. Here, we performed dynamic causal modelling (DCM) on data of 24 healthy young participants collected during functional magnetic resonance imaging to answer this question. The regions of interest (ROI) were defined based on the peak activation within SM1, SMA, and PMd. The model space consisted of bilateral connections between all ROI, with intrinsic self-modulation as inhibitory, and driving inputs set to premotor areas. Our findings revealed that motor slowing was associated with a significant reduction in SM1 self-inhibition, as uncovered by testing the maximum à posteriori against 0 (t(23)=-4.51, p < 0.001). Additionally, the model revealed a significant decrease in the driving input to premotor areas (t(23) > 2.71, p < 0.05) suggesting that structures other than cortical motor areas may contribute to motor fatigability.

A systematic review and Bayesian meta-analysis provide evidence for an effect of acute physical activity on cognition in young adults

Physical exercise is a potential intervention for enhancing cognitive function across the lifespan. However, while studies employing long-term exercise interventions consistently show positive effects on cognition, studies using single acute bouts have produced mixed results. Here, a systematic review and meta-analysis was conducted to determine the impact of acute exercise on cognitive task performance in healthy young adults. A Bayesian hierarchical model quantified probabilistic evidence for a modulatory relationship by synthesizing 651 effect sizes from 113 studies from PsychInfo and Google Scholar representing 4,390 participants. Publication bias was mitigated using the trim-and-fill method. Acute exercise was found to have a small beneficial effect on cognition (g = 0.13 ± 0.04; BF = 3.67) and decrease reaction time. A meta-analysis restricted to executive function tasks revealed improvements in working memory and inhibition. Meta-analytic estimates were consistent across multiple priors and likelihood functions. Physical activities were categorized based on exercise type (e.g., cycling) because many activities have aerobic and anaerobic components, but this approach may limit comparison to studies that categorize activities based on metabolic demands. The current study provides an updated synthesis of the existing literature and insights into the robustness of acute exercise-induced effects on cognition. Funding provided by the United States Army Research Office.

A 27-country test of communicating the scientific consensus on climate change

Communicating the scientific consensus that human-caused climate change is real increases climate change beliefs, worry and support for public action in the United States. In this preregistered experiment, we tested two scientific consensus messages, a classic message on the reality of human-caused climate change and an updated message additionally emphasizing scientific agreement that climate change is a crisis. Across online convenience samples from 27 countries (n = 10,527), the classic message substantially reduces misperceptions (d = 0.47, 95% CI (0.41, 0.52)) and slightly increases climate change beliefs (from d = 0.06, 95% CI (0.01, 0.11) to d = 0.10, 95% CI (0.04, 0.15)) and worry (d = 0.05, 95% CI (-0.01, 0.10)) but not support for public action directly. The updated message is equally effective but provides no added value. Both messages are more effective for audiences with lower message familiarity and higher misperceptions, including those with lower trust in climate scientists and right-leaning ideologies. Overall, scientific consensus messaging is an effective, non-polarizing tool for changing misperceptions, beliefs and worry across different audiences.

Incorporating Individual-Level Treatment Effects and Outcome Preferences Into Personalized Blood Pressure Target Recommendations

BACKGROUND

There are no shared decision-making frameworks for selecting blood pressure (BP) targets for individuals with hypertension. This study addressed whether results from the SPRINT (Systolic Blood Pressure Intervention Trial) could be tailored to individuals using predicted risks and simulated preferences.

METHODS AND RESULTS

Among 8202 SPRINT participants, Cox models were developed and internally validated to predict each individual's absolute difference in risk from intensive versus standard BP lowering for cardiovascular events, cognitive impairment, death, and serious adverse events (AEs). Individual treatment effects were combined using simulated preference weights into a net benefit, which represents a weighted sum of risk differences across outcomes. Net benefits were compared among those above versus below the median AE risk. In simulations for which cardiovascular, cognitive, and death events had much greater weight than the AEs of BP lowering, the median net benefit was 3.3 percentage points (interquartile range [IQR], 2.0-5.7), and 100% of participants had a net benefit favoring intensive BP lowering. When simulating benefits and harms to have similar weights, the median net benefit was 0.8 percentage points (IQR, 0.2-2.2), and 87% had a positive net benefit. Compared with participants at lower risk of AEs from BP lowering, those at higher risk had a greater net benefit from intensive BP lowering despite experiencing more AEs (P<0.001 in both simulations).

CONCLUSIONS

Most SPRINT participants had a predicted net benefit that favored intensive BP lowering, but the degree of net benefit varied considerably. Tailoring BP targets using each patient's risks and preferences may provide more refined BP target recommendations.

Identification of Motivational Determinants for Telemedicine Use Among Patients With Rheumatoid Arthritis in Germany: Secondary Analysis of Data From a Nationwide Cross-Sectional Survey Study

BACKGROUND

Previous studies have demonstrated telemedicine to be an effective tool to complement rheumatology care and address workforce shortage. With the COVID-19 outbreak, telemedicine experienced a massive upswing. An earlier analysis revealed that the motivation of patients with rheumatic and musculoskeletal diseases to use telemedicine is closely connected to their disease. It remains unclear which factors are associated with patients' motivation to use telemedicine in certain rheumatic and musculoskeletal diseases groups, such as rheumatoid arthritis (RA).

OBJECTIVE

This study aims to identify factors that determine the willingness to try telemedicine among patients diagnosed with RA.

METHODS

We conducted a secondary analysis of data from a German nationwide cross-sectional survey among patients with RA. Bayesian univariate logistic regression analysis was applied to the data to determine which factors were associated with willingness to try telemedicine. Predictor variables (covariates) studied individually included sociodemographic factors (eg, age, sex) and health characteristics (eg, health status). All the variables positively and negatively associated with willingness to try telemedicine in the univariate analyses were then considered for Bayesian model averaging analysis after a selection based on the variance inflation factor (≤ 2.5) to identify determinants of willingness to try telemedicine.

RESULTS

Among 438 surveyed patients in the initial study, 210 were diagnosed with RA (47.9%). Among them, 146 (69.5%) answered either yes or no regarding willingness to try telemedicine and were included in the analysis. A total of 22 variables (22/55, 40%) were associated with willingness to try telemedicine (region of practical equivalence %≤5). A total of 9 determinant factors were identified using Bayesian model averaging analysis. Positive determinants included desiring telemedicine services provided by a rheumatologist (odds ratio [OR] 13.7, 95% CI 5.55-38.3), having prior knowledge of telemedicine (OR 2.91, 95% CI 1.46-6.28), residing in a town (OR 2.91, 95% CI 1.21-7.79) or city (OR 0.56, 95% CI 0.23-1.27), and perceiving one's health status as moderate (OR 1.87, 95% CI 0.94-3.63). Negative determinants included the lack of an electronic device (OR 0.1, 95% CI 0.01-0.62), absence of home internet access (OR 0.1, 95% CI 0.02-0.39), self-assessment of health status as bad (OR 0.44, 95% CI 0.21-0.89) or very bad (OR 0.47, 95% CI 0.06-2.06), and being aged between 60 and 69 years (OR 0.48, 95% CI 0.22-1.04) or older than 70 years (OR 0.38, 95% CI 0.16-0.85).

CONCLUSIONS

The results suggest that some patients with RA will not have access to telemedicine without further support. Older patients, those not living in towns, those without adequate internet access, reporting a bad health status, and those not owning electronic devices might be excluded from the digital transformation in rheumatology and might not have access to adequate RA care. These patient groups certainly require support for the use of digital rheumatology care.

Common intentional binding effects across diverse sensory modalities in touch-free voluntary actions

The intentional binding effect refers to the phenomenon where the perceived temporal interval between a voluntary action and its sensory consequence is subjectively compressed. Prior research revealed the importance of tactile feedback from the keyboard on this effect. Here we examined the necessity of such tactile feedback by utilizing a touch-free key-press device without haptic feedback, and explored how initial/outcome sensory modalities (visual/auditory/tactile) and their consistency influence the intentional binding effect. Participants estimated three delay lengths (250, 550, or 850 ms) between the initial and outcome stimuli. Results showed that regardless of the combinations of sensory modalities between the initial and the outcome stimuli (i.e., modal consistency), the intentional binding effect was only observed in the 250 ms delay condition. This findings indicate a stable intentional binding effect both within and across sensory modalities, supporting the existence of a shared mechanism underlying the binding effect in touch-free voluntary actions.

Dissecting the components of error in analogue report tasks

Over the last two decades, the analogue report task has become a standard method for measuring the fidelity of visual representations across research domains including perception, attention, and memory. Despite its widespread use, there has been no methodical investigation of the different task parameters that might contribute to response variability. To address this gap, we conducted two experiments manipulating components of a typical analogue report test of memory for colour hue. We found that human response errors were independently affected by changes in storage and maintenance requirements of the task, demonstrated by a strong effect of set size even in the absence of a memory delay. In contrast, response variability remained unaffected by physical size of the colour wheel, implying negligible contribution of motor noise to task performance, or by its chroma radius, highlighting non-uniformity of the standard colour space. Comparing analogue report to a matched forced-choice task, we found variation in adjustment criterion made a limited contribution to analogue report variability, becoming meaningful only with low representational noise. Our findings validate the analogue report task as a robust measure of representational fidelity for most purposes, while also quantifying non-representational sources of noise that would limit its reliability in specialized settings.

Bayesian estimation in veterinary pharmacology: A conceptual and practical introduction

Sophisticated mathematical and computational tools have become widespread and important in veterinary pharmacology. Although the theoretical basis and practical applications of these have been widely explored in the literature, statistical inference in the context of these models has received less attention. Optimization methods, often with frequentist statistical inference, have been predominant. In contrast, Bayesian statistics have not been widely applied, but offer both practical utility and arguably greater interpretability. Veterinary pharmacology applications are generally well supported by relevant prior information, from either existing substantive knowledge, or an understanding of study and model design. This facilitates practical implementation of Bayesian analyses that can take advantage of this knowledge. This essay will explore the specification of Bayesian models relevant to veterinary pharmacology, including demonstration of prior selection, and illustrate the capability of these models to generate practically useful statistics, including uncertainty statements, that are difficult or impossible to obtain otherwise. Case studies using simulated data will describe applications in clinical trials, pharmacodynamics, and pharmacokinetics, all including multilevel modeling. This content may serve as a suitable starting point for researchers in veterinary pharmacology and related disciplines considering Bayesian estimation for their applied work.

Testing the validity of online psychophysical measurement of body image perception

This body image study tests the viability of transferring a complex psychophysical paradigm from a controlled in-person laboratory task to an online environment. 172 female participants made online judgements about their own body size when viewing images of computer-generated female bodies presented in either in front-view or at 45-degrees in a method of adjustment (MOA) paradigm. The results of these judgements were then compared to the results of two laboratory-based studies (with 96 and 40 female participants respectively) to establish three key findings. Firstly, the results show that the accuracy of online and in-lab estimates of body size are comparable, secondly that the same patterns of visual biases in judgements are shown both in-lab and online, and thirdly online data shows the same view-orientation advantage in accuracy in body size judgements as the laboratory studies. Thus, this study suggests that that online sampling potentially represents a rapid and accurate way of collecting reliable complex behavioural and perceptual data from a more diverse range of participants than is normally sampled in laboratory-based studies. It also offers the potential for designing stratified sampling strategies to construct a truly representative sample of a target population.

The Curve of Learning With and Without Instructions

In skill acquisition, instructing individuals the stimulus-response mappings indicating how to perform and act, yields better performance. Additionally, performance is helped by repeated practice. Whether providing instructions and repeated practice interact to achieve optimal performance remains debated. This paper addresses that question by analyzing the learning curves of individuals learning stimulus-response mappings of varying complexity. We particularly focus on the question whether instructions lead to improved performance in the longer run. Via evidence accumulation modeling, we find no evidence for this assertion. Instructions seem to provide individuals with a head start, leading to better initial performance in the early stages of learning, without long-lasting effects on behavior. We discuss the results in light of related studies that do report long-lasting effects of instructions, and propose that the complexity of a skill determines whether long-lasting benefits of initial instructions exist.

Scientific realism, scientific practice, and science communication: An empirical investigation of academics and science communicators

We argue that the societal consequences of the scientific realism debate, in the context of science-to-public communication are often overlooked and careful theorizing about it needs further empirical groundwork. As such, we conducted a survey experiment with 130 academics (from physics, chemistry, and biology) and 137 science communicators. We provided them with an 11-item questionnaire probing their views of scientific realism and related concepts. Contra theoretical expectations, we find that (a) science communicators are generally more inclined towards scientific antirealism when compared to scientists in the same academic fields, though both groups show an inclination towards realism and (b) academics who engage in more theoretical work are not less (or more) realist than experimentalists. Lastly, (c), we fail to find differences with respect to selective realism but find that science communicators are significantly less epistemically voluntarist compared to their academic counterparts. Overall, our results provide first empirical evidence on the views of scientists and science communicators on scientific realism, with some results running contra to the theoretical expectations, opening up new empirical and theoretical research directions.

10 Hz rhythmic stimulation modulates electrophysiological, but not behavioral markers of suppression

We investigated the role of alpha in the suppression of attention capture by salient but to-be-suppressed (negative and nonpredictive) color cues, expecting a potential boosting effect of alpha-rhythmic entrainment on feature-specific cue suppression. We did so by presenting a rhythmically flickering visual bar of 10 Hz before the cue - either on the cue's side or opposite the cue -while an arrhythmically flickering visual bar was presented on the respective other side. We hypothesized that rhythmic entrainment at cue location could enhance the suppression of the cue. Testing 27 participants ranging from 18 to 39 years of age, we found both behavioral and electrophysiological evidence of suppression: Search times for a target at a negatively cued location were delayed relative to a target away from the cued location (inverse validity effects). In addition, an event-related potential indicative for suppression (the Distractor Positivity, Pd) was observed following rhythmic but not arrhythmic stimulation, indicating that suppression was boosted by the stimulation. This was also echoed in higher spectral power and intertrial phase coherence of EEG at rhythmically versus arrhythmically stimulated electrode sites, albeit only at the second harmonic (20 Hz), but not at the stimulation frequency. In addition, inverse validity effects were not modulated by rhythmic entrainment congruent with the cue side. Hence, we propose that rhythmic visual stimulation in the alpha range could support suppression, though behavioral evidence remains elusive, in contrast to electrophysiological findings.

A Model-Based Approach to the Disentanglement and Differential Treatment of Engaged and Disengaged Item Omissions

Item omissions in large-scale assessments may occur for various reasons, ranging from disengagement to not being capable of solving the item and giving up. Current response-time-based classification approaches allow researchers to implement different treatments of item omissions presumably going back to different mechanisms. These approaches, however, are limited in that they require a clear-cut decision on the underlying missingness mechanism and do not allow to take the uncertainty in classification into account. We present a response-time-based model-based mixture modeling approach that overcomes this limitation. The approach (a) facilitates disentangling item omissions stemming from disengagement from those going back to solution behavior, (b) considers the uncertainty in omission classification, (c) allows for omission mechanisms to vary on the item-by-examinee level, (d) supports investigating person and item characteristics associated with different types of omission behavior, and (e) gives researchers flexibility in deciding on how to handle different types of omissions. The approach exhibits good parameter recovery under realistic research conditions. We illustrate the approach on data from the Programme for the International Assessment of Adult Competencies 2012 and compare it against previous classification approaches for item omissions.

Perceptual similarity judgments do not predict the distribution of errors in working memory

Population coding models provide a quantitative account of visual working memory (VWM) retrieval errors with a plausible link to the response characteristics of sensory neurons. Recent work has provided an important new perspective linking population coding to variables of signal detection, including d-prime, and put forward a new hypothesis: that the distribution of recall errors on, for example, a color wheel, is a consequence of the psychological similarity between points in that stimulus space, such that the exponential-like psychophysical distance scaling function can fulfil the role of population tuning and obviate the need to fit a tuning width parameter to recall data. Using four different visual feature spaces, we measured psychophysical similarity and memory errors in the same participants. Our results revealed strong evidence for a common source of variability affecting similarity judgments and recall estimates but did not support any consistent relationship between psychophysical similarity functions and VWM errors. At the group level, the responsiveness functions obtained from the psychophysical similarity task diverged strongly from those that provided the best fit to working memory errors. At the individual level, we found convincing evidence against an association between observed and best-fitting similarity functions. Finally, our results show that the newly proposed exponential-like responsiveness function has in general no advantage over the canonical von Mises (circular normal) function assumed by previous population coding models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Bayesian pattern-mixture models for dropout and intermittently missing data in longitudinal data analysis

Valid inference can be drawn from a random-effects model for repeated measures that are incomplete if whether the data are missing or not, known as missingness, is independent of the missing data. Data that are missing completely at random or missing at random are two data types for which missingness is ignorable. Given ignorable missingness, statistical inference can proceed without addressing the source of the missing data in the model. If the missingness is not ignorable, however, recommendations are to fit multiple models that represent different plausible explanations of the missing data. A popular choice in methods for evaluating nonignorable missingness is a random-effects pattern-mixture model that extends a random-effects model to include one or more between-subjects variables that represent fixed patterns of missing data. Generally straightforward to implement, a fixed pattern-mixture model is one among several options for assessing nonignorable missingness, and when it is used as the sole model to address nonignorable missingness, understanding the impact of missingness is greatly limited. This paper considers alternatives to a fixed pattern-mixture model for nonignorable missingness that are generally straightforward to fit and encourage researchers to give greater attention to the possible impact of nonignorable missingness in longitudinal data analysis. Patterns of both monotonic and non-monotonic (intermittently) missing data are addressed. Empirical longitudinal psychiatric data are used to illustrate the models. A small Monte Carlo data simulation study is presented to help illustrate the utility of such methods.

A tutorial on Bayesian model-averaged meta-analysis in JASP

Researchers conduct meta-analyses in order to synthesize information across different studies. Compared to standard meta-analytic methods, Bayesian model-averaged meta-analysis offers several practical advantages including the ability to quantify evidence in favor of the absence of an effect, the ability to monitor evidence as individual studies accumulate indefinitely, and the ability to draw inferences based on multiple models simultaneously. This tutorial introduces the concepts and logic underlying Bayesian model-averaged meta-analysis and illustrates its application using the open-source software JASP. As a running example, we perform a Bayesian meta-analysis on language development in children. We show how to conduct a Bayesian model-averaged meta-analysis and how to interpret the results.

A systematic review on the direct approach to elicit the demand-side cost-effectiveness threshold: Implications for low- and middle-income countries

Several literature review studies have been conducted on cost-effectiveness threshold values. However, only a few are systematic literature reviews, and most did not investigate the different methods, especially in-depth reviews of directly eliciting WTP per QALY. Our study aimed to 1) describe the different direct approach methods to elicit WTP/QALY; 2) investigate factors that contribute the most to the level of WTP/QALY value; and 3) investigate the relation between the value of WTP/QALY and GDP per capita and give some recommendations on feasible methods for eliciting WTP/QALY in low- and middle-income countries (LMICs). A systematic review concerning select studies estimating WTP/QALY from a direct approach was carried out in seven databases, with a cut off date of 03/2022. The conversion of monetary values into 2021 international dollars (i$) was performed via CPI and PPP indexes. The influential factors were evaluated with Bayesian model averaging. Criteria for recommendation for feasible methods in LMICs are made based on empirical evidence from the systematic review and given the resource limitation in LMICs. A total of 12,196 records were identified; 64 articles were included for full-text review. The WTP/QALY method and values varied widely across countries with a median WTP/QALY value of i$16,647.6 and WTP/QALY per GDP per capita of 0.53. A total of 11 factors were most influential, in which the discrete-choice experiment method had a posterior probability of 100%. Methods for deriving WTP/QALY vary largely across studies. Eleven influential factors contribute most to the level of values of WTP/QALY, in which the discrete-choice experiment method was the greatest affected. We also found that in most countries, values for WTP/QALY were below 1 x GDP per capita. Some important principles are addressed related to what LMICs may be concerned with when conducting studies to estimate WTP/QALY.

Explaining the flaws in human random generation as local sampling with momentum

In many tasks, human behavior is far noisier than is optimal. Yet when asked to behave randomly, people are typically too predictable. We argue that these apparently contrasting observations have the same origin: the operation of a general-purpose local sampling algorithm for probabilistic inference. This account makes distinctive predictions regarding random sequence generation, not predicted by previous accounts-which suggests that randomness is produced by inhibition of habitual behavior, striving for unpredictability. We verify these predictions in two experiments: people show the same deviations from randomness when randomly generating from non-uniform or recently-learned distributions. In addition, our data show a novel signature behavior, that people's sequences have too few changes of trajectory, which argues against the specific local sampling algorithms that have been proposed in past work with other tasks. Using computational modeling, we show that local sampling where direction is maintained across trials best explains our data, which suggests it may be used in other tasks too. While local sampling has previously explained why people are unpredictable in standard cognitive tasks, here it also explains why human random sequences are not unpredictable enough.

The influence of hatha yoga on stress, anxiety, and suppression: A randomized controlled trial

Engaging in yoga may mitigate stress and anxiety in individuals while potentially enhancing one's capacity to manage distractions. Our research aimed to explore the relation between these two outcomes: Can an eight-week yoga program foster distraction suppression, thereby reducing stress and discomfort? To answer this question, we used Hatha Yoga, the most commonly practiced form of yoga. We tested if the intervention improved participants' ability to suppress distractions and selectively decrease self-reported stress and stress reactivity. In Addition, we investigated whether such an intervention would increase participants' mindfulness. Our study included 98 healthy yoga novices between 18 and 40 years who were randomly assigned to either an experimental or a waitlist condition, with each participant completing pre- and post-intervention assessments, including questionnaires, as well as electrophysiological and behavioral measures. After eight weeks of yoga practice, significant reductions in self-reported stress and stress reactivity levels, as well as increased mindfulness, were observed among those participating in the intervention relative to those in the waitlist control group. There were, however, no significant changes in state or trait anxiety due to the intervention. Changes in stress measures could not be explained by changes in participants' ability to suppress distractors, which was not affected by the intervention. Overall, our findings suggest that regular participation in Hatha Yoga can improve mental health outcomes without impacting cognitive functioning directly related to distractor suppression. CLINICAL TRIAL REGISTRATION NUMBER: NCT05232422.

Metacognition, public health compliance, and vaccination willingness

Metacognition, our ability to reflect on our own beliefs, manifests itself in the confidence we have in these beliefs, and helps us guide our behavior in complex and uncertain environments. Here, we provide empirical tests of the importance of metacognition during the pandemic. Bayesian and frequentist analyses demonstrate that citizens with higher metacognitive sensitivity-where confidence differentiates correct from incorrect COVID-19 beliefs-reported higher willingness to vaccinate against COVID-19, and higher compliance with recommended public health measures. Notably, this benefit of accurate introspection held controlling for the accuracy of COVID-19 beliefs. By demonstrating how vaccination willingness and compliance may relate to insight into the varying accuracy of beliefs, rather than only the accuracy of the beliefs themselves, this research highlights the critical role of metacognitive ability in times of crisis. However, we do not find sufficient evidence to conclude that citizens with higher metacognitive sensitivity were more likely to comply with recommended public health measures when controlling for the absolute level of the confidence citizens had in their COVID-19 beliefs.

Multi-model sequential analysis of MRI data for microstructure prediction in heterogeneous tissue

We propose a general method for combining multiple models to predict tissue microstructure, with an exemplar using in vivo diffusion-relaxation MRI data. The proposed method obviates the need to select a single 'optimum' structure model for data analysis in heterogeneous tissues where the best model varies according to local environment. We break signal interpretation into a three-stage sequence: (1) application of multiple semi-phenomenological models to predict the physical properties of tissue water pools contributing to the observed signal; (2) from each Stage-1 semi-phenomenological model, application of a tissue microstructure model to predict the relative volumes of tissue structure components that make up each water pool; and (3) aggregation of the predictions of tissue structure, with weightings based on model likelihood and fractional volumes of the water pools from Stage-1. The multiple model approach is expected to reduce prediction variance in tissue regions where a complex model is overparameterised, and bias where a model is underparameterised. The separation of signal characterisation (Stage-1) from biological assignment (Stage-2) enables alternative biological interpretations of the observed physical properties of the system, by application of different tissue structure models. The proposed method is exemplified with human prostate diffusion-relaxation MRI data, but has potential application to a wide range of analyses where a single model may not be optimal throughout the sampled domain.

Sensorimotor distance: A grounded measure of semantic similarity for 800 million concept pairs

Experimental design and computational modelling across the cognitive sciences often rely on measures of semantic similarity between concepts. Traditional measures of semantic similarity are typically derived from distance in taxonomic databases (e.g. WordNet), databases of participant-produced semantic features, or corpus-derived linguistic distributional similarity (e.g. CBOW), all of which are theoretically problematic in their lack of grounding in sensorimotor experience. We present a new measure of sensorimotor distance between concepts, based on multidimensional comparisons of their experiential strength across 11 perceptual and action-effector dimensions in the Lancaster Sensorimotor Norms. We demonstrate that, in modelling human similarity judgements, sensorimotor distance has comparable explanatory power to other measures of semantic similarity, explains variance in human judgements which is missed by other measures, and does so with the advantages of remaining both grounded and computationally efficient. Moreover, sensorimotor distance is equally effective for both concrete and abstract concepts. We further introduce a web-based tool ( https://lancaster.ac.uk/psychology/smdistance ) for easily calculating and visualising sensorimotor distance between words, featuring coverage of nearly 800 million word pairs. Supplementary materials are available at https://osf.io/d42q6/ .

Opioid relapse and its predictors among methadone maintenance patients: a multicenter, cross-sectional study in Vietnam

BACKGROUND

Opioid relapse, one of the common and severe problems during methadone maintenance treatment, can give rise to poor treatment outcomes. This study measured the opioid relapse rate and its associated factors among methadone maintenance patients in Vietnam.

METHODS

Information about the demographic characteristics and social support of 655 patients was collected through direct interviews. Medical records were used to gather data on treatment characteristics. Relapse was determined via urine opioid test results.

RESULTS

The overall relapse rate of patients during treatment was 13.1%. According to the multivariate logistic regression model, living in mountainous areas (adjusted odds ratio (aOR) = 3.63, 95% CI 1.90-7.46) and long duration of drug use in the past (aOR = 1.06, 95% CI 1.03-1.09) were associated with an increase in the odds of opioid relapse. By contrast, living with many family members (aOR = 0.69, 95% CI 0.55-0.85), having longer treatment time (aOR = 0.80, 95% CI 0.73-0.87), and completely adhering to treatment (aOR = 0.38, 95% CI 0.23-0.64) were protective for opioid relapse. As per the univariate analyses, the odds of opioid relapse declined by 25% for each increase of one close friend or relative (OR = 0.75, 95% CI 0.66-0.86). Regarding social support (range score: 0-100), each additional increase of one score was associated with a 1% decrease in the odds of opioid relapse (OR = 0.99, 95% CI 0.98-0.99). Patient sex, education level, occupation type, patient's monthly income, family's monthly income, the number of previous treatments, daily methadone dose, comorbidity, and received antiretroviral therapy were not associated with opioid relapse among patients (p > 0.05).

CONCLUSIONS

Residence, the role of family and social support, and treatment adherence should be paid more attention to guarantee and enhance the success of methadone maintenance treatment.

Sustained selective attention to chromatic information enhances visuocortical gain at the population level

Prior work in selective attention research has shown that colour-selective attention enhances neural activity in visuocortical areas sensitive to the attended colour while suppressing activity in areas sensitive to ignored colours. However, it is currently unclear whether this effect is limited to attending to specific colour hues or extends to chromatic information more broadly. To investigate this question, we used steady-state visual evoked potentials (ssVEPs) frequency tagging to quantify participants' visuocortical responses to specific elements embedded in arrays of flickering, randomly moving mid-complex patterns. Participants were instructed to attend to either coloured or greyscale patterns while ignoring the others. We found that attending to either coloured or greyscale patterns produced robust increases in ssVEP amplitudes both compared to ignored stimuli and to baseline. There was however no evidence of suppressed responses to ignored patterns. These findings demonstrate that attentional selection based on the presence or absence of chromatic information prompts selectively enhanced visuocortical processing but this selective amplification is not accompanied by suppression of unattended stimuli. Findings are consistent with theoretical notions that predict strong competition between specific exemplars within a given feature dimension, such as red or green, but weak competition between broadly defined stimulus categories, such as chromatic versus non-chromatic.

The effect of masks on the emotion perception of a facial crowd

The present study investigated the effect of facial masks on people's ability to perceive emotions in crowds. We presented faces with the bottom halves occluded by masks or full faces without occlusion. In two sequentially presented crowds, we varied the number of faces, emotional valence, and intensity of facial expressions, examining the impact of masks on the perception of crowd emotion. Participants reported which of the two crowds they would avoid based on the crowds' average emotions. The participants' ability to judge the average emotion of a crowd, especially a crowd expressing happiness, was impaired when the crowd wore masks. For faces covered by masks, crowd emotion judgments were more negatively biased than those without masks. However, participants could still distinguish the emotional intensities of a crowd wearing masks above chance. Additionally, participants responded more quickly to a crowd with more people without compromising accuracy, despite the perceptual challenges imposed by facial masks. Our results suggest that under ambiguous social situations in which individuals' emotions are partially hidden by masks, a large group may provide stronger social cues than a small group, thereby promoting communication and regulating social behaviors.

Knowledge and attitudes of community pharmacists regarding dementia: A nationwide cross-sectional study in Vietnam

OBJECTIVE

To investigate Vietnamese community pharmacists' knowledge and attitudes towards dementia.

METHODS

1066 community pharmacists in eight provinces/centrally-governed cities were recruited using a non-probability convenience sampling technique. Their dementia knowledge was measured using a set of 14 questions developed through a literature review. The Approaches to Dementia Questionnaire was used to assess pharmacists' attitudes towards dementia. Cronbach's alpha was 0.88 for the overall questionnaire (0.70 for the knowledge part and 0.81 for the attitude part).

RESULTS

Participants were mostly female (74.2%), 20-39 years old (79.1%), and had work experience in pharmacies of less than 10 years (77.0%). Medicines for dementia were available in only 40 community pharmacies (3.8%), including galantamine (3.0%) and donepezil (0.8%). Pharmacists' average knowledge and attitude scores were 8.03 ± 2.61 and 64.81 ± 7.34, respectively. There were considerable differences in pharmacists' knowledge and attitudes between rural and urban areas and among eight provinces (p < 0.001). Higher knowledge and attitude scores were found among those with higher education levels and longer work experience (p < 0.001). Using reliable sources to seek information on dementia, such as books and scientific articles, also helped pharmacists to have better knowledge and more positive attitudes (p < 0.001). There was a positive relationship between knowledge and attitude scores (r = 0.326, p < 0.001).

CONCLUSION

Community pharmacists demonstrated moderate levels of knowledge and attitudes towards dementia. Their knowledge about the symptoms of dementia was inadequate. Educational interventions and training programs are urgently needed to enhance their dementia knowledge and attitudes.

Neurocognitive and self-reported psychosocial and behavioral functioning in siblings of individuals with neurodevelopmental conditions: a study using remote self-administered testing

OBJECTIVE

This study compared and explored the neurocognitive profiles of siblings of persons with and without neurodevelopmental conditions (NDCs) and associations between objective test performance and self-reported psychosocial functioning.

METHODS

Siblings of persons with and without NDCs (64 NDC and 64 control siblings; mean age 19.88 years, range 11-27 years, 73.44% female, 75.78% White Caucasian) completed self-report questionnaires and self-administered computerized neurocognitive tests of executive functioning (EF). Using Bayesian analyses, we examined cross-sectional associations between self-reported psychosocial functioning and cognitive test performance, and predictors of EF over 15 months.

RESULTS

NDC siblings had poorer working memory, inhibition, attention, and shifting compared to controls, as measured by experimental paradigms on the backward Corsi span, N-Back 2-back task, Stop Signal Task, Sustained Attention to Response Task, and the Wisconsin Card Sorting Test (effect size δ ranging 0.49 to 0.64). Bayesian cross-sectional networks revealed negative emotion reactivity and working memory difficulties were central to the NDC sibling network. Over 15 months, poorer EF (k low test scores) was predicted by negative emotion reactivity, sleep problems, and anxiety, over and above effects of age and subclinical autistic and ADHD traits. Siblings of autistic individuals and persons with fetal alcohol spectrum disorder had higher rates of neurocognitive and psychiatric difficulties than other NDCs and controls (Bayes factors >20).

CONCLUSIONS

Neurocognitive difficulties were associated with transdiagnostic vulnerability to poorer wellbeing in NDC siblings. These findings demonstrate the feasibility of remote online cognitive testing and highlight the importance of individualized prevention and intervention for NDC siblings.

Unified tumor growth mechanisms from multimodel inference and dataset integration

Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of cells associated with the SCLC-N or SCLC-A2 subtypes, the transition from the SCLC-A subtype to the SCLC-Y subtype through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor treatment resistance.

Surface texture heterogeneity in maculated bird eggshells

Many of the world's 10 000 bird species lay coloured or patterned eggs. The large diversity of eggshell patterning among birds, achieved through pigment, has been attributed to a few selective agents such as crypsis, thermoregulation, egg recognition, mate signalling, egg strength and protecting the embryo from UV. Pigmentation may influence the texture of eggshells, which in turn may be important for dealing with water and microbes. We measured surface roughness (Sa, nm), surface skewness (Ssk) and surface kurtosis (Sku), which describe different aspects of surface texture, across 204 bird species with maculated (patterned) eggs and 166 species with immaculate (non-patterned) eggs. Using phylogenetically controlled analyses, we tested whether maculated eggshells have different surface topography between the foreground colour and background colour, and between the background colour of maculated eggshells and the surface of immaculate eggshells. Secondly, we determined to what extent variation in eggshell pigmentation of the foreground and background colour is determined by phylogenetic relatedness, and whether certain life-history traits are important predictors of eggshell surface structure. We show that the surface of maculated eggs consists of a rougher foreground pigment compared to the background pigment across 71% of the 204 bird species (54 families) investigated. Species that lay immaculate eggs showed no difference in surface roughness, kurtosis or skewness compared to background pigment of maculated eggs. The difference in eggshell surface roughness between foreground and background pigmentation was greater among species that occupied dense habitats, such as forests with closed canopies, compared to those that nest in open and semi-open habitats (e.g. cities, deserts, grasslands, open shrubland and seashores). Among maculated eggs, foreground texture was correlated with habitat, parental care, diet, nest location, avian group and nest type, while background texture was correlated with clutch size, annual temperature, development mode and annual precipitation. Surface roughness among immaculate eggs was greatest for herbivores, and species that have larger clutch sizes. Together, this suggests that multiple life-history traits have influenced the evolution of eggshell surface textures in modern birds.

Super ensemble based streamflow simulation using multi-source remote sensing and ground gauged rainfall data fusion

Traditional data-driven streamflow predictions usually apply a single model with inconsistent performance in different variability conditions. These days model ensembles or merging the benefits of different models without losing the general character of the data are becoming a trend in hydrology. This study compared three super ensemble learners with eight base models. Twelve years of monthly rolled daily time series data in three river catchments of Ethiopia (Borkena watershed: Awash river basin), (Gummera watershed: Abay river basin), and (Sore watershed: Baro Akobo river basin) is used for single-step daily streamflow simulation using previous thirty-day input timesteps. Five input scenarios are applied: three vegetation indices, three remote sensing-based precipitation products, ground-gauged rainfall, all fused inputs, and selected inputs with the Recursive Feature Elimination (RFE) algorithm. The time series is then divided into training and testing datasets with a ratio of 80:20. The performance of the proposed models was evaluated using the Root Mean Squared Error (RMSE), coefficient of determination (R²), Mean Absolute Error (MAE), and Median Absolute Error (MEDAE). Finally, the result is presented with the corresponding five input scenarios. The catchment's and input scenario's average performance indicated that the three super ensemble learners outperformed the eight base models with relatively stable performance. The top-ranked WASE model exceeded the linear regression baseline by 13.3%. XGB, CNN-GRU, and LSTM proved the highest performance of the base models. This study also revealed that LSTM's key downside is its performance drop in the absence of feature selection criteria. In comparison, XGB showed its superior performance after controlling redundant inputs internally. Moreover, this study uniquely highlights the potential of remote sensing-based vegetation indices in the science of data-driven streamflow modelling for non-gauged catchments with no meteorological time series.

Meta-analyses in psychology often overestimate evidence for and size of effects

Adjusting for publication bias is essential when drawing meta-analytic inferences. However, most methods that adjust for publication bias do not perform well across a range of research conditions, such as the degree of heterogeneity in effect sizes across studies. Sladekova et al. 2022 (Estimating the change in meta-analytic effect size estimates after the application of publication bias adjustment methods. Psychol. Methods) tried to circumvent this complication by selecting the methods that are most appropriate for a given set of conditions, and concluded that publication bias on average causes only minimal over-estimation of effect sizes in psychology. However, this approach suffers from a 'Catch-22' problem-to know the underlying research conditions, one needs to have adjusted for publication bias correctly, but to correctly adjust for publication bias, one needs to know the underlying research conditions. To alleviate this problem, we conduct an alternative analysis, robust Bayesian meta-analysis (RoBMA), which is not based on model-selection but on model-averaging. In RoBMA, models that predict the observed results better are given correspondingly larger weights. A RoBMA reanalysis of Sladekova et al.'s dataset reveals that more than 60% of meta-analyses in psychology notably overestimate the evidence for the presence of the meta-analytic effect and more than 50% overestimate its magnitude.

Associations between DNA methylation and gene regulation depend on chromatin accessibility during transgenerational plasticity

BACKGROUND

Epigenetic processes are proposed to be a mechanism regulating gene expression during phenotypic plasticity. However, environmentally induced changes in DNA methylation exhibit little-to-no association with differential gene expression in metazoans at a transcriptome-wide level. It remains unexplored whether associations between environmentally induced differential methylation and expression are contingent upon other epigenomic processes such as chromatin accessibility. We quantified methylation and gene expression in larvae of the purple sea urchin Strongylocentrotus purpuratus exposed to different ecologically relevant conditions during gametogenesis (maternal conditioning) and modeled changes in gene expression and splicing resulting from maternal conditioning as functions of differential methylation, incorporating covariates for genomic features and chromatin accessibility. We detected significant interactions between differential methylation, chromatin accessibility, and genic feature type associated with differential expression and splicing.

RESULTS

Differential gene body methylation had significantly stronger effects on expression among genes with poorly accessible transcriptional start sites while baseline transcript abundance influenced the direction of this effect. Transcriptional responses to maternal conditioning were 4-13 × more likely when accounting for interactions between methylation and chromatin accessibility, demonstrating that the relationship between differential methylation and gene regulation is partially explained by chromatin state.

CONCLUSIONS

DNA methylation likely possesses multiple associations with gene regulation during transgenerational plasticity in S. purpuratus and potentially other metazoans, but its effects are dependent on chromatin accessibility and underlying genic features.

Predicting the Social-Emotional Competence Based on Childhood Trauma, Internalized Shame, Disability/Shame Scheme, Cognitive Flexibility, Distress Tolerance and Alexithymia in an Iranian Sample Using Bayesian Regression

The purpose of this study was to predict Social Emotional Competence based on childhood trauma, internalized shame, disability/shame scheme, cognitive flexibility, distress tolerance, and alexithymia in an Iranian sample using Bayesian regression. The participants in this research were a sample of 326 (85.3% female and 14.7% male) people living in Tehran in 2021 who were selected by convenience sampling through online platforms. The survey assessments included demographic characteristics (age and gender), presence of childhood trauma, social-emotional competence, internalized shame, the Toronto Alexithymia scales, Young's measure of disability/shame together with measures of cognitive flexibility and distress tolerance. The results from Bayesian regression and Bayesian Model Averaging (BMA) indicated that internalized shame, cognitive flexibility and distress tolerance can be predictive of Social Emotional Competence. These results suggested that Social Emotional Competence can be explained by some important personality factors.

Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades

Stable isotopes are an important tool to uncover animal migration. Geographic natal assignments often require categorizing the spatial domain through a nominal approach, which can introduce bias given the continuous nature of these tracers. Stable isotopes predicted over a spatial gradient (i.e., isoscapes) allow a probabilistic and continuous assignment of origin across space, although applications to marine organisms remain limited. We present a new framework that integrates nominal and continuous assignment approaches by (1) developing a machine-learning multi-model ensemble classifier using Bayesian model averaging (nominal); and (2) integrating nominal predictions with continuous isoscapes to estimate the probability of origin across the spatial domain (continuous). We applied this integrated framework to predict the geographic origin of the Northwest Atlantic mackerel (Scomber scombrus), a migratory pelagic fish comprised of northern and southern components that have distinct spawning sites off Canada (northern contingent) and the US (southern contingent), and seasonally overlap in the US fished regions. The nominal approach based on otolith carbon and oxygen stable isotopes (δ13C/δ18O) yielded high contingent classification accuracy (84.9%). Contingent assignment of unknown-origin samples revealed prevalent, yet highly varied contingent mixing levels (12.5-83.7%) within the US waters over four decades (1975-2019). Nominal predictions were integrated into mackerel-specific otolith oxygen isoscapes developed independently for Canadian and US waters. The combined approach identified geographic nursery hotspots in known spawning sites, but also detected geographic shifts over multi-decadal time scales. This framework can be applied to other marine species to understand migration and connectivity at a high spatial resolution, relevant to management of unit stocks in fisheries and other conservation assessments.

Contralateral delay activity, but not alpha lateralization, indexes prioritization of information for working memory storage

Working memory is inherently limited, which makes it important to select and maintain only task-relevant information and to protect it from distraction. Previous research has suggested the contralateral delay activity (CDA) and lateralized alpha oscillations as neural candidates for such a prioritization process. While most of this work focused on distraction during encoding, we examined the effect of external distraction presented during memory maintenance. Participants memorized the orientations of three lateralized objects. After an initial distraction-free maintenance interval, distractors appeared in the same location as the targets or in the opposite hemifield. This distraction was followed by another distraction-free interval. Our results show that CDA amplitudes were stronger in the interval before compared with the interval after the distraction (i.e., CDA amplitudes were stronger in response to targets compared with distractors). This amplitude reduction in response to distractors was more pronounced in participants with higher memory accuracy, indicating prioritization and maintenance of relevant over irrelevant information. In contrast, alpha lateralization did not change from the interval before distraction compared with the interval after distraction, and we found no correlation between alpha lateralization and memory accuracy. These results suggest that alpha lateralization plays no direct role in either selective maintenance of task-relevant information or inhibition of distractors. Instead, alpha lateralization reflects the current allocation of spatial attention to the most salient information regardless of task-relevance. In contrast, CDA indicates flexible allocation of working memory resources depending on task-relevance.

Higher amplitudes in steady-state visual evoked potentials driven by square-wave versus sine-wave contrast modulation - A dual-laboratory study

Steady-state visual evoked potentials (ssVEPs) are an established tool for assessing visuocortical responses in visual perception and attention. They have the same temporal frequency characteristics as a periodically modulated stimulus (e.g., in contrast or luminance) that drives them. It has been hypothesized that the amplitude of a given ssVEP may depend on the shape of the stimulus modulation function, but the size and robustness of these effects is not well established. The current study systematically compared the effect of the two most common functions in the ssVEP literature, square-wave and sine-wave functions. Across two laboratories, we presented mid-complex color patterns to 30 participants with square-wave or sine-wave contrast modulation and at different driving frequencies (6 Hz, 8.57 Hz, 15 Hz). When ssVEPs were analyzed independently for the samples, with each laboratory's standard processing pipeline, ssVEP amplitudes in both samples decreased at higher driving frequencies and square-wave modulation evoked higher amplitudes at lower frequencies (i.e., 6 Hz, 8.57 Hz) compared to sine-wave modulation. These effects were replicated when samples were aggregated and analyzed with the same processing pipeline. In addition, when using signal-to-noise ratios as outcome measures, this joint analysis indicated a somewhat weaker effect of increased ssVEP amplitudes to square-wave modulation at 15 Hz. The present study suggests that square-wave modulation should be used in ssVEP research when the goal is to maximize signal amplitude or signal-to-noise ratio. Given effects of modulation function across laboratories, and data processing pipelines, the findings appear robust to differences in data collection and analysis.

Characterizing Interoceptive Differences in Autism: A Systematic Review and Meta-analysis of Case-control Studies

Interoception, the body's perception of its own internal states, is thought to be altered in autism, though results of empirical studies have been inconsistent. The current study systematically reviewed and meta-analyzed the extant literature comparing interoceptive outcomes between autistic (AUT) and neurotypical (NT) individuals, determining which domains of interoception demonstrate robust between-group differences. A three-level Bayesian meta-analysis compared heartbeat counting performance, heartbeat discrimination performance, heartbeat counting confidence ratings, and self-reported interoceptive attention between AUT and NT groups (15 studies; n_AUT = 467, n_NT = 478). Autistic participants showed significantly reduced heartbeat counting performance [g = - 0.333, CrI_95% (- 0.535, - 0.138)] and higher confidence in their heartbeat counting abilities [g = 0.430, CrI_95% (0.123, 0.750)], but groups were equivalent on other meta-analyzed outcomes. Implications for future interoception research in autism are discussed.

Bird species occupancy trends in Southeast Mexico over 1900-2020: Accounting for sighting record absences

Long-term land-use change impacts tropical bird communities through population-level and functional diversity effects from habitat loss, degradation and fragmentation, leading to land management and conservation challenges. Assessing the temporal impacts of land-use change on occupancy patterns, population change and functional traits of bird species in tropical areas is limited by the treatment of nondetections as true absences or artefacts of low sampling effort during and throughout years. With this in mind, we developed a novel Bayesian species occupancy framework to account for species absences to evaluate bird community changes in Palenque, Chiapas, Mexico, where there is opportunity for study given exceptional records of change across habitats from rainforest to urban centres. We created a novel dataset of population trends for 244 bird species over the years 1900 to 2020 from published short-term field studies, expert field notes and community science pages. Our results show that open area species had higher population increases than forest specialists over time, represented most evidently by the turnover of rainforest specialists for urban species. Modelled influence of functional traits displayed the importance of main habitat types, body mass and habitat and dietary breadth as factors that associated with bird population trends. On average, species with body masses <6.6 and > 948.4 g showed decreasing trends, while all other species showed increasing or stable trends. Our findings illuminate the value of accounting for species absences from several data sources to discover long-term species population trends and affiliated functional traits whose preservation requires conservation and land management action to protect bird ecosystem services. Primary forest conservation is key to maintaining populations of habitat and dietary specialists, such as small understorey insectivorous and large frugivorous species. Protecting rare natural savanna patches from conversion to cattle pasture is vital to prevent further extirpation of native granivores and to slow colonization by exotic and invasive species.

Cost-Effectiveness of First-Line Pembrolizumab Monotherapy Versus Chemotherapy in High Programmed Death-Ligand 1 Advanced Non-Small Cell Lung Cancer in the Irish Healthcare Setting

OBJECTIVES

This study aimed to assess the cost-effectiveness of pembrolizumab monotherapy in the first-line treatment of advanced non-small cell lung cancer (NSCLC) in adults whose tumors expressed programmed death-ligand 1 (PD-L1) with a tumor proportion score (TPS) ≥ 50% in the Irish healthcare setting.

METHODS

Effectiveness inputs were derived from the 5-year analysis of KEYNOTE-024 phase III clinical trial. The intervention was pembrolizumab monotherapy; the comparator was a weighted average of the 5 chemotherapy regimens from the trial. The population included those with previously untreated advanced PD-L1 TPS ≥ 50% NSCLC. A de novo partitioned survival model was developed. Survival modeling was done using Bayesian model averaging on fitted parametric functions. Costs included drug acquisition, treatment initiation, administration and monitoring, adverse events, subsequent treatments, and terminal care. Costs and health state utilities were sourced from the literature and Irish sources. The model had a 20-year time horizon. The perspective taken was the Health Service Executive. A 4% discount rate was applied. Outcomes were expressed as an incremental cost-effectiveness ratio (ICER), measured in terms of incremental costs per quality-adjusted life-year (QALY). Probabilistic sensitivity analysis and 1-way sensitivity analyses were conducted.

RESULTS

The model estimated a base case ICER of €54 237 per QALY. The probabilistic sensitivity analysis estimated an average ICER of €54 568 per QALY and a 11% probability of cost-effectiveness at the Irish cost-effectiveness threshold of €45 000 per QALY.

CONCLUSION

At the current list price, first-line pembrolizumab monotherapy is not considered cost-effective for the treatment of advanced PD-L1 TPS ≥ 50% NSCLC in the Irish healthcare setting.

Defensive emotions and evaluative judgements: Sensitivity to anger and fear predicts moral judgements, whereas sensitivity to disgust predicts aesthetic judgements

Aesthetic and moral evaluations engage appetitive and defensive emotions. While the role played by pleasure in positive aesthetic and moral judgements has been extensively researched, little is known about how defensive emotions influence negative aesthetic and moral judgements. Specifically, it is unknown which defensive emotions such judgements tap into, and whether both kinds of judgement share a common emotional root. Here, we investigated how participants' individual sensitivity to disgust, fear, anger and sadness predicted subjective judgements of aesthetic and moral stimuli. Bayesian modelling revealed that participants who were more sensitive to anger and fear found conventional and moral transgressions more wrong. In contrast, participants who were more sensitive to disgust disliked asymmetrical geometric patterns and untidy rooms more. These findings suggest that aesthetic and moral evaluations engage multiple defensive emotions, not just disgust, and that they may rely on different defensive emotions as part of their computational mechanism.

Factors Associated With Telemedicine Use Among Patients With Rheumatic and Musculoskeletal Disease: Secondary Analysis of Data From a German Nationwide Survey

BACKGROUND

Previous studies have demonstrated telemedicine (TM) to be an effective tool to complement rheumatology care and address workforce shortage. With the outbreak of the COVID-19 pandemic, TM experienced a massive upswing. A previous study revealed that physicians' willingness to use TM and actual use of TM are closely connected to their knowledge of TM. However, it remains unclear which factors are associated with patients' motivation to use TM.

OBJECTIVE

This study aims to identify the factors that determine patients' willingness to try TM (TM try) and their wish that their rheumatologists offer TM services (TM wish).

METHODS

We conducted a secondary analysis of data from a German nationwide cross-sectional survey among patients with rheumatic and musculoskeletal disease (RMD). Bayesian univariate and multivariate logistic regression analyses were applied to the data to determine which factors were associated with TM try and TM wish. The predictor variables (covariates) studied individually included sociodemographic factors (eg, age and sex) and health characteristics (eg, disease type and health status). All the variables positively or negatively associated with TM try or TM wish in the univariate analyses were then considered for the Bayesian model averaging analysis after a selection based on the variance inflation factor (≤2.5). All the analyses were stratified by sex.

RESULTS

Of the total 102 variables, 59 (57.8%) and 45 (44.1%) variables were found to be positively or negatively associated (region of practical equivalence ≤5%) with TM try and TM wish, respectively. A total of 16 and 8 determinant factors were identified for TM try and TM wish, respectively. Wishing that TM services were offered by rheumatologists, having internet access at home, residing 5 to 10 km away from the general practitioner's office, owning an electronic device, and being aged 40 to 60 years were among the factors positively associated with TM try and TM wish. By contrast, not yet being diagnosed with an RMD, having no prior knowledge of TM, having a bad health status, living in a rural area, not documenting one's health status, not owning an electronic device, and being aged 60 to 80 years were negatively associated with TM try and TM wish.

CONCLUSIONS

Our results suggest that health status, knowledge, age, and access to technical equipment and infrastructure influence the motivation of patients with RMD to use telehealth services. In particular, older patients with RMD living in rural areas, who could likely benefit from using TM, are currently not motivated to use TM and seem to need additional TM support.

Robust Bayesian meta-analysis: Model-averaging across complementary publication bias adjustment methods

Publication bias is a ubiquitous threat to the validity of meta-analysis and the accumulation of scientific evidence. In order to estimate and counteract the impact of publication bias, multiple methods have been developed; however, recent simulation studies have shown the methods' performance to depend on the true data generating process, and no method consistently outperforms the others across a wide range of conditions. Unfortunately, when different methods lead to contradicting conclusions, researchers can choose those methods that lead to a desired outcome. To avoid the condition-dependent, all-or-none choice between competing methods and conflicting results, we extend robust Bayesian meta-analysis and model-average across two prominent approaches of adjusting for publication bias: (1) selection models of p-values and (2) models adjusting for small-study effects. The resulting model ensemble weights the estimates and the evidence for the absence/presence of the effect from the competing approaches with the support they receive from the data. Applications, simulations, and comparisons to preregistered, multi-lab replications demonstrate the benefits of Bayesian model-averaging of complementary publication bias adjustment methods.

Recovering crossed random effects in mixed-effects models using model averaging

Random effects contain crucial information to understand the variability of the processes under study in mixed-effects models with crossed random effects (MEMs-CR). Given that model selection makes all-or-nothing decisions regarding to the inclusion of model parameters, we evaluated if model averaging could deal with model uncertainty to recover random effects of MEMs-CR. Specifically, we analyzed the bias and the root mean squared error (RMSE) of the estimations of the variances of random effects using model averaging with Akaike weights and Bayesian model averaging with BIC posterior probabilities, comparing them with two alternative analytical strategies as benchmarks: AIC and BIC model selection, and fitting a full random structure. A simulation study was conducted manipulating sample sizes for subjects and items, and the variance of random effects. Results showed that model averaging, especially Akaike weights, can adequately recover random variances, given a minimum sample size in the modeled clusters. Thus, we endorse using model averaging to deal with model uncertainty in MEMs-CR. An empirical illustration is provided to ease the usability of model averaging.

Online neurostimulation of Broca's area does not interfere with syntactic predictions: A combined TMS-EEG approach to basic linguistic combination

Categorical predictions have been proposed as the key mechanism supporting the fast pace of syntactic composition in language. Accordingly, grammar-based expectations are formed-e.g., the determiner "a" triggers the prediction for a noun-and facilitate the analysis of incoming syntactic information, which is then checked against a single or few other word categories. Previous functional neuroimaging studies point towards Broca's area in the left inferior frontal gyrus (IFG) as one fundamental cortical region involved in categorical prediction during incremental language processing. Causal evidence for this hypothesis is however still missing. In this study, we combined Electroencephalography (EEG) and Transcranial Magnetic Stimulation (TMS) to test whether Broca's area is functionally relevant in predictive mechanisms for language. We transiently perturbed Broca's area during the first word in a two-word construction, while simultaneously measuring the Event-Related Potential (ERP) correlates of syntactic composition. We reasoned that if Broca's area is involved in predictive mechanisms for syntax, disruptive TMS during the first word would mitigate the difference in the ERP responses for predicted and unpredicted categories in basic two-word constructions. Contrary to this hypothesis, perturbation of Broca's area at the predictive stage did not affect the ERP correlates of basic composition. The correlation strength between the electrical field induced by TMS and the ERP responses further confirmed this pattern. We discuss the present results considering an alternative account of the role of Broca's area in syntactic composition, namely the bottom-up integration of words into constituents, and of compensatory mechanisms within the language predictive network.