Connectionism is a theoretical framework in cognitive science that models mental processes using artificial neural networks. These networks consist of interconnected nodes, similar to neurons in the brain, which work together to process information. Connectionism emphasizes learning through experience, where the strength of connections between nodes adjusts based on input, mimicking how humans learn. This approach contrasts with traditional symbolic AI, which relies on explicit rules and logic. Connectionism has been influential in fields like machine learning and artificial intelligence, leading to advancements in tasks such as image recognition and natural language processing.