Saliency in Context: The Effect of Context on the Diagnosticity of Facial Features

<p>How might faces we have learned be represented in our memory? Researchers believe that our memory for faces is based on building a robust averaged representation comprised of the stable aspects of the face (i.e., eyes, nose, mouth). However, anecdotal evidence suggests this one size fits all approach to face representations may not be correct. A new theory suggests our representation for faces is instead based on a dynamic weighting, wherein what is seen as most diagnostic during learning will be encoded to a greater extent than other features in the face. One factor that may be especially important for a weighted representation is the context in which a face is initially viewed. Dependent on the context of learning, certain features may appear more distinctive than others and therefore be deemed diagnostic and receive representational weight. The current study had participants learn four faces with one manipulated to appear distinctive in the experimental context by having a unique hair colour (Experiment 1), or eye colour (Experiment 2) compared to the other faces. Participants then completed a recognition task where the feature of interest (i.e., hair or eye colour) was either available or unavailable (i.e., bald and eye closed conditions) for recognition. Findings suggested recognition was disrupted when the diagnostic feature was unavailable compared to when that feature was available, across both distinctive and typical faces. Interestingly, Experiment 2 showed a distinctiveness performance advantage compared to Experiment 1, most likely because neighbouring features may be more diagnostic than others during recognition. In addition, further exploratory analysis showed the order of the test could further affect what was encoded.</p>

Saliency in Context: The Effect of Context on the Diagnosticity of Facial Features

<p>How might faces we have learned be represented in our memory? Researchers believe that our memory for faces is based on building a robust averaged representation comprised of the stable aspects of the face (i.e., eyes, nose, mouth). However, anecdotal evidence suggests this one size fits all approach to face representations may not be correct. A new theory suggests our representation for faces is instead based on a dynamic weighting, wherein what is seen as most diagnostic during learning will be encoded to a greater extent than other features in the face. One factor that may be especially important for a weighted representation is the context in which a face is initially viewed. Dependent on the context of learning, certain features may appear more distinctive than others and therefore be deemed diagnostic and receive representational weight. The current study had participants learn four faces with one manipulated to appear distinctive in the experimental context by having a unique hair colour (Experiment 1), or eye colour (Experiment 2) compared to the other faces. Participants then completed a recognition task where the feature of interest (i.e., hair or eye colour) was either available or unavailable (i.e., bald and eye closed conditions) for recognition. Findings suggested recognition was disrupted when the diagnostic feature was unavailable compared to when that feature was available, across both distinctive and typical faces. Interestingly, Experiment 2 showed a distinctiveness performance advantage compared to Experiment 1, most likely because neighbouring features may be more diagnostic than others during recognition. In addition, further exploratory analysis showed the order of the test could further affect what was encoded.</p>

Multivariate Characterization of Morpho-biometric Traits of Indigenous Helmeted Guinea Fowl (Numida meleagris) in Nigeria

This study was embarked upon to characterise phenotypically helmeted guinea fowls in three agro-ecologies in Nigeria using multivariate approach. Eighteen biometric characters, four morphological indices and eleven qualitative (phaneroptic) traits were investigated in a total of 569 adult birds (158 males and 411 females). Descriptive statistics, non-parametric Kruskal–Wallis H test followed by the Mann–Whitney U test for post hoc, Multiple Correspondence Analysis (MCA), General Linear Model, Canonical Discriminant Analysis, Categorical Principal Component Analysis and Decision Trees were employed to discern the effects of agro-ecological zone and sex on the morphostructural parameters. Agro-ecology had significant effect (P<0.05; P <0.01) on all the colour traits. In general, the most frequently observed colour phenotype of guinea fowl had pearl plumage colour (54.0%), pale red skin colour (94.2%), black shank colour (68.7%), brown eye colour (49.7%), white earlobe colour (54.8%) and brown helmet colour (72.6%). The frequencies of helmet shape and wattle size were significantly influenced (P <0.01) by agro-ecology and sex. Overall, birds from the Southern Guinea Savanna zone had significantly higher values (P <0.05) for most biometric traits compared to their Sudano-Sahelian and Tropical Rainforest counterparts. They were also more compact (120.83±1.61 vs. 113.96±0.97 vs. 111.33±1.19) and had lesser condition index (8.542±0.17 vs. 9.92±0.10 vs. 9.61±0.13) than their counterparts in the two other zones. The interaction between agro-ecology and sex had significant effect (P <0.05) on some quantitative variables. The MCA and discriminant analysis revealed considerable intermingling of the phaneroptic, biometric traits and body indices especially between the Sudano-Sahelian and Tropical Rainforest birds. Inspite of the high level of genetic admixture, the guinea fowl populations could best be distinguished using wing length, body length and eye colour. However, further complementary work on genomics will guide future selection and breeding programmes geared towards improving the productivity, survival and environmental adaptation of indigenous helmeted guinea fowls in the tropics.

Clothing Aesthetics: Consistent Colour Choices to Match Fair and Tanned Skin Tones

Fashion stylists advise clothing colours according to personal categories that depend on skin, hair and eye colour. These categories are not defined scientifically, and advised colours are inconsistent. Such caveats may explain the lack of formal tests of clothing colour aesthetics. We assessed whether observers preferred clothing colours that are linked to variation in melanin levels among White women. For this, we presented 12 women's faces: six with fair skin (relatively lower in melanin) and six with tanned skin (relatively higher in melanin). Across two experiments, observers ( N = 96 and 75) selected the colour (hue and saturation or hue and value) of simulated clothing that most suited the skin tone of each face. Observers showed strong preferences for red and blue hues, and in addition favoured ‘cool’ blue hues to match fair skin and ‘warm’ orange/red hues to match tanned skin. This finding suggests that skin tone can determine colour preferences for clothes.

Investigating the genetic architecture of eye colour in a Canadian cohort

The main factors that determine eye colour are the amount of melanin concentrated in iris melanocytes, as well as the shape and distribution of melanosomes. Eye colour is highly variable in populations with European ancestry, in which eye colour categories cover a continuum of low to high quantities of melanin accumulated in the iris. A few polymorphisms in the HERC2/OCA2 locus in chromosome 15 have the largest effect on eye colour in these populations, although there is evidence of other variants in the locus and across the genome also influencing eye colour. To improve our understanding of the genetic loci determining eye colour, we performed a meta-analysis of genome-wide association studies in a Canadian cohort of European ancestry (N= 5,641) and investigated putative causal variants. Our fine-mapping results indicate that there are several candidate causal signals in the HERC2/OCA2 region, whereas other significant loci in the genome likely harbour a single causal signal (TYR, TYRP1, IRF4, SLC24A4). Furthermore, a short subset of the associated eye colour regions was colocalized with the gene expression or methylation profiles of cultured melanocytes (HERC2, OCA2), and transcriptome-wide association studies highlighted the expression of two genes associated with eye colour: SLC24A4 and OCA2. Finally, genetic correlations of eye and hair colour from the same cohort suggest high pleiotropy at the genome level, but locus-level evidence hints at several differences in the genetic architecture of both traits. Overall, we provide a better picture of how polymorphisms modulate eye colour variation, particularly in the HERC2/OCA2 locus, which may be a consequence of specific molecular processes in the iris melanocytes.

What colour are your eyes? Teaching the genetics of eye colour & colour vision. Edridge Green Lecture RCOphth Annual Congress Glasgow May 2019

Eye colour and colour perception are excellent examples to use when teaching genetics as they encompass not simply the basic Mendelian genetics of dominant, recessive and X-linked disorders, but also many of the new concepts such as non-allelic diseases, polygenic disease, phenocopies, genome-wide association study (GWAS), founder effects, gene-environment interaction, evolutionary drivers for variations, copy number variation, insertions deletions, methylation and gene inactivation. Beyond genetics, colour perception touches on concepts involving optics, physics, physiology and psychology and can capture the imagination of the population, as we saw with social media trend of “#the dress”. Television shows such as Game of Thrones focused attention on the eye colour of characters, as well as their Dire-wolves and Dragons. These themes in popular culture can be leveraged as tools to teach and engage everyone in genetics, which is now a key component in all eye diseases. As the explosion of data from genomics, big data and artificial intelligence transforms medicine, ophthalmologists need to be genetically literate. Genetics is relevant, not just for Inherited Retinal Diseases and congenital abnormalities but also for the leading causes of blindness: age-related macular degeneration, glaucoma, myopia, diabetic retinopathy and cataract. Genetics should be part of the armamentarium of every practicing ophthalmologist. We need to ask every patient about their family history. In the near future, patients will attend eye clinics with genetic results showing they are at high risk of certain eye diseases and ophthalmologists will need to know how to screen, follow and treat these patients.

Searching for improvements in predicting human eye colour from DNA

Increasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

Prediction of Eye Colour in Scandinavians Using the EyeColour 11 (EC11) SNP Set

Description of a perpetrator’s eye colour can be an important investigative lead in a forensic case with no apparent suspects. Herein, we present 11 SNPs (Eye Colour 11-EC11) that are important for eye colour prediction and eye colour prediction models for a two-category reporting system (blue and brown) and a three-category system (blue, intermediate, and brown). The EC11 SNPs were carefully selected from 44 pigmentary variants in seven genes previously found to be associated with eye colours in 757 Europeans (Danes, Swedes, and Italians). Mathematical models using three different reporting systems: a quantitative system (PIE-score), a two-category system (blue and brown), and a three-category system (blue, intermediate, brown) were used to rank the variants. SNPs with a sufficient mean variable importance (above 0.3%) were selected for EC11. Eye colour prediction models using the EC11 SNPs were developed using leave-one-out cross-validation (LOOCV) in an independent data set of 523 Norwegian individuals. Performance of the EC11 models for the two- and three-category system was compared with models based on the IrisPlex SNPs and the most important eye colour locus, rs12913832. We also compared model performances with the IrisPlex online tool (IrisPlex Web). The EC11 eye colour prediction models performed slightly better than the IrisPlex and rs12913832 models in all reporting systems and better than the IrisPlex Web in the three-category system. Three important points to consider prior to the implementation of eye colour prediction in a forensic genetic setting are discussed: (1) the reference population, (2) the SNP set, and (3) the reporting strategy.

Non-consensual personified sexbots: an intrinsic wrong

Humanoid robots used for sexual purposes (sexbots) are beginning to look increasingly lifelike. It is possible for a user to have a bespoke sexbot created which matches their exact requirements in skin pigmentation, hair and eye colour, body shape, and genital design. This means that it is possible—and increasingly easy—for a sexbot to be created which bears a very high degree of resemblance to a particular person. There is a small but steadily increasing literature exploring some of the ethical issues surrounding sexbots, however sexbots made to look like particular people is something which, as yet, has not been philosophically addressed in the literature. In this essay I argue that creating a lifelike sexbot to represent and resemble someone is an act of sexual objectification which morally requires consent, and that doing so without the person’s consent is intrinsically wrong. I consider two sexbot creators: Roy and Fred. Roy creates a sexbot of Katie with her consent, and Fred creates a sexbot of Jane without her consent. I draw on the work of Alan Goldman, Rae Langton, and Martha Nussbaum in particular to demonstrate that creating a sexbot of a particular person requires consent if it is to be intrinsically permissible.