Theories of children's developing understanding of mind tend to emphasize either individualistic processes of theory formation, maturation, or introspection, or the process of enculturation. However, such theories must be able to account for the accumulating evidence of the role of social interaction in the development of social understanding. We propose an alternative account, according to which the development of children's social understanding occurs within triadic interaction involving the child's experience of the world as well as communicative interaction with others about (...) their experience and beliefs (Chapman 1991; 1999). It is through such triadic interaction that children gradually construct knowledge of the world as well as knowledge of other people. We contend that the extent and nature of the social interaction children experience will influence the development of children's social understanding. Increased opportunity to engage in cooperative social interaction and exposure to talk about mental states should facilitate the development of social understanding. We review evidence suggesting that children's understanding of mind develops gradually in the context of social interaction. Therefore, we need a theory of development in this area that accords a fundamental role to social interaction, yet does not assume that children simply adopt socially available knowledge but rather that children construct an understanding of mind within social interaction. Key Words: language; Piaget; social interaction; theories of mind; Vygotsky; Wittgenstein. (shrink)

Cross-situational learning is a mechanism for learning the meaning of words across multiple exposures, despite exposure-by-exposure uncertainty as to the word's true meaning. We present experimental evidence showing that humans learn words effectively using cross-situational learning, even at high levels of referential uncertainty. Both overall success rates and the time taken to learn words are affected by the degree of referential uncertainty, with greater referential uncertainty leading to less reliable, slower learning. Words are also learned less successfully and more slowly (...) if they are presented interleaved with occurrences of other words, although this effect is relatively weak. We present additional analyses of participants’ trial-by-trial behavior showing that participants make use of various cross-situational learning strategies, depending on the difficulty of the word-learning task. When referential uncertainty is low, participants generally apply a rigorous eliminative approach to cross-situational learning. When referential uncertainty is high, or exposures to different words are interleaved, participants apply a frequentist approximation to this eliminative approach. We further suggest that these two ways of exploiting cross-situational information reside on a continuum of learning strategies, underpinned by a single simple associative learning mechanism. (shrink)

Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple (...) scales—both within and across trials/situations—learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is considered from the perspective of a process-level understanding of cross-situational learning. (shrink)

Two studies investigated the interaction between utterance and scene processing by monitoring eye movements in agent–action–patient events, while participants listened to related utterances. The aim of Experiment 1 was to determine if and when depicted events are used for thematic role assignment and structural disambiguation of temporarily ambiguous English sentences. Shortly after the verb identified relevant depicted actions, eye movements in the event scenes revealed disambiguation. Experiment 2 investigated the relative importance of linguistic/world knowledge and scene information. When the verb (...) identified either only the stereotypical agent of a (nondepicted) action, or the (nonstereotypical) agent of a depicted action as relevant, verb‐based thematic knowledge and depicted action each rapidly influenced comprehension. In contrast, when the verb identified both of these agents as relevant, the gaze pattern suggested a preferred reliance of comprehension on depicted events over stereotypical thematic knowledge for thematic interpretation. We relate our findings to language comprehension and acquisition theories. (shrink)

Abstract Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations (...) of three feature-based and nine distributional models using a semantic clustering task. Several distributional models demonstrated semantic clustering comparable with clustering-based on feature-based representations. Furthermore, when trained on child-directed speech, the same distributional models perform as well as sensorimotor-based feature representations of children’s lexical semantic knowledge. These results suggest that, to a large extent, information relevant for extracting semantic categories is redundantly coded in perceptual and linguistic experience. Detailed analyses of the semantic clusters of the feature-based and distributional models also reveal that the models make use of complementary cues to semantic organization from the two data streams. Rather than conceptualizing feature-based and distributional models as competing theories, we argue that future focus should be on understanding the cognitive mechanisms humans use to integrate the two sources. (shrink)

How should knowledge be analyzed? Compositionally, as having constituents like belief and justification, or as an atomic concept? In making arguments for or against these perspectives, epistemologists have begun to use experimental evidence from developmental psychology and developmental linguistics. If we were to conclude that knowledge were developmentally prior to belief, then we might have a good basis to claim that belief is not a constituent of knowledge. In this review, I present a broad range of developmental evidence from the (...) past decade and discuss some of the implications it has for the proper analysis of knowledge. The orthodox perspective from the developmental literature was one where children fail to understand belief and knowledge concepts until later in childhood, with typical asymmetries in belief attribution and knowledge attribution. But what emerges from both a discussion of newer findings and a contextualization of older findings is a picture of development whereby core competence with belief and knowledge concepts emerges much earlier than previously thought that apparent failures in later childhood may be explained by other aspects of development than conceptual development and that there is no clear evidence that knowledge attributions emerge earlier than belief attributions. (shrink)

We present a series of three analyses of young children's linguistic input to determine the distributional information it could plausibly offer to the process of grammatical category learning. Each analysis was conducted on four separate corpora from the CHILDES database (MacWhinney, 2000) of speech directed to children under 2;5. We showthat, in accord with other findings, a distributional analysis which categorizeswords based on their co‐occurrence patterns with surroundingwords successfully categorizes the majority of nouns and verbs. In Analyses 2 and 3, (...) we attempt to make our analyses more closely relevant to natural language acquisition by adopting more realistic assumptions about howyoung children represent their input. In Analysis 2, we limit the distributional context by imposing phrase structure boundaries, and find that categorization improves even beyond that obtained from less limited contexts. In Analysis 3, we reduce the representation of input elements which young children might not fully process and we find that categorization is not adversely affected: Although noun categorization is worse than in Analyses 1 and 2, it is still good; and verb categorization actually improves. Overall, successful categorization of nouns and verbs is maintained across all analyses. These results provide promising support for theories of grammatical category formation involving distributional analysis, as long as these analyses are combined with appropriate assumptions about the child learner's computational biases and capabilities. (shrink)

This article provides an overview of a probabilistic constraints framework for thinking about language acquisition and processing. The generative approach attempts to characterize knowledge of language (i.e., competence grammar) and then asks how this knowledge is acquired and used. Our approach is performance oriented: the goal is to explain how people comprehend and produce utterances and how children acquire this skill. Use of language involves exploiting multiple probabilistic constraints over various types of linguistic and nonlinguistic information. Acquisition is the process (...) of accumulating this information, which begins in infancy. The constraint satisfaction processes that are central to language use are the same as the bootstrapping processes that provide entry to language for the child. Framing questions about acquisition in terms of models of adult performance unifies the two topics under a set of common principles and has important consequences for arguments concerning language learnability. (shrink)

Mental-content verbs such as think, believe, imagine and hope seem to pose special problems for the young language learner. One possible explanation for these diYculties is that the concepts that these verbs express are hard to grasp and therefore their acquisition must await relevant conceptual development. According to a diVerent, perhaps complementary, proposal, a major contributor to the diYculty of these items lies with the informational requirements for identifying them from the contexts in which they appear. The experiments reported here (...) explore the implications of these proposals by investigating the contribution of observational and linguistic cues to the acquisition of mental predicate vocabulary. We Wrst demonstrate that particular observed situations can be helpful in prompting reference to mental contents, speciWcally, contexts that include a salient and/or unusual mental state such as a false belief. We then compare the potency of such observational support to the reliability of alternate or concomitant syntactic information (e.g., sentential complementation) in tasks where both children and adults are asked to hypothesize the meaning of novel verbs. The Wndings support the eYcacy of false belief situations for increasing the saliency of mental state descriptions, but also show that.. (shrink)

We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many conditions extracted from (...) naturalistic corpora of parent-child interactions, even though the model maintains far less information than global models. Moreover, Pursuit is found to best capture human experimental findings from several relevant cross-situational word-learning experiments, including those of Yu and Smith (), the paradigm example of a finding believed to support fully global cross-situational models. Implications and limitations of these results are discussed, most notably that the model characterizes only the earliest stages of word learning, when reliance on the co-occurring referent world is at its greatest. (shrink)

Fisher, Jin, and Scott push a central assumption of syntactic bootstrapping: that learners have a universal bias to map each noun in a sentence onto a participant role (i.e., argument of the verb). They propose two enrichments: First, that children use both semantic and syntactic information in representing nouns that accompany a verb; second, that children expect continuity across a discourse. They provide evidence for both learning mechanisms among young children, further spelling out the precise mechanisms underlying syntactic bootstrapping.

We examine the influence of inferring interlocutors' referential intentions from their body movements at the early stage of lexical acquisition. By testing human participants and comparing their performances in different learning conditions, we find that those embodied intentions facilitate both word discovery and word‐meaning association. In light of empirical findings, the main part of this article presents a computational model that can identify the sound patterns of individual words from continuous speech, using nonlinguistic contextual information, and employ body movements as (...) deictic references to discover word‐meaning associations. To our knowledge, this work is the first model of word learning that not only learns lexical items from raw multisensory signals to closely resemble infant language development from natural environments, but also explores the computational role of social cognitive skills in lexical acquisition. (shrink)

Previous research on lexical development has aimed to identify the factors that enable accurate initial word-referent mappings based on the assumption that the accuracy of initial word-referent associations is critical for word learning. The present study challenges this assumption. Adult English speakers learned an artificial language within a cross-situational learning paradigm. Visual fixation data were used to assess the direction of visual attention. Participants whose longest fixations in the initial trials fell more often on distracter images performed significantly better at (...) test than participants whose longest fixations fell more often on referent images. Thus, inaccurate initial word-referent mappings may actually benefit learning. (shrink)

How do children learn the meanings of propositional attitude verbs? We argue that children use information contained in both syntactic distribution and pragmatic function to zero in on the appropriate meanings. Specifically, we identify a potentially universal link between semantic subclasses of attitude verbs, their syntactic distribution and the kinds of indirect speech acts they can be used to perform. As a result, children can use the syntax as evidence about the meaning, which in turn constrains the kinds of pragmatic (...) enrichments they do and do not make in understanding these verbs in conversation. (shrink)

By the time they reach early adulthood, English speakers are familiar with the meaning of thousands of words. In the last decades, computational simulations known as distributional semantic models have demonstrated that it is possible to induce word meaning representations solely from word co-occurrence statistics extracted from a large amount of text. However, while these models learn in batch mode from large corpora, human word learning proceeds incrementally after minimal exposure to new words. In this study, we run a set (...) of experiments investigating whether minimal distributional evidence from very short passages suffices to trigger successful word learning in subjects, testing their linguistic and visual intuitions about the concepts associated with new words. After confirming that subjects are indeed very efficient distributional learners even from small amounts of evidence, we test a DSM on the same multimodal task, finding that it behaves in a remarkable human-like way. We conclude that DSMs provide a convincing computational account of word learning even at the early stages in which a word is first encountered, and the way they build meaning representations can offer new insights into human language acquisition. (shrink)

Normal children learn tens of thousands of words, and do so quickly and efficiently, often in highly impoverished environments. In How Children Learn the Meanings of Words, I argue that word learning is the product of certain cognitive and linguistic abilities that include the ability to acquire concepts, an appreciation of syntactic cues to meaning, and a rich understanding of the mental states of other people. These capacities are powerful, early emerging, and to some extent uniquely human, but they are (...) not special to word learning. This proposal is an alternative to the view that word learning is the result of simple associative learning mechanisms, and it rejects as well the notion that children possess constraints, either innate or learned, that are specifically earmarked for word learning. This theory is extended to account for how children learn names for objects, substances, and abstract entities, pronouns and proper names, verbs, determiners, prepositions, and number words. Several related topics are also discussed, including naïve essentialism, children's understanding of representational art, the nature of numerical and spatial reasoning, and the role of words in the shaping of mental life. Key Words: cognitive development; concepts; meaning; semantics; social cognition; syntax; theory of mind; word learning. (shrink)

Recent research has demonstrated that word learners can determine word-referent mappings by tracking co-occurrences across multiple ambiguous naming events. The current study addresses the mechanisms underlying this capacity to learn words cross-situationally. This replication and extension of Yu and Smith (2007) investigates the factors influencing both successful cross-situational word learning and mis-mappings. Item analysis and error patterns revealed that the co-occurrence structure of the learning environment as well as the context of the testing environment jointly affected learning across observations. Learners (...) also adopted an exclusion strategy, which contributed conjointly with statistical tracking to performance. Implications for our understanding of the processes underlying cross-situational word learning are discussed. (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)

Propositional attitude verbs, such as think and want, have long held interest for both theoretical linguists and language acquisitionists because their syntactic, semantic, and pragmatic properties display complex interactions that have proven difficult to fully capture from either perspective. This paper explores the granularity with which these verbs’ semantic and pragmatic properties are recoverable from their syntactic distributions, using three behavioral experiments aimed at explicitly quantifying the relationship between these two sets of properties. Experiment 1 gathers a measure of 30 (...) propositional attitude verbs’ syntactic distributions using an acceptability judgment task. Experiments 2a and 2b gather measures of semantic similarity between those same verbs using a generalized semantic discrimination task and an ordinal scale task, respectively. Two kinds of analyses are conducted on the data from these experiments. The first compares both the acceptability judgments and the semantic similarity judgments to previous classifications derived from the syntax and semantics literature. The second kind compares the acceptability judgments to the semantic similarity judgments directly. Through these comparisons, we show that there is quite fine-grained information about propositional attitude verbs’ semantics carried in their syntactic distributions—whether one considers the sorts of discrete qualitative classifications that linguists traditionally work with or the sorts of continuous quantitative classifications that can be derived experimentally. (shrink)

Evidence from numerous studies using the visual world paradigm has revealed both that spoken language can rapidly guide attention in a related visual scene and that scene information can immediately influence comprehension processes. These findings motivated the coordinated interplay account (Knoeferle & Crocker, 2006) of situated comprehension, which claims that utterance‐mediated attention crucially underlies this closely coordinated interaction of language and scene processing. We present a recurrent sigma‐pi neural network that models the rapid use of scene information, exploiting an utterance‐mediated (...) attentional mechanism that directly instantiates the CIA. The model is shown to achieve high levels of performance (both with and without scene contexts), while also exhibiting hallmark behaviors of situated comprehension, such as incremental processing, anticipation of appropriate role fillers, as well as the immediate use, and priority, of depicted event information through the coordinated use of utterance‐mediated attention to the scene. (shrink)

Gleitman and Trueswell’s “The easy words” forms a pair with their earlier paper, “Hard words,” completing a circle in which the authors ask how “easy” words (e.g., concrete nouns) are learned. They take up the hypothesis of “cross‐situational learning,” and argue that accumulating observations actually hinders learning if the mechanism requires holding all exemplars in memory over time. They present an alternative hypothesis, “Propose but Verify,” wherein people use one‐trial learning to confirm or disconfirm their current hypothesis—a mechanism distinctly different (...) from cross‐situational learning. (shrink)

Contextual vocabulary acquisition (CVA) is the deliberate acquisition of a meaning for a word in a text by reasoning from context, where “context” includes: (1) the reader’s “internalization” of the surrounding text, i.e., the reader’s “mental model” of the word’s “textual context” (hereafter, “co-text” [3]) integrated with (2) the reader’s prior knowledge (PK), but it excludes (3) external sources such as dictionaries or people. CVA is what you do when you come across an unfamiliar word in your reading, realize that (...) you don’t know what it means, decide that you need to know what it means in order to understand the passage, but there is no one around to ask, and it is not in the dictionary (or you are too lazy to look it up). In such a case, you can try to figure out its meaning “from context”, i.e., from clues in the co-text together with your prior knowledge. Our computational theory of CVA—implemented in a the SNePS knowledge representation and reasoning system [28]—begins with a stored knowledge base containing SNePS representations of relevant PK, inputs SNePS representations of a passage containing an unfamiliar word, and draws inferences from these two (integrated) information sources. When asked to define the word, definition algorithms deductively search the resulting network for information of the sort that might be found in a dictionary definition, outputting a definition frame whose slots are the kinds of features that a definition might contain (e.g., class membership, properties, actions, spatio-temporal information, etc.) and whose slot-fillers contain information gleaned from the network [6–8,20,23,24]. We are investigating ways to make our system more robust, to embed it in a naturallanguage-processing system, and to incorporate morphological information. Our research group, including reading educators, is also applying our methods to the develop-. (shrink)

What makes a word easy to learn? Early‐learned words are frequent and tend to name concrete referents. But words typically do not occur in isolation. Some words are predictable from their contexts; others are less so. Here, we investigate whether predictability relates to when children start producing different words (age of acquisition; AoA). We operationalized predictability in terms of a word's surprisal in child‐directed speech, computed using n‐gram and long‐short‐term‐memory (LSTM) language models. Predictability derived from LSTMs was generally a better (...) predictor than predictability derived from n‐gram models. Across five languages, average surprisal was positively correlated with the AoA of predicates and function words but not nouns. Controlling for concreteness and word frequency, more predictable predicates and function words were learned earlier. Differences in predictability between languages were associated with cross‐linguistic differences in AoA: the same word (when it was a predicate) was produced earlier in languages where the word was more predictable. (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)

Lidz also ponders the theory of syntactic bootstrapping by asking why it is that learners are considered to retain little of extralinguistic environments (i.e., their observations) during word learning, while being able to retain detailed representations of linguistic context, for example, the multiple syntactic frames in which a verb appears. Lidz argues that learners value syntactic information over extralinguistic context from the beginning of learning, consistent with syntactic bootstrapping as a key device for verb learning.

Yang returns to the problem of referential ambiguity, addressed in the opening paper by Gleitman and Trueswell. Using a computational approach, he argues that “big data” approaches to resolving referential ambiguity are destined to fail, because of the inevitable computational explosion needed to keep track of contextual associations present when a word is uttered. Yang tests several computational models, two of which depend on one‐trial learning, as described in Gleitman and Trueswell’s paper. He concludes that such models outperform cross‐situational learning (...) models, thereby formalizing, reinforcing, and advancing the conclusions offered by Gleitman and Trueswell. (shrink)

A variety of mechanisms contribute to word learning. Learners can track co-occurring words and referents across situations in a bottom-up manner. Equally, they can exploit sentential contexts, relying on top–down information such as verb–argument relations and world knowledge, offering immediate constraints on meaning. When combined, CSWL and SLCL potentially modulate each other's influence, revealing how word learners deal with multiple mechanisms simultaneously: Do they use all mechanisms? Prefer one? Is their strategy context dependent? Three experiments conducted with adult learners reveal (...) that learners prioritize SLCL over CSWL. CSWL is applied in addition to SLCL only if SLCL is not perfectly disambiguating, thereby complementing or competing with it. These studies demonstrate the importance of investigating word-learning mechanisms simultaneously, revealing important characteristics of their interaction in more naturalistic learning environments. (shrink)