Language-users shorten words in more predictive contexts when communicating in a miniature artificial language
Jasmeen Kanwal (Edinburgh)
Tuesday 1 November 2016, 11:00–12:30
1.17 Dugald Stewart Building
Zipf’s Law of Abbreviation, the observation that word length tends to be inversely proportional to word frequency, has been confirmed to hold across a wide range of human languages. However, a corpus study by Piantadosi et al. (2011) shows that, for many of these languages, there is in fact a stronger inverse relationship between a word’s length and its predictability in context. Behavioural experiments have shown that predictability in context can affect utterance length in terms of whether phonetic reduction occurs (Gahl and Garnsey 2004), and whether overt morphological (Fedzechkina et al. 2012) or syntactic (Jaeger 2010) markers are used. One study (Mahowald et al. 2013) directly addresses the question of whether predictability in context modulates word length at the level of the lexicon. However, this study is fraught with issues, which I will outline in the talk. I will report the results of a new miniature artificial language learning experiment designed to explicitly test the link between constraints on individual-level behaviour during communication and the structure of the lexicon. We show that, when predictability in context is varied across stimuli rather than bare frequency, language-users shorten words in more predictive contexts, but only when the pressures to communicate both accurately and efficiently are present. I will discuss how these results relate to the burgeoning literature on Uniform Information Density. Finally, I will present some possible ideas for a follow-up experiment.