The probabilistic analysis of language acquisition: Theoretical, computational, and experimental analysis

Abstract

Keywords:Child language acquisition Poverty of the stimulus No negative evidence Bayesian models Minimum description length Simplicity principle Natural language Probabilistic models Identification in the limit a b s t r a c t There is much debate over the degree to which language learning is governed by innate language-specific biases, or acquired through cognition-general principles. Here we examine the probabilistic language acquisition hypothesis on three levels: We outline a novel theoretical result showing that it is possible to learn the exact generative model underlying a wide class of languages, purely from observing samples of the language. We then describe a recently proposed practical framework, which quantifies natural language learnability, allowing specific learnability predictions to be made for the first time. In previous work, this framework was used to make learnability predictions for a wide variety of linguistic constructions, for which learnability has been much debated. Here, we present a new experiment which tests these learnability predictions. We find that our experimental results support the possibility that these linguistic constructions are acquired probabilistically from cognition-general principles.

An amorphous model for morphological processing in visual comprehension based on naive discriminative learning.

“…Ramscar and colleagues made use of the Rescorla-Wagner equations to simulate the time-course of lexical learning. However, there are other relevant psycholinguistic studies which made use of RescorlaWagner model, for example, Hsu, Chater, and Vitányi (2010) and Clair, Monaghan, and Ramscar (2009) on language acquisition, and Ellis (2006), who studied second language learning.…”

Culture shapes the evolution of cognition

“…This restriction to "nativism or nothing" rules out a broad class of possible forms of innateness. Many aspects of human cognition (15), and language acquisition in particular (16), may be better characterized by soft constraints: probabilistic inductive biases that can impose a continuum of preferences ranging from weak to strong. Probabilistic inductive biases in acquisition have been proposed to account for universals concerning word order generalizations (13) and hierarchical phrase structure (17) in syntax, suffixing and prefixing asymmetries in morphosyntax (18), and patterns of vowel harmony (19) and velar palatalization (20) in phonology.…”

“…Fitness can be computed, at the mean field level, with f i ðpÞ = b p i p + 1 − b p i ð1 − pÞ, [16] where p = p · g is the overall expected value for p in the population. Thus, the recursion for biological dynamics becomes…”

On Evaluation Metrics in Optimality Theory

“…The relevance of MDL for grammar induction was already noted by Solomonoff (1964). Over the years, numerous authors have used MDL profitably for grammar induction, either as a methodological principle for the scientist or as a learning criterion for the learner-notably, Berwick (1982), Rissanen and Ristad (1994), Stolcke (1994), Brent and Cartwright (1996), Grünwald (1996), de Marcken (1996, Clark (2001), Goldsmith (2001), Dowman (2007), Hsu and Chater (2010), Hsu, Chater, and Vitányi (2011), Goldsmith and Riggle (2012), and Chater et al (2015), among others.…”