Proceedings of the Annual Meeting of the Cognitive Science Society

The lexica of natural languages are ambiguous, but the de- gree of ambiguity is unequal between words. Some words have more meanings than others. However, the exact prop- erties that favor some words over others when acquiring a new meaning are not very well understood. In recent years, several studies suggested that some words gain more meanings than others based on selection for efficient communication, which could explain the correlation between meaning and frequency discovered by Zipf (Piantadosi, Tily, & Gibson, 2012; Gib- son et al., 2019). The object of this study is to assess the role of selection in the meaning-frequency correlation using a neu- tral model that yields a meaning-frequency correlation without selection pressures. We provide a model where words gain additional meanings through reuse. In the neutral model pre- sented in this paper, words are chosen to be reused at random, independently of their frequency, hence there is no selection mechanism favoring efficient communication. Unlike previous attempts to introduce null models of the meaning-frequency correlation (Caplan, Kodner, & Yang, 2020; Trott & Bergen, 2020), it truly does not rely on selection for frequency. We show that statistical regularities related to ambiguity, such as Zipf’s meaning-frequency correlation, can arise in conditions when words are not undergoing any selective pressures. This model has the additional property of matching word frequency distributions of natural languages. It can provide the baseline against which the presence of selection for efficient communi- cation in natural languages can be assessed.