Noise resistance in communication: Quantifying uniformity and optimality

Abstract

Over the past decade and a half, several lines of research have investigated aspects of the smooth signalling redundancy hypothesis. This hypothesis proposes that speakers distribute the information in linguistic utterances as evenly as possible, in order to make the utterance more robust against noise for the hearer. Several studies have shown evidence for this hypothesis in limited linguistic domains, showing that speakers manipulate acoustic and syntactic features to avoid drastic spikes or troughs in information content. In theory, the mechanism behind this is that these spikes would make utterances more vulnerable to noise events, and thus, communicative failure. However, this previous work doesn't consider information density across entire utterances, and only rarely has this mechanism been directly explored. Here, we introduce a new descriptive statistic that quantifies the uniformity of information across an entire utterance, alongside an algorithm that can measure the uniformity of actual utterances against an optimized distribution. Using a simple simulation, we show that utterances optimized for more uniform distributions of information are, in fact, more robust against noise.

Smooth Signals and Syntactic Change

“…The predictions borne out in the historical data would have been difficult to even imagine without information theory, and yet they follow naturally from an account of language in which speakers optimize information density for noise resistance. We calculate precise measurements of information uniformity in all the clauses in the Icelandic data set, using the descriptive statistic developed for this purpose in Cuskley et al (2020). The present article continues the integration of information theory and linguistics, and leads to deeper explanations for patterns in syntactic optionality and language change.…”

“…A more recent approach to noise resistance optimization in language considers the role of ordering items in a sentence. Following Fenk and Fenk (1980), Cuskley et al (2020) suggest that peaks and troughs of information can be avoided through word order (without necessarily stretching the signal in time), and that orderings which yield greater uniformity help to mitigate against catastrophic communication failures due to noise. In other words, if high-information words are clustered in a string, catastrophic information loss is more likely to result from noise disruption.…”

“…In other words, if high-information words are clustered in a string, catastrophic information loss is more likely to result from noise disruption. Distributing high information units more uniformly throughout the string, however, improves overall resistance to the same noise (Cuskley et al 2020).…”

“…To compute the DORM for a sentence we take the arithmetic mean of every adjacent, overlapping pair of information content values (i.e., a rolling mean), derived from the lemma frequencies in the results. We then compute the sample variance of the resulting list of means (an unbiased estimator, using denominator n − 1) and arrive at a single number, the DORM (see Cuskley et al 2020 for comprehensive description of DORM and its properties).…”

“…If there is a small bias against uniformity in sentences with OV clauses, it could be a selective advantage for VO once it is in competition with OV in a population (and only then). This is worthy of further investigation, particularly if order-based information uniformity is a strategy to prevent catastrophic communication failures in the transmission of a sentence (Cuskley et al 2020). Once a VO variant is innovated in a population (of speakers and of utterances), the VO and OV variants must be tracked by acquirers in some way, and any systematic effect of noise on the parsing of sentences with an OV vP/VP would lead to a systematic over-representation of the frequency of VO on the part of acquirers.…”