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Situativity theorists suggest a model of knowledge and learning that requires thinking on the fly rather than the storage and retrieval of conceptual knowledge. In essence, cognition cannot be separated from the context. Instead, knowing exists in situ, inseparable from context, activity, people, culture, and language. Therefore, learning is ...
The methods of neuro-linguistic programming are the specific techniques used to perform and teach neuro-linguistic programming, [1] [2] which teaches that people are only able to directly perceive a small part of the world using their conscious awareness, and that this view of the world is filtered by experience, beliefs, values, assumptions, and biological sensory systems.
Nonverbal learning disorder (NVLD or NLD) is a proposed neurodevelopmental disorder characterized by core deficits in nonverbal skills, especially visual-spatial processing. People with this condition have normal or advanced verbal intelligence and significantly lower nonverbal intelligence. [ 3 ]
Nonverbal learning disabilities, however, “really impact some of those non-verbal skills” such as “reading body language, reading social cues, all of the non-language areas, non-linguistic ...
A 2020 study estimated that as many as 2.9 million children and adolescents in North America have nonverbal learning disability, or NVLD, which affects a person’s spatial-visual skills.
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision, where a small portion of the data is tagged, and self-supervision.
Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information.It describes a number of artificial neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.
Neural: Symbolic → Neural relies on symbolic reasoning to generate or label training data that is subsequently learned by a deep learning model, e.g., to train a neural model for symbolic computation by using a Macsyma-like symbolic mathematics system to create or label examples. Neural Symbolic uses a neural net that is generated from ...