Connectionism is a theoretical framework in cognitive science that models mental processes using artificial neural networks, emphasizing the interconnected nature of information processing. This approach suggests that cognitive processes emerge from the interactions of simple units or nodes, similar to how neurons operate in the brain. Connectionism contrasts with traditional symbolic approaches, providing insights into learning, memory, and pattern recognition through parallel processing and distributed representation.
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