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 data. This approach contrasts with traditional symbolic models of cognition, which rely on explicit rules and representations. Connectionism has been influential in fields like artificial intelligence and machine learning, providing insights into how complex behaviors and cognitive functions can emerge from simple interactions among many units.