Are there vocal cues to human developmental stability? Relationships between facial fluctuating asymmetry and voice attractiveness

Mate assessment based on physical characteristics: a review and reflection

Mate choice, and sex differences in romantic behaviours, represented one of the first major applications of evolutionary biology to human behaviour. This paper reviews Darwinian approaches to heterosexual mate assessment based on physical characteristics, placing the literature in its historical context (1871-1979), before turning (predominantly) to psychological research on attractiveness judgements based on physical characteristics. Attractiveness is consistently inferred across multiple modalities, with biological theories explaining why we differentiate certain individuals, on average, from others. Simultaneously, it is a judgement that varies systematically in light of our own traits, environment, and experiences. Over 30 years of research has generated robust effects alongside reasons to be humble in our lack of understanding of the precise physiological mechanisms involved in mate assessment. This review concludes with three questions to focus attention in further research, and proposes that our romantic preferences still provide a critical window into the evolution of human sexuality.

Dominance of face over voice in human attractiveness judgments: ERP evidence

The attractiveness of a person, a complex, and socially relevant type of information, is transmitted in many ways, not least through face and voice. However, it is unclear how the stimulus domains carrying attractiveness information interact. The present study explored the audiovisual perception of attractiveness in a Stroop-like paradigm using event-related potentials (ERPs). Participants were presented with face-voice pairs carrying congruent or incongruent attractiveness information and, in turn, judged the attractiveness level of each domain while ignoring the other. Voice attractiveness judgments were influenced by unattended face attractiveness, in terms of both, early perceptual encoding (N170, P200) as well as later evaluative stages (N400, LPC). In contrast, effects of unattended voice attractiveness on face attractiveness judgments were confined to early perceptual encoding (N170). These results demonstrate not only the interaction of multiple domains in human attractiveness perception at different processing stages but also a relative dominance of face over voice attractiveness.

Developmental instability, fluctuating asymmetry, and human psychological science

Developmental instability (DI) is an individual's inability to produce a specific developmental outcome under a given set of conditions, generally thought to result from random perturbations experienced during development. Fluctuating asymmetry (FA) - asymmetry on bilateral features that, on average, are symmetrical (or asymmetry deviating from that arising from design) - has been used to measure DI. Dating to half a century ago, and accelerating in the past three decades, psychological researchers have examined associations between FA (typically measured on bodily or facial features) and a host of outcomes of interest, including psychological disorders, cognitive ability, attractiveness, and sexual behavior. A decade ago, a meta-analysis on findings from nearly 100 studies extracted several conclusions. On average, small but statistically reliable associations between FA and traits of interest exist. Though modest, these associations are expected to greatly underestimate the strength of associations with underlying DI. Despite the massive sample size across studies, we still lack a good handle on which traits are most strongly affected by DI. A major methodological implication of the meta-analysis is that most studies have been, individually, woefully underpowered to detect associations. Though offering some intriguing findings, much research is the past decade too has been underpowered; hence, the newer literature is also likely noisy. Several large-scale studies are exceptions. Future progress depends on additional large-scale studies and researchers' sensitivity to power issues. As well, theoretical assumptions and conceptualizations of DI and FA driving psychological research may need revision to explain empirical patterns.

Non-visual cues and indirect strategies that enable discrimination of asymmetric mates

The postulates of developmental instability-sexual selection hypothesis is intensely debated among evolutionary biologists, wherein despite a large amount of empirical data, evidence for or against it has been largely inconclusive. A key assumption of this hypothesis is that animals assess symmetry in potential mates as an indicator of genetic quality (developmental stability), and consequently use this information to discriminate against those with higher asymmetries while choosing mates. However, the perceptional basis that must underlie such discriminatory behavior (is symmetry a signal or is symmetry signaled) is not clearly defined. It is also argued that since asymmetry levels in natural populations are very low, the low signal-to-noise ratio would make accurate assessment of symmetry both difficult and costly. Rather than attempting to validate this hypothesis or even as to whether animals assess mate symmetry, this review simply aims to examine the plausibility that animals perceive symmetry (directly or indirectly) and consequently discriminate against asymmetric mates in response to perceived irregularities during courtship. For this, we review mate choice and courtship literature to identify potential sensory cues that might advertise asymmetry or lead to discrimination of asymmetric individuals. Although signaling associated with mate choice is commonly multimodal, previous studies on asymmetry have mainly focused on visual perception. In the light of a recent study (Vijendravarma et al., 2022, Proceedings of the National Academy of Sciences of the United States of America, 119, e2116136119), this review attempts to balance this bias by emphasizing on non-visual perception of asymmetry. In conclusion, we discuss the methodological challenges associated with testing the role of multimodal cues in detecting mate asymmetry, and highlight the importance of considering ecological, behavioral, and evolutionary aspects of animals while interpreting empirical data that test such hypothesis.

Vocal modulation in human mating and competition

The human voice is dynamic, and people modulate their voices across different social interactions. This article presents a review of the literature examining natural vocal modulation in social contexts relevant to human mating and intrasexual competition. Altering acoustic parameters during speech, particularly pitch, in response to mating and competitive contexts can influence social perception and indicate certain qualities of the speaker. For instance, a lowered voice pitch is often used to exert dominance, display status and compete with rivals. Changes in voice can also serve as a salient medium for signalling a person's attraction to another, and there is evidence to support the notion that attraction and/or romantic interest can be distinguished through vocal tones alone. Individuals can purposely change their vocal behaviour in attempt to sound more attractive and to facilitate courtship success. Several findings also point to the effectiveness of vocal change as a mechanism for communicating relationship status. As future studies continue to explore vocal modulation in the arena of human mating, we will gain a better understanding of how and why vocal modulation varies across social contexts and its impact on receiver psychology. This article is part of the theme issue 'Voice modulation: from origin and mechanism to social impact (Part I)'.

Do 3D Face Images Capture Cues of Strength, Weight, and Height Better than 2D Face Images do?

Objectives

A large literature exists investigating the extent to which physical characteristics (e.g., strength, weight, and height) can be accurately assessed from face images. While most of these studies have employed two-dimensional (2D) face images as stimuli, some recent studies have used three-dimensional (3D) face images because they may contain cues not visible in 2D face images. As equipment required for 3D face images is considerably more expensive than that required for 2D face images, we here investigated how perceptual ratings of physical characteristics from 2D and 3D face images compare.

Methods

We tested whether 3D face images capture cues of strength, weight, and height better than 2D face images do by directly comparing the accuracy of strength, weight, and height ratings of 182 2D and 3D face images taken simultaneously. Strength, height and weight were rated by 66, 59 and 52 raters respectively, who viewed both 2D and 3D images.

Results

In line with previous studies, we found that weight and height can be judged somewhat accurately from faces; contrary to previous research, we found that people were relatively inaccurate at assessing strength. We found no evidence that physical characteristics could be judged more accurately from 3D than 2D images.

Conclusion

Our results suggest physical characteristics are perceived with similar accuracy from 2D and 3D face images. They also suggest that the substantial costs associated with collecting 3D face scans may not be justified for research on the accuracy of facial judgments of physical characteristics.

Corpus translation system based on Web Services and its application

In order to construct an efficient translation system, this paper constructs a corpus translation system based on Web Services. Moreover, this paper builds a network term detection system based on machine learning algorithms, expands the corpus data with the support of the crawler system, and uses WEB retrieval translation technology. At the same time, in response to the problem of sentence length changes in English abstracts, this paper proposes a method to obtain standard sentence length changes based on edit distance and SVM sorting. Based on requirements, this paper designs the architecture and data integration process of the data integration system. In addition, this paper outlines the detailed design and implementation process of each module of the system, and proposes a system performance optimization plan, and combines translation requirements to construct a corpus translation system based on Web Services. Finally, this paper designs experiments to verify the performance of the model. The research results show that the system constructed in this paper has a good application effect.

Pronunciation correction of students in music classroom based on computer voice simulation

In order to improve the pronunciation effect of students in music classroom, based on computer speech simulation technology, this paper combines speech recognition technology and speech feature extraction technology to summarize the various acoustic parameters of speech and the perception of the information by the human ear, and establishes the physical model and digital model of the speech signal. Through the analysis of voice signals, this paper selects several acoustic parameters that can reflect individual characteristics, and studies the methods of their extraction and adjustment to construct a pronunciation correction system for music students based on computer voice simulation. Finally, this paper designs experiments to verify the performance of the system constructed in this paper. The research results show that the system constructed in this paper has certain practical effects and enriches the expressive power of machine speech. The process of synthesizing speech is simple, and the practical effect is good, which meets the current music teaching needs.

Intelligent English translation system based on evolutionary multi-objective optimization algorithm

The difficulty of obtaining the characteristics of the corpus database of neural machine translation is a factor hindering its development. In order to improve the effect of English intelligent translation, based on the machine learning algorithm, this paper improves the multi-objective optimization algorithm to construct a model based on the English intelligent translation system. Moreover, this paper uses parallel corpus and monolingual corpus for model training and uses semi-supervised neural machine translation method to analyze the data processing path in detail and focuses on the analysis of node distribution and data processing flow. In addition, this paper introduces data-related regularization items through the probabilistic nature of the neural machine translation model and applies it to the monolingual corpus to help the training of the neural machine translation model. Finally, this paper designs experiments to verify the performance of this model. The research results show that the translation model constructed in this paper is highly intelligent and can meet actual translation needs.

Word order detection in English classroom teaching based on improved genetic algorithm of block coding

The performance of the speech recognition system for English classroom teaching is largely affected by the surrounding environment. These interference signals will seriously reduce the quality and intelligibility of the speech signal, thereby greatly reducing the performance of the far-field speech recognition system. Aiming at word order detection in English classroom teaching, this paper proposes an analysis model based on block coding and improved genetic algorithm. Moreover, for DNN-based single-channel speech enhancement algorithms, this paper proposes PDNNs and PLSTMs to solve the problem of serious performance degradation of prototype DNN speech enhancement under low signal-to-noise ratio. This method decomposes the entire enhancement task into multiple subtasks to complete, and the previously completed subtasks will provide prior knowledge for the subsequent subtasks, so that the subsequent subtasks can learn its goals better. In general, the experimental results prove the reliability of the model constructed in this paper.

Evaluation of English intercultural communication ability based on machine learning and fuzzy mathematics

The process of international integration is accelerating continuously, which puts forward certain requirements for the current college students’ communicative ability and English ability. Therefore, it is necessary to further improve the students’ cross-cultural communicative ability in combination with English teaching. This paper combines machine learning and fuzzy mathematics methods to build an evaluation model of English cross-cultural communication ability. Moreover, based on the basic assumptions of college students’ oral communication ability evaluation, this paper builds a basic model for college students’ oral communication ability evaluation. In addition, through factor analysis and correlation analysis, this paper verifies the hypothesis of the student’s oral communication ability evaluation model and obtains an optimized university student’s oral communication ability evaluation model. After the model’s hypothesis testing and a series of statistical analysis, the evaluation system of college students’ oral communication ability is finally obtained. Finally, this article combines the investigation and analysis to test the performance of the model constructed in this article. The research results show that the capability evaluation model constructed in this paper has good performance.

Quality evaluation of English pronunciation based on artificial emotion recognition and gaussian mixture model

At present, the posterior probability measure widely used in English speech recognition has the situation that the posterior probability measure of different phonemes cannot be consistent to measure the pronunciation quality of the phoneme and the acoustic modeling method of voice recognition is inconsistent with the evaluation target. Therefore, in order to improve the evaluation effect of English pronunciation quality in colleges and universities, this article is based on artificial emotion recognition and high-speed hybrid model to analyze and filter various clutters that affect speech quality to improve students’ English speech recognition. Moreover, this article uses the characteristics of the clutter and the target in the data to conform to different distributions and based on the clutter distribution characteristics obtained by statistics, this article realizes the suppression of the clutter to improve the target detection performance. In addition, the method proposed in this paper solves the limitations of the clutter suppression technology in the traditional voice detection system and improves the target detection performance. In order to study the pronunciation quality evaluation effect of this model and its effect in English teaching, this paper designs a controlled experiment to analyze the model’s performance. The research results show that the model constructed in this paper has good performance.

Improvement of English key competences based on machine learning and artificial intelligence technology

How to apply artificial intelligence technology to help education reform is a problem that teaching researchers need to solve urgently. Using artificial intelligence technology to improve the key competences of English subjects is the new direction of current English teaching development. This research combines machine learning technology to analyze the key competences assessment of English teaching disciplines and builds an evaluation model corresponding to the threshold. Moreover, on the basis of orderly mutual information, this study combines the maximum correlation and minimum redundancy theory to select the attribute algorithm to optimize the key competences assessment function of English subjects. In addition, in this study, the performance of the research model is analyzed through a comparative test, and the results are analyzed through actual numerical comparison and error comparison. The research results show that the recognition accuracy of this research model is closer than that of the real score, has higher accuracy, and has certain practical effects.

Artificial intelligence speech recognition model for correcting spoken English teaching

Artificial intelligence speech recognition technology is an important direction in the field of human-computer interaction. The use of speech recognition technology to assist teachers in the correction of spoken English pronunciation in teaching has certain effects and can help students without being constrained by places, time and teachers. Based on artificial intelligence speech recognition technology, this paper improves and analyzes speech recognition algorithms, and uses effective algorithms as the system algorithms of artificial intelligence models. Meanwhile, based on phoneme-level speech error correction, after introducing the basic knowledge, construction and training of acoustic models, the basic process of speech cutting, including the front-end processing of speech and the extraction of feature parameters, is elaborated. In addition, this study designed a control experiment to verify and analyze the artificial intelligence speech recognition correction model. The research results show that the method proposed in this paper has a certain effect.

An artificial intelligence recognition model for English online teaching

The traditional English online teaching model is limited by the teaching location and the difficulty of online teaching, which prevents teachers from controlling students. In order to improve the ability of the English online teaching model to supervise and recognize the status of students, this paper proposes an English online teaching model based on artificial intelligence technology, and adopts a positioning method based on an improved deep belief network for real-time position control and status recognition for students in online learning. Moreover, this study combines intelligent algorithms to build the model structure and verify the performance of the model. The results show that the performance of the model is good. In addition, on the basis of performance testing, the recognition effect of the artificial intelligence-based student online learning recognition model constructed in this paper is recognized. The results show that the model proposed in this paper has a certain effect and meets the actual needs of intelligent teaching.

An artificial intelligence based speech model for linguistics teaching

The linguistic artificial intelligence teaching model can be assisted by the intelligent speech recognition model. The traditional speech recognition algorithm has certain problems, so it cannot effectively eliminate speech noise. Based on the advantages of the linguistics teaching model, this article combines the linguistics teaching model and the artificial intelligence model to build an artificial intelligence assisted teaching model that can be used for classroom teaching. Moreover, this study improves the traditional algorithm and constructs an artificial intelligence linguistics teaching model based on the improved algorithm. The filtering part of noise includes preliminary filtering of speech signals based on the short-term energy detection method, and further detection and recognition of preliminary filtering speech signals based on the artificial intelligence model detection method. After these two steps of filtering and recognition, the voice file is sent to the client for processing and control. In addition, this study set up a control experiment to analyze the performance of the model. The research results show that the algorithm in this paper has a certain effect.

An improved machine learning algorithm for text-voice conversion of English letters into phonemes

Text-to-voice conversion is the core technology of intelligent translation system and intelligent teaching system, which is of great significance to English teaching and expansion. However, there are certain problems with the characteristics of factors in the current text-to- voice conversion. In order to improve the efficiency of text-to- voice conversion, this study improves the traditional machine learning algorithm and proposes an improved model that combines statistical language, factor analysis, and support vector machines. Moreover, the model is constructed as a training module and a testing module. The model combines statistical methods and rule methods in a unified framework to make full use of English language features to achieve automatic conversion of letter strings and phonetic features. In addition, in order to meet the needs of English text-to- voice conversion, this study builds a framework model, this study analyzes the performance of the model, and designs a control experiment to compare the performance of the model. The research results show that the method proposed in this paper has a certain effect.

An artificial intelligence based construction and application of english multimodal online reading mode

English reading plays an important role in promoting oral English and comprehensive English ability. At present, the traditional online reading mode is less effective. In order to change the shortcomings of traditional education, this article builds on the artificial intelligence algorithm and combines the spoken language spectrum algorithm to build the system. Moreover, this article combines with the actual needs to put forward endpoint detection and judgment criteria based on spectral entropy information, establishes a mathematical model of knowledge forgetting, and obtains an intelligent memory algorithm to guide students in personalized learning. In order to verify the effect of the model, this article takes the students in the experimental class and the control class as the experimental objects and compares the spoken pronunciation of the students and the comprehensive English scores of the students after the experiment. The research results show that the artificial intelligence-based English multimodal online reading mode platform constructed in this article has certain effects and can effectively improve students’ English scores.

Simulation of English speech emotion recognition based on transfer learning and CNN neural network

The difference between English and Chinese expressions is that English emphasizes the stress of syllables, so the recognition of English speech emotions plays an important role in learning English. This study uses transfer learning as the technical support to study English speech emotion recognition. The acoustic model based on weight transfer has two different training strategies: single-stage training and two-stage training strategy. By comparing the performance of the English speech emotion recognition model based on CNN neural network and the model proposed in this paper, the statistical comparison data is drawn into a statistical graph. The research results show that transfer learning has certain advantages over other algorithms in English speech emotion recognition. In the subsequent teaching and real-time translation equipment research, transfer learning can be applied to English models.

Feature recognition of spoken Japanese input based on support vector machine

The feature recognition of spoken Japanese is an effective carrier for Sino-Japanese communication. At present, most of the existing intelligent translation equipment only have equipment that converts English into other languages, and some Japanese translation systems have problems with accuracy and real-time translation. Based on this, based on support vector machines, this research studies and recognizes the input features of spoken Japanese, and improves traditional algorithms to adapt to the needs of spoken language recognition. Moreover, this study uses improved spectral subtraction based on spectral entropy for enhancement processing, modifies Mel filter bank, and introduces several improved MFCC feature parameters. In addition, this study selects an improved feature recognition algorithm suitable for this research system and conducts experimental analysis of input feature recognition of spoken Japanese on the basis of this research model. The research results show that this research model has improved the recognition speed and recognition accuracy, and this research model meets the system requirements, which can provide a reference for subsequent related research.

Design of English text-to-speech conversion algorithm based on machine learning

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

Linking human male vocal parameters to perceptions, body morphology, strength and hormonal profiles in contexts of sexual selection

Sexual selection appears to have shaped the acoustic signals of diverse species, including humans. Deep, resonant vocalizations in particular may function in attracting mates and/or intimidating same-sex competitors. Evidence for these adaptive functions in human males derives predominantly from perception studies in which vocal acoustic parameters were manipulated using specialist software. This approach affords tight experimental control but provides little ecological validity, especially when the target acoustic parameters vary naturally with other parameters. Furthermore, such experimental studies provide no information about what acoustic variables indicate about the speaker-that is, why attention to vocal cues may be favored in intrasexual and intersexual contexts. Using voice recordings with high ecological validity from 160 male speakers and biomarkers of condition, including baseline cortisol and testosterone levels, body morphology and strength, we tested a series of pre-registered hypotheses relating to both perceptions and underlying condition of the speaker. We found negative curvilinear and negative linear relationships between male fundamental frequency (fo) and female perceptions of attractiveness and male perceptions of dominance. In addition, cortisol and testosterone negatively interacted in predicting fo, and strength and measures of body size negatively predicted formant frequencies (Pf). Meta-analyses of the present results and those from two previous samples confirmed that fonegatively predicted testosterone only among men with lower cortisol levels. This research offers empirical evidence of possible evolutionary functions for attention to men's vocal characteristics in contexts of sexual selection.

No evidence for an association between facial fluctuating asymmetry and vocal attractiveness in men or women

Facial fluctuating asymmetry (FA), presumably a proxy measure of developmental instability, has been proposed to inversely relate to vocal attractiveness, which may convey information on heritable fitness benefits. Using an improved method of measuring facial FA, we sought to replicate two recent studies that showed an inverse correlation of facial FA with vocal attractiveness. In two samples of men (N = 165) and women (N = 157), we investigated the association of automatically measured facial FA based on 3D face scans with male and female observer-rated attractiveness of voice recordings. No significant associations were found for men or women, also when controlling for facial attractiveness, age, and body mass index. Equivalence tests show that effect sizes were significantly smaller than previous meta-analytic effects, providing robust evidence against a link of facial FA with vocal attractiveness. Thus, our study contradicts earlier findings that vocal attractiveness may signal genetic quality in humans via an association with FA.

A Multivariate Approach to Determine the Dimensionality of Human Facial Asymmetry

Many studies have suggested that developmental instability (DI) could lead to asymmetric development, otherwise known as fluctuating asymmetry (FA). Several attempts to unravel the biological meaning of FA have been made, yet the main step in estimating FA is to remove the effects of directional asymmetry (DA), which is defined as the average bilateral asymmetry at the population level. Here, we demonstrate in a multivariate context that the conventional method of DA correction does not adequately compensate for the effects of DA in other dimensions of asymmetry. This appears to be due to the presence of between-individual variation along the DA dimension. Consequently, we propose to decompose asymmetry into its different orthogonal dimensions, where we introduce a new measure of asymmetry, namely fluctuating directional asymmetry (F-DA). This measure describes individual variation in the dimension of DA, and can be used to adequately correct the asymmetry measurements for the presence of DA. We provide evidence that this measure can be useful in disentangling the different dimensions of asymmetry, and further studies on this measure can provide valuable insight into the underlying biological processes leading to these different asymmetry dimensions.

Human voice attractiveness processing: Electrophysiological evidence

Voice attractiveness plays a significant role in social interaction and mate choice. However, how listeners perceive attractive voices and whether this process is mandatory, is poorly understood. The current study explores this question using event-related brain potentials. Participants listened to syllables spoken by male and female voices with high or low attractiveness while completing an implicit (voice un-related) tone detection task or explicitly judging voice attractiveness. In both tasks, attractive male voices elicited a larger N1 than unattractive voices. However, an effect of voice attractiveness on the late positive complex (LPC) was only seen in the explicit task but it was present to both same- and opposite-sex voices. Taken together, voice attractiveness processing during early stages appears to be rapid and mandatory and related to mate selection, whereas during later elaborated processing, voice attractiveness is strategic and aesthetics-based, requiring attentional resources.

Sex Differences in Facial and Vocal Attractiveness Among College Students in China

This study aims to investigate sex differences in ratings for facial attractiveness (FA) and vocal attractiveness (VA). Participants (60 undergraduates in Study 1 and 111 undergraduates in Study 2) rated the attractiveness of computerized face images and voice recordings of men and women. In Study 1, face images and voice recordings were presented separately. Results indicated that men generally rated voice recordings of women more attractive than those of men, whereas women did not show different attractiveness ratings for voices of men vs. women. In Study 2, face images and voice recordings were paired as multimodal stimuli and presented simultaneously. Results indicated that men rated multimodal stimuli of women as more attractive than those of men, whereas women did not differentiate multimodal stimuli of men vs. women. We found that, compared to VA, FA had a stronger influence on participants' overall evaluations. Finally, we tested the difference between "original multimodal stimuli" (OMS) and "non-original multimodal stimuli" (non-OMS) and found the "OMS-facilitating effect." Taken together, findings indicated some sex differences in FA and VA in the current study, which could be used to interpret behaviors of sexual selection, human mate preferences, and designs and popularization of sex robots.

Measuring asymmetry from high-density 3D surface scans: An application to human faces

Perfect bilateral symmetry is the optimal outcome of the development of bilateral traits in the absence of developmental perturbations. Any random perturbation in this perfect symmetrical state is called Fluctuating Asymmetry (FA). Many studies have been conducted on FA as an indicator of Developmental Instability (DI) and its possible link with stress and individual quality in general and with attractiveness, health and level of masculinity or femininity in humans. Most human studies of facial asymmetry use 2D pictures and a limited number of landmarks. We developed a protocol to utilize high-density 3D scans of human faces to measure the level of asymmetry. A completely symmetric spatially dense anthropometric mask with paired vertices is non-rigidly mapped on target faces using an Iterative Closest Point (ICP) registration algorithm. A set of 19 manually indicated landmarks were used to validate the mapping precision. The protocol's accuracy in FA calculation is assessed, and results show that a spatially dense approach is more accurate. In addition, it generates an integrated asymmetry estimate across the entire face. Finally, the automatic nature of the protocol provides a great advantage by omitting the tedious step of manual landmark indication on the biological structure of interest.

Attractiveness Is Multimodal: Beauty Is Also in the Nose and Ear of the Beholder

Attractiveness plays a central role in human non-verbal communication and has been broadly examined in diverse subfields of contemporary psychology. Researchers have garnered compelling evidence in support of the evolutionary functions of physical attractiveness and its role in our daily lives, while at the same time, having largely ignored the significant contribution of non-visual modalities and the relationships among them. Acoustic and olfactory cues can, separately or in combination, strongly influence the perceived attractiveness of an individual and therefore attitudes and actions toward that person. Here, we discuss the relative importance of visual, auditory and olfactory traits in judgments of attractiveness, and review neural and behavioral studies that support the highly complex and multimodal nature of person perception. Further, we discuss three alternative evolutionary hypotheses aimed at explaining the function of multiple indices of attractiveness. In this review, we provide several lines of evidence supporting the importance of the voice, body odor, and facial and body appearance in the perception of attractiveness and mate preferences, and therefore the critical need to incorporate cross-modal perception and multisensory integration into future research on human physical attractiveness.

Your Cheatin' Voice Will Tell on You: Detection of Past Infidelity from Voice

Evidence suggests that many physical, behavioral, and trait qualities can be detected solely from the sound of a person's voice, irrespective of the semantic information conveyed through speech. This study examined whether raters could accurately assess the likelihood that a person has cheated on committed, romantic partners simply by hearing the speaker's voice. Independent raters heard voice samples of individuals who self-reported that they either cheated or had never cheated on their romantic partners. To control for aspects that may clue a listener to the speaker's mate value, we used voice samples that did not differ between these groups for voice attractiveness, age, voice pitch, and other acoustic measures. We found that participants indeed rated the voices of those who had a history of cheating as more likely to cheat. Male speakers were given higher ratings for cheating, while female raters were more likely to ascribe the likelihood to cheat to speakers. Additionally, we manipulated the pitch of the voice samples, and for both sexes, the lower pitched versions were consistently rated to be from those who were more likely to have cheated. Regardless of the pitch manipulation, speakers were able to assess actual history of infidelity; the one exception was that men's accuracy decreased when judging women whose voices were lowered. These findings expand upon the idea that the human voice may be of value as a cheater detection tool and very thin slices of vocal information are all that is needed to make certain assessments about others.