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 …
Do additional features help or hurt category learning? The curse of dimensionality in human learners
Shows that people are only afflicted by the curse of dimensionality for rule-based categories, not family resemblance ones.
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
When do learned transformations influence similarity and categorization?
The helpfulness of category labels in semi-supervised learning depends on category structure
Do additional features help or harm during category learning? An exploration of the curse of dimensionality in human learners
People are sensitive to hypothesis sparsity during category discrimination
The relevance of labels in semi-supervised learning depends on category structure
Learning time-varying categories