Eliminating unpredictable variation through iterated learning

Human languages may be shaped not only by the (individual psychological) processes of language acquisition, but also by population-level processes arising from repeated language learning and use. One prevalent feature of natural languages is that they avoid unpredictable variation. The current work explores whether linguistic predictability might result from a process of iterated learning in simple diffusion chains of adults. An iterated artificial language learning methodology was used, in which participants were organised into diffusion chains: the first individual in each chain was exposed to an artificial language which exhibited unpredictability in plural marking, and subsequent learners were exposed to the language produced by the previous learner in their chain. Diffusion chains, but not isolate learners, were found to cumulatively increase predictability of plural marking by lexicalising the choice of plural marker. This suggests that such gradual, cumulative population-level processes offer a possible explanation for regularity in language

Constraining generalisation in artificial language learning : children are rational too

Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Examples occur throughout language. For example, English native speakers know that certain noun-adjective combinations are impermissible (e.g. strong winds, high winds, strong breezes, *high breezes). Another example is the restrictions imposed by verb subcategorization, (e.g. I gave/sent/threw the ball to him; I gave/sent/threw him the ball; donated/carried/pushed the ball to him; * I donated/carried/pushed him the ball). Such lexical exceptions have been considered problematic for acquisition: if learners generalize abstract patterns to new words, how do they learn that certain specific combinations are restricted? (Baker, 1979). Certain researchers have proposed domain-specific procedures (e.g. Pinker, 1989 resolves verb subcategorization in terms of subtle semantic distinctions). An alternative approach is that learners are sensitive to distributional statistics and use this information to make inferences about when generalization is appropriate (Braine, 1971). A series of Artificial Language Learning experiments have demonstrated that adult learners can utilize statistical information in a rational manner when determining constraints on verb argument-structure generalization (Wonnacott, Newport & Tanenhaus, 2008). The current work extends these findings to children in a different linguistic domain (learning relationships between nouns and particles). We also demonstrate computationally that these results are consistent with the predictions of domain-general hierarchical Bayesian model (cf. Kemp, Perfors & Tenebaum, 2007)

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

Comparing generalisation in children and adults learning an artificial language

Successful language acquisition involves generalization, but learners must balance this against the acquisition of lexical constraints. Examples occur throughout language. For example, English native speakers know that certain noun-adjective combinations are impermissible (e.g., strong winds, high winds, strong breezes, *high breezes). Another example is the restrictions imposed by verb sub-categorization (e.g., I gave/sent/threw the ball to him; I gave/sent/threw him the ball; I donated/carried/pushed the ball to him; * I donated/carried/pushed him the ball; Baker, 1979). A central debate has been the extent to which learning such patterns depends on semantic cues (Pinker, 1989) and/or distributional statistics (Braine et al., 1990). The current experiments extend previous work which used Artificial Language learning to demonstrate that adults (Wonnacott et al., 2008) and 6 year olds (Wonnacott, 2011) are able to learn lexically based restrictions on generalization using distributional statistics. Here we directly compare the two age groups learning the same artificial language, with a view to exploring maturational differences in language learning. In addition to manipulating frequency (across high and low frequency items) and quantity of exposure (across days), languages were constructed such that a word’s semantic class was helpful for learning the restrictions for some types of lexical items, but potentially misleading for others

Higher order inference in verb argument structure acquisition

Successful language learning combines generalization and the acquisition of lexical constraints. The conflict is particularly clear for verb argument structures, which may generalize to new verbs (John gorped the ball to Bill ->John gorped Bill the ball), yet resist generalization with certain lexical items (John carried the ball to Bill -> *John carried Bill the ball). The resulting learnability “paradox” (Baker 1979) has received great attention in the acquisition literature. Wonnacott, Newport & Tanenhaus 2008 demonstrated that adult learners acquire both general and verb-specific patterns when acquiring an artificial language with two competing argument structures, and that these same constraints are reflected in real time processing. The current work follows up and extends this program of research in two new experiments. We demonstrate that the results are consistent with a hierarchical Bayesian model, originally developed by Kemp, Perfors & Tenebaum (2007) to capture the emergence of feature biases in word learning

Input effects on the acquisition of a novel phrasal construction in five year olds

The present experiments demonstrate that children as young as five years old (M = 5;2) generalize beyond their input on the basis of minimal exposure to a novel argument structure construction. The novel construction that was used involved a non-English phrasal pattern: VN1N2, paired with a novel abstract meaning: N2 approaches N1. At the same time, we find that children are keenly sensitive to the input: they show knowledge of the construction after a single day of exposure but this grows stronger after three days; also, children generalize more readily to new verbs when the input contains more than one verb

Skewing the evidence : the effect of input structure on child and adult learning of lexically based patterns in an artificial language

Successful language acquisition requires both generalization and lexically based learning. Previous research suggests that this is achieved, at least in part, by tracking distributional statistics at and above the level of lexical items. We explored this learning using a semi- artificial language learning paradigm with 6-year-olds and adults, looking at learning of co- occurrence relationships between (meaningless) particles and English nouns. Both age groups showed stronger lexical learning (and less generalization) given “skewed” languages where a majority particle co-occurred with most nouns. In addition, adults, but not children, were affected by overall lexicality, showing weaker lexical learning (more generalization) when some input nouns were seen to alternate (i.e. occur with both particles). The results suggest that restricting generalization is affected by distributional statistics above the level of words/bigrams. Findings are discussed within the framework offered by models capturing generalization as rational inference, namely hierarchical-Bayesian and simplicity-based models

Becoming a written word: eye movements reveal order of acquisition effects following incidental exposure to new words during silent reading

We know that from mid-childhood onwards most new words are learned implicitly via reading; however, most word learning studies have taught novel items explicitly. We examined incidental word learning during reading by focusing on the well-documented finding that words which are acquired early in life are processed more quickly than those acquired later. Novel words were embedded in meaningful sentences and were presented to adult readers early (day 1) or later (day 2) during a five-day exposure phase. At test adults read the novel words in semantically neutral sentences. Participants’ eye movements were monitored throughout exposure and test. Adults also completed a surprise memory test in which they had to match each novel word with its definition. Results showed a decrease in reading times for all novel words over exposure, and significantly longer total reading times at test for early than late novel words. Early-presented novel words were also remembered better in the offline test. Our results show that order of presentation influences processing time early in the course of acquiring a new word, consistent with partial and incremental growth in knowledge occurring as a function of an individual’s experience with each word

Acoustic emphasis in four year olds

Acoustic emphasis may convey a range of subtle discourse distinctions, yet little is known about how this complex ability develops in children. This paper presents a first investigation of the factors which influence the production of acoustic prominence in young children’s spontaneous speech. In a production experiment, SVO sentences were elicited from 4 year olds who were asked to describe events in a video. Children were found to place more acoustic prominence both on ‘new’ words and on words that were ‘given’ but had shifted to a more accessible position within the discourse. This effect of accessibility concurs with recent studies of adult speech. We conclude that, by age four, children show appropriate, adult-like use of acoustic prominence, suggesting sensitivity to a variety of discourse distinctions

Online inference making and comprehension monitoring in children during reading: evidence from eye movements

Inference generation and comprehension monitoring are essential elements of successful reading comprehension. While both improve with age and reading development, little is known about when and how children make inferences and monitor their comprehension during the reading process itself. Over two experiments, we monitored the eye movements of two groups of children (age 8–13 years) as they read short passages and answered questions that tapped local (Experiment 1) and global (Experiment 2) inferences. To tap comprehension monitoring, the passages contained target words which were consistent or inconsistent with the context. Comprehension question location was also manipulated with the question appearing before or after the passage. Children made local inferences during reading, but the evidence was less clear for global inferences. Children were sensitive to inconsistencies that relied on the generation of an inference, consistent with successful comprehension monitoring, although this was seen only very late in the eye movement record. Although question location had a large effect on reading times, it had no effect on global comprehension in one experiment and reading the question first had a detrimental effect in the other. We conclude that children appear to prioritise efficiency over completeness when reading, generating inferences spontaneously only when they are necessary for establishing a coherent representation of the text