Universal Features in Phonological Neighbor Networks

Authors

Kevin S. Brown, Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA & Department of Physics, University of Connecticut, Storrs, CT 06269, USA & Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA & Connecticut Institute for the Brain & Cognitive Sciences, Storrs, CT 06269, USA
Paul D. Allopenna, Department of Psychological Sciences, University of Connecticut, Storrs, CT 06269, USA
William R. Hunt, Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA
Rachael Steiner, Department of Psychological Sciences, University of Connecticut, Storrs, CT 06269, USA
Elliot Saltzman, Department of Physical Therapy and Athletic Training, Boston University, Boston, MA 02215, USA
Ken McRae, Department of Psychology, University of Western Ontario, London, ON N6A 5C2, Canada & Brain & Mind Institute, University of Western Ontario, London, ON N6A 5C2, Canada
James Magnuson, Connecticut Institute for the Brain & Cognitive Sciences, Storrs, CT 06269, USA & Department of Psychological Sciences, University of Connecticut, Storrs, CT 06269, USA

Document Type

Article

Publication Date

7-12-2018

Journal

Entropy

Volume

20

Issue

7

First Page

526

URL with Digital Object Identifier

10.3390/e20070526

Abstract

Human speech perception involves transforming a countinuous acoustic signal into discrete linguistically meaningful units (phonemes) while simultaneously causing a listener to activate words that are similar to the spoken utterance and to each other. The Neighborhood Activation Model posits that phonological neighbors (two forms [words] that differ by one phoneme) compete significantly for recognition as a spoken word is heard. This definition of phonological similarity can be extended to an entire corpus of forms to produce a phonological neighbor network (PNN). We study PNNs for five languages: English, Spanish, French, Dutch, and German. Consistent with previous work, we find that the PNNs share a consistent set of topological features. Using an approach that generates random lexicons with increasing levels of phonological realism, we show that even random forms with minimal relationship to any real language, combined with only the empirical distribution of language-specific phonological form lengths, are sufficient to produce the topological properties observed in the real language PNNs. The resulting pseudo-PNNs are insensitive to the level of lingustic realism in the random lexicons but quite sensitive to the shape of the form length distribution. We therefore conclude that “universal” features seen across multiple languages are really string universals, not language universals, and arise primarily due to limitations in the kinds of networks generated by the one-step neighbor definition. Taken together, our results indicate that caution is warranted when linking the dynamics of human spoken word recognition to the topological properties of PNNs, and that the investigation of alternative similarity metrics for phonological forms should be a priority