Theories of cognitive development must address both the issue of how children bring their knowledge to bear on behavior in-the-moment, and how knowledge changes over time. We argue that seeking answers to these questions requires an appreciation of the dynamic nature of the developing system in its full, reciprocal complexity. We illustrate this dynamic complexity with results from two lines of research on early word learning. The first demonstrates how the child's active engagement with objects and people supports (...) referent selection via memories for what objects were previously seen in a cued location. The second set of results highlights changes in the role of novelty and attentional processes in referent selection and retention as children's knowledge of words and objects grows. Together this work suggests that understanding systems for perception, action, attention, and memory, and their complex interaction, is critical to understand word learning. We review recent literature that highlights the complex interactions between these processes in cognitive development and point to critical issues for future work. (shrink)

Theories of cognitive development must address both the issue of how children bring their knowledge to bear on behavior in‐the‐moment, and how knowledge changes over time. We argue that seeking answers to these questions requires an appreciation of the dynamic nature of the developing system in its full, reciprocal complexity. We illustrate this dynamic complexity with results from two lines of research on early word learning. The first demonstrates how the child's active engagement with objects and people supports (...) referent selection via memories for what objects were previously seen in a cued location. The second set of results highlights changes in the role of novelty and attentional processes in referent selection and retention as children's knowledge of words and objects grows. Together this work suggests that understanding systems for perception, action, attention, and memory, and their complex interaction, is critical to understand word learning. We review recent literature that highlights the complex interactions between these processes in cognitive development and point to critical issues for future work. (shrink)

Ostension is bodily movement that manifests our engagement with things, whether we wish it to or not. Gestures, glances, facial expressions: all betray our interest in something. Ostension enables our first word learning, providing infants with a prelinguistic way to grasp the meaning of words. Ostension is philosophically puzzling; it cuts across domains seemingly unbridgeable -- public--private, inner--outer, mind--body. In this book, Chad Engelland offers a philosophical investigation of ostension and its role in word learning by (...) infants. Engelland discusses ostension (distinguishing it from ostensive definition) in contemporary philosophy, examining accounts by Quine, Davidson, and Gadamer, and he explores relevant empirical findings in psychology, evolutionary anthropology, and neuroscience. He offers original studies of four representative historical thinkers whose work enriches the understanding of ostension: Wittgenstein, Merleau-Ponty, Augustine, and Aristotle. And, building on these philosophical and empirical foundations, Engelland offers a meticulous analysis of the philosophical issues raised by ostension. He examines the phenomenological problem of whether embodied intentions are manifest or inferred; the problem of what concept of mind allows ostensive cues to be intersubjectively available; the epistemological problem of how ostensive cues, notoriously ambiguous, can be correctly understood; and the metaphysical problem of the ultimate status of the key terms in his argument: animate movement, language, and mind. Finally, he argues for the centrality of manifestation in philosophy. Taking ostension seriously, he proposes, has far-reaching implications for thinking about language and the practice of philosophy. (shrink)

I am very grateful to Aaron Cicourel, Penelope Brown, Max Louwerse, and Matthew Ventrura for their constructive comments. Aaron Cicourel provides a helpful summary of my book and his commentary offers a good place to enter the discussion for readers who have not yet read How Children Learn the Meanings of Words. Brown and Louwerse and Ventura raise some critical questions with regard to the text to which I will speak in turn.

Previous research on associative learning has uncovered detailed aspects of the process, including what types of things are learned, how they are learned, and where in the brain such learning occurs. However, perceptual processes, such as stimulus recognition and identification, take time to unfold. Previous studies of learning have not addressed when, during the course of these dynamic recognition processes, learned representations are formed and updated. If learned representations are formed and updated while recognition is ongoing, the (...) result of learning may incorporate spurious, partial information. For example, during word recognition, words take time to be identified, and competing words are often active in parallel. If learning proceeds before this competition resolves, representations may be influenced by the preliminary activations present at the time of learning. In three experiments using word learning as a model domain, we provide evidence that learning reflects the ongoing dynamics of auditory and visual processing during a learning event. These results show that learning can occur before stimulus recognition processes are complete; learning does not wait for ongoing perceptual processing to complete. (shrink)

Infant language learners are faced with the difficult inductive problem of determining how new words map to novel or known objects in their environment. Bayesian inference models have been successful at using the sparse information available in natural child-directed speech to build candidate lexicons and infer speakers’ referential intentions. We begin by asking how a Bayesian model optimized for monolingual input generalizes to new monolingual or bilingual corpora and find that, especially in the case of the bilingual input, the model (...) shows a significant decrease in performance. In the next experiment, we propose the ME Model, a modified Bayesian model, which approximates infants’ mutual exclusivity bias to support the differential demands of monolingual and bilingual learning situations. The extended model is assessed using the same corpora of real child-directed speech, showing that its performance is more robust against varying input and less dependent than the Intentional Model on optimization of its parsimony parameter. We argue that both monolingual and bilingual demands on word learning are important considerations for a computational model, as they can yield significantly different results than when only one such context is considered. (shrink)

Sound-symbolism is the nonarbitrary link between the sound and meaning of a word. Japanese-speaking children performed better in a verb generalization task when they were taught novel sound-symbolic verbs, created based on existing Japanese sound-symbolic words, than novel nonsound-symbolic verbs (Imai, Kita, Nagumo, & Okada, 2008). A question remained as to whether the Japanese children had picked up regularities in the Japanese sound-symbolic lexicon or were sensitive to universal sound-symbolism. The present study aimed to provide support for the latter. (...) In a verb generalization task, English-speaking 3-year-olds were taught novel sound-symbolic verbs, created based on Japanese sound-symbolism, or novel nonsound-symbolic verbs. English-speaking children performed better with the sound-symbolic verbs, just like Japanese-speaking children. We concluded that children are sensitive to universal sound-symbolism and can utilize it in word learning and generalization, regardless of their native language. (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)

At first, Bloom's theory appears inimical to empiricism, since he credits very young children with highly sophisticated cognitive resources (e.g., a theory of mind and a belief that real kinds have essences), and he also attacks the empiricist's favoured learning theory, namely, associationism. We suggest that, on the contrary, the empiricist can embrace much of what Bloom says.

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)

Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of (...) disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations. (shrink)

The roles of linguistic, cognitive, and social-pragmatic processes in word learning are well established. If statistical mechanisms also contribute to word learning, they must interact with these processes; however, there exists little evidence for such mechanistic synergy. Adults use co-occurrence statistics to encode speech–object pairings with detailed sensitivity in stochastic learning environments (Vouloumanos, 2008). Here, we replicate this statistical work with nonspeech sounds and compare the results with the previous speech studies to examine whether exclusion (...) constraints contribute equally to the statistical learning of speech–object and nonspeech–object associations. In environments in which performance could benefit from exclusion, we find a learning advantage for speech over nonspeech, revealing an interaction between statistical and exclusion processes in associative word learning. (shrink)

We propose that Bloom's focus on cognitive factors involved in word learning still lacks a broader perspective. We emphasize the crucial relevance of working memory in learning elements of language. Specifically, we demonstrate through our data that in impaired populations knowledge of some linguistic elements can be dissociated according to the subcomponent of working memory (visual or verbal) involved in a task. Further, although Bloom's concentration on theory of mind as a precondition for word learning (...) is certainly correct, theory of mind being a necessary condition does not make it a sufficient one. On the basis of our studies we point out the importance of a theory of mind related goal preference in acquiring spatial language. In general, we claim that more specific cognitive preferences and constraints should be outlined in detail for the preconditions of acquiring linguistic elements. (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)

At 14 months, children appear to struggle to apply their fairly well-developed speech perception abilities to learning similar sounding words (e.g., bih/dih; Stager & Werker, 1997). However, variability in nonphonetic aspects of the training stimuli seems to aid word learning at this age. Extant theories of early word learning cannot account for this benefit of variability. We offer a simple explanation for this range of effects based on associative learning. Simulations suggest that if infants (...) encode both noncontrastive information (e.g., cues to speaker voice) and meaningful linguistic cues (e.g., place of articulation or voicing), then associative learning mechanisms predict these variability effects in early word learning. Crucially, this means that despite the importance of task variables in predicting performance, this body of work shows that phonological categories are still developing at this age, and that the structure of noninformative cues has critical influences on word learning abilities. (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)

Cognitive Science, Volume 46, Issue 4, April 2022.

In this study, Limburgian and Dutch 2,5- to 4-year-olds and adults took part in a word learning experiment. Following the procedure employed by Quam and Swingley (2010) and Singh et al. (2014), participants learned two novel word-object mappings. After training, word recognition was tested in correct pronunciation (CP) trials and mispronunciation (MP) trials featuring a pitch change. Since Limburgian is considered a restricted tone language, we expected that the pitch change would hinder word recognition in (...) Limburgian, but not in non-tonal Dutch listeners. Contrary to our expectations, both Limburgian and Dutch children appeared to be sensitive to pitch changes in newly learned words, indicated by a significant decrease in target fixation in MP trials compared to CP trials. Limburgian and Dutch adults showed very strong naming effects in both trial types. The results are discussed against the background of the influence of the native prosodic system. (shrink)

Bloom's theory of word learning has difficulty accounting for children's verb acquisition. There is no predominant preverbal event concept, akin to the preverbal object concept, to direct children's early event-verb mappings. Children may take advantage of grammatical and linguistic information in verb acquisition earlier than Bloom allows. A distinction between lexical and grammatical learning is difficult to maintain for verb acquisition.

The extant literature on discourse comprehension distinguishes between two types of texts: narrative and expository. Narrative discourse tells readers a story by giving them an account of events; the narration informs and/or persuades the readership by using textual elements such as theme, plot, and characters. Expository discourse explains or informs the readership by using concepts and techniques such as definition, sequence, categorization, and cause-effect relations. The present study is based on two experiments. In Experiment 1, we compared the two discourse (...) types to examine if college students would be better at extracting the meanings of novel words from one of the two types of discourse structure than from the other. The findings indicated that participants were significantly better at inferring the meaning of novel words from narrative compared to expository discourse. In Experiment 2, we examined the number of situation models that a reader is required to mentally construct, as a possible characteristic that influences the difficulty of learning new word meaning within narrative discourse. Contrary to intuition, fewer novel words were learned in a single-situation, as opposed to a multi-situation model condition, suggesting that the additional inferencing needed to construct multiple models also promotes word learning. Results are discussed with respect to how the structure of written discourse can facilitate word learning in a reader’s native language. Implications for education and assessment are also discussed. (shrink)

Rich sensorimotor interaction facilitates language learning and is presumed to ground conceptual representations. Yet empirical support for early stages of embodied word learning is currently lacking. Finding evidence that sensorimotor interaction shapes learned linguistic representations would provide crucial support for embodied language theories. We developed a gamified word learning experiment in virtual reality in which participants learned the names of six novel objects by grasping and manipulating objects with either their left or right hand. Participants (...) then completed a word–color match task in which they were tested on the same six words and objects. Participants were faster to respond to stimuli in the match task when the response hand was compatible with the hand used to interact with the named object, an effect we refer to as affordance compatibility. In two follow up experiments, we found that merely observing virtual hands interact with the objects was sufficient to acquire a smaller affordance compatibility effect, and we found that the compatibility effect was driven primarily by responses with a compatible hand and not by responses in a compatible spatial location. Our results support theoretical views of language which ground word representations in sensorimotor experiences, and they suggest promising future routes to explore the sensorimotor foundations of higher cognition through immersive virtual experiments. (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)

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)

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)

Previous research on cross-situational word learning has demonstrated that learners are able to reduce ambiguity in mapping words to referents by tracking co-occurrence probabilities across learning events. In the current experiments, we examined whether learners are able to retain mappings over time. The results revealed that learners are able to retain mappings for up to 1 week later. However, there were interactions between the amount of retention and the different learning conditions. Interestingly, the strongest retention was (...) associated with a learning condition that engendered retrieval dynamics that initially challenged the learner but eventually led to more successful retrieval toward the end of learning. The ease/difficulty of retrieval is a critical process underlying cross-situational word learning and is a powerful example of how learning dynamics affect long-term learning outcomes. (shrink)

Modelling with Words is an emerging modelling methodology closely related to the paradigm of Computing with Words introduced by Lotfi Zadeh. This book is an authoritative collection of key contributions to the new concept of Modelling with Words. A wide range of issues in systems modelling and analysis is presented, extending from conceptual graphs and fuzzy quantifiers to humanist computing and self-organizing maps. Among the core issues investigated are - balancing predictive accuracy and high level transparency in learning - (...) scaling linguistic algorithms to high-dimensional data problems - integrating linguistic expert knowledge with knowledge derived from data - identifying sound and useful inference rules - integrating fuzzy and probabilistic uncertainty in data modelling. (shrink)

To construct their first lexicon, infants must determine the relationship between native phonological variation and the meanings of words. This process is arguably more complex for bilingual learners who are often confronted with phonological conflict: phonological variation that is lexically relevant in one language may be lexically irrelevant in the other. In a series of four experiments, the present study investigated English-Mandarin bilingual infants’ abilities to negotiate phonological conflict introduced by learning both a tone and a non-tone language. In (...) a novel word learning task, bilingual children were tested on their sensitivity to tone variation in English and Mandarin contexts. Their abilities to interpret tone variation in a language-dependent manner were compared to those of monolingual Mandarin learning infants. Results demonstrated that at 12 to 13 months, bilingual infants demonstrated the ability to bind tone to word meanings in Mandarin, but to disregard tone variation when learning new words in English. In contrast, monolingual learners of Mandarin did not show evidence of integrating tones into word meanings in Mandarin at the same age even though they were learning a tone language. However, a tone discrimination paradigm confirmed that monolingual Mandarin learning infants were able to tell these tones apart at 12 to 13 months under a different set of conditions. Later, at 17 to 18 months, monolingual Mandarin learners were able to bind tone variation to word meanings when learning new words. Our findings are discussed in terms of cognitive adaptations associated with bilingualism that may ease the negotiation of phonological conflict and facilitate precocious uptake of certain properties of each language. (shrink)