How do connectionist models use artificial neural networks to model human thinking?

Connectionism, also known as parallel-distributed processing, models human thinking by using artificial neural networks as simplified representations of the brain's neurological structure.1

In these models, thinking is not viewed as a series of step-by-step symbolic inferences. Instead, a network consists of simple processing units connected to one another. Thoughts emerge as patterns of simultaneous neural firing activity, where the strength of signals between nodes is determined by activation values and the weights of their connections.1

Key aspects of this approach include:
* Collective Computation: Processes occur in parallel rather than sequentially, allowing the system to perform tasks like facial recognition by generating firing activity across the network.1
* Learning through Experience: The connection weights are refined through past experience, enabling the network to "learn" how to perform cognitive tasks.1
* Constraint Satisfaction: Rather than applying logical rules, the model functions by simultaneously satisfying multiple constraints, making it particularly effective for explaining complex processes like decision making, language learning, and analogical inference.1