In 2006, Perruchet and Pacton (2006) asked whether implicit learning and statistical learning represent two approaches to the same phenomenon. This article represents an important follow‐up to their seminal review article. As in the previous paper, the focus is on the formation of elementary cognitive units. Both approaches favor different explanations on what these units consist of and how they are formed. Perruchet weighs up the evidence for different explanations and concludes with a helpful agenda for future research.

Computational models have shown that purely statistical knowledge about words’ linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that “postman” and “mailman” are semantically similar because they have quantitatively similar patterns of association with other words. In contrast to these computational results, artificial language learning experiments suggest that distributional statistics alone do not facilitate learning of linguistic categories. However, experiments in this paradigm expose participants to entirely novel words, (...) whereas real language learners encounter input that contains some known words that are semantically organized. In three experiments, we show that the presence of familiar semantic reference points facilitates distributional learning and this effect crucially depends both on the presence of known words and the adherence of these known words to some semantic organization. (shrink)

Zuidema et al. illustrate how empirical AGL studies can benefit from computational models and techniques. Computational models can help clarifying theories, and thus in delineating research questions, but also in facilitating experimental design, stimulus generation, and data analysis. The authors show, with a series of examples, how computational modeling can be integrated with empirical AGL approaches, and how model selection techniques can indicate the most likely model to explain experimental outcomes.

Standard accounts hold that regularities of behavior must be arbitrary to constitute a convention. Yet, there is growing consensus that conventionality is a graded phenomenon, and that conventions can be more or less natural. I develop an account of natural conventions that distinguishes two basic dimensions of conventional naturalness: a probabilistic dimension and an inherent one. A convention is probabilistically natural if it is likely to emerge in a population of agents, and inherently natural if its content is a regularity (...) that scores high on relevant measures for naturalness. I motivate the proposal on conceptual grounds and then showcase its descriptive benefits by discussing two case studies in language: the tendency towards word-length optimality and the prevalence of shape opacity in spoken language vocabularies. (shrink)