Who are the best subjects for judgment tasks intended to test grammatical hypotheses? Michael Devitt ( [2006a] , [2006b] ) argues, on the basis of a hypothesis concerning the psychology of such judgments, that linguists themselves are. We present empirical evidence suggesting that the relevant divide is not between linguists and non-linguists, but between subjects with and without minimally sufficient task-specific knowledge. In particular, we show that subjects with at least some minimal exposure to or knowledge of such tasks tend (...) to perform consistently with one another—greater knowledge of linguistics makes no further difference—while at the same time exhibiting markedly greater in-group consistency than those who have no previous exposure to or knowledge of such tasks and their goals. (shrink)

Michael Devitt ([2006a], [2006b]) argues that, insofar as linguists possess better theories about language than non-linguists, their linguistic intuitions are more reliable. (Culbertson and Gross [2009]) presented empirical evidence contrary to this claim. Devitt ([2010]) replies that, in part because we overemphasize the distinction between acceptability and grammaticality, we misunderstand linguists' claims, fall into inconsistency, and fail to see how our empirical results can be squared with his position. We reply in this note. Inter alia we argue that Devitt's focus (...) on grammaticality intuitions, rather than acceptability intuitions, distances his discussion from actual linguistic practice. We close by questioning a demand that drives his discussion—viz., that, for linguistic intuitions to supply evidence for linguistic theorizing, a better account of why they are evidence is required. (shrink)

Cognitive Science, Volume 46, Issue 3, March 2022.

According to classical arguments, language learning is both facilitated and constrained by cognitive biases. These biases are reflected in linguistic typology—the distribution of linguistic patterns across the world's languages—and can be probed with artificial grammar experiments on child and adult learners. Beginning with a widely successful approach to typology (Optimality Theory), and adapting techniques from computational approaches to statistical learning, we develop a Bayesian model of cognitive biases and show that it accounts for the detailed pattern of results of artificial (...) grammar experiments on noun-phrase word order (Culbertson, Smolensky, & Legendre, 2012). Our proposal has several novel properties that distinguish it from prior work in the domains of linguistic theory, computational cognitive science, and machine learning. This study illustrates how ideas from these domains can be synthesized into a model of language learning in which biases range in strength from hard (absolute) to soft (statistical), and in which language-specific and domain-general biases combine to account for data from the macro-level scale of typological distribution to the micro-level scale of learning by individuals. (shrink)