scholarly journals Neural networks supporting social evaluation of bodies based on body shape

2018 ◽  
Author(s):  
Inez Greven ◽  
Paul Downing ◽  
Richard Ramsey
Keyword(s):  
Neural Networks ◽  
Body Shape ◽  
Neural Mechanism ◽  
Relevant Information ◽  
The Body ◽  
Social Judgments ◽  
Social Evaluation ◽  
Fmri Experiment ◽  
Socially Relevant ◽  
Social Inferences

Body shape cues inferences regarding personality and health, but the neural processes underpinning such inferences remain poorly understood. Across two fMRI experiments, we test the extent to which neural networks associated with body perception and theory-of-mind (ToM) support social inferences based on body shape. Participants observed obese, muscular, and slim bodies that cued distinct social inferences as pilot experiments revealed. To investigate judgment intentionality, the first fMRI experiment required participants to detect repeat presentations of bodies, whereas in fMRI Experiment 2 participants intentionally formed an impression. Body and ToM networks were localized using independent functional localisers. Experiment 1 revealed no differential network engagement for muscular or obese compared to slim bodies. By contrast, in Experiment 2, compared to slim bodies, forming impressions of muscular bodies engaged the body-network more, whereas the ToM-network was engaged more when forming impressions of obese bodies. These results demonstrate that social judgments based on body shape do not rely on a single neural mechanism, but rather on multiple mechanisms that are separately sensitive to body fat and muscularity. Moreover, dissociable responses are only apparent when intentionally forming an impression. Thus, these experiments show how segregated networks operate to extract socially-relevant information cued by body shape.

scholarly journals Abnormal Processing of Social Information from Faces in Autism

2001 ◽  
Vol 13 (2) ◽  
pp. 232-240 ◽  
Author(s):  
Ralph Adolphs ◽  
Lonnie Sears ◽  
Joseph Piven
Keyword(s):  
Social Cognition ◽  
Social Information ◽  
Relevant Information ◽  
Neural Substrates ◽  
Social Knowledge ◽  
Social Judgments ◽  
High Functioning ◽  
Impaired Ability ◽  
Socially Relevant ◽  
Underlying Processes

Autism has been thought to be characterized, in part, by dysfunction in emotional and social cognition, but the pathology of the underlying processes and their neural substrates remain poorly understood. Several studies have hypothesized that abnormal amygdala function may account for some of the impairments seen in autism, specifically, impaired recognition of socially relevant information from faces. We explored this issue in eight high-functioning subjects with autism in four experiments that assessed recognition of emotional and social information, primarily from faces. All tasks used were identical to those previously used in studies of subjects with bilateral amygdala damage, permitting direct comparisons. All subjects with autism made abnormal social judgments regarding the trustworthiness of faces; however, all were able to make normal social judgments from lexical stimuli, and all had a normal ability to perceptually discriminate the stimuli. Overall, these data from subjects with autism show some parallels to those from neurological subjects with focal amygdala damage. We suggest that amygdala dysfunction in autism might contribute to an impaired ability to link visual perception of socially relevant stimuli with retrieval of social knowledge and with elicitation of social behavior.

scholarly journals Aberrant neural networks for the recognition memory of socially relevant information in patients with schizophrenia

2016 ◽  
Vol 7 (1) ◽  
pp. e00602 ◽  
Author(s):  
Jooyoung Oh ◽  
Ji-Won Chun ◽  
Eunseong Kim ◽  
Hae-Jeong Park ◽  
Boreom Lee ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recognition Memory ◽  
Relevant Information ◽  
Socially Relevant

scholarly journals Neural networks supporting social evaluation of bodies based on body shape

2018 ◽  
Vol 14 (3) ◽  
pp. 328-344 ◽  
Author(s):  
Inez M. Greven ◽  
Paul E. Downing ◽  
Richard Ramsey
Keyword(s):  
Neural Networks ◽  
Body Shape ◽  
Social Evaluation

Updating long-held assumptions about fat stigma: For women, body shape plays a critical role

2020 ◽  
Author(s):  
Jaimie Krems ◽  
Steven L. Neuberg
Keyword(s):  
Body Shape ◽  
Critical Role ◽  
The Body ◽  
Theoretical Assumption ◽  
Obesity Stigma ◽  
Obese Female ◽  
Three Samples ◽  
Female Bodies ◽  
Different Parts ◽  
Fat Stigma

Heavier bodies—particularly female bodies—are stigmatized. Such fat stigma is pervasive, painful to experience, and may even facilitate weight gain, thereby perpetuating the obesity-stigma cycle. Leveraging research on functionally distinct forms of fat (deposited on different parts of the body), we propose that body shape plays an important but largely underappreciated role in fat stigma, above and beyond fat amount. Across three samples varying in participant ethnicity (White and Black Americans) and nation (U.S., India), patterns of fat stigma reveal that, as hypothesized, participants differently stigmatized equally-overweight or -obese female targets as a function of target shape, sometimes even more strongly stigmatizing targets with less rather than more body mass. Such findings suggest value in updating our understanding of fat stigma to include body shape and in querying a predominating, but often implicit, theoretical assumption that people simply view all fat as bad (and more fat as worse).

Importance of Oral Health in Pregnancy: A Mini-symposium

2019 ◽  
Vol 15 (3) ◽  
pp. 156-158
Author(s):  
Aditi Priyamvara ◽  
Amit K. Dey ◽  
Antara Bagchi ◽  
Raveena Kelkar ◽  
Rajaram Sharma
Keyword(s):  
Oral Health ◽  
Oral Cavity ◽  
Tooth Enamel ◽  
Relevant Information ◽  
The Body ◽  
Systemic Impact ◽  
Low Birth Weight Babies ◽  
Adverse Pregnancy ◽  
In Pregnancy

Background: It is known that hormonal imbalances during pregnancy make women more susceptible to dental problems. High levels of progesterone and estrogen during pregnancy, lead to an increased inflammatory response to dental plaque thus causing predisposing to gum diseases such as gingivitis. If untreated, gingivitis leads to chronic periodontitis which may manifest systemically in form of cardiovascular, endocrine or even respiratory disorders. Also, hyperacidity in the oral cavity due to gastric reflux and vomiting leads to decreased pH thus damaging the tooth enamel making the oral cavity more prone to tooth decay and tooth loss. Studies also show that periodontal disease can also lead to adverse pregnancy outcomes such as pre-term and low birth weight babies. Objectives: We sought to understand the role of oral health in pregnancy. Methods: We identified major articles of interest in the field of oral health in pregnancy and drafted a mini-symposium based on relevant information. Conclusion: Regular dental visits and cognizant efforts to sustain a healthy oral environment can help women in the prevention and treatment of dental issues during pregnancy. The paper highlights the common oral manifestations during pregnancy and their local and systemic impact on the body during pregnancy. Furthermore, it also emphasizes the importance of good oral health practices to counteract the oral complications and the significance of oral health awareness in pregnant women.

Chaos in body–vortex interactions

2010 ◽  
Vol 466 (2119) ◽  
pp. 1871-1891 ◽  
Author(s):  
Johan Roenby ◽  
Hassan Aref
Keyword(s):  
Body Shape ◽  
Mass Density ◽  
Relative Equilibrium ◽  
Large Body ◽  
Point Vortex ◽  
Cylindrical Body ◽  
The Body ◽  
Body Motion ◽  
Single Vortex ◽  
Vortex Interactions

The model of body–vortex interactions, where the fluid flow is planar, ideal and unbounded, and the vortex is a point vortex, is studied. The body may have a constant circulation around it. The governing equations for the general case of a freely moving body of arbitrary shape and mass density and an arbitrary number of point vortices are presented. The case of a body and a single vortex is then investigated numerically in detail. In this paper, the body is a homogeneous, elliptical cylinder. For large body–vortex separations, the system behaves much like a vortex pair regardless of body shape. The case of a circle is integrable. As the body is made slightly elliptic, a chaotic region grows from an unstable relative equilibrium of the circle-vortex case. The case of a cylindrical body of any shape moving in fluid otherwise at rest is also integrable. A second transition to chaos arises from the limit between rocking and tumbling motion of the body known in this case. In both instances, the chaos may be detected both in the body motion and in the vortex motion. The effect of increasing body mass at a fixed body shape is to damp the chaos.

scholarly journals Sea stars generate downforce to stay attached to surfaces

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mark Hermes ◽  
Mitul Luhar
Keyword(s):  
Body Shape ◽  
Control Volume ◽  
Water Channel ◽  
Morphological Plasticity ◽  
The Body ◽  
Sea Star ◽  
Sea Stars ◽  
Hydrodynamic Loads ◽  
Spherical Dome ◽  
Star Models

AbstractIntertidal sea stars often function in environments with extreme hydrodynamic loads that can compromise their ability to remain attached to surfaces. While behavioral responses such as burrowing into sand or sheltering in rock crevices can help minimize hydrodynamic loads, previous work shows that sea stars also alter body shape in response to flow conditions. This morphological plasticity suggests that sea star body shape may play an important hydrodynamic role. In this study, we measured the fluid forces acting on surface-mounted sea star and spherical dome models in water channel tests. All sea star models created downforce, i.e., the fluid pushed the body towards the surface. In contrast, the spherical dome generated lift. We also used Particle Image Velocimetry (PIV) to measure the midplane flow field around the models. Control volume analyses based on the PIV data show that downforce arises because the sea star bodies serve as ramps that divert fluid away from the surface. These observations are further rationalized using force predictions and flow visualizations from numerical simulations. The discovery of downforce generation could explain why sea stars are shaped as they are: the pentaradial geometry aids attachment to surfaces in the presence of high hydrodynamic loads.

scholarly journals Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1581
Author(s):  
Jimy Oblitas ◽  
Jezreel Mejia ◽  
Miguel De-la-Torre ◽  
Himer Avila-George ◽  
Lucía Seguí Gil ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cucurbita Pepo ◽  
Performance Metrics ◽  
Microstructural Analysis ◽  
Relevant Information ◽  
Machine Learning Techniques ◽  
Radial Basis Neural Networks ◽  
Vegetable Foods ◽  
Vegetal Tissue

Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)—when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods.

The development and validation of the body shape questionnaire

1987 ◽  
Vol 6 (4) ◽  
pp. 485-494 ◽  
Author(s):  
Peter J. Cooper ◽  
Melanie J. Taylor ◽  
Zafra Cooper ◽  
Christopher G. Fairbum
Keyword(s):  
Body Shape ◽  
The Body ◽  
Development And Validation
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