How Visual is Visual Prediction?

Infants are readily able to use their recent experience to shape their future behavior. Recent work has confirmed that infants generate neural predictions based on their recent experience (Emberson, Richards, & Aslin, 2015) and that neural predictions trigger visual system activity similar to that elicited by visual stimulation. This study uses behavioral methods to ask, how visual is visual prediction? In Experiment 1, we confirmed that when additional trials provide additional visual experience with the experimental shape, infants exhibit a robust novelty preference. In Experiment 2, we removed the visual stimulus from some trials and presented the predictive auditory cue alone, allowing the effects of neural prediction to be assessed. We found no evidence of looking preferences at test, suggesting that visual prediction does not contribute to the computation of visual familiarity. In Experiment 3, we provided infants with a degraded visual stimulus to test whether visual prediction could bias visual perception under ambiguous conditions. Again, we found no evidence of looking preferences at test, suggesting that visual prediction is not biasing perception of an uncertain stimulus. Overall, our results suggest that visual prediction is not visual, in the strictest sense, despite the presence of visual system activation.     

A Mixed-Methods Inquiry Into Trans* Environmental Microaggressions on College Campuses: Experiences and Outcomes

ABSTRACT

The experiences, including with environmental microaggressions, and well-being of trans* collegians remain an under-researched topic. In this mixed-methods study of a survey sample of 152 trans* collegians, multivariable regression findings suggest that the frequency of experiencing select trans* environmental microaggressions (e.g., not having access to comfortable bathrooms as a trans* person) are associated with increased risk for poorer academic outcomes (e.g., developmental challenges) but are not associated with mental health outcomes. Furthermore, interviews with 18 trans* collegians suggest that students face several systemic microaggressions, including difficulties advancing trans* inclusion. Various reactions, including paths of resistance, were identified. Implications are discussed.     

Differences in Experiences of Discrimination in Accessing Social Services Among Transgender/Gender Nonconforming Individuals by (Dis)Ability

Transgender and gender nonconforming (GNC) individuals frequently experience discrimination and potentially a lack of respect from service providers, suggesting they have decreased access to professionals with cultural competency. Similarly, people with disabilities experience higher levels of discrimination in social services than their nondisabled counterparts. From an intersectional perspective, this study examines rates of discrimination in accessing social services faced by transgender and GNC people, comparing across ability. Data indicate that although transgender and GNC individuals of all abilities experience gender-based discrimination when accessing social services, those with disabilities experience higher levels of antitransgender discrimination in mental health centers, rape crisis centers, and domestic violence shelters.     

The expected and unexpected media effects on youth’s (mis)perceptions of peer norms: Chinese college students’ overestimation of peer smoking prevalence

Youth’s perceptions of peer norms have profound effects on their attitudes and behaviors. Unfortunately, their perceptions of peers tend to be biased. To investigate the role of media in the formation of misperceived peer norms, the current study tested and compared two media-effect mechanisms: the direct exemplar mechanism and the indirect influence of presumed influence (IPI) mechanism. The two mechanisms were tested in a sample (n = 1746) that is representative of college students in one large university in China, based on which the actual and perceived campus smoking rates were examined. The individual-level perceived peer smoking prevalence was then analyzed in comparison with the campus-level actual smoking prevalence. The results revealed serious overestimations of peer smoking prevalence. As predicted, pro-smoking media content significantly heightened smoking prevalence estimates through both exemplar and IPI mechanisms. In contrast, anti-smoking messages did not elicit any intended effect through the IPI mechanism, but showed a boomerang effect through the exemplar mechanism. These findings suggest that both pro- and anti-smoking media content may be responsible for inflated perceptions of peer smoking among youth.     

Comparison of dichotomized and distributional approaches in rare event clinical trial design: a fixed Bayesian design

This research was motivated by our goal to design an efficient clinical trial to compare two doses of docosahexaenoic acid supplementation for reducing the rate of earliest preterm births (ePTB) and/or preterm births (PTB). Dichotomizing continuous gestational age (GA) data using a classic binomial distribution will result in a loss of information and reduced power. A distributional approach is an improved strategy to retain statistical power from the continuous distribution. However, appropriate distributions that fit the data properly, particularly in the tails, must be chosen, especially when the data are skewed. A recent study proposed a skew-normal method. We propose a three-component normal mixture model and introduce separate treatment effects at different components of GA. We evaluate operating characteristics of mixture model, beta-binomial model, and skew-normal model through simulation. We also apply these three methods to data from two completed clinical trials from the USA and Australia. Finite mixture models are shown to have favorable properties in PTB analysis but minimal benefit for ePTB analysis. Normal models on log-transformed data have the largest bias. Therefore we recommend finite mixture model for PTB study. Either finite mixture model or beta-binomial model is acceptable for ePTB study.     

FDR control of detected regions by multiscale matched filtering

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed technique is a multiscale kernel regression in conjunction with statistical multiple testing for region detection while controlling the false discovery rate (FDR) and maximizing the signal-to-noise ratio via matched filtering. This is achieved by considering a one-dimensional region detection problem as its equivalent zero-dimensional peak detection problem. The detection method does not require a priori knowledge of the shape of the nonzero regions. However, if the shape of the nonzero regions is known a priori, e.g., rectangular pulse, the signal regions can also be reconstructed from the detected peaks, seen as their topological point representatives. Simulations show that the method can effectively perform signal detection and reconstruction in the simulated data under high noise conditions, while controlling the FDR of detected regions and their reconstructed length.     

Estimating function method for product autoregressive models

The first-order product autoregressive (PAR(1)) model introduced by McKenzie in 1982 McKenzie, E. D. (1982). Product autoregression: A time series characterization of the gamma distribution. Journal of Applied Probability 19:463–468. [Google Scholar] did not attract the attention of practitioners due to the unavailability of a proper estimation method. This article proposes an estimating function (EF) method to fill the gap. In particular, we suggest an optimal combination of linear and quadratic EFs to overcome the problem of parameter identification. The procedure is applied to Weibull and Gamma PAR(1) models. Simulation and data analysis show that the proposed method performs better than the existing methods.     

Effects of some design factors on the distribution of similarity indices in cluster analysis

This article investigates the effects of number of clusters, cluster size, and correction for chance agreement on the distribution of two similarity indices, namely, Jaccard and Rand indices. Skewness and kurtosis are calculated for the two indices and their corrected forms then compared with those of the normal distribution. Three clustering algorithms are implemented: complete linkage, Ward, and K-means. Data were randomly generated from bivariate normal distributions with specified means and variance covariance matrices. Three-way ANOVA is performed to assess the significance of the design factors using skewness and kurtosis of the indices as responses. Test statistics for testing skewness and kurtosis and observed power are calculated. Simulation results showed that independent of the clustering algorithms or the similarity indices used, the interaction effect cluster size x number of clusters and the main effects of cluster size and number of clusters were found always significant for skewness and kurtosis. The three way interaction of cluster size x correction x number of clusters was significant for skewness of Rand and Jaccard indices using all clustering algorithms, but was not significant using Ward's method for both Rand and Jaccard indices, while significant for Jaccard only using complete linkage and K-means algorithms. The correction for chance agreement was significant for skewness and kurtosis using Rand and Jaccard indices when complete linkage method is used. Hence, such design factors must be taken into consideration when studying distribution of such indices.     

Weighted kappa as a function of unweighted kappas

The kappa coefficient is a widely used measure for assessing agreement on a nominal scale. Weighted kappa is an extension of Cohen's kappa that is commonly used for measuring agreement on an ordinal scale. In this article, it is shown that weighted kappa can be computed as a function of unweighted kappas. The latter coefficients are kappa coefficients that correspond to smaller contingency tables that are obtained by merging categories.     

Bayesian prediction of order statistics with fixed and random sample sizes based on k-record values from Pareto distribution

In this paper, the two-parameter Pareto distribution is considered and the problem of prediction of order statistics from a future sample and that of its geometric mean are discussed. The Bayesian approach is applied to construct predictors based on observed k-record values for the cases when the future sample size is fixed and when it is random. Several Bayesian prediction intervals are derived. Finally, the results of a simulation study and a numerical example are presented for illustrating all the inferential procedures developed here.