Jennifer J. Richler

Bio: Jennifer J. Richler is an academic researcher from Vanderbilt University. The author has contributed to research in topics: Face perception & Psychology. The author has an hindex of 24, co-authored 45 publications receiving 4557 citations.

Effect size estimates: Current use, calculations, and interpretation.

Abstract: The Publication Manual of the American Psychological Association (American Psychological Association, 2001, American Psychological Association, 2010) calls for the reporting of effect sizes and their confidence intervals. Estimates of effect size are useful for determining the practical or theoretical importance of an effect, the relative contributions of factors, and the power of an analysis. We surveyed articles published in 2009 and 2010 in the Journal of Experimental Psychology: General, noting the statistical analyses reported and the associated reporting of effect size estimates. Effect sizes were reported for fewer than half of the analyses; no article reported a confidence interval for an effect size. The most often reported analysis was analysis of variance, and almost half of these reports were not accompanied by effect sizes. Partial η2 was the most commonly reported effect size estimate for analysis of variance. For t tests, 2/3 of the articles did not report an associated effect size estimate; Cohen's d was the most often reported. We provide a straightforward guide to understanding, selecting, calculating, and interpreting effect sizes for many types of data and to methods for calculating effect size confidence intervals and power analysis.

Holistic Processing Predicts Face Recognition

Abstract: The concept of holistic processing is a cornerstone of face-recognition research. In the study reported here, we demonstrated that holistic processing predicts face-recognition abilities on the Cambridge Face Memory Test and on a perceptual face-identification task. Our findings validate a large body of work that relies on the assumption that holistic processing is related to face recognition. These findings also reconcile the study of face recognition with the perceptual-expertise work it inspired; such work links holistic processing of objects with people's ability to individuate them. Our results differ from those of a recent study showing no link between holistic processing and face recognition. This discrepancy can be attributed to the use in prior research of a popular but flawed measure of holistic processing. Our findings salvage the central role of holistic processing in face recognition and cast doubt on a subset of the face-perception literature that relies on a problematic measure of holistic processing.

A meta-analysis and review of holistic face processing.

Abstract: The concept of holistic processing is a cornerstone of face recognition research, yet central questions related to holistic processing remain unanswered, and debates have thus far failed to reach a resolution despite accumulating empirical evidence. We argue that a considerable source of confusion in this literature stems from a methodological problem. Specifically, 2 measures of holistic processing based on the composite paradigm (complete design and partial design) are used in the literature, but they often lead to qualitatively different results. First, we present a comprehensive review of the work that directly compares the 2 designs, and which clearly favors the complete design over the partial design. Second, we report a meta-analysis of holistic face processing according to both designs and use this as further evidence for one design over the other. The meta-analysis effect size of holistic processing in the complete design is nearly 3 times that of the partial design. Effect sizes were not correlated between measures, consistent with the suggestion that they do not measure the same thing. Our meta-analysis also examines the correlation between conditions in the complete design of the composite task, and suggests that in an individual differences context, little is gained by including a misaligned baseline. Finally, we offer a comprehensive review of the state of knowledge about holistic processing based on evidence gathered from the measure we favor based on the 1st sections of our review�the complete design�and outline outstanding research questions in that new context. (PsycINFO Database Record (c) 2014 APA, all rights reserved)

Meanings, Mechanisms, and Measures of Holistic Processing

Abstract: Few concepts are more central to the study of face recognition than holistic processing. Progress toward understanding holistic processing is challenging because the term “holistic” has many meanings, with different researchers addressing different mechanisms and favoring different measures. While in principle the use of different measures should provide converging evidence for a common theoretical construct, convergence has been slow to emerge. We explore why this is the case. One challenge is that “holistic processing” is often used to describe both a theoretical construct and a measured effect, which may not have a one-to-one mapping. Progress requires more than greater precision in terminology regarding different measures of holistic processing or different hypothesized mechanisms of holistic processing. Researchers also need to be explicit about what meaning of holistic processing they are investigating so that it is clear whether different researchers are describing the same phenomenon or not. Face recognition differs from object recognition, and not all meanings of holistic processing are equally suited to help us understand that important difference.

Gut microbiome modulates response to anti–PD-1 immunotherapy in melanoma patients

Abstract: Preclinical mouse models suggest that the gut microbiome modulates tumor response to checkpoint blockade immunotherapy; however, this has not been well-characterized in human cancer patients. Here we examined the oral and gut microbiome of melanoma patients undergoing anti-programmed cell death 1 protein (PD-1) immunotherapy (n = 112). Significant differences were observed in the diversity and composition of the patient gut microbiome of responders versus nonresponders. Analysis of patient fecal microbiome samples (n = 43, 30 responders, 13 nonresponders) showed significantly higher alpha diversity (P < 0.01) and relative abundance of bacteria of the Ruminococcaceae family (P < 0.01) in responding patients. Metagenomic studies revealed functional differences in gut bacteria in responders, including enrichment of anabolic pathways. Immune profiling suggested enhanced systemic and antitumor immunity in responding patients with a favorable gut microbiome as well as in germ-free mice receiving fecal transplants from responding patients. Together, these data have important implications for the treatment of melanoma patients with immune checkpoint inhibitors.

The New Statistics Why and How

Abstract: We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data- analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.

Bayesian Estimation Supersedes the t Test

Abstract: Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms.

How Big Is “Big”? Interpreting Effect Sizes in L2 Research

Abstract: The calculation and use of effect sizes—such as d for mean differences and r for correlations—has increased dramatically in second language (L2) research in the last decade. Interpretations of these effects, however, have been rare and, when present, have largely defaulted to Cohen's levels of small (d = .2, r = .1), medium (.5, .3), and large (.8, .5), which were never intended as prescriptions but rather as a general guide. As Cohen himself and many others have argued, effect sizes are best understood when interpreted within a particular discipline or domain. This article seeks to promote more informed and field-specific interpretations of d and r by presenting a description of L2 effects from 346 primary studies and 91 meta-analyses (N > 604,000). Results reveal that Cohen's benchmarks generally underestimate the effects obtained in L2 research. Based on our analysis, we propose a field-specific scale for interpreting effect sizes, and we outline eight key considerations for gauging relative magnitude and practical significance in primary and secondary studies, such as theoretical maturity in the domain, the degree of experimental manipulation, and the presence of publication bias.

Introduction To Robust Estimation And Hypothesis Testing

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