How Efficiency Shapes Human Language

Abstract

Cognitive science applies diverse tools and perspectives to study human language. Recently, an exciting body of work has examined linguistic phenomena through the lens of efficiency in usage: what otherwise puzzling features of language find explanation in formal accounts of how language might be optimized for communication and learning? Here, we review studies that deploy formal tools from probability and information theory to understand how and why language works the way that it does, focusing on phenomena ranging from the lexicon through syntax. These studies show how a pervasive pressure for efficiency guides the forms of natural language and indicate that a rich future for language research lies in connecting linguistics to cognitive psychology and mathematical theories of communication and inference.

Multiscale kinematic analysis reveals structural properties of change in evolving manual languages in the lab

“…Furthermore, the gestures were coded for the amount of marking for the functional category, and this showed that such gestures occurred more often at later generations. Finally, average gesture duration -as a measure of communicative efficiency -did not reliably change over the generations, which ran counter to predictions that more mature communication systems tend towards maximal efficiency (Gibson et al, 2019).…”

“…Based on the full sequences of the referential components that were uniquely expressed in each gesture, entropy was computed, which expresses compressibility of the gesture content, i.e., the amount of information that is needed to compress the signal. When a lot of referential components in the gesture utterance recurs between other gestures, the system has a more simple structure and indicates systematic reuse of gestural components (e.g., Gibson et al, 2019). Dovetailing with the qualitative observations and other studies in this field (e.g., Verhoef et al, 2016), it was found that gesture-component entropy decreased over the generations.…”

“…Entropy is a measure that quantifies the compressibility of data structures, and has been used to gauge the combinatorial structure of communicative tokens in the field of language evolution (e.g., Verhoef et al, 2016; for theoretical grounding see Gibson et al, 2019). In the original experiment, Motamedi and colleagues (2019) computed entropy from the gesture content codings, which captured recurrent information units between gestures.…”

Language design and communicative competence: The minimalist perspective

“…When claims are made about ambiguity being beneficial for communicative efficiency, the domain of inquiry is typically restricted to a small range of ambiguities, like simple forms of polysemy or basic forms of syntactic ambiguity (as in Gibson et al 2019 and their focus on why SVO syntax might be more communicatively efficient than SOV syntax), ignoring more complex polysemy (e.g. copredication; Collins 2017) and syntactic phenomena (e.g.…”

A simple repair mechanism can alleviate computational demands of pragmatic reasoning: simulations and complexity analysis

“…To investigate the computational plausibility of this potential gain in communicative efficiency we present a theoretical analysis of other-initiated repair and pragmatic reasoning. Following Gibson et al (2019), we define efficient communication as communication in which participants reach mutual understanding while requiring minimal effort in terms of resource costs (deconstructed here as the sum of computational and interactional cost). We compare a novel agent-based model of otherinitiated repair with one of pragmatic reasoning (Goodman and Frank, 2016) for both their communicative success and use of computational and interactional resources.…”