Symbolic modelling involves using abstract symbols to represent different aspects of a model, whereas connectionist modelling involves using networks of interconnected nodes that represent the processing of information in the brain. While symbolic modelling is good for representing logical and linguistic processes, connectionist modelling is better suited for understanding patterns and associations in data.