Facial Photographs Research Articles

Clues for Facial Perceived Age: Exercise, Sun Protection, Photoaging, and Anthropometric Properties: An Observational Cross-Sectional Study.

The effect of environmental and genetic factors on the aging process is widely acknowledged. Yet, the extent to which each factor decisively contributes to the perception of looking younger or older remains a subject of debate. This study seeks to identify the factors linked to the perceived age among Turkish women. Ten assessors scored the perceived ages of 250 female patients based on facial photographs. The study aimed to assess the impact of environmental factors and anthropometric measurements on the perception of aging. A comprehensive analysis involved conducting 9 perioral and 6 periorbital anthropometric measurements on all study participants. Exercise (p = .001), mild photodamage (stage 1-2) (p = .001), consistent sunscreen use (p = .001), the length of the palpebral fissure (p = .043), and the height of the upper vermilion (p = .019) demonstrated significant associations with a more youthful appearance. Environmental factors, including exercise, photoprotection, sunscreen use, and anthropometric measurements such as palpebral fissure length and upper vermilion height, play a significant role in contributing to a more youthful appearance.

A Comparative Study: Photogrammetry and Cephalometric Analysis for Orthodontic Treatment Planning

Introduction: Cephalometric analysis constitutes the benchmark for diagnosing craniofacial morphology in orthodontic practice. However, the increasing demand for aesthetic standards has clinicians questioning whether ideal cephalometric measurements are synonymous with facial aesthetics since soft tissues do not necessarily follow the underlying skeletal profile. In addition, rising concerns about radiation exposure have necessitated a non-radiographic, non-invasive and cost-effective alternative. Facial photography has been an integral part of orthodontic treatment planning but usually as an adjunct to record keeping. However, recent studies have proven the reliability of using photographs for soft-tissue analysis. It provides an easily reproducible, economical and low technique-sensitive procedure to evaluate cranial morphology, especially for epidemiologic research. The primary aim of this study is to determine if photographic analysis could be utilised with precision for orthodontic diagnosis. Materials and Methods: The study was conducted in the Department of Orthodontics, J. S. S. Dental College and Hospital, Mysuru. This study selected 100 patients aged 12–28 years who had reported to the department for orthodontic treatment. Standardised profile photographs and lateral cephalograms were obtained from the patients as part of the records required. Traditional cephalometric (angular and linear) measurements and corresponding photographic ones used for sagittal and vertical assessment were analysed to draw inferences. Results: The reliability of the photographic technique was proven with intraclass correlation coefficients above 0.79. A highly significant correlation (P < 0.001) was found between all parameters. N-Sn-Pog/N’-Sn-Pog’ (0.946), SN-MP/TrN’-MP” (0.844) and FMA/FMA’ showed the highest coefficients of correlation. The photographic variable that best explained the variability of its analogous cephalometric measurement was the N-SN-Pog angle (r 2 = 0.895). Conclusion: The photographic method has proven to be repeatable and reproducible. With a homogeneous protocol, the photographic method is a practical alternative to lateral cephalograms, especially in cases that require a non-invasive, low-cost option.

A Dataset for Kinship Estimation from Image of Hand Using Machine Learning

Kinship (Familial relationships) detection is crucial in many fields and has applications in biometric security, adoption, forensic investigations, and more. It is also essential during wars and natural disasters like earthquakes since it may aid in reunion, missing person searches, establishing emergency contacts, and providing psychological support. The most common method of determining kinship is DNA analysis which is highly accurate. Another approach, which is noninvasive, uses facial photos with computer vision and machine learning algorithms for kinship estimation. Each part of the Human -body has its own embedded information that can be extracted and adopted for identification, verification, or classification of that person. Kinship recognition is based on finding traits that are shared by every family. We investigate the use of hand geometry for kinship detection, which is a new approach. Because of the available hand image Datasets do not contain kinship ground truth; therefore, we created our own dataset. This paper describes the tools, methodology, and details of the collected MKH, which stands for the Mosul Kinship Hand, images dataset. The images of MKH dataset were collected using a mobile phone camera with a suitable setup and consisted of 648 images for 81 individuals from 14 families (8 hand situations per person). This paper also presents the use of this dataset in kinship prediction using machine learning. Google MdiaPipe was used for hand detection, segmentation, and geometrical key points finding. Handcraft feature extraction was used to extract 43 distinctive geometrical features from each image. A neural network classifier was designed and trained to predict kinship, yielding about 93% prediction accuracy. The results of this novel approach demonstrated that the hand possesses biometric characteristics that may be used to establish kinship, and that the suggested method is a promising way as a kinship indicator.

Sex differences in social brain neural responses in autism: temporal profiles of configural face-processing within data-driven time windows

Face-processing timing differences may underlie visual social attention differences between autistic and non-autistic people, and males and females. This study investigates the timing of the effects of neurotype and sex on face-processing, and their dependence on age. We analysed EEG data during upright and inverted photographs of faces from 492 participants from the Longitudinal European Autism Project (141 neurotypical males, 76 neurotypical females, 202 autistic males, 73 autistic females; age 6–30 years). We detected timings of sex/diagnosis effects on event-related potential amplitudes at the posterior–temporal channel P8 with Bootstrapped Cluster-based Permutation Analysis and conducted Growth Curve Analysis (GCA) to investigate the timecourse and dependence on age of neural signals. The periods of influence of neurotype and sex overlapped but differed in onset (respectively, 260 and 310 ms post-stimulus), with sex effects lasting longer. GCA revealed a smaller and later amplitude peak in autistic female children compared to non-autistic female children; this difference decreased in adolescence and was not significant in adulthood. No age-dependent neurotype difference was significant in males. These findings indicate that sex and neurotype influence longer latency face processing and implicates cognitive rather than perceptual processing. Sex may have more overarching effects than neurotype on configural face processing.

AI-generated faces show lower morphological diversity than real faces do

Some recent studies suggest that artificial intelligence can create realistic human faces subjectively unrecognizable from faces of real people. We have compared static facial photographs of 197 real men with a sample of 200 male faces generated by artificial intelligence to test whether they converge in basic morphological characteristic such as shape variation and bilateral asymmetry. Both datasets depicted standardized faces of European men with a neutral expression. Then we used geometric morphometrics to investigate their facial morphology and calculate the measures of shape variation and asymmetry. We found that the natural faces of real individuals were more variable in their facial shape than the artificially generated faces were. Moreover, the artificially synthesized faces showed lower levels of facial asymmetry than the control group. Despite the rapid development of generative adversarial networks, natural faces are thus still statistically distinguishable from the artificial ones by objective measurements. We recommend the researchers in face perception, that aim to use artificially generated faces as ecologically valid stimuli, to check whether their stimuli morphological variance is comparable with that of natural faces in a target population.

Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressions.

In many medical disciplines, facial attractiveness is part of the diagnosis, yet its scoring might be confounded by facial expressions. The intent was to apply deep convolutional neural networks (CNN) to identify how facial expressions affect facial attractiveness and to explore whether a dedicated training of the CNN is able to reduce the bias of facial expressions. Frontal facial images (n = 840) of 40 female participants (mean age 24.5 years) were taken adapting a neutral facial expression and the six universal facial expressions. Facial attractiveness was computed by means of a face detector, deep convolutional neural networks, standard support vector regression for facial beauty, visual regularized collaborative filtering and a regression technique for handling visual queries without rating history. CNN was first trained on random facial photographs from a dating website and then further trained on the Chicago Face Database (CFD) to increase its suitability to medical conditions. Both algorithms scored every image for attractiveness. Facial expressions affect facial attractiveness scores significantly. Scores from CNN additionally trained on CFD had less variability between the expressions (range 54.3-60.9 compared to range: 32.6-49.5) and less variance within the scores (P ≤ .05), but also caused a shift in the ranking of the expressions' facial attractiveness. Facial expressions confound attractiveness scores. Training on norming images generated scores less susceptible to distortion, but more difficult to interpret. Scoring facial attractiveness based on CNN seems promising, but AI solutions must be developed on CNN trained to recognize facial expressions as distractors.

Development and validation of point-of-care mobile solution to guide the diagnosis of malnutrition: A multicenter, prospective cohort study.

11042 Background: Malnutrition has negative effects on patients with chronic diseases, leading to reduced treatment tolerance, increased risk of clinical complications, and even death. This research was aimed to develop a point-of-care program based on facial features to screen malnourished inpatient patients. Methods: In this prospective, multicenter, cohort study, we retrieved facial photograph and malnutrition screening scales from 4 different hospitals. We utilized a variety of machine learning models to explore whether the ocular area could serve as an enhanced region for facial recognition nutrition. Last, we utilized a facial area segmentation and weighted approach to retain the information of full-face features using a BP neural network and validated using Delong-test, IDI-test, and NRI-test. Overall, 619 inpatients’ facial photographs and their corresponding nutrition screening questionnaires were used to train, validate and test the machine learning model. Results: The Pearson correlation analysis showed a significant correlation (p<0.05) between the two questionnaires in all groups. The average AUC obtained from the five-fold cross-validation set was 0.886 (CI 0.843-0.930), 0.834 (CI 0.764-0.904), and 0.927 (CI 0.899-0.955) for the Cancer Inpatient Group, Other Inpatient Group, and All Inpatient Group, respectively, with the corresponding AUC obtained from the external validation set being 0.860 (CI 0.817-0.904), 0.843 (CI 0.796-0.889), and 0.887 (CI 0.829-0.944). Conclusions: The facial photograph-based point of-care mobile solution can screen malnutrition with good accuracy, showing its potential for screening malnutrition in inpatients in the hospital in different types of diseases.

Domain Adaptation and Semantic Drawing Driven Sketch-to-Photo Retrieval using Collaborative Generative Representation Learning

Abstract: Sketch-based face recognition is an interesting task in vision and multimedia research yet it is quite challenging due to the great difference between face photos and sketches. In this paper we propose a novel approach for photo-sketch generation aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars we develop a fully convolutional network to learn the end-to-end photosketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning in which the architecture is stacked by only convolutional kernels of very small sizes. The exemplar-based method is most frequently used in face sketch synthesis because of its efficiency in representing the nonlinear mapping between face photos and sketches. However, the sketches synthesized by existing exemplar-based methods suffer from block artifacts and blur effects. In addition, most exemplar-based methods ignore the training sketches in the weight representation process. To improve synthesis performance, a novel joint training model is proposed in this paper, taking sketches into consideration. First, we construct the joint training photo and sketch by concatenating the original photo and its sketch with a high-pass filtered image of their corresponding sketch. Then, an offline random sampling strategy is adopted for each test photo patch to select the joint training photo and sketch patches in the neighboring region. Finally, a novel locality constraint is designed to calculate the reconstruction weight, allowing the synthesized sketches to have more detailed information

A computer-aid speech rehabilitation system with mirrored video generating.

Aphasia is a communication disorder that affects the ability to process and produce language, which severely impacting their lives. Computer-aid exercise rehabilitation has shown to be highly effective for these patients. In our study, we proposed a speech rehabilitation system with mirrored therapy. The study goal is to construct a effective rehabilitation software for aphasia patients. This system collects patients' facial photos for mirrored video generation and speech synthesis. The visual feedback provided by the mirror creates an engaging and motivating experience for patients. And the evaluation platform employs machine learning technologies for assessing speech similarity. The sophisticated task-oriented rehabilitation training with mirror therapy is also presented for experiments performing. The performance of three tasks reaches the average scores of 83.9% for vowel exercises, 74.3% for word exercies and 77.8% for sentence training in real time. The user-friendly application system allows patients to carry out daily training tasks instructed by the therapists or the prompt information of menu. Our work demonstrated a promising intelligent mirror software system for reading-based aphasia rehabilitation.

Machine Learning Analysis of Facial Photographs for Predicting Bicuspid Aortic Valve

Machine Learning Analysis of Facial Photographs for Predicting Bicuspid Aortic Valve

Detecting Fake Images Using Convolutional Neutral Networks - A Deep Learning Approach

One popular generative model with many uses is the Generative Adversarial Network (GAN). Based on this unique concept, recent investigations have shown that it is feasible to produce high-quality fake face photos. The misuse of those fictitious faces in picture manipulation might lead to moral, ethical, and legal issues. To identify fake face images produced by the best method available at the moment, we first propose a Convolutional Neural Network (CNN) based method in this paper [20]. We also present experimental evidence demonstrating that the proposed method can achieve satisfactory results with an average accuracy over 99.4%. To further bolster the logic of our approach, we also offer comparison findings based on a few variations of the suggested CNN design, such as the high pass filter, the quantity of layer groups, and the activation function.

Changes in interoceptive accuracy related to emotional interference in somatic symptom disorder

ObjectiveThe somatic symptom disorder (SSD) is characterized by one or more distressing or disabling somatic symptoms accompanied by an excessive amount of time, energy and emotion related to the symptoms. These manifestations of SSD have been linked to alterations in perception and appraisal of bodily signals. We hypothesized that SSD patients would exhibit changes in interoceptive accuracy (IA), particularly when emotional processing is involved.MethodsTwenty-three patients with SSD and 20 healthy controls were recruited. IA was assessed using the heartbeat perception task. The task was performed in the absence of stimuli as well as in the presence of emotional interference, i.e., photographs of faces with an emotional expression. IA were examined for correlation with measures related to their somatic symptoms, including resting-state heart rate variability (HRV).ResultsThere was no significant difference in the absolute values of IA between patients with SSD and healthy controls, regardless of the condition. However, the degree of difference in IA without emotional interference and with neutral facial interference was greater in patients with SSD than in healthy controls (p = 0.039). The IA of patients with SSD also showed a significant correlation with low-frequency HRV (p = 0.004) and high-frequency HRV (p = 0.007).ConclusionSSD patients showed more significant changes in IA when neutral facial interference was given. These results suggest that bodily awareness is more affected by emotionally ambiguous stimuli in SSD patients than in healthy controls.

Digital Planning to Enhance Diagnosis and Precision in Correcting Excessive Gingival Display in the Presence of Asymmetrical Maxillary Position: A Case Report.

This case report addresses the treatment of excessive gingival display (EGD) in the context of maxillary asymmetry, zenith irregularities, and occlusal plane inclination. Digital planning was pivotal in formulating a precise treatment strategy by incorporating facial photographs, digital models, and cone-beam computed tomography data. Parameters, including occlusal plane inclination, teeth position, and lip-to-gingival margin relationships, were considered to ensure treatment alignment with the patient's facial characteristics. Notably, during the planning phase, it was evident that the conventional approach using the cementoenamel junction as the apical limit for incisions would result in asymmetry. Consequently, the gingival margin position was defined in accordance with facial and lip features. The chosen treatment, flapless crown lengthening, was tailored to the patient's thin phenotype and guided by measurements derived from digital planning. Postsurgery, the patient experienced a swift and painless recovery. A harmonious smile with a stable gingival margin position was achieved at the 1-year follow-up, seamlessly complementing the patient's facial attributes. This case underscores the importance of personalized EGD treatment and the value of digital planning in enhancing diagnostic accuracy and precise treatment planning, ultimately facilitating optimal treatment strategies.

Photographic Parameters in Three-Dimensional Facial Image Acquisition. A Scoping Review.

Orthognathic surgery is a viable and reproducible treatment for facial deformities. Despite the precision of the skeletal planning of surgical procedures, there is little information about the relations between hard and soft tissues in three-dimensional (3D) analysis, resulting in unpredictable soft tissue outcomes. Three-dimensional photography is a viable tool for soft tissue analysis because it is easy to use, has wide availability, low cost, and is harmless. This review aims to establish parameters for acquiring consistent and reproducible 3D facial images. A scoping review was conducted across PubMed, SCOPUS, Scientific Electronic Library Online (SciELO), and Web of Science databases, adhering to "Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews" guidelines. Articles presenting 3D facial photographs in the diagnostic phase were considered. A total of 79 articles were identified, of which 29 were selected for analysis. The predominant use of automated systems like 3dMD and VECTRA M3 was noted. User positioning has highest agreement among authors. Noteworthy aspects include the importance of proper lighting, facial expression, and dental positioning, with observed discrepancies and inconsistencies among authors. Finally, the authors proposed a 3D image acquisition protocol based on this research findings.

Risk factors for acne scarring in Ecuador.

Acne is a common disease that is associated with scarring and substantial psychosocial burden. The Global Burden of Skin Disease reported that the burden from acne as measured by disability-adjusted life years (DALYs) from 188 countries and specifically that it is greatest in Western Europe, high-income North America and Southern Latin America. This paper aimed to identify risk factors for acne scarring specific to the Ecuadorian population in order to adapt the 4-ASRAT tool accordingly. This was an observational prospective study. Participants were recruited to complete a survey that was developed based on the potential risk factors for acne scarring and had facial photographs taken. To determine risk factors and their respective weighting, a logistic regression was performed. The study included 404 participants. Results from univariate analyses indicated that being male (OR = 2.76 95%CI [1.72; 4.43]), having severe or very severe acne scarring (OR = 4.28 95%CI [1.24; 14.79]), acne duration over 1 year (OR = 1.71 95%CI [1.12; 2.60]), oily skin (OR = 2.02 95%CI [1.27; 3.22]) and the presence of acne on the neck (OR = 2.26 95%CI [1.30; 3.92]), were all significantly associated with the presence of acne scarring. Male sex (2.56 95%CI [1.58;4.17]), oily skin (1.96 95%CI [1.20;3.20]) and severe or very severe acne (3.75 95%CI [1.05;13.37]) remained significant risk factors for acne scarring in the multivariate analysis. By identifying acne scarring risk factors and applying the tool in everyday dermatology visits, we can reduce the physical and psychological burden that acne scarring causes in the adolescent and adult populations. Further research should be conducted to reassess potential risk factors and complete the adaptation of the tool for the Ecuadorian population, with a larger and more representative study population.

Abstract PO2-19-12: Enhancing skin appearance and quality of life in breast cancer survivors on aromatase inhibitor therapy

Abstract Background: Post-menopausal women with estrogen receptor positive breast cancer (BC) are typically prescribed endocrine therapy with an aromatase inhibitor (AI) for 5-10 years following BC diagnosis. AIs have multiple established side effects related to estrogen deprivation including vasomotor symptoms, vaginal dryness, and accelerated bone thinning. Many patients also complain about the impact of AIs on skin quality; however, limited data currently exists, particularly with objective measurements of skin quality. Canfield Sciences VISIA-CA is a device that can be used to objectively measure skin changes using patented comparison to norms analysis with a skin feature database to grade the skin relative to others in the same age and skin type. Herein, we aim to evaluate changes in skin quality with the Canfield Sciences VISIA-CA Device after initiation of AI therapy and investigate potential benefits of skincare intervention. Trial Design: This is a pilot study aiming to describe changes in skin quality after initiation of AI therapy in BC survivors through use of VISIA-CA technology and patient reported outcome measures. This study will allow us to collect data and identify the trend (if any) for a future larger study. Patients who consent for participation are given a baseline survey to assess self-reported quality of life, perception of skin quality, and self-image prior to initiation of AI. Patients also receive baseline skin measurement with the VISIA-CA device and facial skin photography. Baseline skincare regimen is documented. Any patients on prescription tretinoin products at time of baseline assessment are excluded from the study. Patients return 6 months after initiation of AI for follow-up surveys, updated VISIA-CA measurement, and facial skin photography. Patients then meet with a Mayo Clinic Cosmetic Center aesthetician for skincare intervention with recommended topical treatments including daily facial cleanser, exfoliator, toner, hydrating crème, and retinol-based product. Patients return for additional visits with updated survey, VISIA-CA measurement, and facial skin photography at 1 month, 3 months, and 6 months after skincare intervention. Patients are asked to complete skincare compliance diary to document adherence on a weekly basis. Eligibility Criteria: Post-menopausal patients with newly diagnosed ER-positive BC at Mayo Clinic Florida are identified for participation in the study prior to beginning AI therapy. Specific Aims: 1.) Evaluate the incidence and severity of skin changes after initiation of AI therapy in BC survivors through use of Canfield Sciences VISIA-CA technology, facial skin photography, and validated patient reported outcome measures. 2.) Assess for objective improvements in skin quality measurements after skincare intervention including a retinol-based product Statistical Methods: Continuous variables will be summarized as mean (standard deviation) or median (range) and categorical variables will be reported as frequency (percentage). The changes of skin measurement and patient reported outcome measurements will be calculated and summarized for the following time frames: 1) from initiation of AI therapy to 26 weeks after 2) from the initiation of skincare intervention and 4 weeks, 12 weeks, and 26 weeks after. Appropriate statistical plots will be used to illustrate the change. Target Accrual: 50 patients. Schedule of Events Citation Format: Lauren Cornell, Leila Tolaymat, Pooja Advani, Saranya Chumsri, Pincelli Thais. Enhancing skin appearance and quality of life in breast cancer survivors on aromatase inhibitor therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-19-12.

Facial Skin Aging Characteristics of the Old-Perceived Age in a 20-40 Years Old Chinese Female Population.

In the quest for a youthful appearance, women use a variety of anti- aging cosmetics. Defining skin problems is especially important for the selection of anti-aging solutions. However, the skin problems faced by Chinese women at different ages are different. This study aimed at Chinese women aged 20-40 years old and analyzed facial skin aging characteristics of those with old-perceived age. The total of 400 standard facial photographs from Chinese female volunteers aged 20-40 was assessed by another 126 Chinese women. The facial areas and skin aging characteristics that influenced age estimation were collected at the same time. Skin aging characteristics, including wrinkles, skin tone, pigmentation and pores, were analyzed based on facial photographs. Groupings were made based on deviation of perceived age from chronological age, and skin aging characteristics among groups were compared. The perceived age of Chinese women aged 20-40 has a moderate correlation with chronological age. Women aged 20-30 generally had an old-perceived age. Deep skin tone was a prominent problem in this age group, with those who had the older-perceived age observed the darker and redder skin tone. Women aged 31-40 were perceived partly old but appeared with wrinkle aggravation, as well as deepening of redness, enlarged pores, and increased pigmentation at the mid-face. The perceived older women also had more visible frown lines and darker skin tone at the upper face. The perceived age of Chinese women aged 20-40 tends to deviate from their chronological age. Women aged 20-30 with old-perceived age are associated with deep skin tone, even found darker and redder in older-perceived women group, while women aged 31-40 are associated with wrinkles and deterioration at mid-face area and upper-face problems drive more attention in older-perceived women group.

Facial Expression Recognition Using Convolutional Neural Network

Understanding others' intentions through nonverbal cues like facial emotions is crucial in human communication. To design and train Deep Learning Models, this paper describes in detail how Convolutional Neural Network Models are developed using tf. Keras. The aim is to Sort facial photos into one of the seven face detection classifiers, our model is developed in such a manner that it learns hidden nonlinearity from the entered facial images, which is vital for discriminating the form of emotion someone is expressing. The model proposed on the Lenet-5 architecture by Yann LeCun uses the subsampling, feature map, and activation function (ReLu) in between the convolutional layer and fully connected layer for the output soft-max activation function will be used. The FER-2013 dataset, which consists of 35,887 structured 48x48 pixel grayscale images, was used to train the CNN models. The training dataset has 28,709 elements, while the testing dataset has 3,589 elements, and while validation has 3,589 elements. Train and test are the two folder names used to organize the FER dataset. separated even further into distinct files, each holding a different kind of FER dataset class. To mitigate the overfitting of the dropout, batch normalization and the model are employed. Since this is a multiclass classification problem, we are utilizing the Soft-max activation function and the Rectified linear unit for non-linear operation (ReLu). We are training a categorical cross- entropy and matrix for accuracy based on the parameters to assess the constructed CNN model's performance by examining the training epoch history and we have used optimizer Adam (Adaptive Moment Estimation) with the learning rate of 0.0001. We obtained the accuracy of the LeNet-5 model on training at 95.49% and testing at 49.47% [13].

Face matching and self-insight: A Registered Report investigating individual differences in metacognitive sensitivity, efficiency, and bias.

Deciding whether two face photographs depict the same person or not can be a challenging task, and there are substantial individual differences in face matching ability. Far less is known about differences in metacognitive ability; that is, how well people can estimate the quality of their own face matching judgements. The purpose of this Registered Report was to determine the relationship between first-order performance in a face matching task, and three metacognitive measures: metacognitive sensitivity (the information exploited by metacognition), metacognitive efficiency (the quality of metacognitive processing itself), and metacognitive bias (the overall tendency towards high or low confidence). Participants completed a 200-trial unfamiliar face matching task, providing a second-order (metacognitive) confidence rating after each first-order (cognitive) response. Cognitive performance and metacognitive sensitivity showed a substantial positive association (rs = .60), suggesting that they were based on overlapping information, with poorer performers having lower quality information available. Once these differences in the information available to metacognition were accounted for, the quality of metacognitive processing itself (metacognitive efficiency) did not vary systematically with cognitive performance (rs = .00). Moreover, poor performers were not less or more confident overall than good performers, as cognitive performance and metacognitive bias did not correlate significantly (rs = .11). These findings allow us to move beyond consideration of group-level insight and inform regarding individual differences in cognitive and metacognitive abilities.

Michaels verpasste genetische Diagnose

AbstractMichael, after whom the Michael Foundation is named, had been diagnosed with neonatal-onset refractory epilepsy that challenged numerous epileptologists at the time. The seizures disappeared in adolescence but a neurodevelopmental disorder with intellectual disability remained. Still, to the best of our knowledge, genetic testing was never considered. Facial photographs of Michael potentially containing the clue to his diagnosis had been published multiple times in the past few decades and are known to a broad audience. Michael passed away in 2023 at the age of 80 years. A post-mortem analysis of Michael’s photographs employing both the human eye and artificial intelligence as well as retrospective anamnesis of Michael’s clinical symptoms revealed Williams–Beuren syndrome to be the by far most likely cause of Michael’s disorder. This diagnosis has implications for affected individuals as well as for the family and relatives and therefore illustrates the importance of genetic testing of neurodevelopmental disorders even in older individuals.