The Perceived Match Between Observed and Own Bodies, but Not Its Accuracy, Is Influenced by Movement Dynamics and Clothing Cues.

Own-perceived body matching - the ability to match one's own body with an observed body - is a difficult task for both general and clinical populations. Thus far, however, own-perceived body matching has been investigated in situations that are incongruent with how we are used to experience and perceive our body in daily life. In the current study, we aimed to examine own-perceived body matching in a context that more closely resembles real life. More specifically, we investigated the effects of body movement dynamics and clothing cues on own-perceived body matching. We asked participants to match their own body with an externally perceived body that was a 3D-generated avatar based on participants' real bodies, fitted with a computer-generated dress. This perceived body was (1) either static (non-walking avatar) or dynamic (walking avatar), (2) either bigger, smaller, or the same size as participants' own body size, and (3) fitted with a dress with a size either bigger, smaller, or the same as participants' own dress size. Our results suggest that movement dynamics cues did not improve the accuracy of own-perceived body matching, but that confidence about dress fit was higher for dynamic avatars, and that the difference between dynamic and static avatars was dependent on participants' self-esteem. Furthermore, when participants were asked to rate the observed body in reference to how they wanted to represent themselves to others, dynamic avatars were rated lower than static avatars for the biggest-sized bodies only, possibly reflecting the influence of movement cues on amplifying socio-cultural stereotypes. Finally, while smaller body/dress sizes were systematically rated higher than bigger body/dress sizes for several self-report items, the interplay between body and dress size played an important role in participants' self-report as well. Thus, while our research suggests that movement and garment dynamics, allowing for realistic, concrete situations that are reminiscent of daily life, influence own-body perception, these cues did not lead to an improvement in accuracy. These findings provide important insights for research exploring (own-) body perception and bodily self-awareness, with practical (e.g., development of online avatars) and clinical (e.g., anorexia nervosa and body dysmorphic disorder) implications.

Use of a real-life practical context changes the relationship between implicit body representations and real body measurements.

A mismatch exists between people's mental representations of their own body and their real body measurements, which may impact general well-being and health. We investigated whether this mismatch is reduced when contextualizing body size estimation in a real-life scenario. Using a reverse correlation paradigm, we constructed unbiased, data-driven visual depictions of participants' implicit body representations. Across three conditions-own abstract, ideal, and own concrete body-participants selected the body that looked most like their own, like the body they would like to have, or like the body they would use for online shopping. In the own concrete condition only, we found a significant correlation between perceived and real hip width, suggesting that the perceived/real body match only exists when body size estimation takes place in a practical context, although the negative correlation indicated inaccurate estimation. Further, participants who underestimated their body size or who had more negative attitudes towards their body weight showed a positive correlation between perceived and real body size in the own abstract condition. Finally, our results indicated that different body areas were implicated in the different conditions. These findings suggest that implicit body representations depend on situational and individual differences, which has clinical and practical implications.

Toenails as a biomarker of exposure to arsenic: A review.

This systematic review summarizes the current evidence related to the reliability of toenail total arsenic concentrations (thereafter "arsenic") as a biomarker of long-term exposure. Specifically, we reviewed literature on consistency of repeated measures over time, association with other biomarkers and metal concentrations, factors influencing concentrations, and associations with health effects. We identified 129 papers containing quantitative original data on arsenic in toenail samples covering populations from 29 different countries. We observed geographic differences in toenail arsenic concentrations, with highest median or mean concentrations in Asian countries. Arsenic-contaminated drinking water, occupational exposure or living in specific industrial areas were associated with an increased toenail arsenic content. The effects of other potential determinants and sources of arsenic exposure including diet, gender and age on the concentrations in toenails need further investigations. Toenail arsenic was correlated with the concentrations in hair and fingernails, and with urine arsenic mainly among highly exposed populations with a toenail mean or median ≥1 µg/g. Overall, there is a growing body of evidence suggesting that arsenic content from a single toenail sample may reflect long-term internal dose-exposure. Toenail arsenic can serve as a reliable measure of toxic inorganic arsenic exposure in chronic disease research, particularly promising for cancer and cardiovascular conditions.

Author Correction: Perceived match between own and observed models' bodies: influence of face, viewpoints, and body size.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Perceived match between own and observed models' bodies: influence of face, viewpoints, and body size.

People are generally unable to accurately determine their own body measurements and to translate this knowledge to identifying a model/avatar that best represents their own body. This inability has not only been related to health problems (e.g. anorexia nervosa), but has important practical implications as well (e.g. online retail). Here we aimed to investigate the influence of three basic visual features-face presence, amount of viewpoints, and observed model size-on the perceived match between own and observed models' bodies and on attitudes towards these models. Models were real-life models (Experiment 1) or avatar models based on participants' own bodies (Experiment 2). Results in both experiments showed a strong effect of model size, irrespective of participants' own body measurements. When models were randomly presented one by one, participants gave significantly higher ratings to smaller- compared to bigger-sized models. The reverse was true, however, when participants observed and compared models freely, suggesting that the mode of presentation affected participants' judgments. Limited evidence was found for an effect of facial presence or amount of viewpoints. These results add evidence to research on visual features affecting the ability to match observed bodies with own body image, which has biological, clinical, and practical implications.

Yarn-Level Cloth Simulation with Sliding Persistent Contacts.

Cloth is made of yarns that are stitched together forming semi-regular patterns. Due to the complexity of stitches and patterns, the macroscopic behavior of cloth is dictated by the contact interactions between yarns, not by the mechanical properties of yarns alone. The computation of cloth mechanics at the yarn level appears as a computationally complex and costly process at first sight, due to the need to resolve many fine-scale contact interactions. We propose instead an efficient representation of cloth at the yarn level that treats yarn-yarn contacts as persistent, but with the possibility to slide, thereby avoiding expensive contact handling altogether. We introduce a compact representation of yarn geometry and kinematics, capturing the essential deformation modes of yarn crossings, loops, stitches, and stacks, with a minimum cost. Based on this representation, we design force models that reproduce the characteristic macroscopic behavior of yarn-based fabrics. Our approach is suited for both woven and knitted fabrics. We demonstrate the efficiency of our method on simulations with millions of degrees of freedom (hundreds of thousands of yarn loops), almost one order of magnitude faster than previous techniques. We also compare the different macroscopic behavior under woven and knitted patterns with the same yarn density.