Facial Expressions and Emotions Research Papers (original) (raw)

2025, Journal of Experimental Psychopathology

The present study examined (a) processing biases for emotional facial stimuli in a sample of 355 4- to 12-year-old non-clinical children, (b) developmental patterns of such biases, and (c) to what extent biases were related to social anxiety and the temperamental trait of behavioral inhibition in children of various ages. Processing biases were assessed with a dot probe task and a dynamic emotion recognition paradigm (i.e., morph task), whereas children's levels of social anxiety and behavioral inhibition were measured by means of parent-report. Results showed that on the morph task children were generally faster in detecting happy faces compared to angry faces, and this effect was not qualified by age, social anxiety, or behavioral inhibition. Further analyses revealed no significant effect of age on bias scores. However, analyses did reveal two classes in the data with one class mainly consisting of younger children and the other class predominantly composed of older children:...

2025

VISION BASED ARTIFICIAL INTELLIGENCE FOR OPTIMIZING E-COMMERCE EXPERIENCES IN VIRTUAL REALITY Advancements in artificial intelligence (AI) and digital technologies have deeply reshaped consumer behavior and marketing strategies, demanding innovative approaches to decoding and optimizing customer engagement. This dissertation explores the potential of vision deep neural networks, generative AI, and virtual reality (VR) to analyze emotional and behavioral responses and enhance strategic business insights in digital commerce. This research focuses on convolutional neural network (CNN) architectures and evaluates their effectiveness in predicting consumer engagement through facial emotion recognition (FER). The dissertation addresses limitations in FER datasets by integrating synthetic data generated using generative adversarial networks (GANs) and real-world open data extracted from social media. This hybrid approach enhances model generalizability across diverse demographics and advertisement categories. The dissertation further investigates the role of immersive VR environments in influencing consumer engagement and purchase intent. By leveraging multi-modal causal analysis, it examines the interplay between VR design complexity, exposure sequencing, and emotional responses, providing actionable insights for optimizing e-commerce experiences. Ethical considerations are central to this research, which address biases, privacy concerns, and transparency in AI-driven decision-making. The findings contribute to the development of robust, inclusive, and scalable frameworks for personalized commerce, offering a transformative approach to understanding consumer behavior in digital environments. Through a systematic integration of vision deep learning, generative AI, and VR technologies, this dissertation bridges critical gaps in systems engineering research and business applications; DEDICATION To the infinite game of learning, may curiosity forever guide the way. ABSTRACT .

2025, Scientific Bulletin-Economic Sciences

This research investigates the potential contributions of neuroscience literature and techniques to marketing management, employing biosensors such as eye-tracking glasses, facial expression analysis, and galvanic skin response (GSR). By leveraging neuroscientific methods to understand consumer reactions to marketing stimuli, marketers can glean valuable insights to enhance their marketing strategies and optimize customer interactions. The study use a qualitative analysis and aims to bridge the gap between consumer neuroscience and traditional marketing practices, utilizing neuromarketing tools to decipher customer behavior.

2025, Cognition and Emotion

It is well established that categorizing the emotional content of facial expressions may differ depending on contextual information. Whether this malleability is observed in the auditory domain and in genuine emotion expressions is poorly explored. We examined the perception of authentic laughter and crying in the context of happy, neutral and sad facial expressions. Participants rated the vocalizations on separate unipolar scales of happiness and sadness, and on arousal. Although they were instructed to focus exclusively on the vocalizations, consistent context effects were found: For both laughter and crying, emotion judgments were shifted towards the information expressed by the face. These modulations were independent of response latencies and were larger for more emotionally ambiguous vocalizations. No effects of context were found for arousal ratings. These findings suggest that the automatic encoding of contextual information during emotion perception generalizes across modalities, to purely nonverbal vocalizations, and is not confined to acted expressions.

2025, The Fragile Equilibrium: Punishment, Mercy, and the Entropy of Societies

The concept of entropy, central to thermodynamics, can be applied to social systems to understand societal stability and collapse. This paper explores how social entropy, driven by increasing disorder and randomness, mirrors the lifecycle of societies from establishment to decline. Punishment and mercy are crucial elements in this context, balancing societal order and compassion. Punishment deters undesirable behaviors, ensures justice, and maintains order, while mercy humanizes justice, fostering social cohesion and rehabilitation. However, the implementation of punishment is complex, involving challenges like proportionality, consistency, effectiveness, and ethical considerations. Psychological aversion to punishment, rooted in the fear of pain, complicates its application, leading to resistance, demands for leniency, and public perception issues. Mercy, defined as compassion or forgiveness towards someone within one's power to punish, plays a critical role in tempering justice with empathy. The paper categorizes mercy into four types based on personality: balanced, narcissistic, self-flagellating, and psychopathic, each influencing societal dynamics and law enforcement differently. A balanced approach to punishment and mercy is essential for maintaining social stability. Excessive mercy, however, can lead to social stratification, lawlessness, undermining deterrence, and eroding public trust. The concept of "narcissistic empathy," a self-serving form of empathy, can impact legal outcomes and societal trust in the justice system. Achieving a nuanced and adaptive balance between punishment and mercy is crucial for sustaining a just and cohesive society, where both justice and compassion are upheld. This balance helps societies counteract the natural progression towards disorder, ensuring long-term stability and harmony.

2025, Routledge International Handbook of Criminology and Affect Theory

Across courtroom proceedings and particularly during sentencing, criminal-legal decision-makers often want to see that a defendant has recognized the harm they have caused and will take action to minimize the risks of it repeating. This prioritization of acknowledging, regretting, and repairing harm is frequently recognized through emotions, body language, words, and behaviors. Yet remorse, often the sought-after emotional response in criminal-legal contexts, is far more complicated. An emotion that conveys empathy, self-reflection, and growth could also communicate guilt, manipulation, and selfishness. Highlighting the multifaceted nature of remorse and its competing emotional interpretations, this chapter discusses how the allowance and evaluation of remorse in the criminal-legal system operates as a double-edged sword for defendants and decision-makers. Specifically, the methods and findings of our prior work with probation officers and judges help to extrapolate these emotional complexities.

2025, HAL (Le Centre pour la Communication Scientifique Directe)

Face occlusion is a very challenging problem in face recognition. The performance of face recognition system can decrease drastically due to the presence of partial occlusion on the face. One approach to overcome this problem is to first pre-classify faces into two classes: the clean face and the occluded face; then faces in different classes are treated by different recognition systems. In this case an algorithm which is able to automatically detect the presence of occlusions on the face will be a useful tool to increase the performances of the system. In this paper we present a scarf detection algorithm. In the experimental results the performances of the algorithm are reported and compared with state of the art systems.

2025, IJNRD

I.INTRODUCTION An image is a visual representation of a two dimensional data; an image can be formed by using million number of pixels which is an integrated unit of digital image analysis. RGB image consists of these independent image one in each of the primary colors red, green and blue. Gray scale image means one kind of black and white image with monochromatic nature of pixels. LAB image means lightness channel A, channel B, its global color model where y As we move our main criteria is clustering of image which is a method to define group of pixels; therefore, the pixels in the group define a class in the segmented image, segmentation is aprocess of partitioning n image into parts or region. Here in this method we have used very common filtering method foredge detection of contours they are prewitt, sobel and canny filtering. Here pewit filtering is a special filtering used in image processing to identify processes for subsequent processing of an image. Canny filtering method is used for multistage edge detector based on the derivative of a Gaussian in order to compute the intensity of the gradients. Sobel is an edge detector filter used as a gradient based method that look strong changes in the first derivative of the image.ou can specify any given color by giving numeric values. 1.1 What Is Kmeans Clustering: K Means is a clustering algorithm. Clustering algorithms are unsupervised algorithms which means that there is no labelled data available. It is used to identifydifferent classes or clusters in the given data based on how similar the data is. Datapoints in the same group are more similar to other data points in that same group than those in other groups. K-means clustering is one of the most commonly used clustering algorithms. Here, k represents the number of clusters. k-means is method of cluster analysis using a pre-specified no. of clusters. It requires advance knowledge of 'K'. k-means, using a pre-specified number of clusters, the method assignsrecords to each cluster to find the mutually exclusive cluster of spherical shape based on distance. Here k means clustering is just an example because syntaxes are easily available in matlab, and method of clustering is very easy. If you want to use any complex clustering method, you can use it but results of image contour may be differentfor that reason signal to noise ratio of each contour of the image will be changed accordingly. 1.2 What Is Mean Square Error in Image Processing MSE (Mean Square Error) MSE is the most common estimator of image quality measurement metric. It is a full reference metric and the values closer tozero are the better. It is the second moment of the error. The variance of the estimator and its bias are both incorporated with mean squared error.

2025

Στην παρούσα έρευνα διερευνήσαµε την επίδραση των αναλογιών στην κατανόηση επιστηµονικών µη-διαισθητικών εξηγήσεων µέσα από κείµενα. Τα κείµενα παρουσίαζαν την επιστηµονική εξήγηση της εναλλαγής µέρας/νύχτας είτε µε τη χρήση αναλογίας (πειραµατική οµάδα) είτε χωρίς (οµάδα ελέγχου). Υποθέσαµε ότι η χρήση της αναλογίας θα συνέβαλε στην κατανόηση της επιστηµονικής µηδιαισθητικής εξήγησης γιατί παρουσιάζει έναν µη-οικείο µηχανισµό µέσα από ένα εξαιρετικά οικείο πεδίο. Τα αποτελέσµατα έδειξαν µεγαλύτερη ανάκληση πληροφοριών, λιγότερη παραποίηση νοήµατος και περισσότερες επιστηµονικές εξηγήσεις στον µεταέλεγχο σε σύγκριση µε τον προέλεγχο από τα παιδιά της πειραµατικής οµάδας συγκριτικά µε τα παιδιά της οµάδας ελέγχου.

2025, South Eastern European Journal of Public Health, ISSN 2197-5248

Crowd behavior is a critical aspect of numerous applications such as crowd management, urban planning, and safety monitoring in the current era of the world. Convolutional Neural Networks (CNNs), one of the most recent advancements in deep learning, have demonstrated potential in the analysis of crowd behavior patterns. However, computational limitations frequently make it difficult to implement complex CNN models for crowd analysis tasks, particularly in real-time applications. The utilization of a lightweight CNN model for crowd behavior analysis on the Motion Emotion Dataset (MED) is proposed in our study. The MED dataset has diverse scenes with varying crowd emotional and behavioral aspects, making it an ideal benchmark for evaluating crowd analysis algorithms. The 2D CNN model is applied to the MED datasets to extract the features and annotations for training the lightweight CNN. The model is validated in the validation set and achieved an accuracy of 99.4% on the Emotion Dataset and 94.35% on the Behavior Dataset. The results are validated using the confusion matrix. The results indicate that the lightweight CNN model achieves competitive performance on the MED dataset while exhibiting reduced computational overhead compared to more complex models. The discoveries made aid in the advancement of effective and scalable strategies for crowd surveillance and control, with applications spanning across diverse sectors such as public safety, transportation, and event coordination.

2025, Cheiron Program and Abstracts, June 2022

2025, Fronteiras - estudos midiáticos

A representação de emoções, sentimentos, afetos através da imagem é um dos atributos de que são investidos pintores, escultores, fotógrafos ou cineastas. Boa parte da experiência de criação desses artistas é fomentada através do encontro de seus corpos com o de seus modelos ou atores. Nas possibilidades produzidas por esta associação encontram-se alguns mistérios das conexões recíprocas entre corpos e seus instrumentos técnicos. Neste artigo, nos concentraremos em alguns célebres estudos sobre as expressões humanas [Le Brun, Darwin] para que possamos mapear os vestígios dos modos de olhar e de agir durante o ato artístico. Nosso foco será orientado por exemplos da fotografia [Rejlander, Duchene, Nadar] os quais, a nosso ver, guardam as sementes da imagem em movimento e que colocam em perspectiva algumas fronteiras entre campos do saber normalmente tidos como independentes como as artes e as ciências.

2025, 3rd International Scientific Research Congress Book

In recent years, the growing prevalence of mental health issues and emotional disorders-partly associated with remote work and social isolation-has led to increased interest in Facial Emotion Recognition (FER) methods as tools for the early detection of affective disturbances. Assessing the effectiveness of these methods may support the development of solutions aimed at identifying reduced psychological wellbeing among employees, allowing for timely intervention and professional support. This publication provides a review of the most frequently used FER techniques based on visible-spectrum imaging systems. The selected methods were implemented and empirically compared using the publicly available FER-2013 dataset. The analysis emphasizes key performance parameters and metrological aspects, with particular attention to their potential applications in psychological, medical, and research settings. Laboratory tests allowed for the identification of practical strengths and limitations of each method, offering a basis for considering their integration into mental health assessment tools and suggesting directions for future research.

2025

Pain is a multifaceted experience with both sensory and emotional components. This paper proposes a speech-based desktop application that uses artificial intelligence (AI) to classify pain vocal expressions as physical or emotional. The tool is designed with a privacy-first approach, operating entirely offline to ensure that sensitive audio data never leaves the user's device. We present a conceptual framework and methodology, including an overview of relevant background literature on vocal pain indicators and speech emotion recognition. Key components of the proposed method are detailed, such as acoustic feature extraction techniques (e.g., mel-frequency cepstral coefficients, prosodic features), classification models (machine learning and deep learning approaches), and the offline system design. This tool could benefit clinicians in assessing patient pain, aid researchers in studies of pain and emotion, and support mental health practitioners by providing objective insights into a client's emotional distress. We discuss the potential advantages of an offline, privacy-preserving approach for such a sensitive application, as well as anticipated limitations, including data variability and classification challenges. Conclusion: The proposed system offers a novel intersection of pain assessment and affective computing, laying the groundwork for future development and empirical validation of AI-driven, privacy-conscious pain classification tools.

2025, Advanced International Journal of Multidisciplinary Research

In order to study the application of convolutional neural networks in facial expression recognition, a 10-layer convolutional neural network model is designed to recognize facial expressions. The last layer uses the Softmax function to output the classification results of expressions. First, the convolution and pooling algorithms of convolutional neural networks were studied and the structure of the model was designed. Secondly, in order to more vividly display the features extracted by the convolutional layer, the extracted features are visualized and displayed in the form of feature maps. The convolutional neural network model in this work was tested on the Fer-2013 data set, and the experimental results demonstrated the superiority of the recognition rate. It is known that the Fer-2013 dataset contains data collected in an experimental environment, and in order to verify the generalization ability of model recognition, a self-made facial expression data set in natural state was created, and performed a series of preprocessing on the face images such as cropping, grayscale and pixel adjustment. The trained model, which was previously applied to the Fer-2013 dataset, was tested out on the new dataset. The experiment yielded promising results, one of which in the form of a recognition accuracy rate as high as 85.1%.

2025

In an age where genuine human connection is increasingly valued yet harder to foster, this article offers ten practical strategies for making social interactions warmer, more authentic, and emotionally engaging. Drawing from personal experience and supported by psychological insights, the author emphasizes the importance of name usage, eye contact, body language, and active listening in everyday conversations. The article encourages readers to deepen interactions by adopting expressive gestures, mirroring emotions, asking open-ended questions, and giving timely affirmations. Through these accessible yet impactful techniques, the paper aims to help individuals build more respectful, meaningful, and lasting connections in both personal and professional contexts.

2025, Mind

By looking at it you come to know that a thing is an apple. How? A natural answer is that this is down to how it looks - its superficial visual appearance. Looks Views treat our acquaintance with such looks as accounting for how visual... more

2025, International Journal of Intelligent Systems and Applications in Engineering

Facial Expressions are quite personalized and may look different for different individuals. Whereas there are certain facial muscles which shows some common features for certain human expressions across the cultures and facial shapes. Convolution Neural Network have shown tremendous success in Facial Expression Recognition task. In recent past many researchers have proposed multiple models with manageable size solution to Facial Expression Recognition task. In the current work, we have considered shape, complexion and other identity related information separate from certain specified muscle movements which are specific for emotion recognition. This is done by a novel Emotion-Generative Adversarial Network. This saves a lot of effort and simplifies the Facial Expression Recognition process. We then apply Scale Invariant Feature Transformation and vola john's face extraction method for pre-processing and face image extraction from background. This enables us to train our model accurately irrespective of scale, orientation, illumination etc and with very less training samples accurately. We feed the feature extracted facial image to an attention-based Convolutional Neural Network. This will ensure more emphasis on critical areas for expression recognition of facial image. Finally, we have used Local Binary Pattern for classification of the input image to a particular emotion class. We have tested our model on CK+, OULU-Casia and FER-2013 datasets and it is at par with performance of all major state-of-art models. Proposed model may be utilized by various automated interactive systems, such as robot to human communication, automated customer care systems etc. The proposed work may also be quite useful for observing reaction of viewers to a particular advertisement or article automatically and use this information for various purposes like user's interest, product feedback etc.

2025, PAÑHABYĀKARAṆA-SUTTA

DISCURSO DE LAS RESPUESTAS A PREGUNTAS 42. Cattār'imāni bhikkhave pañhabyākaraṇāni. 2 Katamāni cattāri? Atthi bhikkhave pañho ekaṃsa-byākaraṇīyo, atthi bhikkhave pañho vibhajja-byākaraṇīyo, atthi bhikkhave pañho paṭipucchā-byākaraṇīyo, atthi bhikkhave pañho ṭhapanīyo. Imāni kho bhikkhave cattāri pañhabyākaraṇānī ti. Bhikkhus, hay estas cuatro respuestas a preguntas. ¿Cuáles cuatro? Bhikkhus, existe la pregunta que debe ser respondida directamente. Bhikkhus, existe la pregunta que debe ser respondida analíticamente. Bhikkhus, existe la pregunta que debe ser respondida con una pregunta. Bhikkhus, existe la pregunta que debe ser puesta a un lado. Bhikkhus, éstas son las cuatro respuestas a preguntas. Ekaṃsavacanaṃ ekaṃ, vibhajjavacanāparaṃ. Tatiyaṃ paṭipuccheyya, catutthaṃ pana ṭhāpaye. Una se contesta directamente, otra se contesta analíticamente, la tercera, respondería uno con contrarréplica, pero la cuarta la debería poner a un lado. Yo ca tesaṃ 3 tattha tattha, jānāti anudhammataṃ. Catu-pañhassa kusalo, āhu bhikkhuṃ tathāvidhaṃ. Ese que sabe, aquí y allí, la respuesta correcta de éstas, dicen que este bhikkhu es hábil en los cuatro tipos de preguntas. Durāsado duppasaho, gambhīro duppadhaṃsiyo. Atho atthe anatthe ca, ubhayassa hoti kovido. 4 Es difícil de atacar, difícil de derrotar, profundo, difícil de superar, porque es experto en ambas cosas: en lo beneficioso y lo perjudicial.

2025, The Indian Journal of Technical Education, ISSN 0971-3034

Facial expression recognition (FER) is an essential task in computer vision that has a wide range of applications, including human-computer interaction and affective computing. This study involved a thorough analysis to compare the precision and effectiveness of different machine learning algorithms in identifying facial expressions. The analysis was conducted using a dataset specifically designed for facial expressions. The objective was to determine the most efficient algorithm for precisely categorizing various facial expressions depicted in still images. In order to accomplish this goal, we examined six well-known machine learning algorithms: Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), Decision Trees, and Gradient Boosting Machines (GBM). The evaluation of these algorithms was conducted using various performance metrics, such as accuracy, precision, recall, and F1 score. The dataset utilized in this investigation consisted of a varied collection of facial expressions captured in still images, encompassing a range of emotions including happiness, sadness, anger, surprise, fear, and disgust. Every image was annotated with the corresponding ground truth label indicating the facial expression. The experimental findings unveiled substantial disparities in the efficacy of various algorithms. CNN achieved the highest accuracy, reaching 96.3%, closely followed by GBM with an accuracy of 97.2%. SVM, Random Forest, and KNN demonstrated competitive performance, whereas Decision Trees showed slightly lower accuracy in comparison to the other algorithms.

2025, Ομιλία στο στο Συνέδριο «Με το βλέμμα στραμμένο στο μέλλον. Παιδαγωγικό Τμήμα Δημοτικής Εκπαίδευσης: Είκοσι χρόνια δημιουργίας και προσφοράς στην επιστήμη, την εκπαίδευση και την κοινωνία», Παιδαγωγικό Τμήμα Δημοτικής Εκπαίδευσης, Πανεπιστήμιο Αθηνών, Αθήνα, 4-5 Ιουνίου 2004

Συνέδριο «Με το βλέμμα στραμμένο στο μέλλον. Παιδαγωγικό Τμήμα Δημοτικής Εκπαίδευσης: Είκοσι χρόνια δημιουργίας και προσφοράς στην επιστήμη, την εκπαίδευση και την κοινωνία»

2025, Ανακοίνωση στο Συμπόσιο του Κλάδου Αναπτυξιακής Ψυχολογίας "Αναδυόμενη ενηλικίωση: Ανθρώπινη ανάπτυξη σε καιρούς ξένους", 18ο Πανελλήνιο Συνέδριο Ψυχολογικής Έρευνας, Ελληνική Ψυχολογική Εταιρεία, Πάντειο Πανεπιστήμιο, Αθήνα, 5-9 Οκτωβρίου 2022

Σκοπός του Συμποσίου είναι να παρουσιάσει ερευνητικά δεδομένα που έχουν συλλεγεί στην Ελλάδα (και σε συνεργασία με ερευνητές σε άλλες χώρες) πάνω σε ένα εύρος διεργασιών που αφορούν την ανάπτυξη, τη μάθηση και την ευημερία των αναδυόμενων ενηλίκων, με μεγαλύτερη εστίαση στους φοιτητές. Δίνεται ιδιαίτερη έμφαση, εκτός από τους ατομικούς, στους δομικούς παράγοντες των πλαισίων της ανάπτυξης και, ειδικότερα, σε παράγοντες κινδύνου, όπως η πρόσφατη οικονομική κρίση, η πανδημία και οι κοινωνικοοικονομικές διαφορές, καθώς και σε προστατευτικούς παράγοντες, που συνδέονται με την ψυχική ανθεκτικότητα. Οι συμμετέχοντες στις εν λόγω έρευνες προέρχονται από τον γενικό πληθυσμό και από ειδικούς πληθυσμούς και οι μέθοδοι ανάλυσης των δεδομένων είναι ποσοτικές και ποιοτικές. Το Συμπόσιο αναμένεται να αναδείξει ενδιαφέροντα ευρήματα για την κατάσταση των νέων ανθρώπων που οδεύουν προς την ενηλικίωση σε καιρούς κρίσης, ευρήματα χρήσιμα για τη διαμόρφωση πολιτικών στα πεδία της εκπαίδευσης και της ψυχικής υγείας των νέων.

2025, Diadorim

The interface between Functional Linguistics and Construction Grammar has already proved to be productive for analyses of a wide range of phenomena on several languages. These theoretical frameworks share similarities in terms of the relevance of use in linguistic research. In Functional Linguistics, use is the fundamental reality of language, since language is shaped by the uses we make of it in communicative situations in which we engage. In Constructional Grammar, constructs – concrete instantiations of constructional schemes – are the means by which we build generalizations about patterns that emerge from our experience of language use. Functional Linguistics and Construction Grammar also come closer by recognizing the close relationship between form and function, so crucial to functionalist studies and captured by Construction Grammar in the concept of construction.

2025, Proceedings of the 15th International Conference on Greek Linguistics (ICGL 15/1)

This paper investigates the role of semantic transparency for the mental representation of derived words in Standard Modern Greek. Specifically, we seek to examine whether semantic transparency affects morphological representation and whether any potential effect is dichotomous or graded. To this end, we conducted a lexical decision task employing the immediate cross-modal priming paradigm, which is considered to tap into an abstract, modality independent level of representation in the core of the mental lexicon. Forty-eight university students, native speakers of Standard Modern Greek, participated in the experiment. Ninety prime–target pairs, each comprising a derived (or pseudo-derived) word as auditory prime and its (real or putative) noun-base as visual target, were equally distributed in 3 critical semantic transparency conditions (opaque, e.g., tsiγarízo–tsiγáro ‘to frizzle’–‘cigarette’; semitransparent, e.g., piθikízo–píθikos ‘to imitate’–‘ape’; transparent, e.g., psiθirízo–psíθiros ‘to whisper’–‘whisper’), according to their mean scores from 3 semantic relatedness rating pretests. The analyses of the behavioral data (i.e., reaction times, error rates) revealed statistically significant and equivalent priming in the transparent and semitransparent condition but no priming in the opaque condition. This dichotomous pattern of priming implies morphologically structured lexical representations for the transparent and semitransparent derived stems on the one hand, while holistic, morphologically unstructured lexical representations for the opaque derived and pseudo-derived stems on the other hand. Therefore, we suggest a semantically constrained dual-route account of morphological representation rather than a full-decomposition or a graded (distributed connectionist, discriminative learning) account. To conclude, our experimental findings point that the mental representation of derived words is crucially influenced by semantic transparency in Standard Modern Greek, as is the case with many other Indo-European languages (e.g., English, French, Italian, Spanish, Bulgarian, Serbian, Croatian, Polish, Russian) and in contrast to Semitic languages (e.g., Arabic, Hebrew, Maltese).

2025

Rainforest biodiversity is particularly vulnerable to loss, since the distribution of forests is limited and the vertebrate species that live within these forests have a limited potential to re-colonize deforested areas, especially when... more

2025, The Evolution of Nonverbal Communication

Nonverbal Communication: A Key Aspect of Human Interaction
Nonverbal communication is an essential component of meaning transmission between individuals, structured as a complex system of extralinguistic signals manifested through body kinetics, facial expressions, proxemics, eye contact, and paralinguistic elements. It plays a crucial role in modulating and interpreting verbal communication, as well as in constructing autonomous meanings. While it is a universal phenomenon, its expression and interpretation are culturally determined and subject to contextual variability. Numerous studies have demonstrated that nonverbal communication accounts for a significant portion of human interaction, contributing to the creation and maintenance of interpersonal relationships, conflict management, and the expression of individual and collective identity.
From a neurobiological perspective, nonverbal communication is deeply rooted in the evolutionary mechanisms of the human species, with its origins in primordial brain structures such as the limbic system and the amygdala, which regulate emotional responses to environmental stimuli. The ability to decode nonverbal signals is present from the first months of life and develops through interaction with the social environment, representing a fundamental element in the maturation of communicative and relational skills.
Furthermore, nonverbal communication plays a decisive role in regulating group dynamics, establishing leadership, and shaping perceptions of authority. Analyzing nonverbal cues allows for a better understanding of interlocutors’ intentions and the adaptation of one’s communication for greater effectiveness. Recent studies in social psychology have shown that a conscious use of nonverbal communication can significantly influence persuasion and conviction in negotiation and interpersonal contexts.
The Importance of Nonverbal Communication in Human Interactions
The study of nonverbal communication is critically important in the fields of cognitive sciences, social psychology, and applied linguistics. It plays a key role in regulating social interactions, providing implicit information that often surpasses the explanatory capacity of verbal language. Research in cognitive neuroscience and evolutionary psychology suggests that the human brain processes and responds to nonverbal signals more quickly and effectively than to linguistic ones, indicating that these signals constitute a primordial and instinctive mechanism of communication.
According to the studies of Albert Mehrabian, in emotionally charged interactions, verbal content has only a marginal impact compared to nonverbal elements, with body language and vocal tone playing a predominant role in conveying meaning. However, the influence of these variables differs based on situational and cultural factors, necessitating an interdisciplinary analysis to fully understand their communicative function.
From an applied perspective, nonverbal communication has significant implications in multiple professional and personal contexts. In interpersonal relationships, it enables the expression of empathy, affiliation, and social status, while in the workplace, it is a key element in negotiation, leadership, and personnel management. The ability to accurately interpret nonverbal signals can enhance the quality of interactions, facilitating cooperation and preventing misunderstandings. Moreover, the strategic use of nonverbal communication is crucial in media and advertising, where gestures, expressions, and postures are carefully studied to evoke emotional responses from the audience.
Nonverbal communication also plays a significant role in shaping individual and collective identity. Bodily signals, expressions, and nonverbal behaviors are used to assert a sense of belonging to a social group and to convey shared values and norms. Cultural differences in nonverbal communication are particularly relevant in the context of globalization and intercultural interactions, as they influence the perception and interpretation of communicative messages.
Objectives and Structure of the Essay
This essay aims to explore the various dimensions of nonverbal communication and its impact on relational dynamics, with a particular focus on similarities and differences between humans and non-human primates. The adopted approach will be multidisciplinary, combining insights from cognitive psychology, comparative ethology, semiotics, and social neuroscience to develop a comprehensive theoretical and applied framework.
The work will be structured into the following chapters:
Body Language and Gestures – An analysis of major gestural and postural codes, with particular attention to their pragmatic function and intercultural differences. Different types of gestures will be examined, categorized into emblems, illustrators, regulators, and adaptors, analyzing their role in conscious and unconscious communication.
Facial Expressions and Emotions – A study of microexpressions, their universality, and the role of primary emotions, referencing Paul Ekman’s research and neuroscientific studies on limbic circuits. The neurophysiological mechanisms of facial expressions and their impact on social perception will be discussed.
Eye Contact and Proxemics – An in-depth exploration of gaze management and interpersonal space, with a cross-cultural analysis of their variations. The function of eye contact in building trust and regulating conversation will be examined.
Voice Tone and Paralinguistic Elements – A study of the acoustic components of nonverbal communication, with a focus on intonation patterns, rhythm, and vocal modulation in conveying communicative intent. The implications of prosody in emotional perception and speaker credibility will also be explored.
Comparison Between Humans and Primates – A comparative analysis of human and primate communication methods, focusing on the neurobiological and evolutionary foundations of nonverbal communication. Ethological studies highlighting similarities between the gestural behavior of chimpanzees, bonobos, and humans will be discussed.
The objective of this essay is to provide an advanced analysis of nonverbal communication, outlining its central role in the construction of social meaning and communicative competence, with implications in clinical psychology, sociolinguistics, and cognitive neuroscience. Additionally, the essay will explore the practical applications of nonverbal communication in therapeutic, educational, and technological contexts, highlighting how emerging technologies such as artificial intelligence and facial recognition can contribute to the understanding and strategic use of nonverbal signals.

2025, Journal for the Study of the Old Testament

Although shame is frequently evoked by the biblical writers for ethical ends, many recent studies impose either a shame-honour binary or limit shame to an extrinsic, social construct shorn of moral connotations. The result is a deficient understanding of the nuances of shame and its literary and rhetorical functions within the Hebrew Bible. This lacuna is even more pronounced in narrative texts whose laconic style means shame dynamics may be present even without the use of technical terms. Accordingly, this article investigates the semantic domain of בושׁ in the Hebrew Bible. Exploring associated lexemes, collocations, and motifs not only aids definitional clarity but identifies a matrix of attendant shame markers. That these markers can indicate the operation of shame apart from specific lexeme use is confirmed through an analysis of Genesis 38. The Judah-Tamar episode implicitly evokes shame to aid characterisation which, in turn, supports the goal of moral formation.

2025

This study aims to explore the influence of employee attractiveness on service recovery paradox by using Eye Tracker and Facial Recognition Software. Based on two service recovery scenarios an experiment was carried out with the participation of 20 subjects in Turkey. In the experiment participants’ pre-and post-satisfaction levels before the service failure and after the service recovery attempt have been measured. The participants were also asked to respond to Likert type questions to explain to explain how they felt about the service encounter they were exposed to in the scenarios. The analysis of data showed that although attractiveness reduced the negative of the responses, increased interest in the attractive service employee. However, the results showed that in both scenarios (with attractive and less attractive service employee photos) the service recovery paradox did not occur.

2025, Sensors

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interaction and communication. While many studies suggest that individuals with ASD struggle with emotion processing, the association between emotion processing and autistic traits in non-clinical populations is still unclear. We examine whether neurotypical adults' facial emotion recognition and expression imitation are associated with autistic traits. We recruited 32 neurotypical adults; each received two computerized tasks, the Dynamic Emotion Recognition and Expression Imitation, and two standardized measures: the Chinese version AQ and the Twenty-Item Prosopagnosia Index (PI-20). Results for the dynamic emotion recognition showed that happiness has the highest mean accuracy, followed by surprise, sadness, anger, fear, and disgust. For expression imitation, it was easiest to imitate surprise and happiness, followed by disgust, while the accuracy of imitating sadness, anger, and fear was much lower. Importantly, individual AQ scores negatively correlated with emotion recognition accuracy and positively correlated with PI-20. The AQ imagination, communication sub-scores, and PI-20 positively correlated with the expression imitation of surprise. In summary, we found a significant link between recognizing emotional expressions and the level of autistic traits in non-clinical populations, supporting the concept of broader autism phenotype.

2025

Rasa ingin tahu berpengaruh terhadap berkembangnya ilmu pengetahuan. Penelitian ini bertujuan untuk mengetahui pengaruh keterampilan bertanya dan mengadakan variasi secara parsial maupun secara simultan terhadap rasa ingin tahu siswa. Penelitian ini menggunakan pendekatan kuantitatif dengan metode ex-postfacto. Populasi dalam penelitian ini adalah siswa kelas IV SD se-Kecamatan Temon, Kabupaten Kulon Progo yang berjumlah 418 dengan sampel berjumlah 204 siswa yang diambil secara acak dengan menggunakan rumus Slovin. Teknik pengumpulan data menggunakan skala untuk mengumpulkan data keterampilan bertanya, keterampilan mengadakan variasi, dan rasa ingin tahu siswa. Instrumen penelitian ini diujicobakan kepada 31 siswa. Uji validitas instrumen yang digunakan adalah validitas isi dengan teknik expert judgment, sedangkan untuk mencari daya beda menggunakan rumus korelasi product moment. Reliabilitas instrumen menggunakan rumus Alpha Cronbach. Uji prasyarat analisis yang dilakukan adalah uj...

2025, HAL (Le Centre pour la Communication Scientifique Directe)

2025, BioMedical Engineering OnLine

Background: Facial expression muscles serve a fundamental role in the orofacial system, significantly influencing the overall health and well-being of an individual. They are essential for performing basic functions such as speech, chewing, and swallowing. The purpose of this study was to determine whether surface electromyography could be used to evaluate the health, function, or dysfunction of three facial muscles by measuring their electrical activity in healthy people. Additionally, to ascertain whether pattern recognition and artificial intelligence may be used for tasks that differ from one another. Results: The study included 24 participants and examined three muscles (m. Orbicularis Oris, m. Zygomaticus Major, and m. Mentalis) during five different facial expressions. Prior to thorough statistical analysis, features were extracted from the acquired electromyographs. Finally, classification was done with the use of logistic regression, random forest classifier and linear discriminant analysis. A statistically significant difference in muscle activity amplitudes was demonstrated between muscles, enabling the tracking of individual muscle activity for diagnostic and therapeutic purposes. Additionally other time domain and frequency domain features were analyzed, showing statistical significance in differentiation between muscles as well. Examples of pattern recognition showed promising avenues for further research and development. Conclusion: Surface electromyography is a useful method for assessing the function of facial expression muscles, significantly contributing to the diagnosis and treatment of oral motor function disorders. Results of this study show potential for further research and development in this field of research.

2025

Broader Autism Phenotype (BAP) defines heritable features present in unaffected relatives of individuals with autism. BAP affects face perception, an impairment associated with the magnocellular (M) visual pathway that processes information of low spatial frequency and the parvocellular (P) visual pathway that processes information of high spatial frequency. Here we tested the hypothesis that parents of children with Autism Spectrum Disorder (pASD), who are BAP candidates, present altered M and P pathways integration for the processing of facial emotions information as compared to parents of typically developing children (pTD). For this end, we carried out electroencephalographic recordings in pTD and pASD, while they had to recognize emotions of face pictures composed by the same or different emotions (happiness or anger) presented in different spatial frequencies. We found no significant differences in the accuracy between groups but lower amplitude in a late frontoparietal potent...

2025, Neural Processing Letters

2025

Giving appropriate treatment and assistance to persons afflicted by autism spectrum disorder (ASD) requires early identification. Previous research indicates that early facial expressions can be a valuable tool for identifying ASD. As people's facial expressions vary from ethnicity to ethnicity, so it is essential to choose a specific ethnicity for building a more accurate model of that ethnicity. The purpose of this study is to create and evaluate how well different deep learning models identify ASD from facial expressions in the Bangladeshi population. Because of this study's primary objective is to examine the facial expressions of toddlers and teenagers from Bangladesh, we produced two datasets with 1500 photos for each of them. We used a variety of cutting-edge deep learning models, such as EfficientNetV2L, EfficientNetB0, EfficientNetB7, MobileNetV2, ResNet50, ResNet101, DenseNet201, VGG16, and VGG19 for this study. Our investigation provided excellent findings, with the EfficientNetB0 model discriminating ASD from non-ASD people with an accuracy of 99% for toddlers and 94% for teenagers. Other models also showed excellent precision, with an average accuracy of 96% for toddlers and 82.5% for teenagers. The results highlight the effectiveness of transfer learning in the Bangladeshi context for detecting ASD using facial expression analysis, laying the groundwork for future studies and possible practical applications.

2025, K. Droß-Krüpe, K. Ruffing (Hrsg.), Markt, Märkte und Marktgebäude in der antiken Welt

Methodische Überlegungen zur Archäologie des Marktgeschehens in der griechischen Polis anhand einiger ausgewählter Beispiele

2025, Safety & Fire Technology

Purpose: This article aims to explore and advance the field of public speaking training through the use of virtual reality (VR) and virtual avatars. The focus is on addressing the challenges faced in traditional public speaking training methods by leveraging the capabilities of VR technology. Project and methods: The project involved developing an innovative algorithm designed to control the behaviour of virtual avatars during public speaking training in a VR environment. This algorithm integrates multiple aspects of human communication, including posture modelling, voice modulation, greetings mannerisms, eye contact management, gestural communication and interactive responsiveness. The methodology combined theoretical research and practical implementation, involving a comprehensive review of existing solutions and the development of a sophisticated, integrated algorithm. The project utilised advanced programming techniques and the latest VR technology, tested in simulated public speaking scenarios. Results: The results of implementing this algorithm in VR-based training applications showed a significant improvement in the authenticity and effectiveness of public speaking training. The virtual avatars, powered by the algorithm, were able to simulate realistic human behaviours and responses, thus providing a more engaging and immersive learning experience for users. The application offered a variety of realistic training scenarios, interactive avatar feedback, customisation options, and progress tracking and analysis features. The study found that the algorithm successfully enhanced participants' public speaking skills, reducing anxiety and improving their overall communication abilities. Conclusions: The research conducted in an "e-Zawody" project concludes that the integration of VR and virtual avatars significantly enhances public speaking training, offering a novel, effective and engaging approach. The development of the algorithm marks a pivotal advancement in educational technology, providing a platform that surpasses traditional training methods. However, the study acknowledges technological limitations and the need for ongoing research and development. Future efforts should focus on enhancing the realism and interactivity of virtual avatars and expanding the application of VR-based training across various fields. The findings suggest a promising direction for the future of public speaking training, with VR technology poised to play a crucial role in the evolution of educational methodologies.

2025, TELKOMNIKA (Telecommunication Computing Electronics and Control)

Analisis komponen utama (PCA) dan analisis deskriminan linear (LDA) merupakan metode ekstraksi berbasis penampakan yang menghasilkan fitur-fitur dengan struktur global. Fitur-fitur dengan struktur global mempunyai kelemahan, yaitu fitur-fitur dengan struktur lokal tidak dapat dicirikan. Proyeksi pelestarian lokalitas (LPP) dan wajah-Laplacian orthogonal (OLF) merupakan metode ekstraksi model penampakan yang menghasilkan fitur-fitur dengan struktur lokal, namun fitur struktur global diabaikan.

2025

Within the broad spectrum of studies examining gender disparities in the recognition of emotional expressions, a recurring pattern emerges: females outperform males. This focus on female and male differences overlooks the non-binary nature of gender and fails to encompass the nuanced spectrum of gender identities. This study aimed to address this gap. Drawing upon a sample of 100 participants (N = 100; 40 female, 30 non-binary, 30 male) aged between 18 and 65 (M = 31.2, SD = 5.7), the current study investigated gender differences in accuracy of response for emotion recognition using picture stimuli of 7 different facial expressions (anger, disgust, fear, sadness, surprise, happiness, and neutral). Overall, the statistical analyses conRirmed gender identity as a predictor of individual differences in facial emotion recognition (p = .030). Pairwise comparisons revealed that only nonbinary cohorts showed marginally signiRicant results compared to women (p= .053). However, distinct gender-related variations were observed in emotions like sadness (p = .034) and anger (p = .007). These Rindings highlight the necessity of analyzing emotions separately rather than to rely solely on an overall facial emotion recognition accuracy score to understand the relationship between the variables under investigation.

2025

Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence. The evaluation can be performed on both conventional and dynamic MRI acquisition protocols, while the latter is also checked longitudinally across dynamic series. The assessment provides an overall image quality score and information on the types of artifacts and degrading factors as well as a number of objective metrics for automated evaluation across series (BRISQUE score, Total Variation, PSNR, SSIM, FSIM, MS-SSIM). Moreover, the user can define specific regions of interest (ROIs) to calculate the regional signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thus individualizing the quality output to specific use cases, such as tissue-specific contrast or regional noise quantification.

2025, Revista de psicología y educación

origen principal de la actividad recogida por los electrodos, su localización (corteza cerebral) y su actividad (principalmente rítmica). A continuación se muestra con un ejemplo cómo estas técnicas pueden utilizarse en el estudio de funciones cognitivas o sus alteraciones. Concretamente, se presentan dos estudios realizados sobre alteraciones de la percepción lingüística, específicamente sobre dislexia. En un primer estudio se pretendía una introducción al campo, con lo que los resultados no fueron concluyentes dada la gran disparidad de resultados encontrados hasta la fecha por los distintos autores. Debido a que la heterogeneidad de las muestras puede ser la causa de esta disparidad, en un segundo estudio se utiliza una muestra muy homogénea de disléxicos puros y se comprueba la hipótesis de que la dislexia es un trastorno principalmente funcional del cerebro.

2025

Proper pain therapy requires adequate pain assessment. This study evaluated the reliability and validity of the Unesp-Botucatu horse acute pain scale (UHAPS), the Orthopedic Composite Pain Scale (CPS) and unidimensional scales in horses admitted for orthopedic and soft tissue surgery. Forty-two horses were assessed and videotaped before surgery, up to 4 hours postoperatively, up to 3 hours after analgesic treatment, and 24 hours postoperatively (168 video clips). After six evaluators viewing each edited video clip twice in random order at a 20-day interval, they chose whether analgesia would be indicated and applied the Simple Descriptive, Numeric and Visual Analog scales, CPS, and UHAPS. For all evaluators, intraobserver reliability of UHAPS and CPS ranged from 0.70 to 0.97. Reproducibility was variable among the evaluators and ranged from poor to very good for all scales. Principal component analysis showed a weak association among 50% and 62% of the UHAPS and CPS items, respectively. Criterion validity based on Spearman correlation among all scales was above 0.67. Internal consistency was minimally acceptable (0.51-0.64). Item-total correlation was acceptable (0.3-0.7) for 50% and 38% of UHAPS and CPS items, respectively. UHAPS and CPS were specific (90% and 79% respectively), but both were not sensitive (43 and 38%, respectively). Construct validity (responsiveness) was confirmed for all scales because pain scores increased after surgery. The cutoff point for rescue analgesia was � 5 and � 7 for the UHAPS and CPS, respectively. All scales presented adequate repeatability,

2025, Research Square (Research Square)

Emotional states can be expressed in facial expressions, speech, or body language. This study aims to create an effective system that can recognize emotional states from speech. The study proposes a methodology framework that uses acoustic features to recognize emotions. Initially, various methods for extracting features from speech are examined, and extensive statistical values are derived from the feature data. To recognize emotions from different speakers, a method to standardize the statistical features is presented. Normalization is proven to be necessary for building a high-performance system. Using normalized values, a score is calculated for each speech utterance, and the feature patterns for different emotions such as Anger, Boredom, Fear, Sadness, Happiness, and Neutral are examined. The study suggests that pitch, rst formant, and speaking rate are the best features to distinguish emotions such as Anger, Fear, Boredom, Happiness, and Sadness. The study also shows that Support Vector Machine (SVM) can yield satisfactory performance in recognizing emotions. The system has achieved a high level of accuracy in detecting emotions such as Fear, Boredom, and Sadness. However, the method used in this study is not effective in recognizing emotions of Anger and Happiness.

2025

New demands associated with living in a highly-technological and globally-competitive world require today's students to develop a very different set of competencies than previous generations of students needed. The general goal of education is to prepare young people to live independent and productive lives. Unfortunately, our current educational system is not keeping pace with these changes and new demands. The world is becoming increasingly complex and to make progress toward fixing educational woes, we need to have a good sense of bearings-where we are, and where we're heading. This paper is intended to provide such bearings, specifically in terms of a fresh vision for education. We envision new modes of learning and teaching using stimulating online environments such as games and simulations, coupled with an assessment infrastructure covering a broad set of competencies and other attributes to support learning. This represents a long view of the field to inform current R&D efforts.

2025, Interpersona: an international journal on personal relationships

The purpose of this research was to analyze the psychometric characteristics of the short-form UCLA Loneliness Scale (ULS-6) among Palestinian university students. The sample consisted of 288 university students (56% women and 44% men), aged 18-22 years. The psychometric characteristics of the ULS-6 were examined using confirmatory factor analysis, reliability analysis, and criterion-related validity methods. The unidimensionality of the ULS-6 was supported among Palestinian university students. The ULS-6 showed good psychometric characteristics, with adequate internal consistency. In addition, the ULS-6 was negatively correlated with significant others support, family support, friends support, self-esteem and satisfaction with life. The results of the present study suggested that the Arabic version of the ULS-6 constitutes a concise psychometrically sound tool to assess loneliness.

2025, Journal of Psychopharmacology

We recently demonstrated that alcohol elicits a difference between men and women in perceptual threshold for facial expressions of sadness. However, this study did not include a manipulation of alcohol expectancy. Therefore, we sought to determine whether these effects may be due to the expectation of having consumed alcohol. Male and female participants ( n = 100) were randomised using a balanced-placebo design to receive either an alcoholic or a non-alcoholic drink and to be told that this was alcoholic or non-alcoholic. Participants completed a psychophysical task which presented male and female faces expressing angry, happy, and sad emotions. Analysis of threshold data indicated a significant two-way interaction of drink × target emotion, reflecting a higher threshold for the detection of sad facial expressions of emotion, compared with angry or happy expressions, in the alcohol condition compared with the placebo condition. We did not observe any evidence of sex differences in ...