Exploring the role that encoding and retrieval play in sampling effects

A growing body of literature suggests that making different sampling assumptions about how data are generated can lead to qualitatively different patterns of inference based on that data. However, relatively little is known about how sampling …

Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization

Points out that people make opposing inferences in categorisation and generalisation tasks, and suggests that it is because of different assumptions about how items are sampled.

Representational and sampling assumptions drive individual differences in single category generalisation

A cognitive analysis of deception without lying

Leaping to conclusions: Why premise relevance affects argument strength

Demonstrates that premise non-monotonicity can be explained by people's assumptions about how data are sampled and captured by a Bayesian model of generalisation.

How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning

People ignore token frequency when deciding how widely to generalize

The role of sampling assumptions in generalization with multiple categories

To catch a liar: The effects of truthful and deceptive testimony on inferential learning

Similarity, feature discovery, and the size principle