The Cultural Evolution of Structured Languages in an Open‐Ended, Continuous World

Abstract

Language maps signals onto meanings through the use of two distinct types of structure. First, the space of meanings is discretized into categories that are shared by all users of the language. Second, the signals employed by the language are compositional: The meaning of the whole is a function of its parts and the way in which those parts are combined. In three iterated learning experiments using a vast, continuous, open‐ended meaning space, we explore the conditions under which both structured categories and structured signals emerge ex nihilo. While previous experiments have been limited to either categorical structure in meanings or compositional structure in signals, these experiments demonstrate that when the meaning space lacks clear preexisting boundaries, more subtle morphological structure that lacks straightforward compositionality—as found in natural languages—may evolve as a solution to joint pressures from learning and communication.

Compositional Hierarchical Structure Evolves through Cultural Transmission: An Experimental Study

“…Although similar linguistic structure evolved across experiments, it did so to varying degrees determined by the nature of the pressure for expressivity, that is, either an artificial pressure against ambiguity in production or communicative interaction. Results show that the substitution of an artificial pressure for communicative interaction eases the evolution of linguistic structure (for a similar conclusion, see Carr et al 2016). With the inclusion of communicative interaction, structure emerged more rapidly, and languages become significantly structured by the first generation.…”

“…Our results further suggest the evolution of morphologically complex constituents which constitute a nominal syntactic category; they all share the same distribution within sentences and thus can be interchanged with each other to derive grammatical structures. Moreover, all nominal constituents within a given language share a morphological category marker and thus high stringsimilarity (for similar results, see Carr, Smith, Cornish, & Kirby 2016;Nowak & Baggio 2016). These nominal constituents combine with each other and verbal elements to form more complex linguistic expressions.…”

“…We introduced a pressure for expressivity into the experimental model via a highly restrictive filter against ambiguity, which prevented languages to become degenerate (i.e., it guaranteed that each sentence corresponded to a single scene): every time a participant used the same description for more than one meaning (thus introducing homonymous descriptions), they were warned and asked to provide an alternative description. This and similar artificial filters against ambiguity (Carr et al 2016;Kirby et al 2008;Silvey et al 2015;Verhoef 2012) have been previously used as an analogue of a pressure to be expressive which comes from the need to communicate accurately in natural language use. Nonetheless, with a complex meaning space where the discriminating features of meanings might not be clear to the participant and in the absence of a goal to communicate meanings successfully, participants who do not discriminate all meaning features systematically do not have any natural reason to do so in production.…”

Adults are more efficient in creating and transmitting novel signalling systems than children

“…Second, introducing a homonymy filter may not have imposed a sufficiently strong expressivity pressure to avoid ambiguous signals (Flaherty & Kirby, 2008;Carr, Smith, Cornish & Kirby, 2017). A better way of directing learners' attention to the requirement for having unique signals for all meanings would have been to combine iterated learning with a referential communication task, as in Kirby et al (2015).…”

How social learning adds up to a culture: from birdsong to human public opinion

“…Cultural transmission of a highly stereotyped signal with distinct categories (or symbols) should be easier, and it is more likely to remain stable over iterations compared with a graded signal. Interestingly, spontaneous emergence of a categorical signal has been reported in language evolution studies (Carr et al, 2016). In these experiments, human subjects were instructed to learn an artificial language, composed of arbitrary words, each representing objects differing in a visual feature (such as color and shape) and in movement.…”