  Humans and animals are able to generalize or transfer information from
previous experience so that they can behave appropriately in novel situations.
What mechanisms--computations, representations, and neural systems--give rise
to this remarkable ability? The members of this Generative Adversarial
Collaboration (GAC) come from a range of academic backgrounds but are all
interested in uncovering the mechanisms of generalization. We started out this
GAC with the aim of arbitrating between two alternative conceptual accounts:
(1) generalization stems from integration of multiple experiences into summary
representations that reflect generalized knowledge, and (2) generalization is
computed on-the-fly using separately stored individual memories. Across the
course of this collaboration, we found that--despite using different
terminology and techniques, and although some of our specific papers may
provide evidence one way or the other--we in fact largely agree that both of
these broad accounts (as well as several others) are likely valid. We believe
that future research and theoretical synthesis across multiple lines of
research is necessary to help determine the degree to which different candidate
generalization mechanisms may operate simultaneously, operate on different
scales, or be employed under distinct conditions. Here, as the first step, we
introduce some of these candidate mechanisms and we discuss the issues
currently hindering better synthesis of generalization research. Finally, we
introduce some of our own research questions that have arisen over the course
of this GAC, that we believe would benefit from future collaborative efforts.
