  Human speech perception involves transforming a countinous acoustic signal
into discrete linguistically meaningful units, such as phonemes, while
simultaneously causing a listener to activate words that are similar to the
spoken utterance and to each other. The Neighborhood Activation Model
(NAM~\cite{Luce:1986,Luce:1998}) 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~\cite{Vitevitch:2008} (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 lingusitic 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.
