Connectionism is a theoretical framework and computational modeling approach within cognitive science, artificial intelligence, and neuroscience that posits that mental phenomena and intelligent behavior can be understood as emergent properties of networks of simple, interconnected processing units. It investigates processes such as learning, memory, and perception by emphasizing distributed representations and parallel processing, where knowledge is encoded in the strength and pattern of connections between units, offering an alternative to symbolic manipulation paradigms.