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WebRef.org: odd things found in strange places.
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Observational learning (social learning)
Observational learning, also known as social learning or modeling, is a type of learning that occurs through observing and imitating the behaviors, attitudes, and outcomes of others. Proposed by psychologist Albert Bandura as part of his social learning theory, observational learning emphasizes the importance of social influences in shaping behavior and cognition. Key concepts of…
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Motivation crowding theory
Motivation crowding theory, also known as the crowding-out effect or the overjustification effect, is a psychological theory that suggests external incentives such as rewards or punishments can undermine intrinsic motivation. Proposed by psychologists Edward Deci and Richard Ryan, motivation crowding theory posits that when individuals are offered external rewards for engaging in activities they intrinsically…
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Elaboration likelihood model
The Elaboration Likelihood Model (ELM) is a dual-process theory of persuasion developed by Richard E. Petty and John T. Cacioppo in the 1980s. It proposes that there are two distinct routes through which persuasive messages can lead to attitude change: the central route and the peripheral route. The route individuals take depends on their level…
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Drive theory
Drive theory, also known as the drive-reduction theory, is a psychological theory proposed by Clark Hull in the 1940s. It suggests that biological needs create internal states of tension or arousal called drives, which motivate individuals to engage in behaviors that will reduce or satisfy these needs and restore homeostasis or equilibrium. Key concepts of…
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Cognitive dissonance
Cognitive dissonance theory, proposed by psychologist Leon Festinger in 1957, suggests that individuals experience psychological discomfort, or dissonance, when they hold conflicting beliefs, attitudes, or behaviors. This discomfort motivates them to reduce the inconsistency and restore cognitive harmony. Key concepts of cognitive dissonance theory include: Dissonance: Cognitive dissonance refers to the uncomfortable feeling of tension…
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Attribution theory
Attribution theory is a social psychological framework that focuses on how individuals interpret and explain the causes of behavior, events, and outcomes. It explores the cognitive processes involved in making attributions, or judgments about the reasons behind observed phenomena. Developed by Fritz Heider and further elaborated by Harold Kelley and others, attribution theory helps understand…
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Robotics
Robotics is an interdisciplinary field that combines aspects of engineering, computer science, mathematics, and physics to design, build, and operate robots. Robots are autonomous or semi-autonomous machines that can perform tasks autonomously or under human control. Robotics encompasses a wide range of subfields, including robot design, control systems, perception, artificial intelligence, and human-robot interaction. Here…
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Natural language processing
Natural Language Processing (NLP) is a field of artificial intelligence (AI) and linguistics that focuses on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and useful. NLP techniques allow machines to interact with humans through natural language, enabling tasks such as language translation, sentiment analysis, chatbots, and…
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Evolutionary computing
Evolutionary computing is a family of computational techniques inspired by principles of natural evolution and Darwinian theory. These techniques are used to solve optimization and search problems by mimicking the process of natural selection, mutation, and reproduction observed in biological evolution. Here are some key concepts and topics within evolutionary computing: Genetic Algorithms (GAs): Genetic…
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Machine learning
Machine learning is a subset of artificial intelligence (AI) that focuses on developing algorithms and techniques that enable computers to learn from data and improve their performance on specific tasks without being explicitly programmed. In other words, machine learning algorithms allow computers to automatically learn patterns and relationships from data and make predictions or decisions…
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