Lecture 1: Connectionism

Topics covered: Associative learning via the Rescorla-Wagner rule. Connection to other error driven learning rules. Using networks as classifiers. More complex networks and pattern matching.

Lecture 2: Statistical learning

Topics covered: Introduction to Bayesian reasoning. A model for judging coincidences. Comments on conservative belief upating. A model of the perceptual magnet effect. Bayesian program induction for concept learning.

Lecture 3: Semantic networks

Topics covered: Semantic priming and spreading activation. The small world of words project. Local network structure. Predicting remote associations. Structure of semantic networks. Developmental trajectory

Lecture 4: The wisdom of crowds

Topics covered: Galton’s vox populi. Surowiecki’s criteria. Wisdom of crowds for ranking data. Example from category learning. Wisdom of crowds in combinatorial optimisation problems. Compensating for strategic behaviour. Application in forensic science

Lecture 5: Cultural transmission

Topics covered: The iterated learning paradigm. Theoretical argument that it reveals inductive biases. Illustration with function learning task. Limitations when individual differences exist. Cumulative cultural evolution in a language game.

Lecture 6: Summary

Classroom discussion, no slides