Emotions are central to human life and, as such, are of primary interest in basic psychological research. There is widespread agreement that emotions involve subjective experiences that can be described with discrete natural language labels and often involve changes in bodily states, but there is ongoing debate about how specific and differentiated these bodily states are, and how they relate to emotional labels. Recent work showed that objective measures derived from the face can be used to accurately classify discrete emotional labels. However, it remains possible that the facial patterns associated with discrete emotions merely convey valence and arousal (i.e., “core affect”) and that this information can be utilized to deduce discrete emotion categories. In light of this, in the current work, we examined whether the facial patterns that reliably distinguish between emotional states are reducible to valence and arousal. Our findings support the position that the human face contains rich information that can be used to predict people’s emotional states and that this information is not reducible to core affect. We discuss the implications of this work to the debate concerning the nature of emotions.

This study compares response time and accuracy in a binary data labeling task on two platforms: a desktop computer and a mobile phone. Three methods were used on the computer: a keyboard, a mouse, and a mouse with perceptual variability that was designed to combat vigilance decrement. Three additional methods were used on the mobile phone: a tap, a swipe touch-gesture, and a tap with perceptual variability. Results of the study show that the fastest was the keyboard, which may explain its popularity in labeling tasks. The second fastest was the tap interface, suggesting the unexploited potential of mobile devices for "labeling while waiting for the bus." In terms of accuracy, we found clear evidence for a speed-accuracy tradeoff, and the advantage of the perceptual variability methods to be more accurate than the matching methods without perceptual variability. The results suggest that different user input methods should be employed depending on whether response time or accuracy is more valued.

Sensor array geometry has a direct impact on the direction-of-arrival (DOA) estimation of a seismic signal. In this paper, we design a planar array that aims to optimize the DOA estimation of a narrowband signal in the sense of the minimum mean-squared-periodic-error (MSPE) obtained by the maximum a-posteriori (MAP) estimator of the DOA. We investigate the MSPE of the MAP estimator as a main design criterion and compare it with the criteria: 1) the cyclic Bayesian Cramér-Rao Bound (CBCRB); and 2) the expected log-likelihood (ELL). The theoretical properties of these criteria are discussed. We show that minimizing the CBCRB is equivalent to maximizing the expected Fisher information matrix. Additionally, maximizing the ELL under a uniform prior is equivalent to minimizing the Kullback-Leibler divergence between the posterior PDF and its estimation. The criteria are compared across three different array geometries, specifically: small arrays, uniform circular arrays (UCAs), and concentric circular arrays (CCAs). Simulation results show that 1) direct MAP-MSPE optimization notably exceeds CBCRB- and ELL-based designs, especially in small arrays; 2) UCAs have suboptimal performance compared to non-circular arrays in many scenarios; 3) under the MAP-MSPE criterion, CCAs match unconstrained design performance with lower computational complexity, making them preferable for smaller arrays; 4) for CCAs and larger UCAs, CBCRB and MAP-MSPE designs yield similar results, while the ELL design excels in the case of small UCAs. Our results highlight the need for selecting suitable array geometries and design criteria in accordance with the scenario and array size in order to achieve the best DOA estimation results.

In many practical parameter estimation problems, such as phase, frequency, and direction-of-arrival (DOA) estimation, the observation model is periodic with respect to the unknown parameters, and thus, the appropriate estimation criterion is periodic in the parameter space. However, iterative estimation methods, such as the Fisher-Scoring method, do not take into consideration the periodic information in order to improve the accuracy of the estimation. In this paper, we present a new iterative method - periodic Fisher-Scoring, which takes into account the signal’s periodic properties through the utilization of the cyclic Cramér-Rao bound (CRB). The cyclic CRB is a lower bound on the mean cyclic error (MCE) of unbiased estimators and, thus, is more appropriate for the derivation of the iterative method. In addition, the periodic Fisher-Scoring method uses the modulo $2 \pi$ operator at each iteration. Simulation results for DOA estimation in seismic arrays show that the proposed periodic Fisher-Scoring estimator has a lower MCE compared to the conventional Fisher-Scoring estimator. The performance improvement is more significant around the edges of the range $[-\pi,\pi]$ and under the misspecified model, i.e. under the mismatched assumption of white noise. We also show that the periodic Fisher-Scoring estimator achieves the cyclic CRB much faster than the CRB.

In the current study, we set out to examine the viability of a novel approach to modeling human personality. Research in psychology suggests that people’s personalities can be effectively described using five broad dimensions (the Five-Factor Model; FFM); however, the FFM potentially leaves room for improved predictive accuracy. We propose a novel approach to modeling human personality that is based on the maximization of the model’s predictive accuracy. Unlike the FFM, which performs unsupervised dimensionality reduction, we utilized a supervised machine learning technique for dimensionality reduction of questionnaire data, using numerous psychologically meaningful outcomes as data labels (e.g., intelligence, well-being, sociability). The results showed that our five-dimensional personality summary, which we term the “Predictive Five” (PF), provides predictive performance that is better than the FFM on two independent validation datasets, and on a new set of outcome variables selected by an independent group of psychologists. The approach described herein has the promise of eventually providing an interpretable, low-dimensional personality representation, which is also highly predictive of behavior.

The Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), is a crucial tool for conservation decision-making. However, despite substantial effort, numerous species remain unassessed or have insufficient data available to be assigned a Red List extinction risk category. Moreover, the Red Listing process is subject to various sources of uncertainty and bias. The development of robust automated assessment methods could serve as an efficient and highly useful tool to accelerate the assessment process and offer provisional assessments. Here, we aimed to (1) present a machine learning-based automated extinction risk assessment method that can be used on less known species; (2) offer provisional assessments for all reptiles-the only major tetrapod group without a comprehensive Red List assessment; and (3) evaluate potential effects of human decision biases on the outcome of assessments. We use the method presented here to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the IUCN. The models used in our predictions were 90% accurate in classifying species as threatened/nonthreatened, and 84% accurate in predicting specific extinction risk categories. Unassessed and Data Deficient reptiles were considerably more likely to be threatened than assessed species, adding to mounting evidence that these species warrant more conservation attention. The overall proportion of threatened species greatly increased when we included our provisional assessments. Assessor identities strongly affected prediction outcomes, suggesting that assessor effects need to be carefully considered in extinction risk assessments. Regions and taxa we identified as likely to be more threatened should be given increased attention in new assessments and conservation planning. Lastly, the method we present here can be easily implemented to help bridge the assessment gap for other less known taxa.

Visual skill learning is the process of improving responses to surrounding visual stimuli.1 For individuals with autism spectrum disorders (ASDs), efficient skill learning may be especially valuable due to potential difficulties with sensory processing2 and challenges in adjusting flexibly to changing environments.3,4 Standard skill learning protocols require extensive practice with multiple stimulus repetitions,5-7 which may be difficult for individuals with ASD and create abnormally specific learning with poor ability to generalize.4 Motivated by findings indicating that brief memory reactivations can facilitate skill learning,8,9 we hypothesized that reactivation learning with few stimulus repetitions will enable efficient learning in individuals with ASD, similar to their learning with standard extensive practice protocols used in previous studies.4,10,11 We further hypothesized that in contrast to experience-dependent plasticity often resulting in specificity, reactivation-induced learning would enable generalization patterns in ASD. To test our hypotheses, high-functioning adults with ASD underwent brief reactivations of an encoded visual learning task, consisting of only 5 trials each instead of hundreds. Remarkably, individuals with ASD improved their visual discrimination ability in the task substantially, demonstrating successful learning. Furthermore, individuals with ASD generalized learning to an untrained visual location, indicating a unique benefit of reactivation learning mechanisms for ASD individuals. Finally, an additional experiment showed that without memory reactivations ASD subjects did not demonstrate efficient learning and generalization patterns. Taken together, the results provide proof-of-concept evidence supporting a distinct route for efficient visual learning and generalization in ASD, which may be beneficial for skill learning in other sensory and motor domains.

Background In the first stage of a two-stage study , the researcher uses a statistical model to impute the unobserved exposures. In the second stage, imputed exposures serve as covariates in epidemiological models. Imputation error in the first stage operate as measurement errors in the second stage, and thus bias exposure effect estimates. Objective This study aims to improve the estimation of exposure effects by sharing information between the first and second stages. Methods At the heart of our estimator is the observation that not all second-stage observations are equally important to impute. We thus borrow ideas from the optimal-experimental-design theory, to identify individuals of higher importance. We then improve the imputation of these individuals using ideas from the machine-learning literature of domain adaptation. Results Our simulations confirm that the exposure effect estimates are more accurate than the current best practice. An empirical demonstration yields smaller estimates of PM effect on hyperglycemia risk, with tighter confidence bands. Significance Sharing information between environmental scientist and epidemiologist improves health effect estimates. Our estimator is a principled approach for harnessing this information exchange, and may be applied to any two stage study.

Spatial predictions, like other supervised learning tasks, require some criterion for a predictor’s quality. Typical data-splitting schemes, such as holdouts and $k$ -fold cross-validation, ignore the fact that the training data are usually not available where predictions are being made. The common data-splitting schemes are thus biased estimates of a predictor’s performance, which in turn may lead to choosing suboptimal predictors. In this contribution, we borrow ideas from the domain adaptation machine-learning literature, to suggest the importance-weighted source risk (IWSR). IWSR is a principled approach for weighting the prediction risk, which allows the practitioner to explicitly state the target locations for prediction. IWSR essentially consists of down-weighting training locations and up-weighting target locations. We show that, unlike the usual (unweighted) empirical risk, IWSR is an unbiased estimator of the prediction error. Equipped with this risk estimator, we use it to learn a model in the empirical risk minimization framework and to evaluate the existing predictors. We show the superiority of this weighted risk, using both simulated data and an empirical control: air-temperature prediction in France.

The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is significantly better than chance level. This method of signal detection is particularly popular in neuroimaging and genetics. We provide evidence that using a classifier's accuracy as a test statistic can be an underpowered strategy for finding differences between populations, compared to a bona fide statistical test. It is also computationally more demanding than a statistical test. Via simulation, we compare test statistics that are based on classification accuracy, to others based on multivariate test statistics. We find that the probability of detecting differences between two distributions is lower for accuracy-based statistics. We examine several candidate causes for the low power of accuracy-tests. These causes include: the discrete nature of the accuracy-test statistic, the type of signal accuracy-tests are designed to detect, their inefficient use of the data, and their suboptimal regularization. When the purpose of the analysis is the evaluation of a particular classifier, not signal detection, we suggest several improvements to increase power. In particular, to replace V-fold cross-validation with the Leave-One-Out Bootstrap.

Given a multivariate parameter, ? and appropriate data, prevalence estimation deals with the counting of the number of entries in ? that depart from their hypothesized null values. The problem has two main motivations: First, in the case, a population consists of two sub-populations, we may want to know the prevalence of each. Examples include a sub-population of respondents and a sub-population of non-respondents in personalized medicine; active and inactive subjects in neuroimaging; associated and non-associated genes in genetic studies (GWAS); tolerant and intolerant animals in toxicology, etc. Second, various multiple testing algorithms may bene?t from knowledge of the effect's prevalence. The Adaptive Benjamini-Hochberg algorithm is one of many such algorithms. The algorithm """adapts""" by estimating the signal's prevalence before a multiple testing stage. In this chapter, we will cover the vast literature on prevalence estimators, try to organize it along design principles and statistical guarantees, with recommendations to the practitioner.

In the current study, we set out to examine the viability of a novel approach to modeling human personality. Current research in psychology suggests that people’s personalities can be effectively described using five broad dimensions (the Five-Factor Model; FFM); however, the FFM has been criticized for its relatively limited predictive ability. We propose a novel approach to modeling human personality that is based on the maximization of the model’s predictive accuracy. Unlike the FFM, which performs unsupervised dimensionality reduction, we utilized supervised machine learning techniques for dimensionality reduction of questionnaire data, using numerous psychologically meaningful outcomes as data labels (e.g., intelligence, well-being, sociability). The results showed that our five dimensional personality summary, which we term the Predictive Five (PF), provides predictive performance that is superior to the FFM in independent validation datasets, and on a new set of outcome variables selected by an independent group of psychologists. Furthermore, we examine the between-participants’ replicability of the PF representation and show that the PF has good test-retest reliability, and as such provides an important addition to the psychologists' toolbox. The approach described herein has the promise of providing an interpretable low-dimensional personality representation, which is also predictive of behaviour.

Mapping of near-surface air temperature (Ta) at high spatio-temporal resolution is essential for unbiased assessment of human health exposure to temperature extremes, not least given the observed trend of urbanization and global climate change. Data constraints have led previous studies to focus merely on daily Ta metrics, rather than hourly ones, making them insufficient for intra-day assessment of health exposure. In this study, we present a three-stage machine learning-based ensemble model to estimate hourly Ta at a high spatial resolution of 1 × 1 km2, incorporating remotely sensed surface skin temperature (Ts) from geostationary satellites, reanalysis synoptic variables, and observations from weather stations, as well as auxiliary geospatial variables, which account for spatio-temporal variability of Ta. The Stage 1 model gap-fills hourly Ts at 4 × 4 km2 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which are subsequently fed into the Stage 2 model to estimate hourly Ta at the same spatio-temporal resolution. The Stage 3 model downscales the residuals between estimated and measured Ta to a grid of 1 × 1 km2, taking into account additionally the monthly diurnal pattern of Ts derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In each stage, the ensemble model synergizes estimates from the constituent base learners—random forest (RF) and extreme gradient boosting (XGBoost)—by applying a geographically weighted generalized additive model (GAM), which allows the weights of results from individual models to vary over space and time. Demonstrated for Israel for the period 2004–2017, the proposed ensemble model outperformed each of the two base learners. It also attained excellent five-fold cross-validated performance, with overall root mean square error (RMSE) of 0.8 and 0.9 °C, mean absolute error (MAE) of 0.6 and 0.7 °C, and R2 of 0.95 and 0.98 in Stage 1 and Stage 2, respectively. The Stage 3 model for downscaling Ta residuals to 1 km MODIS grids achieved overall RMSE of 0.3 °C, MAE of 0.5 °C, and R2 of 0.63. The generated hourly 1 × 1 km2 Ta thus serves as a foundation for monitoring and assessing human health exposure to temperature extremes at a larger geographical scale, helping to further minimize exposure misclassification in epidemiological studies.

Estimation of the direction of arrival (DOA) of a seismic signal is required for accurate localization of seismic events, such as earthquakes and human-made explosions. Currently, seismic DOA estimation algorithms are based on the assumption that the additive seismic noise is uncorrelated between sensors. However, in this paper we show by analyzing real data sets that seismic sensors exhibit noise correlation. We calculate a robust estimator of the noise covariance matrix from off-line real data. Then, we present three estimators: 1) the seismic-wave DOA maximum likelihood estimator (MLE) that acknowledges the correlated noise between sensors; 2) the MLE for uncorrelated noise with spherical covariance matrix; and 3) the beamforming Bartlett estimator, which is the method used in seismic applications. We show by numerical simulations on real-data statistics that DOA estimates that do not consider these correlations depart from the true direction and have significantly higher values of mean-squared-error and bias.

Ensuring the integrity of data from large sensor networks is a challenging task that is relevant in many domains. Precision agriculture is one instance of this challenge, where dendrometer sensors provide data used for plot specific irrigation decisions, with critical implications for yields and water savings. To aid the identification of malfunctioning dendrometer sensors, we introduce a pipeline for detecting various types of anomalies and investigating their root causes using visual analytics. Our pipeline is unique not only in that it borrows from web technologies to provide interactivity, but also because it incorporates detection algorithms from several fields, such as robust multivariate statistics, unsupervised machine learning, and social-network analysis.

Wilson (1) proposes a multiple testing procedure based on the harmonic mean p-value (HMP). While this is a potentially useful method, he makes several claims that are not supported by the theory. Herein we identify 4 errors, for clarity described in terms of the version with equal weights 1 / L , so that w R = | R | / L . [↵][1]1To whom correspondence may be addressed. Email: j.j.goeman{at}lumc.nl. [1]: #xref-corresp-1-1

Rising global temperatures over the last decades have increased heat exposure among populations worldwide. An accurate estimate of the resulting impacts on human health demands temporally explicit and spatially resolved monitoring of near-surface air temperature (T-a). Neither ground-based nor satellite-borne observations can achieve this individually, but the combination of the two provides synergistic opportunities. In this study, we propose a two-stage machine learning-based hybrid model to estimate 1 x 1 km(2) gridded intra-daily T-a from surface skin temperature (T-s) across the complex terrain of Israel during 2004-2016. We first applied a random forest (RF) regression model to impute missing T-s from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra satellites, integrating T-s from the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellite and synoptic variables from European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. The imputed T-s are in turn fed into the Stage 2 RF-based model to estimate T-a at the satellite overpass hours of each day. We evaluated the model's performance applying out-of-sample fivefold cross validation. Both stages of the hybrid model perform very well with out-of-sample fivefold cross validated R-2 of 0.99 and 0.96, MAE of 0.42 degrees C and 1.12 degrees C, and RMSE of 0.65 degrees C and 1.58 degrees C (Stage 1: imputation of T-s, and Stage 2: estimation of T-a from T-s, respectively). The newly proposed model provides excellent computationally efficient estimation of near-surface air temperature at high resolution in both space and time, which helps further minimize exposure misclassification in epidemiological studies.

Studying the effects of air-pollution on health is a key area in environmental epidemiology. An accurate estimation of air-pollution effects requires spatio-temporally resolved datasets of air-pollution, especially, Fine Particulate Matter (PM). Satellite-based technology has greatly enhanced the ability to provide PM assessments in locations where direct measurement is impossible. Indirect PM measurement is a statistical prediction problem. The spatio-temporal statistical literature offer various predictive models: Gaussian Random Fields (GRF) and Linear Mixed Models (LMM), in particular. GRF emphasize the spatio-temporal structure in the data, but are computationally demanding to fit. LMMs are computationally easier to fit, but require some tampering to deal with space and time. Recent advances in the spatio-temporal statistical literature propose to alleviate the computation burden of GRFs by approximating them with Gaussian Markov Random Fields (GMRFs). Since LMMs and GMRFs are both computationally feasible, the question arises: which is statistically better? We show that despite the great popularity of LMMs in environmental monitoring and pollution assessment, LMMs are statistically inferior to GMRF for measuring PM in the Northeastern USA.

The most prevalent approach to activation localization in neuroimaging is to identify brain regions as contiguous supra-threshold clusters, check their significance using random field theory, and correct for the multiple clusters being tested. Besides recent criticism on the validity of the random field assumption, a spatial specificity paradox remains: the larger the detected cluster, the less we know about the location of activation within that cluster. This is because cluster inference implies "there exists at least one voxel with an evoked response in the cluster", and not that "all the voxels in the cluster have an evoked response". Inference on voxels within selected clusters is considered bad practice, due to the voxel-wise false positive rate inflation associated with this circular inference. Here, we propose a remedy to the spatial specificity paradox. By applying recent results from the multiple testing statistical literature, we are able to quantify the proportion of truly active voxels within selected clusters, an approach we call All-Resolutions Inference (ARI). If this proportion is high, the paradox vanishes. If it is low, we can further "drill down" from the cluster level to sub-regions, and even to individual voxels, in order to pinpoint the origin of the activation. In fact, ARI allows inference on the proportion of activation in all voxel sets, no matter how large or small, however these have been selected, all from the same data. We use two fMRI datasets to demonstrate the non-triviality of the spatial specificity paradox, and its resolution using ARI. We verify that the endless circularity permitted by ARI does not render its estimates overly conservative using both simulation, and a data split.

Human perception thresholds can improve through learning. Here we report findings challenging the fundamental 'practice makes perfect' basis of procedural learning theory, showing that brief reactivations of encoded visual memories are sufficient to improve perceptual discrimination thresholds. Learning was comparable to standard practice-induced learning and was not due to short training per se, nor to an epiphenomenon of primed retrieval enhancement. The results demonstrate that basic perceptual functions can be substantially improved by memory reactivation, supporting a new account of perceptual learning dynamics.