Jennifer Culbertson

Learning biases predict a word order universal

How recurrent typological patterns, or universals, emerge from the extensive diversity found across the worlds languages constitutes a central question for linguistics and cognitive science. Recent challenges to a fundamental assumption of generative linguistics-that universal properties of the human language acquisition faculty constrain the types of grammatical systems which can occur-suggest the need for new types of empirical evidence connecting typology to biases of learners. Using an artificial language learning paradigm in which adult subjects are exposed to a mix of grammatical systems (similar to a period of linguistic change), we show that learners biases mirror a word-order universal, first proposed by Joseph Greenberg, which constrains typological patterns of adjective, numeral, and noun ordering. We briefly summarize the results of a probabilistic model of the hypothesized biases and their effect on learning, and discuss the broader implications of the results for current theories of the origins of cross-linguistic word-order preferences.

Are Linguists Better Subjects

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. 1. Introduction2. Background and Clarification3. Previous Experiments4. Our Experiment5. The Context of Devitts Claim and His Psychological Model Appendix Introduction Background and Clarification Previous Experiments Our Experiment The Context of Devitts Claim and His Psychological Model Appendix

Revisited Linguistic Intuitions

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 Devitts 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. 1u2003Introduction 2u2003Acceptability and Grammaticality 3u2003Our Prima Facie Inconsistency 4u2003Our Experimental Results 5u2003Explaining why Intuitions are Evidence 1u2003Introduction 2u2003Acceptability and Grammaticality 3u2003Our Prima Facie Inconsistency 4u2003Our Experimental Results 5u2003Explaining why Intuitions are Evidence

Cognitive Biases, Linguistic Universals, and Constraint‐Based Grammar Learning

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 worlds 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.

A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word‐Order Universal

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment (Culbertson, Smolensky, & Legendre, 2012) targeting the learning of word-order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross-linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization-Greenbergs Universal 18-which bans a particular word-order pattern relating nouns, adjectives, and numerals.

Blocking effects at the lexicon/semantics interface and bidirectional optimization in French

We document two cases of partial blocking at the lexicon/semantics interface concerning the interpretation of inchoative French verbs (craquer snap, se briser break, (se) casser break). One concerns the obligatorily referential interpretation with non-reflexive-marked verbs like casser in a sentence whose pronominal subject il is potentially ambiguous between a referential and a non-referential interpretation (Il a cassé plusieurs branches ‘he/there broke several branches’). The other concerns aspectual interpretations arising in contexts traditionally used as telicity diagnostics. In such contexts, one form (reflexive-marked or not, depending on the particular aspectual context), by default, has the target interpretation; if the lexicon does not provide that form then the other form takes on the target interpretation, resulting in the absence of an overall one-to-one mapping between form and interpretation. This general pattern supports an analysis relying on competition among expression/interpretation pairs, which is here formalized in a bidirectional OT architecture.