A remarkable ability of the cognitive system is the creation of
new knowledge based on prior experiences. What cognitive
mechanisms support such knowledge creation? We propose
that statistical learning not only extracts existing relationships
between objects, but also generates new associations between
objects that have never been directly associated. Participants
viewed a continuous color sequence consisting of base pairs
(e.g., A-B, B-C), and learned these pairs. Importantly, they also
successfully learned a novel pair (A-C) that could only be
associated through transitive relations between the base pairs
(Exp1). This learning, however, was not successful with three
base pairs (e.g., learning A-D from A-B, B-C, C-D), revealing
a limit in this transitive process (Exp2). Beyond temporal
associations, novel transitive associations can also be formed
across categorical hierarchies (Exp3), but with limits
(Exp4&5). The current findings suggest that statistical learning
provides an efficient scaffold through which new object
associations are transitively created.