The role of sound symbolism in language learning

Certain correspondences between the sound and meaning of words can be observed in subsets of the vocabulary. These sound-symbolic relationships have been suggested to result in easier language acquisition, but previous studies have explicitly tested effects of sound symbolism on learning category distinctions but not on word learning. In 2 word learning experiments, we varied the extent to which phonological properties related to a rounded-angular shape distinction and we distinguished learning of categories from learning of individual words. We found that sound symbolism resulted in an advantage for learning categories of sound-shape mappings but did not assist in learning individual word meanings. These results are consistent with the limited presence of sound symbolism in natural language. The results also provide a reinterpretation of the role of sound symbolism in language learning and language origins and a greater specification of the conditions under which sound symbolism proves advantageous for learning. (PsycINFO Database Record (c) 2012 APA, all rights reserved).     

Interactions between statistical and semantic information in infant language development

Infants can use statistical regularities to form rudimentary word categories (e.g. noun, verb), and to learn the meanings common to words from those categories. Using an artificial language methodology, we probed the mechanisms by which two types of statistical cues (distributional and phonological regularities) affect word learning. Because linking distributional cues vs. phonological information to semantics make different computational demands on learners, we also tested whether their use is related to language proficiency. We found that 22-month-old infants with smaller vocabularies generalized using phonological cues; however, infants with larger vocabularies showed the opposite pattern of results, generalizing based on distributional cues. These findings suggest that both phonological and distributional cues marking word categories promote early word learning. Moreover, while correlations between these cues are important to forming word categories, we found infants' weighting of these cues in subsequent word-learning tasks changes over the course of early language development.     

Consonants and vowels: different roles in early language acquisition

Language acquisition involves both acquiring a set of words (i.e. the lexicon) and learning the rules that combine them to form sentences (i.e. syntax). Here, we show that consonants are mainly involved in word processing, whereas vowels are favored for extracting and generalizing structural relations. We demonstrate that such a division of labor between consonants and vowels plays a role in language acquisition. In two very similar experimental paradigms, we show that 12-month-old infants rely more on the consonantal tier when identifying words (Experiment 1), but are better at extracting and generalizing repetition-based srtuctures over the vocalic tier (Experiment 2). These results indicate that infants are able to exploit the functional differences between consonants and vowels at an age when they start acquiring the lexicon, and suggest that basic speech categories are assigned to different learning mechanisms that sustain early language acquisition.     

No frills: Simple regularities in language can go a long way in the development of word knowledge

Recent years have seen a flourishing of Natural Language Processing models that can mimic many aspects of human language fluency. These models harness a simple, decades-old idea: It is possible to learn a lot about word meanings just from exposure to language, because words similar in meaning are used in language in similar ways. The successes of these models raise the intriguing possibility that exposure to word use in language also shapes the word knowledge that children amass during development. However, this possibility is strongly challenged by the fact that models use language input and learning mechanisms that may be unavailable to children. Across three studies, we found that unrealistically complex input and learning mechanisms are unnecessary. Instead, simple regularities of word use in children's language input that they have the capacity to learn can foster knowledge about word meanings. Thus, exposure to language may play a simple but powerful role in children's growing word knowledge. A video abstract of this article can be viewed at https://youtu.be/dT83dmMffnM .

Research Highlights

• Natural Language Processing (NLP) models can learn that words are similar in meaning from higher-order statistical regularities of word use.
• Unlike NLP models, infants and children may primarily learn only simple co-occurrences between words.
• We show that infants' and children's language input is rich in simple co-occurrence that can support learning similarities in meaning between words.
• We find that simple co-occurrences can explain infants' and children's knowledge that words are similar in meaning.

     

Sound-symbolism: A Piece in the Puzzle of Word Learning

Sound-symbolism is the idea that the relationship between word sounds and word meaning is not arbitrary for all words, but rather that there are subsets of words in the world’s languages for which sounds and their symbols have some degree of correspondence. The present research investigates sound-symbolism as a possible route to the learning of an unknown word’s meaning. Three studies compared the guesses that adult participants made regarding the potential meanings of sound-symbolic and non-sound symbolic obsolete words. In each study, participants were able to generate better definitions for sound-symbolic words when compared to non-sound symbolic words. Participants were also more likely to recognize the meanings of sound symbolic words. The superior performance on sound-symbolic words held even when definitions generated on the basis of sound association were eliminated. It is concluded that sound symbolism is a word property that influences word learning.     

Why is number word learning hard? Evidence from bilingual learners

Young children typically take between 18 months and 2 years to learn the meanings of number words. In the present study, we investigated this developmental trajectory in bilingual preschoolers to examine the relative contributions of two factors in number word learning: (1) the construction of numerical concepts, and (2) the mapping of language specific words onto these concepts. We found that children learn the meanings of small number words (i.e., one, two, and three) independently in each language, indicating that observed delays in learning these words are attributable to difficulties in mapping words to concepts. In contrast, children generally learned to accurately count larger sets (i.e., five or greater) simultaneously in their two languages, suggesting that the difficulty in learning to count is not tied to a specific language. We also replicated previous studies that found that children learn the counting procedure before they learn its logic – i.e., that for any natural number, n, the successor of n in the count list denotes the cardinality n + 1. Consistent with past studies, we found that children’s knowledge of successors is first acquired incrementally. In bilinguals, we found that this knowledge exhibits item-specific transfer between languages, suggesting that the logic of the positive integers may not be stored in a language-specific format. We conclude that delays in learning the meanings of small number words are mainly due to language-specific processes of mapping words to concepts, whereas the logic and procedures of counting appear to be learned in a format that is independent of a particular language and thus transfers rapidly from one language to the other in development.     

Sound to meaning correspondences facilitate word learning

A fundamental assumption regarding spoken language is that the relationship between sound and meaning is essentially arbitrary. The present investigation questioned this arbitrariness assumption by examining the influence of potential non-arbitrary mappings between sound and meaning on word learning in adults. Native English-speaking monolinguals learned meanings for Japanese words in a vocabulary-learning task. Spoken Japanese words were paired with English meanings that: (1) matched the actual meaning of the Japanese word (e.g., “hayai” paired with fast); (2) were antonyms for the actual meaning (e.g., “hayai” paired with slow); or (3) were randomly selected from the set of antonyms (e.g., “hayai” paired with blunt). The results showed that participants learned the actual English equivalents and antonyms for Japanese words more accurately and responded faster than when learning randomly paired meanings. These findings suggest that natural languages contain non-arbitrary links between sound structure and meaning and further, that learners are sensitive to these non-arbitrary relationships within spoken language.     

The logic in language: How all quantifiers are alike,but each quantifier is different

Quantifier words like each, every, all and three are among the most abstract words in language. Unlike nouns, verbs and adjectives, the meanings of quantifiers are not related to a referent out in the world. Rather, quantifiers specify what relationships hold between the sets of entities, events and properties denoted by other words. When two quantifiers are in the same clause, they create a systematic ambiguity. “Every kid climbed a tree” could mean that there was only one tree, climbed by all, or many different trees, one per climbing kid. In the present study, participants chose a picture to indicate their preferred reading of different ambiguous sentences – those containing every, as well as the other three quantifiers. In Experiment 1, we found large systematic differences in preference, depending on the quantifier word. In Experiment 2, we then manipulated the choice of a particular reading of one sentence, and tested how this affected participants’ reading preference on a subsequent target sentence. We found a priming effect for all quantifiers, but only when the prime and target sentences contained the same quantifier. For example, all-a sentences prime other all-a sentences, while each-a primes each-a, but sentences with each do not prime sentences with all or vice versa. In Experiment 3, we ask whether the lack of priming across quantifiers could be due to the two sentences sharing one fewer word. We find that changing the verb between the prime and target sentence does not reduce the priming effect. In Experiment 4, we discover one case where there is priming across quantifiers – when one number (e.g. three) is in the prime, and a different one (e.g. four) is in the target. We discuss how these findings relate to linguistic theories of quantifier meaning and what they tell us about the division of labor between conceptual content and combinatorial semantics, as well as the mental representations of quantification and of the abstract logical structure of language.     

A probabilistic computational model of cross-situational word learning

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.     

Mirror neurons, the representation of word meaning, and the foot of the third left frontal convolution

Previous neuroimaging research has attempted to demonstrate a preferential involvement of the human mirror neuron system (MNS) in the comprehension of effector-related action word (verb) meanings. These studies have assumed that Broca's area (or Brodmann's area 44) is the homologue of a monkey premotor area (F5) containing mouth and hand mirror neurons, and that action word meanings are shared with the mirror system due to a proposed link between speech and gestural communication. In an fMRI experiment, we investigated whether Broca's area shows mirror activity solely for effectors implicated in the MNS. Next, we examined the responses of empirically determined mirror areas during a language perception task comprising effector-specific action words, unrelated words and nonwords. We found overlapping activity for observation and execution of actions with all effectors studied, i.e., including the foot, despite there being no evidence of foot mirror neurons in the monkey or human brain. These "mirror" areas showed equivalent responses for action words, unrelated words and nonwords, with all of these stimuli showing increased responses relative to visual character strings. Our results support alternative explanations attributing mirror activity in Broca's area to covert verbalisation or hierarchical linearisation, and provide no evidence that the MNS makes a preferential contribution to comprehending action word meanings.     

The Effect of Prosody on Distributional Learning in 12‐ to 13‐Month‐Old Infants

Distributional information is a potential cue for learning syntactic categories. Recent studies demonstrate a developmental trajectory in the level of abstraction of distributional learning in young infants. Here we investigate the effect of prosody on infants' learning of adjacent relations between words. Twelve‐ to thirteen‐month‐old infants were exposed to an artificial language comprised of 3‐word‐sentences of the form aXb and cYd, where X and Y words differed in the number of syllables. Training sentences contained a prosodic boundary between either the first and the second word or the second and the third word. Subsequently, infants were tested on novel test sentences that contained new X and Y words and also contained a flat prosody with no grouping cues. Infants successfully discriminated between novel grammatical and ungrammatical sentences, suggesting that the learned adjacent relations can be abstracted across words and prosodic conditions. Under the conditions tested, prosody may be only a weak constraint on syntactic categorization. Copyright © 2011 John Wiley & Sons, Ltd.     

Gestural development and its relation to a child's early vocabulary

Gesture and language are tightly connected during the development of a child's communication skills. Gestures mostly precede and define the way of language development; even opposite direction has been found. Few recent studies have focused on the relationship between specific gestures and specific word categories, emphasising that the onset of one gesture type predicts the onset of certain word categories or of the earliest word combinations.The aim of this study was to analyse predicative roles of different gesture types on the onset of first word categories in a child's early expressive vocabulary. Our data show that different types of gestures predict different types of word production. Object gestures predict open-class words from the age of 13 months, and gestural routines predict closed-class words and social terms from 8 months. Receptive vocabulary has a strong mediating role for all linguistically defined categories (open- and closed-class words) but not for social terms, which are the largest word category in a child's early expressive vocabulary. Accordingly, main contribution of this study is to define the impact of different gesture types on early expressive vocabulary and to determine the role of receptive vocabulary in gesture-expressive vocabulary relation in the Croatian language.     

Capacities underlying word learning

Children are strikingly good at learning the meanings of words. Current controversy focuses on the relative importance of different capacities in this learning process including principles of association, low-level attentional mechanisms, special word learning constraints, syntactic cues and theory of mind. We argue that children succeed at word learning because they possess certain conceptual biases about the external world, the ability to infer the referential intentions of others and an appreciation of syntactic cues to word meaning. Support for this view comes from studies exploring the phenomena of fast mapping, the whole object bias, the acquisition of names for entities belonging to different ontological kinds and the effect of lexical contrast. Word learning is not the result of a general associative learning process, nor does it involve specialized constraints. The ability to learn the meanings of words depends on a number of capacities, some of which are specific to language and unique to humans, others of which are potentially shared with other species.     

Word learning does not end at fast-mapping: evolution of verb meanings through reorganization of an entire semantic domain

This paper explores the process through which children sort out the relations among verbs belonging to the same semantic domain. Using a set of Chinese verbs denoting a range of action events that are labeled by carrying or holding in English as a test case, we looked at how Chinese-speaking 3-, 5-, and 7-year-olds and adults apply 13 different verbs to a range of carrying/holding events. We asked how children learning Chinese originally divide and label the semantic space in this domain, how they discover the boundaries between different words, and how the meanings of verbs in the domain as a whole evolve toward the representations of adults. We also addressed the question of what factors make verb meaning acquisition easy or hard. Results showed that the pattern of children’s verb use is largely different from that of adults and that it takes a long time for children to be able to use all verbs in this domain in the way adults do. We also found that children start to use broad-covering and frequent verbs the earliest, but use of these verbs tends to converge on adult use more slowly because children could not use these verbs as adults did until they had identified boundaries between these verbs and other near-synonyms with more specific meanings. This research highlights the importance of systematic investigation of words that belong to the same domain as a whole, examining how word meanings in a domain develop as parts of a connected system, instead of examining each word on its own: learning the meaning of a verb invites restructuring of the meanings of related, neighboring verbs.     

Semantic Ambiguity: Do Multiple Meanings Inhibit or Facilitate Word Recognition?

It is not clear whether multiple unrelated meanings inhibit or facilitate word recognition. Some studies have found a disadvantage for words having multiple meanings with respect to unambiguous words in lexical decision tasks (LDT), whereas several others have shown a facilitation for such words. In the present study, we argue that these inconsistent findings may be due to the approach employed to select ambiguous words across studies. To address this issue, we conducted three LDT experiments in which we varied the measure used to classify ambiguous and unambiguous words. The results suggest that multiple unrelated meanings facilitate word recognition. In addition, we observed that the approach employed to select ambiguous words may affect the pattern of experimental results. This evidence has relevant implications for theoretical accounts of ambiguous words processing and representation.     

The Specificity of Sound Symbolic Correspondences in Spoken Language

Although language has long been regarded as a primarily arbitrary system, sound symbolism, or non‐arbitrary correspondences between the sound of a word and its meaning, also exists in natural language. Previous research suggests that listeners are sensitive to sound symbolism. However, little is known about the specificity of these mappings. This study investigated whether sound symbolic properties correspond to specific meanings, or whether these properties generalize across semantic dimensions. In three experiments, native English‐speaking adults heard sound symbolic foreign words for dimensional adjective pairs (big/small, round/pointy, fast/slow, moving/still) and for each foreign word, selected a translation among English antonyms that either matched or mismatched with the correct meaning dimension. Listeners agreed more reliably on the English translation for matched relative to mismatched dimensions, though reliable cross‐dimensional mappings did occur. These findings suggest that although sound symbolic properties generalize to meanings that may share overlapping semantic features, sound symbolic mappings offer semantic specificity.     

Word learning as Bayesian inference

The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of the observed examples. The theory addresses shortcomings of the two best known approaches to modeling word learning, based on deductive hypothesis elimination and associative learning. Three experiments with adults and children test the Bayesian account's predictions in the context of learning words for object categories at multiple levels of a taxonomic hierarchy. Results provide strong support for the Bayesian account over competing accounts, in terms of both quantitative model fits and the ability to explain important qualitative phenomena. Several extensions of the basic theory are discussed, illustrating the broader potential for Bayesian models of word learning.     

Predictive coding accelerates word recognition and learning in the early stages of language development

The ability to predict future events in the environment and learn from them is a fundamental component of adaptive behavior across species. Here we propose that inferring predictions facilitates speech processing and word learning in the early stages of language development. Twelve‐ and 24‐month olds’ electrophysiological brain responses to heard syllables are faster and more robust when the preceding word context predicts the ending of a familiar word. For unfamiliar, novel word forms, however, word‐expectancy violation generates a prediction error response, the strength of which significantly correlates with children's vocabulary scores at 12 months. These results suggest that predictive coding may accelerate word recognition and support early learning of novel words, including not only the learning of heard word forms but also their mapping to meanings. Prediction error may mediate learning via attention, since infants’ attention allocation to the entire learning situation in natural environments could account for the link between prediction error and the understanding of word meanings. On the whole, the present results on predictive coding support the view that principles of brain function reported across domains in humans and non‐human animals apply to language and its development in the infant brain. A video abstract of this article can be viewed at: http://hy.fi/unitube/video/e1cbb495-41d8-462e-8660-0864a1abd02c . [Correction added on 27 January 2017, after first online publication: The video abstract link was added.]     

The secret is in the sound: from unsegmented speech to lexical categories

When learning language, young children are faced with many seemingly formidable challenges, including discovering words embedded in a continuous stream of sounds and determining what role these words play in syntactic constructions. We suggest that knowledge of phoneme distributions may play a crucial part in helping children segment words and determine their lexical category, and we propose an integrated model of how children might go from unsegmented speech to lexical categories. We corroborated this theoretical model using a two‐stage computational analysis of a large corpus of English child‐directed speech. First, we used transition probabilities between phonemes to find words in unsegmented speech. Second, we used distributional information about word edges – the beginning and ending phonemes of words – to predict whether the segmented words from the first stage were nouns, verbs, or something else. The results indicate that discovering lexical units and their associated syntactic category in child‐directed speech is possible by attending to the statistics of single phoneme transitions and word‐initial and final phonemes. Thus, we suggest that a core computational principle in language acquisition is that the same source of information is used to learn about different aspects of linguistic structure.     

Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis

The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting a computational experiment using Binder et al.'s (2016) featural conceptual representations based on neurobiologically motivated attributes. In an experiment, these conceptual vectors are predicted from text-based word vectors using a neural network and linear transformation, and prediction performance is compared among various types of information. The analysis demonstrates that abstract information is generally predicted more accurately by word vectors than perceptual and spatiotemporal information, and specifically, the prediction accuracy of cognitive and social information is higher. Emotional information is also found to be successfully predicted for abstract words. These results indicate that language can be a major source of knowledge about abstract attributes, and they support the recent view that emphasizes the importance of language for abstract concepts. Furthermore, we show that word vectors can capture some types of perceptual and spatiotemporal information about concrete concepts and some relevant word categories. This suggests that language statistics can encode more perceptual knowledge than often expected.