Women's Greater Ability to Perceive Happy Facial Emotion Automatically: Gender Differences in Affective Priming

There is evidence that women are better in recognizing their own and others' emotions. The female advantage in emotion recognition becomes even more apparent under conditions of rapid stimulus presentation. Affective priming paradigms have been developed to examine empirically whether facial emotion stimuli presented outside of conscious awareness color our impressions. It was observed that masked emotional facial expression has an affect congruent influence on subsequent judgments of neutral stimuli. The aim of the present study was to examine the effect of gender on affective priming based on negative and positive facial expression. In our priming experiment sad, happy, neutral, or no facial expression was briefly presented (for 33 ms) and masked by neutral faces which had to be evaluated. 81 young healthy volunteers (53 women) participated in the study. Subjects had no subjective awareness of emotional primes. Women did not differ from men with regard to age, education, intelligence, trait anxiety, or depressivity. In the whole sample, happy but not sad facial expression elicited valence congruent affective priming. Between-group analyses revealed that women manifested greater affective priming due to happy faces than men. Women seem to have a greater ability to perceive and respond to positive facial emotion at an automatic processing level compared to men. High perceptual sensitivity to minimal social-affective signals may contribute to women's advantage in understanding other persons' emotional states.     

Recognition of Intensive Valence and Arousal Affective States via Facial Electromyographic Activity in Young and Senior Adults

Background

Research suggests that interaction between humans and digital environments characterizes a form of companionship in addition to technical convenience. To this effect, humans have attempted to design computer systems able to demonstrably empathize with the human affective experience. Facial electromyography (EMG) is one such technique enabling machines to access to human affective states. Numerous studies have investigated the effects of valence emotions on facial EMG activity captured over the corrugator supercilii (frowning muscle) and zygomaticus major (smiling muscle). The arousal emotion, specifically, has not received much research attention, however. In the present study, we sought to identify intensive valence and arousal affective states via facial EMG activity.

Methods

Ten blocks of affective pictures were separated into five categories: neutral valence/low arousal (0VLA), positive valence/high arousal (PVHA), negative valence/high arousal (NVHA), positive valence/low arousal (PVLA), and negative valence/low arousal (NVLA), and the ability of each to elicit corresponding valence and arousal affective states was investigated at length. One hundred and thirteen participants were subjected to these stimuli and provided facial EMG. A set of 16 features based on the amplitude, frequency, predictability, and variability of signals was defined and classified using a support vector machine (SVM).

Results

We observed highly accurate classification rates based on the combined corrugator and zygomaticus EMG, ranging from 75.69% to 100.00% for the baseline and five affective states (0VLA, PVHA, PVLA, NVHA, and NVLA) in all individuals. There were significant differences in classification rate accuracy between senior and young adults, but there was no significant difference between female and male participants.

Conclusion

Our research provides robust evidences for recognition of intensive valence and arousal affective states in young and senior adults. These findings contribute to the successful future application of facial EMG for identifying user affective states in human machine interaction (HMI) or companion robotic systems (CRS).     

Tactile sensitivity,tactile acuity,and affective touch: from childhood to early adolescence

The development of somatosensation and affective touch acquires a central role throughout our lives, for several reasons. In adults, these functions are driven by different, neuroanatomically and functionally segregated fibres. To date, very little is known about the basic features of these fibres in childhood and this lack of knowledge is mirrored in the affective touch domain, where there are no studies on the main physiological features of the tactile processes linked to the stimulation of the hairy skin, namely the preferential site of affective touch. Thus, our study aims to analyze (1) tactile sensitivity and tactile acuity of children’s hairy forearms; (2) a possible dissociation between somatosensation and the affective touch; and (3) the presence/absence of the perception of affective touch already in childhood. To these aims, participants (160 children, aged 6 to 14?years), were administered with the Von Frey (tactile sensitivity) and the 2 Point Discrimination (tactile acuity) tests. Affective touch was measured following the classic protocol and pleasantness ratings were recorded. Our findings showed a correlation between age and somatosensation, suggesting a progressive reduction of sensitivity and acuity as age grows. Further, there was no overlap between affective touch and somatosensation, suggesting a behavioural segregation. Lastly, we found higher pleasantness ratings for Affective versus Neutral stimulations at all ages and an enhanced preference for Affective as age grows. We concluded that both somatosensation and affective touch are already present as two separate components of touch in childhood and change as a function of age.     

Odor Valence Linearly Modulates Attractiveness,but Not Age Assessment,of Invariant Facial Features in a Memory-Based Rating Task

Scented cosmetic products are used across cultures as a way to favorably influence one''s appearance. While crossmodal effects of odor valence on perceived attractiveness of facial features have been demonstrated experimentally, it is unknown whether they represent a phenomenon specific to affective processing. In this experiment, we presented odors in the context of a face battery with systematic feature manipulations during a speeded response task. Modulatory effects of linear increases of odor valence were investigated by juxtaposing subsequent memory-based ratings tasks – one predominantly affective (attractiveness) and a second, cognitive (age). The linear modulation pattern observed for attractiveness was consistent with additive effects of face and odor appraisal. Effects of odor valence on age perception were not linearly modulated and may be the result of cognitive interference. Affective and cognitive processing of faces thus appear to differ in their susceptibility to modulation by odors, likely as a result of privileged access of olfactory stimuli to affective brain networks. These results are critically discussed with respect to potential biases introduced by the preceding speeded response task.     

Implicit Processing of Visual Emotions Is Affected by Sound-Induced Affective States and Individual Affective Traits

The ability to recognize emotions contained in facial expressions are affected by both affective traits and states and varies widely between individuals. While affective traits are stable in time, affective states can be regulated more rapidly by environmental stimuli, such as music, that indirectly modulate the brain state. Here, we tested whether a relaxing or irritating sound environment affects implicit processing of facial expressions. Moreover, we investigated whether and how individual traits of anxiety and emotional control interact with this process. 32 healthy subjects performed an implicit emotion processing task (presented to subjects as a gender discrimination task) while the sound environment was defined either by a) a therapeutic music sequence (MusiCure), b) a noise sequence or c) silence. Individual changes in mood were sampled before and after the task by a computerized questionnaire. Additionally, emotional control and trait anxiety were assessed in a separate session by paper and pencil questionnaires. Results showed a better mood after the MusiCure condition compared with the other experimental conditions and faster responses to happy faces during MusiCure compared with angry faces during Noise. Moreover, individuals with higher trait anxiety were faster in performing the implicit emotion processing task during MusiCure compared with Silence. These findings suggest that sound-induced affective states are associated with differential responses to angry and happy emotional faces at an implicit stage of processing, and that a relaxing sound environment facilitates the implicit emotional processing in anxious individuals.     

Classification and Visualization Based on Derived Image Features: Application to Genetic Syndromes

Data transformations prior to analysis may be beneficial in classification tasks. In this article we investigate a set of such transformations on 2D graph-data derived from facial images and their effect on classification accuracy in a high-dimensional setting. These transformations are low-variance in the sense that each involves only a fixed small number of input features. We show that classification accuracy can be improved when penalized regression techniques are employed, as compared to a principal component analysis (PCA) pre-processing step. In our data example classification accuracy improves from 47% to 62% when switching from PCA to penalized regression. A second goal is to visualize the resulting classifiers. We develop importance plots highlighting the influence of coordinates in the original 2D space. Features used for classification are mapped to coordinates in the original images and combined into an importance measure for each pixel. These plots assist in assessing plausibility of classifiers, interpretation of classifiers, and determination of the relative importance of different features.     

Support vector machines for detecting age-related changes in running kinematics

Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts.     

Age correction in dementia--matching to a healthy brain

In recent research, many univariate and multivariate approaches have been proposed to improve automatic classification of various dementia syndromes using imaging data. Some of these methods do not provide the possibility to integrate possible confounding variables like age into the statistical evaluation. A similar problem sometimes exists in clinical studies, as it is not always possible to match different clinical groups to each other in all confounding variables, like for example, early-onset (age<65 years) and late-onset (age≥65) patients with Alzheimer's disease (AD). Here, we propose a simple method to control for possible effects of confounding variables such as age prior to statistical evaluation of magnetic resonance imaging (MRI) data using support vector machine classification (SVM) or voxel-based morphometry (VBM). We compare SVM results for the classification of 80 AD patients and 79 healthy control subjects based on MRI data with and without prior age correction. Additionally, we compare VBM results for the comparison of three different groups of AD patients differing in age with the same group of control subjects obtained without including age as covariate, with age as covariate or with prior age correction using the proposed method. SVM classification using the proposed method resulted in higher between-group classification accuracy compared to uncorrected data. Further, applying the proposed age correction substantially improved univariate detection of disease-related grey matter atrophy using VBM in AD patients differing in age from control subjects. The results suggest that the approach proposed in this work is generally suited to control for confounding variables such as age in SVM or VBM analyses. Accordingly, the approach might improve and extend the application of these methods in clinical neurosciences.     

Recognizing Age-Separated Face Images: Humans and Machines

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components - facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one''s gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario.     

Women's attractiveness is linked to expected age at menopause

A great number of studies have shown that features linked to immediate fertility explain a large part of the variance in female attractiveness. This is consistent with an evolutionary perspective, as men are expected to prefer females at the age at which fertility peaks (at least for short‐term relationships) in order to increase their reproductive success. However, for long‐term relationships, a high residual reproductive value (the expected future reproductive output, linked to age at menopause) becomes relevant as well. In that case, young age and late menopause are expected to be preferred by men. However, the extent to which facial features provide cues to the likely age at menopause has never been investigated so far. Here, we show that expected age at menopause is linked to facial attractiveness of young women. As age at menopause is heritable, we used the mother's age at menopause as a proxy for her daughter's expected age of menopause. We found that men judged faces of women with a later expected age at menopause as more attractive than those of women with an earlier expected age at menopause. This result holds when age, cues of immediate fertility and facial ageing were controlled for. Additionally, we found that the expected age at menopause was not correlated with any of the other variables considered (including immediate fertility cues and facial ageing). Our results show the existence of a new correlate of women's facial attractiveness, expected age at menopause, which is independent of immediate fertility cues and facial ageing.     

Improved classification of Alzheimer's disease data via removal of nuisance variability

Diagnosis of Alzheimer's disease is based on the results of neuropsychological tests and available supporting biomarkers such as the results of imaging studies. The results of the tests and the values of biomarkers are dependent on the nuisance features, such as age and gender. In order to improve diagnostic power, the effects of the nuisance features have to be removed from the data. In this paper, four types of interactions between classification features and nuisance features were identified. Three methods were tested to remove these interactions from the classification data. In stratified analysis, a homogeneous subgroup was generated from a training set. Data correction method utilized linear regression model to remove the effects of nuisance features from data. The third method was a combination of these two methods. The methods were tested using all the baseline data from the Alzheimer's Disease Neuroimaging Initiative database in two classification studies: classifying control subjects from Alzheimer's disease patients and discriminating stable and progressive mild cognitive impairment subjects. The results show that both stratified analysis and data correction are able to statistically significantly improve the classification accuracy of several neuropsychological tests and imaging biomarkers. The improvements were especially large for the classification of stable and progressive mild cognitive impairment subjects, where the best improvements observed were 6% units. The data correction method gave better results for imaging biomarkers, whereas stratified analysis worked well with the neuropsychological tests. In conclusion, the study shows that the excess variability caused by nuisance features should be removed from the data to improve the classification accuracy, and therefore, the reliability of diagnosis making.     

Affective responses in tamarins elicited by species-specific music

Theories of music evolution agree that human music has an affective influence on listeners. Tests of non-humans provided little evidence of preferences for human music. However, prosodic features of speech (‘motherese’) influence affective behaviour of non-verbal infants as well as domestic animals, suggesting that features of music can influence the behaviour of non-human species. We incorporated acoustical characteristics of tamarin affiliation vocalizations and tamarin threat vocalizations into corresponding pieces of music. We compared music composed for tamarins with that composed for humans. Tamarins were generally indifferent to playbacks of human music, but responded with increased arousal to tamarin threat vocalization based music, and with decreased activity and increased calm behaviour to tamarin affective vocalization based music. Affective components in human music may have evolutionary origins in the structure of calls of non-human animals. In addition, animal signals may have evolved to manage the behaviour of listeners by influencing their affective state.     

Semi-parametric area under the curve regression method for diagnostic studies with ordinal data

The classification accuracy of new diagnostic tests is based on receiver operating characteristic (ROC) curves. The area under the ROC curve (AUC) is one of the well-accepted summary measures for describing the accuracy of diagnostic tests. The AUC summary measure can vary by patient and testing characteristics. Thus, the performance of the test may be different in certain subpopulation of patients and readers. For this purpose, we propose a direct semi-parametric regression model for the non-parametric AUC measure for ordinal data while accounting for discrete and continuous covariates. The proposed method can be used to estimate the AUC value under degenerate data where certain rating categories are not observed. We will discuss the non-standard asymptotic theory, since the estimating functions were based on cross-correlated random variables. Simulation studies based on different classification models showed that the proposed model worked reasonably well with small percent bias and percent mean-squared error. The proposed method was applied to the prostate cancer study to estimate the AUC for four readers, and the carotid vessel study with age, gender, history of previous stroke, and total number of risk factors as covariates, to estimate the accuracy of the diagnostic test in the presence of subject-level covariates.     

Testosterone-mediated sex differences in the face shape during adolescence: subjective impressions and objective features

Sex identification of a face is essential for social cognition. Still, perceptual cues indicating the sex of a face, and mechanisms underlying their development, remain poorly understood. Previously, our group described objective age- and sex-related differences in faces of healthy male and female adolescents (12–18 years of age), as derived from magnetic resonance images (MRIs) of the adolescents' heads. In this study, we presented these adolescent faces to 60 female raters to determine which facial features most reliably predicted subjective sex identification. Identification accuracy correlated highly with specific MRI-derived facial features (e.g. broader forehead, chin, jaw, and nose). Facial features that most reliably cued male identity were associated with plasma levels of testosterone (above and beyond age). Perceptible sex differences in face shape are thus associated with specific facial features whose emergence may be, in part, driven by testosterone.     

Access to Awareness for Faces during Continuous Flash Suppression Is Not Modulated by Affective Knowledge

It is a controversially debated topic whether stimuli can be analyzed up to the semantic level when they are suppressed from visual awareness during continuous flash suppression (CFS). Here, we investigated whether affective knowledge, i.e., affective biographical information about faces, influences the time it takes for initially invisible faces with neutral expressions to overcome suppression and break into consciousness. To test this, we used negative, positive, and neutral famous faces as well as initially unfamiliar faces, which were associated with negative, positive or neutral biographical information. Affective knowledge influenced ratings of facial expressions, corroborating recent evidence and indicating the success of our affective learning paradigm. Furthermore, we replicated shorter suppression durations for upright than for inverted faces, demonstrating the suitability of our CFS paradigm. However, affective biographical information did not modulate suppression durations for newly learned faces, and even though suppression durations for famous faces were influenced by affective knowledge, these effects did not differ between upright and inverted faces, indicating that they might have been due to low-level visual differences. Thus, we did not obtain unequivocal evidence for genuine influences of affective biographical information on access to visual awareness for faces during CFS.     

The MPI facial expression database--a validated database of emotional and conversational facial expressions

The ability to communicate is one of the core aspects of human life. For this, we use not only verbal but also nonverbal signals of remarkable complexity. Among the latter, facial expressions belong to the most important information channels. Despite the large variety of facial expressions we use in daily life, research on facial expressions has so far mostly focused on the emotional aspect. Consequently, most databases of facial expressions available to the research community also include only emotional expressions, neglecting the largely unexplored aspect of conversational expressions. To fill this gap, we present the MPI facial expression database, which contains a large variety of natural emotional and conversational expressions. The database contains 55 different facial expressions performed by 19 German participants. Expressions were elicited with the help of a method-acting protocol, which guarantees both well-defined and natural facial expressions. The method-acting protocol was based on every-day scenarios, which are used to define the necessary context information for each expression. All facial expressions are available in three repetitions, in two intensities, as well as from three different camera angles. A detailed frame annotation is provided, from which a dynamic and a static version of the database have been created. In addition to describing the database in detail, we also present the results of an experiment with two conditions that serve to validate the context scenarios as well as the naturalness and recognizability of the video sequences. Our results provide clear evidence that conversational expressions can be recognized surprisingly well from visual information alone. The MPI facial expression database will enable researchers from different research fields (including the perceptual and cognitive sciences, but also affective computing, as well as computer vision) to investigate the processing of a wider range of natural facial expressions.     

An illustration of variable precision rough set theory: the gender classification of the European barn swallow (Hirundo rustica)

This paper introduces a new technique in the investigation of object classification and illustrates the potential use of this technique for the analysis of a range of biological data, using avian morphometric data as an example. The nascent variable precision rough sets (VPRS) model is introduced and compared with the decision tree method ID3 (through a ‘leave n out’ approach), using the same dataset of morphometric measures of European barn swallows (Hirundo rustica) and assessing the accuracy of gender classification based on these measures. The results demonstrate that the VPRS model, allied with the use of a modern method of discretization of data, is comparable with the more traditional non-parametric ID3 decision tree method. We show that, particularly in small samples, the VPRS model can improve classification and to a lesser extent prediction aspects over ID3. Furthermore, through the ‘leave n out’ approach, some indication can be produced of the relative importance of the different morphometric measures used in this problem. In this case we suggest that VPRS has advantages over ID3, as it intelligently uses more of the morphometric data available for the data classification, whilst placing less emphasis on variables with low reliability. In biological terms, the results suggest that the gender of swallows can be determined with reasonable accuracy from morphometric data and highlight the most important variables in this process. We suggest that both analysis techniques are potentially useful for the analysis of a range of different types of biological datasets, and that VPRS in particular has potential for application to a range of biological circumstances.     

The Morphometrics of “Masculinity” in Human Faces

In studies of social inference and human mate preference, a wide but inconsistent array of tools for computing facial masculinity has been devised. Several of these approaches implicitly assumed that the individual expression of sexually dimorphic shape features, which we refer to as maleness, resembles facial shape features perceived as masculine. We outline a morphometric strategy for estimating separately the face shape patterns that underlie perceived masculinity and maleness, and for computing individual scores for these shape patterns. We further show how faces with different degrees of masculinity or maleness can be constructed in a geometric morphometric framework. In an application of these methods to a set of human facial photographs, we found that shape features typically perceived as masculine are wide faces with a wide inter-orbital distance, a wide nose, thin lips, and a large and massive lower face. The individual expressions of this combination of shape features—the masculinity shape scores—were the best predictor of rated masculinity among the compared methods (r = 0.5). The shape features perceived as masculine only partly resembled the average face shape difference between males and females (sexual dimorphism). Discriminant functions and Procrustes distances to the female mean shape were poor predictors of perceived masculinity.     

Perception of emotional intonation of noisy speech signal with different acoustic parameters by adults of different age and gender

The listener-distinctive features of recognition of different emotional intonations (positive, negative and neutral) of male and female speakers in the presence or absence of background noise were studied in 49 adults aged 20-79 years. In all the listeners noise produced the most pronounced decrease in recognition accuracy for positive emotional intonation ("joy") as compared to other intonations, whereas it did not influence the recognition accuracy of "anger" in 65-79-year-old listeners. The higher emotion recognition rates of a noisy signal were observed for speech emotional intonations expressed by female speakers. Acoustic characteristics of noisy and clear speech signals underlying perception of speech emotional prosody were found for adult listeners of different age and gender.