Research in category learning has been dominated by a
‘reference point’ view in which items are classified based on
attention-weighted similarity to reference points (e.g.,
prototypes, exemplars, clusters) in a multidimensional space.
Although much work has attempted to distinguish between
particular types of reference point models, they share a core
design principle that items will be classified as belonging to
the category of the most proximal reference point(s). In this
paper, we present an original experiment challenging this
distance assumption. After classification training on a
modified XOR category structure, we find that many learners
generalize their category knowledge to novel exemplars in a
manner that violates the distance assumption. This pattern of
performance reveals a fundamental limitation in the reference
point framework and suggests that stimulus generalization is
not a reliable foundation for explaining human category
learning.