How does connectionism explain thinking and learning?

In cognitive science, connectionism—also known as parallel-distributed processing—explains thinking and learning by modeling the brain’s neural structure through artificial neural networks.1

Thinking as Neural Activity

Connectionists view thinking not as a series of logical, step-by-step symbolic inferences, but as the result of collective, parallel processing. In this framework:1
* Mental Representations: Thoughts are represented as patterns of simultaneous neural firing activity across a network of simple processing units.1
* Constraint Satisfaction: Cognitive tasks, such as decision making, are performed by the simultaneous satisfaction of multiple constraints rather than the application of explicit rules.1

Learning through Experience

Learning is viewed as a mechanical process of adjusting the network's internal structure based on past experience. This involves:21
* Weight Adjustment: The strength of connections between neurons is represented by "weights". Learning occurs when an external agent or experience adjusts these weights.2
* Tuning: By increasing or decreasing these weights, the network becomes more or less likely to produce a specific output in response to a given input. Over time, this "tuning" of pathways allows the network to respond correctly to patterns without human intervention.23