Nonverbal communicationNonverbal communication (NVC) is the transmission of messages or signals through a nonverbal platform such as eye contact, facial expressions, gestures, posture, use of objects and body language. It includes the use of social cues, kinesics, distance (proxemics) and physical environments/appearance, of voice (paralanguage) and of touch (haptics). A signal has three different parts to it, including the basic signal, what the signal is trying to convey, and how it is interpreted.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Sensory cueA sensory cue is a statistic or signal that can be extracted from the sensory input by a perceiver, that indicates the state of some property of the world that the perceiver is interested in perceiving. A cue is some organization of the data present in the signal which allows for meaningful extrapolation. For example, sensory cues include visual cues, auditory cues, haptic cues, olfactory cues and environmental cues. Sensory cues are a fundamental part of theories of perception, especially theories of appearance (how things look).
ConversationConversation is interactive communication between two or more people. The development of conversational skills and etiquette is an important part of socialization. The development of conversational skills in a new language is a frequent focus of language teaching and learning. Conversation analysis is a branch of sociology which studies the structure and organization of human interaction, with a more specific focus on conversational interaction.
Human communicationHuman communication, or anthroposemiotics, is a field of study dedicated to understanding how humans communicate. Humans' ability to communicate with one another would not be possible without an understanding of what we are referencing or thinking about. Because humans are unable to fully understand one another's perspective, there needs to be a creation of commonality through a shared mindset or viewpoint. The field of communication is very diverse, as there are multiple layers of what communication is and how we use its different features as human beings.
Unsupervised learningUnsupervised learning, is paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data. Neural network tasks are often categorized as discriminative (recognition) or generative (imagination). Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see Venn diagram); however, the separation is very hazy. For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.
Haptic communicationHaptic communication is a branch of nonverbal communication that refers to the ways in which people and animals communicate and interact via the sense of touch. Touch is the most sophisticated and intimate of the five senses. Touch or haptics, from the ancient Greek word haptikos is extremely important for communication; it is vital for survival. Touch is the first sense to develop in the fetus. The development of an infant's haptic senses and how it relates to the development of the other senses such as vision has been the target of much research.
Online machine learningIn computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms.
Social behaviorSocial behavior is behavior among two or more organisms within the same species, and encompasses any behavior in which one member affects the other. This is due to an interaction among those members. Social behavior can be seen as similar to an exchange of goods, with the expectation that when you give, you will receive the same. This behavior can be affected by both the qualities of the individual and the environmental (situational) factors. Therefore, social behavior arises as a result of an interaction between the two—the organism and its environment.
Weak supervisionWeak supervision, also called semi-supervised learning, is a paradigm in machine learning, the relevance and notability of which increased with the advent of large language models due to large amount of data required to train them. It is characterized by using a combination of a small amount of human-labeled data (exclusively used in more expensive and time-consuming supervised learning paradigm), followed by a large amount of unlabeled data (used exclusively in unsupervised learning paradigm).
Artificial neural networkArtificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.
Feature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.
Intercultural communicationIntercultural communication is a discipline that studies communication across different cultures and social groups, or how culture affects communication. It describes the wide range of communication processes and problems that naturally appear within an organization or social context made up of individuals from different religious, social, ethnic, and educational backgrounds. In this sense, it seeks to understand how people from different countries and cultures act, communicate, and perceive the world around them.
Interpersonal communicationInterpersonal communication is an exchange of information between two or more people. It is also an area of research that seeks to understand how humans use verbal and nonverbal cues to accomplish a number of personal and relational goals. Interpersonal communication research addresses at least six categories of inquiry: 1) how humans adjust and adapt their verbal communication and nonverbal communication during face-to-face communication; 2) how messages are produced; 3) how uncertainty influences behavior and information-management strategies; 4) deceptive communication; 5) relational dialectics; and 6) social interactions that are mediated by technology.
Self-supervised learningSelf-supervised learning (SSL) is a paradigm in machine learning for processing data of lower quality, rather than improving ultimate outcomes. Self-supervised learning more closely imitates the way humans learn to classify objects. The typical SSL method is based on an artificial neural network or other model such as a decision list. The model learns in two steps. First, the task is solved based on an auxiliary or pretext classification task using pseudo-labels which help to initialize the model parameters.
Social relationA social relation is the fundamental unit of analysis within the social sciences, and describes any voluntary or involuntary interpersonal relationship between two or more individuals within and/or between groups. The group can be a language or kinship group, a social institution or organization, an economic class, a nation, or gender. Social relations are derived from human behavioral ecology, and, as an aggregate, form a coherent social structure whose constituent parts are best understood relative to each other and to the social ecosystem as a whole.
Types of social groupsIn the social sciences, social groups can be categorized based on the various group dynamics that define social organization. In sociological terms, groups can fundamentally be distinguished from one another by the extent to which their nature influence individuals and how. A primary group, for instance, is a small social group whose members share close, personal, enduring relationships with one another (e.g. family, childhood friend).
Ensemble learningIn statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
Face perceptionFacial perception is an individual's understanding and interpretation of the face. Here, perception implies the presence of consciousness and hence excludes automated facial recognition systems. Although facial recognition is found in other species, this article focuses on facial perception in humans. The perception of facial features is an important part of social cognition. Information gathered from the face helps people understand each other's identity, what they are thinking and feeling, anticipate their actions, recognize their emotions, build connections, and communicate through body language.
Active learning (machine learning)Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs. In statistics literature, it is sometimes also called optimal experimental design. The information source is also called teacher or oracle. There are situations in which unlabeled data is abundant but manual labeling is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels.
