Using Object Knowledge in Visual Tracking and Reaching

Nine‐month‐old infants were presented with an engaging and challenging task of visually tracking and reaching for a rolling ball that disappeared and reappeared from behind an occluder. On some trials, the infant observed the experimenter place a barrier on the ball's track; the barrier remained partially visible above the occluder throughout the remainder of the trial. When the task involved only predictive tracking, infants' anticipatory gaze shifts were faster when no barrier was present. When the task involved both tracking and reaching, there were more reaches when no barrier was present. If the infant reached, the timing and extension of the reach and the accompanying gaze shift did not differ with regard to the barrier. Because catching the ball was quite difficult for these infants, task demands interfered with the integration of visual information and visuospatial reasoning about the barrier with the reaching action.     

Small area estimation: the EBLUP estimator based on spatially correlated random area effects

This paper deals with small area indirect estimators under area level random effect models when only area level data are available and the random effects are correlated. The performance of the Spatial Empirical Best Linear Unbiased Predictor (SEBLUP) is explored with a Monte Carlo simulation study on lattice data and it is applied to the results of the sample survey on Life Conditions in Tuscany (Italy). The mean squared error (MSE) problem is discussed illustrating the MSE estimator in comparison with the MSE of the empirical sampling distribution of SEBLUP estimator. A clear tendency in our empirical findings is that the introduction of spatially correlated random area effects reduce both the variance and the bias of the EBLUP estimator. Despite some residual bias, the coverage rate of our confidence intervals comes close to a nominal 95%.     

Children's participation in foster care placements

This article presents results from a study of the birth children of foster carers and their experiences of growing up with foster siblings. Data were gathered from focus groups (17 participants), discussion groups (16 participants), a questionnaire (684 responses) and eight individual interviews. The children's participation in the fostering placements is in focus. Accounts from the children show that several of them participate in care-giving activities in everyday life. Their participation in the official assignment is however limited and can lead to children taking on responsibility with no support from adults. The tension between children's agency and position in society is addressed. The study shows how power relations between children and adults come into play, how both children and adults (re)-produce positions where children's rights to have a say in questions that concern them are not reflected within fostering processes.     

Training Intraverbal Naming to Establish Matching-to-Sample Performances

The current study evaluated whether training intraverbal naming would be sufficient to establish visual-visual matching-to-sample (MTS) performances in college students. In the first experiment, we used a multiple-probe design across stimulus sets to assess whether six participants could match arbitrary visual stimuli (AB) after learning to tact their two experimentally defined classes (A’ and B’) and then intraverbally relate their names (i.e., “A’ goes with B’”). All participants matched the stimuli accurately after training, as well as emitted the trained intraverbals. In the second experiment, we used a multiple baseline design across four participants to assess whether the same training would produce bi-directional intraverbals in the form of “B’ goes with A’,” and MTS performance consistent with symmetry (BA). All participants responded accurately during matching and intraverbal tests. Across both experiments, participants stated the trained intraverbals while performing the matching task. Results showed that MTS performance can be established solely by verbal behavior training.     

Perceived Inflation Persistence

This article constructs and estimates a measure called perceived inflation persistence that can be used to determine if professional forecasters’ inflation forecasts indicate there has been a change in inflation persistence. This measure is built via the implied autocorrelation function that follows from the estimates obtained using a forecaster-specific state-space model. Findings indicate that U.S. perceived inflation persistence has changed since the mid-1990s with more consensus among forecasters at lower levels of persistence. When compared to the autocorrelation function for actual inflation, forecasters typically react less to shocks to inflation than the actual inflation data would suggest.     

Recognizing Structural Nonidentifiability: When Experiments Do Not Provide Information About Important Parameters and Misleading Models Can Still Have Great Fit

In the quest to model various phenomena, the foundational importance of parameter identifiability to sound statistical modeling may be less well appreciated than goodness of fit. Identifiability concerns the quality of objective information in data to facilitate estimation of a parameter, while nonidentifiability means there are parameters in a model about which the data provide little or no information. In purely empirical models where parsimonious good fit is the chief concern, nonidentifiability (or parameter redundancy) implies overparameterization of the model. In contrast, nonidentifiability implies underinformativeness of available data in mechanistically derived models where parameters are interpreted as having strong practical meaning. This study explores illustrative examples of structural nonidentifiability and its implications using mechanistically derived models (for repeated presence/absence analyses and dose–response of Escherichia coli O157:H7 and norovirus) drawn from quantitative microbial risk assessment. Following algebraic proof of nonidentifiability in these examples, profile likelihood analysis and Bayesian Markov Chain Monte Carlo with uniform priors are illustrated as tools to help detect model parameters that are not strongly identifiable. It is shown that identifiability should be considered during experimental design and ethics approval to ensure generated data can yield strong objective information about all mechanistic parameters of interest. When Bayesian methods are applied to a nonidentifiable model, the subjective prior effectively fabricates information about any parameters about which the data carry no objective information. Finally, structural nonidentifiability can lead to spurious models that fit data well but can yield severely flawed inferences and predictions when they are interpreted or used inappropriately.     

GENDER AND PROSOCIAL VALUES DURING ADOLESCENCE: A Research Note

To date, relatively little is known about the prosocial values of adolescents. Research has shown that females attach more importance to certain prosocial values than males do in late adolescence but has not considered whether this gender difference is evident across stages of adolescence and whether it varies by race. We examine values that focus on doing things for others, using data from a nationwide survey of 12- to 17-year-olds. We find that girls place more importance on these prosocial values than boys at younger ages. However, we also find important patterns in this gender difference across racial groups as well as across age. White boys appear to attach less importance to prosocial values than other race and gender groups. Furthermore, the gender gap in prosocial values is larger at older ages, with older adolescent boys placing less importance on prosocial values than younger ones.     

Shape analysis for automated sulcal classification and parcellation of MRI data

We describe geometric invariants that characterize the shape of curves and surfaces in 3D space: curvature, Gauss integrals and moments. We apply these invariants to neuroimaging data to determine if they have application for automatically classifying and parcellating cortical data. The curves of sulci and gyri on the cortical surface can be obtained by reconstructing cortical surface representations of the human brain from magnetic resonance imaging (MRI) data. We reconstructed gray matter surfaces for 15 subjects, traced 10 sulcal curves on each surface and computed geometric invariants for each curve. These geometric features were used classify the curves into sulcal and hemispheric classes. The best classification results were obtained when moment-based features were computed on the sulcal curves in native space. Gauss integral measures showed that they were useful for differentiating the hemispheric location of a single sulcus. These promising results may indicate that moment invariants are useful for characterizing shape on a global scale. Gauss integral invariants are potentially useful measures for characterizing cortical shape on a local, rather than global scale. Gauss integrals have found biological significance in characterizing proteins so it is worthwhile to consider their possible application to neuroscientific data.