  Category, or property generalization is a central function in the human
cognition. It plays a crucial role in a variety of domains, such as learning,
everyday reasoning, specialized reasoning, and decision making. Judging the
content of a dish as edible, a hormone level as healthy, a building as
belonging to the same architectural style as previously seen buildings, are
examples of category generalization. In this paper, we propose self-organizing
maps as candidates to explain the psychological mechanisms underlying category
generalization. Self-organizing maps are psychologically and biologically
plausible neural network models that learn after limited exposure to positive
category examples, without any need of contrastive information. Just like
humans. They reproduce human behavior in category generalization, in particular
for what concerns the well-known Numerosity and Variability effects, which are
usually explained with Bayesian tools. Where category generalization is
concerned, self-organizing maps are good candidates to bridge the gap between
the computational level of analysis in Marr's hierarchy (where Bayesian models
are situated) and the algorithmic level of aanalysis in Marr's hierarchy (where
Bayesian models are situated) and the algorithmic level of analysis in which
plausible mechanisms are described.
