Correction to: Choosing face: The curse of self in profile image selection.
Ontology highlight
ABSTRACT:
SUBMITTER: White D
PROVIDER: S-EPMC8363730 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
Publications
Correction to: Choosing face: The curse of self in profile image selection.
White David D Sutherland Clare A M CAM Burton Amy L AL
Cognitive research: principles and implications 20210813 1
Similar Datasets
Project description:People draw automatic social inferences from photos of unfamiliar faces and these first impressions are associated with important real-world outcomes. Here we examine the effect of selecting online profile images on first impressions. We model the process of profile image selection by asking participants to indicate the likelihood that images of their own face ("self-selection") and of an unfamiliar face ("other-selection") would be used as profile images on key social networking sites. Across two large Internet-based studies (n = 610), in line with predictions, image selections accentuated favorable social impressions and these impressions were aligned to the social context of the networking sites. However, contrary to predictions based on people's general expertise in self-presentation, other-selected images conferred more favorable impressions than self-selected images. We conclude that people make suboptimal choices when selecting their own profile pictures, such that self-perception places important limits on facial first impressions formed by others. These results underscore the dynamic nature of person perception in real-world contexts.
| S-EPMC5391387 | biostudies-literature
Project description:Many essential organelles and endosymbionts exhibit a strict matrilineal pattern of inheritance. The absence of paternal transmission of such extranuclear components is thought to preclude a response to selection on their effects on male viability and fertility. We overturn this dogma by showing that two mechanisms, inbreeding and kin selection, allow mitochondria to respond to selection on both male viability and fertility. Even modest levels of inbreeding allow such a response to selection when there are direct fitness effects of mitochondria on male fertility because inbreeding associates male fertility traits with mitochondrial matrilines. Male viability effects of mitochondria are also selectable whenever there are indirect fitness effects of males on the fitness of their sisters. When either of these effects is sufficiently strong, we show that there are conditions that allow the spread of mitochondria with direct effects that are harmful to females, contrary to standard expectation. We discuss the implications of our findings for the evolution of organelles and endosymbionts and genomic conflict.
| S-EPMC2664385 | biostudies-literature
Project description:We consider an Empirical Bayes method to correct for the Winner's Curse phenomenon in genome-wide association studies. Our method utilizes the collective distribution of all odds ratios (ORs) to determine the appropriate correction for a particular single-nucleotide polymorphism (SNP). We can show that this approach is squared error optimal provided that this collective distribution is accurately estimated in its tails. To improve the performance when correcting the OR estimates for the most highly associated SNPs, we develop a second estimator that adaptively combines the Empirical Bayes estimator with a previously considered Conditional Likelihood estimator. The applications of these methods to both simulated and real data suggest improved performance in reducing selection bias.
| S-EPMC4048064 | biostudies-literature
Project description:Purpose of reviewHuman reproduction is a common process and one that unfolds over a relatively short time, but pregnancy and birth processes are challenging to study. Selection occurs at every step of this process (e.g., infertility, early pregnancy loss, and stillbirth), adding substantial bias to estimated exposure-outcome associations. Here we focus on selection in perinatal epidemiology, specifically, how it affects research question formulation, feasible study designs, and interpretation of results.Recent findingsApproaches have recently been proposed to address selection issues in perinatal epidemiology. One such approach is the ongoing pregnancies denominator for gestation-stratified analyses of infant outcomes. Similarly, bias resulting from left truncation has recently been termed "live birth bias," and a proposed solution is to control for common causes of selection variables (e.g., fecundity, fetal loss) and birth outcomes. However, these approaches have theoretical shortcomings, conflicting with the foundational epidemiologic concept of populations at risk for a given outcome.SummaryWe engage with epidemiologic theory and employ thought experiments to demonstrate the problems of using denominators that include units not "at risk" of the outcome. Fundamental (and commonsense) concerns of outcome definition and analysis (e.g., ensuring that all study participants are at risk for the outcome) should take precedence in formulating questions and analysis approach, as should choosing questions that stakeholders care about. Selection and resulting biases in human reproductive processes complicate estimation of unbiased exposure- outcome associations, but we should not focus solely (or even mostly) on minimizing such biases.
| S-EPMC6510491 | biostudies-literature
Project description: Not available
| S-EPMC10700517 | biostudies-literature
Project description:KomNet is a face image dataset originated from three media sources which can be used to recognize faces. KomNET contains face images which were collected from three different media sources, i.e. mobile phone camera, digital camera, and media social. The collected face dataset was frontal face image or facing the camera. The face dataset originated from the three media were collected without certain conditions such as lighting, background, haircut, mustache and beard, head cover, glasses, and differences of expression. KomNet dataset were collected from 50 clusters in which each of them consisted of 24 face images. To increase the number of training data, the face images were propagated with augmentation image technique, in which ten augmentations were used such as Rotate, Flip, Gaussian Blur, Gamma Contrast, Sigmoid Contrast, Sharpen, Emboss, Histogram Equalization, Hue and Saturation, Average Blur so the face images became 240 face images per cluster. The author trained the dataset by using CNN-based transfer learning VGGface. KomNET dataset are freely available on https://data.mendeley.com/datasets/hsv83m5zbb/2.
| S-EPMC7243044 | biostudies-literature
Project description:The FASSEG repository is composed by four subsets containing face images useful for training and testing automatic methods for the task of face segmentation. Threesubsets, namely frontal01, frontal02, and frontal03 are specifically built for performing frontal face segmentation. Frontal01 contains 70 original RGB images and the corresponding roughly labelledground-truth masks. Frontal02 contains the same image data, with high-precision labelled ground-truth masks. Frontal03 consists in 150 annotated face masks of twins captured in various orientations, illumination conditions and facial expressions. The last subset, namely multipose01, contains more than 200 faces in multiple poses and the corresponding ground-truth masks. For all face images, ground-truth masks are labelled on six classes (mouth, nose, eyes, hair, skin, and background).
| S-EPMC6454221 | biostudies-literature
Project description:Background and purposeIn clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis.MethodsWe present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images.ResultsExperimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively.
| S-EPMC4880324 | biostudies-literature
Project description:Psychophysical experiments suggested a relative importance of a narrow band of spatial frequencies for recognition of face identity in humans. There exists, however, no conclusive evidence of why it is that such frequencies are preferred. To address this question, I examined the amplitude spectra of a large number of face images and observed that face spectra generally fall off more steeply with spatial frequency compared with ordinary natural images. When external face features (such as hair) are suppressed, then whitening of the corresponding mean amplitude spectra revealed higher response amplitudes at those spatial frequencies which are deemed important for processing face identity. The results presented here therefore provide support for that face processing characteristics match corresponding stimulus properties.
| S-EPMC2603213 | biostudies-literature
Project description:The human ventral visual stream contains regions that respond selectively to faces over objects. However, it is unknown whether responses in these regions correlate with how face-like stimuli appear. Here, we use parameterized face silhouettes to manipulate the perceived face-likeness of stimuli and measure responses in face- and object-selective ventral regions with high-resolution fMRI. We first use "concentric hyper-sphere" (CH) sampling to define face silhouettes at different distances from the prototype face. Observers rate the stimuli as progressively more face-like the closer they are to the prototype face. Paradoxically, responses in both face- and object-selective regions decrease as face-likeness ratings increase. Because CH sampling produces blocks of stimuli whose variability is negatively correlated with face-likeness, this effect may be driven by more adaptation during high face-likeness (low-variability) blocks than during low face-likeness (high-variability) blocks. We tested this hypothesis by measuring responses to matched-variability (MV) blocks of stimuli with similar face-likeness ratings as with CH sampling. Critically, under MV sampling, we find a face-specific effect: responses in face-selective regions gradually increase with perceived face-likeness, but responses in object-selective regions are unchanged. Our studies provide novel evidence that face-selective responses correlate with the perceived face-likeness of stimuli, but this effect is revealed only when image variability is controlled across conditions. Finally, our data show that variability is a powerful factor that drives responses across the ventral stream. This indicates that controlling variability across conditions should be a critical tool in future neuroimaging studies of face and object representation.
| S-EPMC3404198 | biostudies-literature