Developmental Stages of Perception and Language Acquisition in a Perceptually Grounded Robot

The objective of this research is to develop a system for language learning based on a minimum of pre-wired language-specific functionality, that is compatible with observations of perceptual and language capabilities in the human developmental trajectory. In the proposed system, meaning (in terms of descriptions of events and spatial relations) is extracted from video images based on detection of position, motion, physical contact and their parameters. Mapping of sentence form to meaning is performed by learning grammatical constructions that are retrieved from a construction inventory based on the constellation of closed class items uniquely identifying the target sentence structure. The resulting system displays robust acquisition behavior that reproduces certain observations from developmental studies, with very modest “innate” language specificity

Acquiring and processing verb argument structure : distributional learning in a miniature language

Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings

Variability, negative evidence, and the acquisition of verb argument constructions

We present a hierarchical Bayesian framework for modeling the acquisition of verb argument constructions. It embodies a domain-general approach to learning higher-level knowledge in the form of inductive constraints (or overhypotheses), and has been used to explain other aspects of language development such as the shape bias in learning object names. Here, we demonstrate that the same model captures several phenomena in the acquisition of verb constructions. Our model, like adults in a series of artificial language learning experiments, makes inferences about the distributional statistics of verbs on several levels of abstraction simultaneously. It also produces the qualitative learning patterns displayed by children over the time course of acquisition. These results suggest that the patterns of generalization observed in both children and adults could emerge from basic assumptions about the nature of learning. They also provide an example of a broad class of computational approaches that can resolve Baker's Paradox

Distributional effects and individual differences in L2 morphology learning

Second language (L2) learning outcomes may depend on the structure of the input and learners’ cognitive abilities. This study tested whether less predictable input might facilitate learning and generalization of L2 morphology while evaluating contributions of statistical learning ability, nonverbal intelligence, phonological short-term memory, and verbal working memory. Over three sessions, 54 adults were exposed to a Russian case-marking paradigm with a balanced or skewed item distribution in the input. Whereas statistical learning ability and nonverbal intelligence predicted learning of trained items, only nonverbal intelligence also predicted generalization of case-marking inflections to new vocabulary. Neither measure of temporary storage capacity predicted learning. Balanced, less predictable input was associated with higher accuracy in generalization but only in the initial test session. These results suggest that individual differences in pattern extraction play a more sustained role in L2 acquisition than instructional manipulations that vary the predictability of lexical items in the input

Adults are more efficient in creating and transmitting novel signalling systems than children

Iterated language learning experiments have shown that meaningful and structured signalling systems emerge when there is pressure for signals to be both learnable and expressive. Yet such experiments have mainly been conducted with adults using language-like signals. Here we explore whether structured signalling systems can also emerge when signalling domains are unfamiliar and when the learners are children with their well-attested cognitive and pragmatic limitations. In Experiment 1, we compared iterated learning of binary auditory sequences denoting small sets of meanings in chains of adults and 5-7-year old children. Signalling systems became more learnable even though iconicity and structure did not emerge despite applying a homonymy filter designed to keep the systems expressive. When the same types of signals were used in referential communication by adult and child dyads in Experiment 2, only the adults, but not the children, were able to negotiate shared iconic and structured signals. Referential communication using their native language by 4-5-year old children in Experiment 3 showed that only interaction with adults, but not with peers resulted in informative expressions. These findings suggest that emergence and transmission of communication systems is unlikely to be driven by children, and point to the importance of cognitive maturity and pragmatic expertise of learners as well as feedback-based scaffolding of communicative effectiveness by experts during language evolution

BEYOND STATISTICAL LEARNING IN THE ACQUISITION OF PHRASE STRUCTURE

The notion that children use statistical distributions present in the input to acquire various aspects of linguistic knowledge has received considerable recent attention. But the roles of learner's initial state have been largely ignored in those studies. What remains unclear is the nature of learner's contribution. At least two possibilities exist. One is that all that learners do is to collect and compile accurately predictive statistics from the data, and they do not have antecedently specified set of possible structures (Elman, et al. 1996; Tomasello 2000). On this view, outcome of the learning is solely based on the observed input distributions. A second possibility is that learners use statistics to identify particular abstract syntactic representations (Miller & Chomsky 1963; Pinker 1984; Yang 2006). On this view, children have predetermined linguistic knowledge on possible structures and the acquired representations have deductive consequences beyond what can be derived from the observed statistical distributions alone. This dissertation examines how the environment interacts with the structure of the learner, and proposes a linking between distributional approach and nativist approach to language acquisition. To investigate this more general question, we focus on how infants, adults and neural networks acquire the phrase structure of their target language. This dissertation presents seven experiments, which show that adults and infants can project their generalizations to novel structures, while the Simple Recurrent Network fails. Moreover, it will be shown that learners' generalizations go beyond the stimuli, but those generalizations are constrained in the same ways that natural languages are constrained. This is compatible with the view that statistical learning interacts with inherent representational system, but incompatible with the view that statistical learning is the sole mechanism by which the existence of phrase structure is discovered. This provides novel evidence that statistical learning interacts with innate constraints on possible representations, and that learners have a deductive power that goes beyond the input data. This suggests that statistical learning is used merely as a method for mapping the surface string to abstract representation, while innate knowledge specifies range of possible grammars and structures

Do as I say, not as I do:a lexical distributional account of English locative verb class acquisition

Children overgeneralise verbs to ungrammatical structures early in acquisition, but retreat from these overgeneralisations as they learn semantic verb classes. In a large corpus of English locative utterances (e.g., the woman sprayed water onto the wall/wall with water), we found structural biases which changed over development and which could explain overgeneralisation behaviour. Children and adults had similar verb classes and a correspondence analysis suggested that lexical distributional regularities in the adult input could help to explain the acquisition of these classes. A connectionist model provided an explicit account of how structural biases could be learned over development and how these biases could be reduced by learning verb classes from distributional regularities