While most morphemes in the world’s language involve continuous structure or concatenation (e.g., prefixes and suffixes), many languages show some form of non-adjacent, non-concatenative morphology. Non-concatenative morphology poses a challenge for statistical learning approaches to morpheme segmentation because the combinatorial possibilities greatly increase for non-adjacent dependencies. The present study explores the types of dependencies that human learners (school-aged children and adults) are able to extract from exposure to a miniature, artificial non-concatenative system. In Experiment 1, participants were exposed to 12 CCC ‘roots’ that fit into 72 CVCVC skeletons with a high variety of VV ‘residue’. Experiment 2 extended Experiment 1 to school-aged children (with adult controls). Experiment 3 replicated Experiment 1, but with ‘mixed’ consonant-vowel roots and residues. Across all three experiments, participants were able to recognize familiar items compared to novel items, but had limited ability to generalize the CCC roots to novel items, suggesting a limited ability to parse consonantal roots. Adults were better at generalizing to novel items compared to children.
Quantum Chemistry Meets Machine Learning
