Australasian Mathematical Psychology Conference 2019

The diversity effect in inductive reasoning depends on sampling assumptions

Brett Hayes
Psychology, University of New South Wales
Danielle Navarro
University of New South Wales
Rachel G. Stephens
University of New South Wales
Keith Ransom
University of Adelaide
Natali Dilevski
University of Sydney

A key phenomenon in inductive reasoning is the diversity effect, whereby a novel property is more likely to be generalized when it is shared by an evidence sample composed of diverse instances than a sample composed of similar instances. We describe a Bayesian model and an experimental study that show that the diversity effect depends on a belief that samples of evidence were selected by a helpful agent (strong sampling). Inductive arguments with premises containing either diverse or non-diverse evidence samples were presented under different sampling conditions, where instructions and filler items indicated that the samples were selected intentionally (strong sampling) or randomly (weak sampling). A robust diversity effect was found under strong sampling but was attenuated under weak sampling. As predicted by our Bayesian model, the largest effect of sampling was on arguments with non-diverse evidence, where strong sampling led to more restricted generalization than weak sampling. These results show that the characteristics of evidence deemed relevant to an inductive reasoning problem depend on beliefs about how the evidence was generated.