Connectionist models are computational frameworks that simulate cognitive processes through networks of simple units, often inspired by the neural architecture of the brain. These models represent knowledge as patterns of activation across a network, allowing for parallel processing and learning through adjustment of connection strengths. This approach is especially relevant in understanding language and cognition as it provides insights into how concepts, meanings, and language structures are formed and organized in the brain.
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