Measuring auditory event-related potentials at the external ear canal: A demonstrative study using a new electrode and error-feedback paradigm.

Although ear canal electroencephalogram (EEG) recording has received interest from basic and applied research communities, evidence on how it can be implemented in practice is limited. The present study involving eight male participants including the authors presents the utility of our ear canal electrode and method by demonstrating both comparability of ear canal EEG to those at nearby sites and distinctiveness that ear canal event-related potentials (ERPs) could have. For this purpose, we used the balanced noncephalic electrode reference and an experimental paradigm with an error-feedback sound. Clear auditory ERPs were detected at the ear canal sites with a sufficiently low noise level comparable with those at conventional sites. The N1c, a temporal maximum subcomponent, spread over the bilateral temporal sites, including the ear canals and earlobes. While consecutive signals are generally highly similar between the ear canal and the earlobe, the N1c was larger at the ear canal than the earlobe, as demonstrated by the conventional frequentist and the hierarchical Bayesian modelling approaches. Although an evident caveat is that our sample was limited in terms of size and sex, the general capability indicates that the structure of our ear canal electrode provides EEG measurement that can be used in basic and applied settings. Our experimental method can also be an ERP-based test that conveniently assesses the capability of existing and future ear canal electrodes. The distinctive nature of the ERPs to the error-feedback sound may be utilized to examine the basic aspects of auditory ERPs and to test the processes involved in feedback-guided behaviour of participants.

Boosting confidence without boosting performance: item strength creates the illusion of source accuracy.

People often express high confidence for misremembered sources. Starns and Ksander ([2016]. Item strength influences source confidence and alters source memory zROC slopes. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(3), 351-365; hereafter SK16) found that this happens more often when a person is highly confident in memory for the item itself, and that simply increasing item memory can increase high-confidence source errors. Under the decision heuristic account, this pattern emerges because strong item memories contaminate source judgments by promoting high confidence responses even when source evidence is relatively weak. Consequently, strengthening item memory is predicted to increase confidence for both correct and incorrect source responses; however, SK16 could not assess this key prediction because their item-strength manipulation also impaired source memory. We report two experiments with new item-strengthening manipulations designed to minimise source memory impairments. Results replicated the evidence for the decision heuristic account reported by SK16 and provided additional support by showing a boost in source confidence for both correct and error responses when item memory was strengthened without accompanying source impairments .

Spatial small area smoothing models for handling survey data with nonresponse.

Spatial smoothing models play an important role in the field of small area estimation. In the context of complex survey designs, the use of design weights is indispensable in the estimation process. Recently, efforts have been made in these spatial smoothing models, in order to obtain reliable estimates of the spatial trend. However, the concept of missing data remains a prevalent problem in the context of spatial trend estimation as estimates are potentially subject to bias. In this paper, we focus on spatial health surveys where the available information consists of a binary response and its associated design weight. Furthermore, we investigate the impact of nonresponse as missing data on a range of spatial models for different missingness mechanisms and different degrees of missingness by means of an extensive simulation study. The computations were performed in R, using INLA and other existing packages. The results show that weight adjustment to correct for missingness has a beneficial effect on the bias in the missing at random setting for all models. Furthermore, we estimate the geographical distribution of perceived health at the district level based on the Belgian Health Interview Survey (2001). Copyright © 2017 John Wiley & Sons, Ltd.

The effect of special educational assistance in early childhood education and care on psycho-social difficulties in elementary school children.

BACKGROUND: Three to seven percent of pre-schoolers have developmental problems or child psychiatric disorders. Randomized controlled trials (RCTs) indicate that interventions in early childhood education and care (ECEC) improve long-term outcomes of children from disadvantaged backgrounds. It is unknown if such effects generalize beyond the well-structured context of RCTs and to children who may not have a disadvantaged background but have developmental problems or psychiatric disorders. METHODS: We used data from the population-based Norwegian Mother, Father and Child Cohort Study, recruiting pregnant women from 1999 to 2009, with child follow-up from ages 6, 18, and 36 months to ages 5, 7, and 8 years. This sub-study included 2499 children with developmental problems or psychiatric disorders at age five. We investigated the effects of special educational assistance at age five on mother-reported internalizing, externalizing, and communication problems at age eight. We analysed bias due to treatment by indication with directed acyclic graphs, adjusted for treatment predictors to reduce bias, and estimated effects in different patient groups and outcome domains with a hierarchical Bayesian model. RESULTS: In the adjusted analysis, pre-schoolers who received special educational assistance had on average by 0.1 (0.04-0.16) standardised mean deviation fewer psycho-social difficulties in elementary school. CONCLUSION: In a sample of children from mostly higher socioeconomic backgrounds we estimate a positive effects of special educational assistance during the transition from preschool to the school years. It may therefore be considered as an intervention for pre-schoolers with developmental or behaviour problems. More research with improved measurements of treatment and outcomes is needed to solidify the findings and identify success factors for the implementation of special educational assistance in ECEC.

Bayesian Estimation for Reliability Engineering: Addressing the Influence of Prior Choice.

Over the last few decades, reliability analysis has attracted significant interest due to its importance in risk and asset integrity management. Meanwhile, Bayesian inference has proven its advantages over other statistical tools, such as maximum likelihood estimation (MLE) and least square estimation (LSE), in estimating the parameters characterizing failure modelling. Indeed, Bayesian inference can incorporate prior beliefs and information into the analysis, which could partially overcome the lack of data. Accordingly, this paper aims to provide a closed-mathematical representation of Bayesian analysis for reliability assessment of industrial components while investigating the effect of the prior choice on future failures predictions. To this end, hierarchical Bayesian modelling (HBM) was tested on three samples with distinct sizes, while five different prior distributions were considered. Moreover, a beta-binomial distribution was adopted to represent the failure behavior of the considered device. The results show that choosing strong informative priors leads to distinct predictions, even if a larger sample size is considered. The outcome of this research could help maintenance engineers and asset managers in integrating their prior beliefs into the reliability estimation process.

A Joint Modelling Approach to Analyze Risky Decisions by Means of Diffusion Tensor Imaging and Behavioural Data.

Understanding dependencies between brain functioning and cognition is a challenging task which might require more than applying standard statistical models to neural and behavioural measures to be accomplished. Recent developments in computational modelling have demonstrated the advantage to formally account for reciprocal relations between mathematical models of cognition and brain functional, or structural, characteristics to relate neural and cognitive parameters on a model-based perspective. This would allow to account for both neural and behavioural data simultaneously by providing a joint probabilistic model for the two sources of information. In the present work we proposed an architecture for jointly modelling the reciprocal relation between behavioural and neural information in the context of risky decision-making. More precisely, we offered a way to relate Diffusion Tensor Imaging data to cognitive parameters of a computational model accounting for behavioural outcomes in the popular Balloon Analogue Risk Task (BART). Results show that the proposed architecture has the potential to account for individual differences in task performances and brain structural features by letting individual-level parameters to be modelled by a joint distribution connecting both sources of information. Such a joint modelling framework can offer interesting insights in the development of computational models able to investigate correspondence between decision-making and brain structural connectivity.

A Bayesian assessment of polychlorinated biphenyl contamination of fish communities in the Laurentian Great Lakes.

Polychlorinated biphenyl (PCB) contamination has historically posed constraints on the recreational and commercial fishing industry in the Great Lakes. Empirical evidence suggests that PCB contamination represents a greater health risk from fish consumption than other legacy contaminants. The present study attempts a rigorous assessment of the spatio-temporal PCB trends in multiple species across the Canadian waters of the Great Lakes. We applied a Bayesian modelling framework, whereby we initially used dynamic linear models to delineate PCB levels and rates of change, while accounting for the role of fish length and lipid content as covariates. We then implemented Bayesian hierarchical modelling to evaluate the temporal PCB trends during the dreissenid pre- and post-invasion periods, as well as the variability among and within the water bodies of the Great Lakes system. Our analysis indicates that Lake Ontario is characterized by the highest PCB levels among nearly all of the fish species examined. Historically contaminated local areas, designated as Areas of Concern, and embayments receiving riverine inputs displayed higher concentrations within each of the water bodies examined. The general temporal trend across the Great Lakes was that the high PCB concentrations during the early 1970s followed a declining trajectory throughout the late 1980s/early 1990s, likely as a result of the reductions in industrial emissions and other management actions. Nonetheless, after the late 1990s/early 2000s, our analysis provided evidence of a decline in the rate at which PCB concentrations in fish were dropping, accompanied by a gradual establishment of species-specific, steady-state concentrations, around which there is considerable year-to-year variability. The overall trends indicate that reduced contaminant emissions have brought about distinct beneficial changes in fish PCB concentrations, but past historical contamination along with other external or internal stressors (e.g., invasive species, climate change) continue to modulate the current levels, thereby posing potential risks to humans through fish consumption.

Natural transformation of chlordecone into 5b-hydrochlordecone in French West Indies soils: statistical evidence for investigating long-term persistence of organic pollutants.

Chlordecone (CLD) was an organochlorine insecticide whose previous use resulted in an extensive pollution of the environment with severe health effects and social consequences. A closely related compound, 5b-hydrochlordecone (5b-hydroCLD), has been searched for and often detected in environmental matrices from the geographical area where CLD was applied. The current consensus considered that its presence was not the result of a biotic or abiotic dechlorination of CLD in these matrices but rather the consequence of its presence as impurity (synthesis by-product) in the CLD released into the environment. The aim of the present study was to determine if and to what extent degradation of CLD into 5b-hydroCLD occurred in the field. To test this hypothesis, the ratios of 5b-hydroCLD and CLD concentrations in a dataset of 810 soils collected between 2006 and 2012 in Martinique were compared to the ratios measured in 3 samples of the CLD dust commercial formulations applied in the banana fields of French West Indies (FWI) and 1 sample of the technical-grade CLD corresponding to the active ingredient used in such formulations. Soil data were processed with a hierarchical Bayesian model to account for random measurement errors and data censoring. Any pathway of CLD transformation into 5b-hydroCLD occurring over the long term in FWI soils would indeed change the ratio of 5b-hydroCLD/CLD compared to what it was in the initially applied formulations. Results showed a significant increase of the 5b-hydroCLD/CLD ratio in the soils-25 times greater in soil than in commercial formulations-which suggested that natural CLD transformation into 5b-hydroCLD over the long term occurred in these soils. Results from this study may impact future decisions for the remediation of the polluted areas.

Accounting for genotype uncertainty in the estimation of allele frequencies in autopolyploids.

Despite the increasing opportunity to collect large-scale data sets for population genomic analyses, the use of high-throughput sequencing to study populations of polyploids has seen little application. This is due in large part to problems associated with determining allele copy number in the genotypes of polyploid individuals (allelic dosage uncertainty-ADU), which complicates the calculation of important quantities such as allele frequencies. Here, we describe a statistical model to estimate biallelic SNP frequencies in a population of autopolyploids using high-throughput sequencing data in the form of read counts. We bridge the gap from data collection (using restriction enzyme based techniques [e.g. GBS, RADseq]) to allele frequency estimation in a unified inferential framework using a hierarchical Bayesian model to sum over genotype uncertainty. Simulated data sets were generated under various conditions for tetraploid, hexaploid and octoploid populations to evaluate the model's performance and to help guide the collection of empirical data. We also provide an implementation of our model in the R package polyfreqs and demonstrate its use with two example analyses that investigate (i) levels of expected and observed heterozygosity and (ii) model adequacy. Our simulations show that the number of individuals sampled from a population has a greater impact on estimation error than sequencing coverage. The example analyses also show that our model and software can be used to make inferences beyond the estimation of allele frequencies for autopolyploids by providing assessments of model adequacy and estimates of heterozygosity.