Greater learnability is not sufficient to produce cultural universals

Abstract

Looking across human societies reveals regularities in the languages that people speak and the concepts that they use. One explanation that has been proposed for these “cultural universals” is differences in the ease with which people learn particular languages and concepts. A difference in learnability means that languages and concepts possessing a particular property are more likely to be accurately transmitted from one generation of learners to the next. Intuitively, this difference could allow languages and concepts that are more learnable to become more prevalent after multiple generations of cultural transmission. If this is the case, the prevalence of languages and concepts with particular properties can be explained simply by demonstrating empirically that they are more learnable. We evaluate this argument using mathematical analysis and behavioral experiments. Specifically, we provide two counter-examples that show how greater learnability need not result in a property becoming prevalent. First, more learnable languages and concepts can nonetheless be less likely to be produced spontaneously as a result of transmission failures. We simulated cultural transmission in the laboratory to show that this can occur for memory of distinctive items: these items are more likely to be remembered, but not generated spontaneously once they have been forgotten. Second, when there are many languages or concepts that lack the more learnable property, sheer numbers can swamp the benefit produced by greater learnability. We demonstrate this using a second series of experiments involving artificial language learning. Both of these counter-examples show that simply finding a learnability bias experimentally is not sufficient to explain why a particular property is prevalent in the languages or concepts used in human societies: explanations for cultural universals based on cultural transmission need to consider the full set of hypotheses a learner could entertain and all of the kinds of errors that can occur in transmission.

Phonological Concept Learning

“…Earlier work on the typological frequencies of natural classes corresponding to the SHJ Types has found a surprising resemblance, not to the results of Experiment 2, but to those of the classical I > II > III , IV , V > VI order (Moreton & Pertsova, ). A full account of typology will require modeling of not only the inductive biases and the channel biases, but of their interaction during iterated learning (Griffiths & Kalish, ; Griffiths, Kalish, & Lewandowsky, ; Rafferty, Griffiths, & Ettlinger, ; see also Pater & Moreton, , for preliminary work on iterated learning with MaxEnt grammars, and for references to related work on agent‐based modeling).…”

Learning nonadjacent dependencies in phonology: Transparent vowels in vowel harmony

“…Indo-European, Altaic, Niger-Congo). While typologically infrequent patterns may be more difficult to learn, it is not necessarily the case that learning difficulties will directly translate into a typological difference (Rafferty et al 2013). This suggests that learning may play a role in the relative typological difference between transparent and opaque vowels, but additional factors may keep the existence of transparent vowels stable crosslinguistically.…”

The Relationship Between Artificial and Second Language Learning

“…ALL studies have also been used to explore the degree to which language learning may be explained by domain-general learning mechanisms (Aslin & Newport, 2008;Newport, Hauser, Spaepen, & Aslin, 2004;Saffran et al, 1996). Finally, ALL experiments have recently been incorporated into iterated learning studies where findings come not from seeing whether the languages are learned, but rather, seeing what happens to the artificial languages after several cycles of learning and transmission as a method of exploring principles of language evolution and change (Galantucci, 2005;Kirby, Cornish, & Smith, 2008;Rafferty, Griffiths, & Ettlinger, 2013;Reali & Griffiths, 2009;Smith & Wonnacott, 2010). In addressing these and other issues, artificial languages can be viewed as serving as test-tube models of natural language that allow researchers to examine precise issues about language that are not readily testable with natural language (e.g., Morgan-Short et al, 2012).…”