Why are some words easier to learn than others? And what enables the eventual learning of the more difficult words? These questions were addressed for nouns using a paradigm in which adults were exposed to naturalistic maternal input that was manipulated to simulate access to several different information sources, both alone and in combination: observation of the extralinguistic contexts in which the target word was used, the words that co‐occurred with the target word, and the target word's syntactic context. (...) Words that were not accurately identified from observation alone were both abstract (e.g., music) and concrete (e.g., tail). Whether a noun could be learned from observation depended on whether it labeled a basic‐level object category (BLOC). However, the difference between BLOC labels and non‐BLOC labels was eliminated when observation was supplemented with linguistic context. Thus, although BLOC labels can be learned from observation alone, non‐BLOC labels require richer linguistic context. These findings support a model of vocabulary growth in which an important role is played by changes in the information to which learners have access. (shrink)

Learning to map words onto their referents is difficult, because there are multiple possibilities for forming these mappings. Cross-situational learning studies have shown that word-object mappings can be learned across multiple situations, as can verbs when presented in a syntactic context. However, these previous studies have presented either nouns or verbs in ambiguous contexts and thus bypass much of the complexity of multiple grammatical categories in speech. We show that noun word learning in adults is robust (...) when objects are moving, and that verbs can also be learned from similar scenes without additional syntactic information. Furthermore, we show that both nouns and verbs can be acquired simultaneously, thus resolving category-level as well as individual word-level ambiguity. However, nouns were learned more quickly than verbs, and we discuss this in light of previous studies investigating the noun advantage in word learning. (shrink)

In English, some common nouns, like 'dog', can combine with determiners like 'a' and 'many', but not with 'much', while other nouns, like 'water', can be used together with 'much', but not with 'a' and 'many'. These common nouns have been respectively called count nouns (CNs) and mass nouns (MNs). How do children learn to use CNs and MNs in the appropriate contexts? Gaining a better understanding of this is the goal of this paper. To do so, it is important (...) to first get clear on the nature of the distinction between CNs and MNs. Is it a grammatical distinction? Does the distinction apply to nouns, to their senses, or only to their occurrences within noun phrases (NPs)? Showing that the count-mass distinction really is grammatical and applies to nouns is the matter of my first part. Then the question occurs as to whether the distinction corresponds to a systematic difference in the sense of count and mass expressions. If it does, children's acquisition of the distinction may simply follow from their ability to learn the senses of these expressions and determiners. In a second part, I thus discuss various semantic characterizations that have been proposed, and make explicit the exceptions from which they suffer. Now, understanding the sense of an expression is interpreting it correctly as it occurs in an utterance. Formal characterizations of our interpretations help to clarify what is involved in learning and understanding these expressions. In my third part, I examine several formal characterizations with the purpose to specify what would be an adequate representation of the interpretations of mass and count nominal expressions. The understanding gained in these first three parts is used to identify what abilities are exercised by children when they acquire the count-mass distinction. The picture that emerges differs from earlier views of the acquisition in several respects. I thus describe these views and highlight the differences between them and my own proposal. In a final, fifth part, I critically examine the experimental evidence that proponents of some of the accounts of the acquisition of the count-mass distinction have cited in their favor. (shrink)

Previous research suggests that children learning a variety of languages acquire similar early noun vocabularies and do so by similar and universal processes. We report here results from two studies that show differences in the early noun learning of English- and Japanese-speaking children. Experiment 1 examined the relative numbers of animal names and object names in vocabularies of English-speaking and Japanese-speaking children. English-speaking children's vocabularies were heavily lopsided with many more object than animal names whereas Japanese-speaking (...) children's vocabularies were more evenly balanced. Experiment 2 used a novel noun extension task to examine what young children know about the different organizations of animal and artifact categories. The results suggest that early learners of English but not Japanese over-generalize what they know about object categories to animal categories. The role of culture, input and linguistic structure in early noun acquisitions is discussed. (shrink)

It is unclear how children learn labels for multiple overlapping categories such as “Labrador,” “dog,” and “animal.” Xu and Tenenbaum suggested that learners infer correct meanings with the help of Bayesian inference. They instantiated these claims in a Bayesian model, which they tested with preschoolers and adults. Here, we report data testing a developmental prediction of the Bayesian model—that more knowledge should lead to narrower category inferences when presented with multiple subordinate exemplars. Two experiments did not support this prediction. Children (...) with more category knowledge showed broader generalization when presented with multiple subordinate exemplars, compared to less knowledgeable children and adults. This implies a U-shaped developmental trend. The Bayesian model was not able to account for these data, even with inputs that reflected the similarity judgments of children. We discuss implications for the Bayesian model, including a combined Bayesian/morphological knowledge account that could explain the demonstrated U-shaped trend. (shrink)

Learning depends on attention. The processes that cue attention in the moment dynamically integrate learned regularities and immediate contextual cues. This paper reviews the extensive literature on cued attention and attentional learning in the adult literature and proposes that these fundamental processes are likely significant mechanisms of change in cognitive development. The value of this idea is illustrated using phenomena in children's novel word learning.

There is growing evidence that words that are acquired early in life are processed faster and more accurately than words acquired later, even by adults. As neuropsychological and neuroimaging studies have implicated different brain networks in the processing of action verbs and concrete nouns, the present study was aimed at contrasting reaction times to early and later-acquired action verbs and concrete nouns, in order to determine whether effects of word learning age express differently for the two types of words. (...) Our results show that while word frequency affected both types of words in the same way, distinct learning age effects were observed for action verbs and concrete nouns. A further experiment specified that this difference was observed for verbs describing actions belonging to the human motor repertoire, but not for verbs denoting actions past this repertoire (e.g., to neigh). We interpret these data within a recently emerging framework according to which language processing is associated with sensory motor programs. (shrink)

Many NLP tasks rely on accurately estimating word dependency probabilities P(w1|w2), where the words w1 and w2 have a particular relationship (such as verb-object). Because of the sparseness of counts of such dependencies, smoothing and the ability to use multiple sources of knowledge are important challenges. For example, if the probability P(N |V ) of noun N being the subject of verb V is high, and V takes similar objects to V , and V is synonymous to V , (...) then we want to conclude that P(N |V ) should also be reasonably high—even when those words did not cooccur in the training data. (shrink)

Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers (premarkers, e.g., gendered articles) or succeeding category markers (postmarkers, e.g., gendered suffixes). Given that numerous systems for marking grammatical categories exist in natural languages, it follows that a better understanding of these findings can shed light on the factors underlying this diversity. In two discriminative learning simulations and an artificial language (...) learning experiment, we identify two factors that modulate linear order effects in linguistic category learning: category structure and the level of abstraction in a category hierarchy. Regarding category structure, we find that postmarking brings an advantage for learning category diagnostic stimulus dimensions, an effect not present when categories are non‐confusable. Regarding levels of abstraction, we find that premarking of super‐ordinate categories (e.g., noun class) facilitates learning of subordinate categories (e.g., nouns). We present detailed simulations using a plausible candidate mechanism for the observed effects, along with a comprehensive analysis of linear order effects within an expectation‐based account of learning. Our findings indicate that linguistic category learning is differentially guided by pre‐ and postmarking, and that the influence of each is modulated by the specific characteristics of a given category system. (shrink)

Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers (premarkers, e.g., gendered articles) or succeeding category markers (postmarkers, e.g., gendered suffixes). Given that numerous systems for marking grammatical categories exist in natural languages, it follows that a better understanding of these findings can shed light on the factors underlying this diversity. In two discriminative learning simulations and an artificial language (...) learning experiment, we identify two factors that modulate linear order effects in linguistic category learning: category structure and the level of abstraction in a category hierarchy. Regarding category structure, we find that postmarking brings an advantage for learning category diagnostic stimulus dimensions, an effect not present when categories are non‐confusable. Regarding levels of abstraction, we find that premarking of super‐ordinate categories (e.g., noun class) facilitates learning of subordinate categories (e.g., nouns). We present detailed simulations using a plausible candidate mechanism for the observed effects, along with a comprehensive analysis of linear order effects within an expectation‐based account of learning. Our findings indicate that linguistic category learning is differentially guided by pre‐ and postmarking, and that the influence of each is modulated by the specific characteristics of a given category system. (shrink)

The concept of the universal was born in the lands we now call Europe, yet it is precisely the universal that is Europe's undoing. All European politics is caught in a tension: to assert a European identity is to be open to multiplicity, but this very openness could dissolve Europe as such. This book reflects on Europe and its changing boundaries over the span of twenty centuries. A work of philosophy, it consistently draws on concrete events. From ancient Greece and (...) Rome, to Christianity, to the Reformation, to the national revolutions of the twentieth century, what we today call "Europe" has been a succession of projects in the name of ecclesia or community. Empire, Church, and EU: all have been constructed in contrast to an Oriental "other." The stakes of Europe, then, are as much metaphysical as political. Redefining a series of key concepts such as world, place, transportation, and the common, this book sheds light on Europe as process by engaging with the most significant philosophical debates on the subject, including the work of Marx, Husserl, Heidegger, Patocka, and Nancy. (shrink)

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)

In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2-year-olds seem to intuit the whole range of things in a category from hearing a single instance named—they have word-learning biases. This is not the case for children with relatively small vocabularies. We present a computational model that accounts for the emergence of word-learning biases in children at both ends of the vocabulary spectrum based solely on vocabulary structure. The results (...) of Experiment 1 show that late-talkers' and early-talkers' noun vocabularies have different structures and that neural networks trained on the vocabularies of individual late talkers acquire different word-learning biases than those trained on early-talker vocabularies. These models make novel predictions about the word-learning biases in these two populations. Experiment 2 tests these predictions on late- and early-talking toddlers in a novel noun generalization task. (shrink)

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—Greenberg's Universal 18—which bans a particular word‐order pattern relating nouns, adjectives, and numerals. (shrink)

This article presents two learning processes in order to explain how children at an early age can transform a complex sensory input to concepts and categories. The first process constructs the perceptual structures that emerge in children’s cognitive development by detecting invariants in the sensory input. The invariant structures involve a reduction in dimensionality of the sensory information. It is argued that this process generates the primary domains of space, objects and actions and that these domains can be represented (...) as conceptual spaces. Once the primary domains have been established, the second process utilizes covariances between different dimensions of the domains in order to identify natural clusters of entities. The clusters are then are used to determine concepts as regions in the spaces. As an application, the processes are used to resolve the so-called ‘complex first paradox’ that emerges from the fact that children, in general, learn nouns earlier than adjectives, even though nouns are semantically more complex than adjectives. (shrink)

Prior research has shown that people can learn many nouns from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a (...) recent associative model, we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings. (shrink)

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects (...) based on shared and non-shared features. Feature distinctiveness predicted order of acquisition across all measures: Words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non-shared features and did not include shared features. This indicates that shared features may play less of a role in early word learning than non-shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects' distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning. (shrink)

Fodor's and Pylyshyn's stand on systematicity in thought and language has been debated and criticized. Van Gelder and Niklasson, among others, have argued that Fodor and Pylyshyn offer no precise definition of systematicity. However, our concern here is with a learning based formulation of that concept. In particular, Hadley has proposed that a network exhibits strong semantic systematicity when, as a result of training, it can assign appropriate meaning representations to novel sentences (both simple and embedded) which contain words (...) in syntactic positions they did not occupy during training. The experience of researchers indicates that strong systematicity in any form is difficult to achieve in connectionist systems.Herein we describe a network which displays strong semantic systematicity in response to Hebbian, connectionist training. During training, two-thirds of all nouns are presented only in a single syntactic position (either as grammatical subject or object). Yet, during testing, the network correctly interprets thousands of sentences containing those nouns in novel positions. In addition, the network generalizes to novel levels of embedding. Successful training requires a, corpus of about 1000 sentences, and network training is quite rapid. The architecture and learning algorithms are purely connectionist, but classical insights are discernible in one respect, viz, that complex semantic representations spatially contain their semantic constituents. However, in other important respects, the architecture is distinctly non-classical. (shrink)

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge lengths computed using various distance measures. Feature distinctiveness was computed as a distance measure, showing how far an object in a network is from other objects (...) based on shared and non‐shared features. Feature distinctiveness predicted order of acquisition across all measures: Words that were further away from other words in the network space were learned earlier. The best distance measures were based only on non‐shared features (object dissimilarity) and did not include shared features (object similarity). This indicates that shared features may play less of a role in early word learning than non‐shared features. In addition, the strongest effects were found for visual form and surface features. Cluster analysis further revealed that this effect is a localized effect in the object feature space, where objects' distances from their cluster centroid were inversely correlated with their age of acquisition. Together, these results suggest a role for feature distinctiveness in early word learning. (shrink)

A robust result in research on the production of grammatical agreement is that speakers are more likely to produce an erroneous verb with phrases such as the key to the cabinets, with a singular noun followed by a plural one, than with phrases such as the keys to the cabinet, where a plural noun is followed by a singular. These asymmetries are thought to reflect core language production processes. Previous accounts have attributed error patterns to a syntactic number (...) feature present on plurals but not singulars. An alternative approach is presented in which a process similar to structural priming contributes to the error asymmetry via speakers' past experiences with related agreement constructions. A corpus analysis and two agreement production studies test this account. The results suggest that agreement production is shaped by statistical learning from past language experience. Implications for accounts of agreement are discussed. (shrink)

The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic‐level concepts (carrot). A feature‐based attractor network with a single layer of semantic features developed representations of both basic‐level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat (...) attractor network. Experiment and Simulation 2 show that, as with basic‐level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory. (shrink)

To help infer the meanings of novel words, children frequently capitalize on their current linguistic knowledge to constrain the hypothesis space. Children's syntactic knowledge of function words has been shown to be especially useful in helping to infer the meanings of novel words, with most previous research focusing on how children use preceding determiners and pronouns/auxiliary to infer whether a novel word refers to an entity or an action, respectively. In the current visual world experiment, we examined whether 28‐ to (...) 32‐month‐olds could exploit their lexical semantic knowledge of an additional class of function words—prepositions—to learn novel nouns. During the experiment, children were tested on their ability to use the prepositions in, on, under, and next to to identify novel creatures displayed on a screen (e.g., The wug is on the table), as well as their ability to later identify the creature without accompanying prepositions (e.g., Look at the wug). Children overall demonstrated understanding of all the prepositions but next to and were able to use their knowledge of prepositions to learn the associations between novel words and their intended referents, as shown by greater‐than chance looks to the target referent when no prepositional phrase was provided. (shrink)

How Children Learn the Meanings of Words (HCLMW) defends the theory that words are learned through sophisticated and early-emerging cognitive abilities that have evolved for other purposes; there is no dedicated mental mechanism that is special to word learning. The commentators raise a number of challenges to this theory: Does it correctly characterize the nature and development of early abilities? Does it attribute too much to children, or too little? Does it only apply to nouns, or can it also (...) explain the acquisition of words such as verbs and determiners? More general issues are discussed as well, including the role of the input, the relationship between words and concepts, and debates over nativism, adaptationism, and modularity. (shrink)

Abstract:There is evidence that children learn both proper names and count nouns from the outset of lexical development. Furthermore, children's first proper names are typically words for people, whereas their first count nouns are commonly terms for other objects, including artifacts. I argue that these facts represent a challenge for two well‐known theoretical accounts of object word learning. I defend an alternative account, which credits young children with conceptual resources to acquire words for both individual objects and object categories, (...) and conceptual biases to construe some objects (notably people) as individuals in their own right and most other objects as instances of their category. (shrink)

There is evidence that children learn both proper names and count nouns from the outset of lexical development. Furthermore, children's first proper names are typically words for people, whereas their first count nouns are commonly terms for other objects, including artifacts. I argue that these facts represent a challenge for two well-known theoretical accounts of object word learning. I defend an alternative account, which credits young children with conceptual resources to acquire words for both individual objects and object categories, (...) and conceptual biases to construe some objects (notably people) as individuals in their own right and most other objects as instances of their category. (shrink)

Fodor's and Pylyshyn's stand on systematicity in thought and language has been debated and criticized. Van Gelder and Niklasson, among others, have argued that Fodor and Pylyshyn offer no precise definition of systematicity. However, our concern here is with a learning based formulation of that concept. In particular, Hadley has proposed that a network exhibits strong semantic systematicity when, as a result of training, it can assign appropriate meaning representations to novel sentences (both simple and embedded) which contain words (...) in syntactic positions they did not occupy during training. The experience of researchers indicates that strong systematicity in any form is difficult to achieve in connectionist systems.Herein we describe a network which displays strong semantic systematicity in response to Hebbian, connectionist training. During training, two-thirds of all nouns are presented only in a single syntactic position (either as grammatical subject or object). Yet, during testing, the network correctly interprets thousands of sentences containing those nouns in novel positions. In addition, the network generalizes to novel levels of embedding. Successful training requires a, corpus of about 1000 sentences, and network training is quite rapid. The architecture and learning algorithms are purely connectionist, but ‘classical’ insights are discernible in one respect, viz, that complex semantic representations spatially contain their semantic constituents. However, in other important respects, the architecture is distinctly non-classical. (shrink)

Here we study polysemy as a potential learning bias in vocabulary learning in children. Words of low polysemy could be preferred as they reduce the disambiguation effort for the listener. However, such preference could be a side-effect of another bias: the preference of children for nouns in combination with the lower polysemy of nouns with respect to other part-of-speech categories. Our results show that mean polysemy in children increases over time in two phases, i.e. a fast growth till (...) the 31st month followed by a slower tendency towards adult speech. In contrast, this evolution is not found in adults interacting with children. This suggests that children have a preference for non-polysemous words in their early stages of vocabulary acquisition. Interestingly, the evolutionary pattern described above weakens when controlling for syntactic category (noun, verb, adjective or adverb) but it does not disappear completely, suggesting that it could result from a combination of a standalone bias for low polysemy and a preference for nouns. (shrink)

Non-verbal enrichment in the form of pictures or gesture can support word learning in first and foreign languages. The present study seeks to compare the effects of viewing pictures vs. imitating iconic gestures on learning second language vocabulary. In our study participants learned L2 words together with a virtual, pedagogical agent. The to-be-learned items were either enriched with pictures, or with gestures that had to be imitated, or without any non-verbal enrichment as control. Results showed that gesture imitation (...) was particularly supportive for learning nouns, whereas pictures showed to be most beneficial for memorizing verbs. These findings, suggesting that the type of vocabulary learning strategy has to match with the type of linguistic material to be learned, have important educational implications for L2 classrooms and technology-enhanced tutoring systems. (shrink)