Event Detail

Event Type: 
Mathematical Biology Seminar
Date/Time: 
Wednesday, May 15, 2019 - 12:00 to 12:50
Location: 
Kidder 237

Speaker Info

Institution: 
Oregon State University, Pharmacy
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.