On Empirical Methodology, Constraints, and Hierarchy in Artificial Grammar Learning

Abstract

This paper considers the AGL literature from a psycholinguistic perspective. It first presents a taxonomy of the experimental familiarization test procedures used, which is followed by a consideration of shortcomings and potential improvements of the empirical methodology. It then turns to reconsidering the issue of grammar learning from the point of view of acquiring constraints, instead of the traditional AGL approach in terms of acquiring sets of rewrite rules. This is, in particular, a natural way of handling long‐distance dependences. The final section addresses an underdeveloped issue in the AGL literature, namely how to detect latent hierarchical structure in AGL response patterns.

Editors' Review and Introduction: Learning Grammatical Structures: Developmental, Cross‐Species, and Computational Approaches

“…They make clear how being more explicit in defining different structures can help to identify and test their presence in carefully designed AGL experiments—in this case the detection of hierarchical structures as opposed to sequential ones. They illustrate this by showing how behavioral (see also Levelt, ) as well as neuroimaging methods and data can reveal signatures of hierarchical processing in humans. If combined with a model comparison approach, the framework provided by Uddén et al () holds much promise for future progress in demonstrating and understanding hierarchical processing.…”

“…Being more explicit about assumptions and theoretical considerations is also the theme of the contributions by Zuidema et al () and Levelt (). Zuidema et al illustrate how empirical AGL studies can benefit from computational models and techniques.…”

“…In the final contribution to the special issue, Levelt () distills decades of hard‐fought experience with empirical linguistic research to advise the AGL community. He considers the value of AGL from a psycholinguistic perspective and remarks on the various gaps and overlooked venues.…”

Finding hierarchical structure in binary sequences : evidence from Lindenmayer grammar learning

“…There's a plethora of evidence that nested structures are represented and used by adults in sentence processing (e.g., Lewis & Phillips, 2015) as well as in other cognitive domains like mathematical expressions (Maruyama et al, 2012;Monti et al, 2012;Nakai & Sakai, 2014), motor action (Hunt & Aslin, 2001;, musical melody (Koelsch, 2005) and rhythm Kotz et al, 2018). Nevertheless, the experimental demonstration of the learning of nested structures in sequence processing has proven difficult and the field of artificial grammar learning (AGL) has produced very few empirical studies showing conclusive evidence (Kovács & Endress, 2014;Fitch, 2014;Honing & Zuidema, 2014;Levelt, 2019). This difficulty comes from the fact that in the test cases classically used, a sequence can be processed without necessarily building a nested structure as other, possibly simpler ways of representing it can give rise to similar learning performance.…”

The semantics-syntax interface: Learning grammatical categories and hierarchical syntactic structure through semantics.

“…However, previous artificial language learning studies of HCEs have not yet adequately addressed the productive use of recursive structures expressing dependencies between categories of words and constituents. Hence, thus far, artificial language studies cannot yet inform us about the natural acquisition of these complex sequential structures typical for natural language (Levelt, 2019). For instance, previous studies have isolated only aspects of HCE structures (that append one constituent to the end of another, or insert one constituent within another) using finite state grammars (Fitch et al, 2005).…”