title: Discovery of a Recursive Principle: An Artificial Grammar Investigation of Human Learning of a Counting Recursion Language.
abstract: Learning is typically understood as a process in which the behavior of an organism is progressively shaped until it closely approximates a target form. It is easy to comprehend how a motor skill or a vocabulary can be progressively learned-in each case, one can conceptualize a series of intermediate steps which lead to the formation of a proficient behavior. With grammar, it is more difficult to think in these terms. For example, center embedding recursive structures seem to involve a complex interplay between multiple symbolic rules which have to be in place simultaneously for the system to work at all, so it is not obvious how the mechanism could gradually come into being. Here, we offer empirical evidence from a new artificial language (or "artificial grammar") learning paradigm, Locus Prediction, that, despite the conceptual conundrum, recursion acquisition occurs gradually, at least for a simple formal language. In particular, we focus on a variant of the simplest recursive language, a (n) b (n) , and find evidence that (i) participants trained on two levels of structure (essentially ab and aabb) generalize to the next higher level (aaabbb) more readily than participants trained on one level of structure (ab) combined with a filler sentence; nevertheless, they do not generalize immediately; (ii) participants trained up to three levels (ab, aabb, aaabbb) generalize more readily to four levels than participants trained on two levels generalize to three; (iii) when we present the levels in succession, starting with the lower levels and including more and more of the higher levels, participants show evidence of transitioning between the levels gradually, exhibiting intermediate patterns of behavior on which they were not trained; (iv) the intermediate patterns of behavior are associated with perturbations of an attractor in the sense of dynamical systems theory. We argue that all of these behaviors indicate a theory of mental representation in which recursive systems lie on a continuum of grammar systems which are organized so that grammars that produce similar behaviors are near one another, and that people learning a recursive system are navigating progressively through the space of these grammars.
authors.affiliation: Department of Psychology, University of ConnecticutStorrs, CT, USA; Haskins LaboratoriesNew Haven, CT, USA.
authors.forename: Pyeong Whan
authors.initials: PW
authors.lastname: Cho
authors.affiliation: Department of Psychology, University of Connecticut Storrs, CT, USA.
authors.forename: Emily
authors.initials: E
authors.lastname: Szkudlarek
authors.affiliation: Department of Psychology, University of ConnecticutStorrs, CT, USA; Haskins LaboratoriesNew Haven, CT, USA.
authors.forename: Whitney
authors.initials: W
authors.lastname: Tabor
keywords: artificial grammar learning; center embeddings; context-free grammar; counting recursion; graded state machine; sequence learning