sampling

When people use samples of evidence to make inferences, they consider both the sample contents and how the sample was generated (“sampling assumptions”). The current studies examined whether people can update their sampling assumptions – whether they …

GPT4 is similar to humans on category-based induction tasks unless they involve sampling assumptions

Sampling assumptions — the assumptions people make about how an example of a category or concept has been chosen — help us learn from examples efficiently. One context where sampling assumptions are particularly important is in social contexts, where …

People need to know how data were generated when they encode it; they can't revise it later if their assumptions were wrong

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 …

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.

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.