Abstract
Shepard’s (1987) universal law of generalisation (ULG) illustrates that an invariant gradient of generalisation across species
and across stimuli conditions can be obtained by mapping the probability of a
generalisation response onto the representations of similarity between individual
stimuli. Tenenbaum and Griffiths (2001) Bayesian
account of generalisation expands ULG towards generalisation from multiple
examples. Though the Bayesian model starts from Shepard’s account it refrains from
any commitment to the notion of psychological similarity to explain categorisation.
This chapter presents the conceptual spaces theory (Gärdenfors 2000, 2014) as a mediator between Shepard’s
and Tenenbaum & Griffiths’ conflicting views on the role of psychological similarity
for a successful model of categorisation. It suggests that the conceptual spaces
theory can help to improve the Bayesian model while finding an explanatory role
for psychological similarity.