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Cognitive architecture refers to the underlying structure and processes of the mind that enable intelligent behavior, providing a blueprint for building artificial systems that mimic human cognition. It integrates theories from psychology, neuroscience, and computer science to create models that simulate human thought and learning processes.
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Mental representation refers to the mental imagery or cognitive symbols that stand in for external reality, enabling the mind to process, store, and retrieve information about the world. These representations are crucial for perception, thought, and language, as they allow individuals to navigate and interpret their environment effectively.
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Information processing is the transformation, storage, and retrieval of information within a system, often modeled after human cognition. It is fundamental to understanding how both biological and artificial systems handle data and make decisions.
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Cognitive Load Theory suggests that learning is hindered when the working memory is overwhelmed by too much information at once, emphasizing the need to design instructional materials that optimize cognitive processing. It differentiates between intrinsic, extraneous, and Germane Cognitive Loads to enhance learning efficiency and retention.
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Schema theory posits that all knowledge is organized into units or frameworks, known as schemas, which help individuals understand and interpret information by providing a structure for experiences and expectations. These schemas are dynamic, evolving with new information and experiences, and play a critical role in cognitive processes such as memory, perception, and learning.
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Connectionism is a cognitive science approach that models mental or behavioral phenomena as the emergent processes of interconnected networks of simple units, akin to neurons in the brain. This approach emphasizes learning from experience and the parallel processing of information, forming the basis for modern neural network models in artificial intelligence.
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Symbolic AI, also known as classical AI, is an approach to artificial intelligence that relies on the manipulation of symbols and the use of formal logic to mimic human reasoning. It contrasts with sub-symbolic approaches like neural networks, focusing on explicit knowledge representation and rule-based systems to solve complex problems.
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Bayesian inference is a statistical method that updates the probability of a hypothesis as more evidence or information becomes available, utilizing Bayes' Theorem to combine prior beliefs with new data. It provides a flexible framework for modeling uncertainty and making predictions in complex systems, often outperforming traditional methods in scenarios with limited data or evolving conditions.
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Dual-process theory posits that human cognition operates through two distinct processes: an automatic, fast, and intuitive system, and a controlled, slow, and deliberative system. This framework helps explain a wide range of cognitive phenomena, from decision-making to moral reasoning, by highlighting the interplay between instinctual and analytical thought processes.
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Cognitive bias refers to systematic patterns of deviation from norm or rationality in judgment, where individuals create their own 'subjective reality' from their perception of the input. These biases often result from the brain's attempt to simplify information processing, leading to errors in decision-making and judgment.
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Metaphor in thought refers to the cognitive process where abstract concepts are understood and structured through more concrete experiences, shaping how we perceive and interact with the world. This mechanism is fundamental in human cognition, influencing language, reasoning, and perception by mapping familiar experiences onto new or complex ideas.
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Information Behavior refers to the ways individuals seek, manage, use, and share information in various contexts, influenced by their needs, motivations, and the environment. It encompasses a range of activities from recognizing an information need to the final use or dissemination of information, shaped by cognitive, social, and technological factors.
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Information seeking is the process by which individuals actively search for and utilize information to satisfy a specific need or solve a problem. It involves various strategies and behaviors that are influenced by the individual's context, prior knowledge, and the availability of resources.
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Theoretical knowledge refers to understanding principles, frameworks, and ideas that form the basis for practical application. It provides the foundational insights necessary for developing hypotheses, models, and systematic approaches to problem-solving across various disciplines.
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Organizational memory refers to the accumulated body of data, information, and knowledge created in the course of an organization's existence, which can be used to guide future decision-making and practices. It encompasses both tangible resources like documents and databases, as well as intangible assets such as employee expertise and organizational culture.
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Information Seeking Behavior refers to the ways individuals search for and utilize information to satisfy their needs, influenced by cognitive, social, and emotional factors. This behavior is shaped by the context of the information need, the availability of information resources, and the individual's skills and preferences in navigating these resources.
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Differential grounding refers to the consideration of different interlocutors' backgrounds, beliefs, and experiences to ensure mutual understanding in communication. It emphasizes adjusting explanations or conversations to suit an individual's unique perspective and needs to facilitate more effective dialogue.
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The Psychology of Representation explores how mental images and symbols within the human mind depict, process, and understand the external world. It examines how these representations influence perceptions, decision-making, and behaviors in both individual and social contexts.
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