A Bootstrapping Model of Frequency and Context Effects in Word Learning

Prior research has shown that people can learn many nouns (i.e., word–object mappings) 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 (Kachergis, Yu, & Shiffrin, 2012), 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.     

Not all is forgotten: Children's associative matrices for features of a word learning episode

Word learning studies traditionally examine the narrow link between words and objects, indifferent to the rich contextual information surrounding objects. This research examined whether children attend to this contextual information and construct an associative matrix of the words, objects, people, and environmental context during word learning. In Experiment 1, preschool-aged children (age: 3;2–5;11 years) were presented with novel words and objects in an animated storybook. Results revealed that children constructed associations beyond words and objects. Specifically, children attended to and had the strongest associations for features of the environmental context but failed to learn word-object associations. Experiment 2 demonstrated that children (age: 3;0–5;8 years) leveraged strong associations for the person and environmental context to support word-object mapping. This work demonstrates that children are especially sensitive to the word learning context and use associative matrices to support word mapping. Indeed, this research suggests associative matrices of the environment may be foundational for children's vocabulary development.     

Rapid learning of syllable classes from a perceptually continuous speech stream

To learn a language, speakers must learn its words and rules from fluent speech; in particular, they must learn dependencies among linguistic classes. We show that when familiarized with a short artificial, subliminally bracketed stream, participants can learn relations about the structure of its words, which specify the classes of syllables occurring in first and last word positions. By studying the effect of familiarization length, we compared the general predictions of associative theories of learning and those of models postulating separate mechanisms for quickly extracting the word structure and for tracking the syllable distribution in the stream. As predicted by the dual-mechanism model, the preference for structurally correct items was negatively correlated with the familiarization length. This result is difficult to explain by purely associative schemes; an extensive set of neural network simulations confirmed this difficulty. Still, we show that powerful statistical computations operating on the stream are available to our participants, as they are sensitive to co-occurrence statistics among non-adjacent syllables. We suggest that different learning mechanisms analyze speech on-line: A rapid mechanism extracting structural information about the stream, and a slower mechanism detecting statistical regularities among the items occurring in it.     

婴幼儿词语习得中的社会性认知加工过程

有关婴幼儿词语习得的研究主要有三种理论假设:联想学习理论、制约限制理论和社会语用理论.本文将从以下四个方面着重介绍社会语用理论的相关研究:婴幼儿词语习得过程涉及哪些社会性认知加工过程,社会语用理论如何应对来自前两种理论的质疑和挑战,社会性认知加工过程的相对权重如何以及自闲症儿童词语习得存在怎样的社会性认知加工障碍.相关研究结果表明社会性认知加工过程在婴幼儿词语习得中具有基础性作用.最后对今后在该领域的研究中需要进一步深入探讨的问题提出了一些思考.     

Atypical shape bias and categorization in autism: Evidence from children and computational simulations

The shape bias, a preference for mapping new word labels onto the shape rather than the color or texture of referents, has been postulated as a word‐learning mechanism. Previous research has shown deficits in the shape bias in children with autism even though they acquire sizeable lexicons. While previous explanations have suggested the atypical use of color for label extension in individuals with autism, we hypothesize an atypical mapping of novel labels to novel objects, regardless of the physical properties of the objects. In Experiment 1, we demonstrate this phenomenon in some individuals with autism, but the novelty of objects only partially explains their lack of shape bias. In a second experiment, we present a computational model that provides a developmental account of the shape bias in typically developing children and in those with autism. This model is based on theories of neurological dysfunctions in autism, and it integrates theoretical and empirical findings in the literature of categorization, word learning, and the shape bias. The model replicates the pattern of results of our first experiment and shows how individuals with autism are more likely to categorize experimental objects together on the basis of their novelty. It also provides insights into possible mechanisms by which children with autism learn new words, and why their word referents may be idiosyncratic. Our model highlights a developmental approach to autism that emphasizes deficient representations of categories underlying an impaired shape bias.     

An associative account of the development of word learning

Word learning is a notoriously difficult induction problem because meaning is underdetermined by positive examples. How do children solve this problem? Some have argued that word learning is achieved by means of inference: young word learners rely on a number of assumptions that reduce the overall hypothesis space by favoring some meanings over others. However, these approaches have difficulty explaining how words are learned from conversations or text, without pointing or explicit instruction. In this research, we propose an associative mechanism that can account for such learning. In a series of experiments, 4-year-olds and adults were presented with sets of words that included a single nonsense word (e.g. dax). Some lists were taxonomic (i.,e., all items were members of a given category), some were associative (i.e., all items were associates of a given category, but not members), and some were mixed. Participants were asked to indicate whether the nonsense word was an animal or an artifact. Adults exhibited evidence of learning when lists consisted of either associatively or taxonomically related items. In contrast, children exhibited evidence of word learning only when lists consisted of associatively related items. These results present challenges to several extant models of word learning, and a new model based on the distinction between syntagmatic and paradigmatic associations is proposed.     

Cross‐Situational Learning: An Experimental Study of Word‐Learning Mechanisms

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.     

Learning times for large lexicons through cross-situational learning

Cross-situational learning is a mechanism for learning the meaning of words across multiple exposures, despite exposure-by-exposure uncertainty as to a word's true meaning. Doubts have been expressed regarding the plausibility of cross-situational learning as a mechanism for learning human-scale lexicons in reasonable timescales under the levels of referential uncertainty likely to confront real word learners. We demonstrate mathematically that cross-situational learning facilitates the acquisition of large vocabularies despite significant levels of referential uncertainty at each exposure, and we provide estimates of lexicon learning times for several cross-situational learning strategies. This model suggests that cross-situational word learning cannot be ruled out on the basis that it predicts unreasonably long lexicon learning times. More generally, these results indicate that there is no necessary link between the ability to learn individual words rapidly and the capacity to acquire a large lexicon.     

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.     

On leveraged learning in lexical acquisition and its relationship to acceleration

Children at about age 18 months experience acceleration in word learning. This vocabulary explosion is a robust phenomenon, although the exact shape and timing vary from child to child. One class of explanations, which we term collectively as leveraged learning , posits that knowledge of some words helps with the learning of others. In this framework, the child initially knows no words and so learning is slow. As more words are acquired, new words become easier and thus it is the acquisition of early words that fuels the explosion in learning. In this paper we examine the role of leveraged learning in the vocabulary spurt by proposing a simple model of leveraged learning. Our results show that leverage can change both the shape and timing of the acceleration, but that it cannot create acceleration if it did not exist in the corresponding model without leveraging. This model is then applied to the Zipfian distribution of word frequencies, which confirm that leveraging does not create acceleration, but that the relationship between frequency and the difficulty of learning a word may be complex.     

Children learn words easier when they are interested in the category to which the word belongs

The overall pattern of vocabulary development is relatively similar across children learning different languages. However, there are considerable differences in the words known to individual children. Historically, this variability has been explained in terms of differences in the input. Here, we examine the alternate possibility that children's individual interest in specific natural categories shapes the words they are likely to learn – a child who is more interested in animals will learn a new animal name easier relative to a new vehicle name. Two‐year‐old German‐learning children (N = 39) were exposed to four novel word–object associations for objects from four different categories. Prior to the word learning task, we measured their interest in the categories that the objects belonged to. Our measure was pupillary change following exposure to familiar objects from these four categories, with increased pupillary change interpreted as increased interest in that category. Children showed more robust learning of word–object associations from categories they were more interested in relative to categories they were less interested in. We further found that interest in the novel objects themselves influenced learning, with distinct influences of both category interest and object interest on learning. These results suggest that children's interest in different natural categories shapes their word learning. This provides evidence for the strikingly intuitive possibility that a child who is more interested in animals will learn novel animal names easier than a child who is more interested in vehicles.     

Exploring the robustness of cross-situational learning under Zipfian distributions

Cross-situational learning has recently gained attention as a plausible candidate for the mechanism that underlies the learning of word-meaning mappings. In a recent study, Blythe and colleagues have studied how many trials are theoretically required to learn a human-sized lexicon using cross-situational learning. They show that the level of referential uncertainty exposed to learners could be relatively large. However, one of the assumptions they made in designing their mathematical model is questionable. Although they rightfully assumed that words are distributed according to Zipf's law, they applied a uniform distribution of meanings. In this article, Zipf's law is also applied to the distribution of meanings, and it is shown that under this condition, cross-situational learning can only be plausible when referential uncertainty is sufficiently small. It is concluded that cross-situational learning is a plausible learning mechanism but needs to be guided by heuristics that aid word learners with reducing referential uncertainty.     

Using variability to guide dimensional weighting: associative mechanisms in early word learning

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.     

What Is the Buzz About Iconicity? How Iconicity in Caregiver Speech Supports Children's Word Learning

One cue that may facilitate children's word learning is iconicity, or the correspondence between a word's form and meaning. Some have even proposed that iconicity in the early lexicon may serve to help children learn how to learn words, supporting the acquisition of even noniconic, or arbitrary, word–referent associations. However, this proposal remains untested. Here, we investigate the iconicity of caregivers’ speech to young children during a naturalistic free-play session with novel stimuli and ask whether the iconicity of caregivers’ speech facilitates children's learning of the noniconic novel names of those stimuli. Thirty-four 1.5-2-year-olds (19 girls; half monolingual English learners and half bilingual English-Spanish learners) participated in a naturalistic free-play task with their caregivers followed by a test of word-referent retention. We found that caregivers’ use of iconicity, particularly in utterances in which they named the novel stimuli, was associated with the likelihood that children learned that novel name. This result held even when controlling for other factors associated with word learning, such as the concreteness and frequency of words in caregiver speech. Together, the results demonstrate that iconicity not only can serve to help children identify the referent of novel words (as in previous research) but can also support their ability to retain even noniconic word-referent mappings.     

学习方法对陌生语言字形学习的影响

采用学习——测查的范式,以陌生视觉单词(韩字)为材料,考察了学习方法对陌生语言字形学习效果的影响。在学习阶段,材料分别以连续重复(集中学习)和间隔重复(分散学习)方式呈现6遍,在学习之后半小时、1天以及1个星期之后,分别进行再认记忆测查。结果发现,虽然随着测查间隔时间的增长,两种学习条件的成绩都显著降低,但是在三次测查中,分散学习的成绩都明显好于集中学习。这一结果说明在排除语音、语义等其他言语元素的干扰之后,以分散学习的方式学习新字形的效果好于集中学习。     

Associative memory or algorithmic search: a comparative study on learning strategies of bats and shrews

Two common strategies for successful foraging are learning to associate specific sensory cues with patches of prey ("associative learning") and using set decision-making rules to systematically scan for prey ("algorithmic search"). We investigated whether an animal's life history affects which of these two foraging strategies it is likely to use. Natterer's bats (Myotis nattereri) have slow life-history traits and we predicted they would be more likely to use associative learning. Common shrews (Sorex araneus) have fast life-history traits and we predicted that they would rely more heavily on routine-based search. Apart from their marked differences in life-history traits, these two mammals are similar in body size, brain weight, habitat, and diet. We assessed foraging strategy, associative learning ability, and retention time with a four-arm maze; one arm contained a food reward and was marked with four sensory stimuli. Bats and shrews differed significantly in their foraging strategies. Most bats learned to associate the sensory stimuli with the reward and remembered this association over time. Most shrews searched the maze using consistent decision-making rules, but did not learn or remember the association. We discuss these results in terms of life-history traits and other key differences between these species. Our results suggest a link between an animal's life-history strategy and its use of associative learning.     

Word Meanings Evolve to Selectively Preserve Distinctions on Salient Dimensions

Words refer to objects in the world, but this correspondence is not one‐to‐one: Each word has a range of referents that share features on some dimensions but differ on others. This property of language is called underspecification. Parts of the lexicon have characteristic patterns of underspecification; for example, artifact nouns tend to specify shape, but not color, whereas substance nouns specify material but not shape. These regularities in the lexicon enable learners to generalize new words appropriately. How does the lexicon come to have these helpful regularities? We test the hypothesis that systematic backgrounding of some dimensions during learning and use causes language to gradually change, over repeated episodes of transmission, to produce a lexicon with strong patterns of underspecification across these less salient dimensions. This offers a cultural evolutionary mechanism linking individual word learning and generalization to the origin of regularities in the lexicon that help learners generalize words appropriately.     

类别学习的阻碍效应与双机制理论

通过对类别学习中的阻碍效应进行系统研究,尝试性的提出了类别学习的双机制理论。三个实验分别考察：当样例特征随机呈现时;定义特征固定呈现在样例首位时;以及刺激材料为知觉图形材料时,类别学习中的阻碍效应。三个实验的研究结果都发现：在类别学习中存在一定程度的阻碍效应,支持类别学习同时存在联结学习机制和认知学习机制的双机制观点。     

A test of foreign language acquisition: paired‐association learning

This experiment investigated college student difficulties with foreign language acquisition. We developed a word‐sound, paired‐associates learning test that discriminated 92% of the control participants and 95% of the experimental participants. The results are interpreted in terms of an associative model of foreign language learning disability. We discuss how to integrate the test into a larger diagnostic battery for predicting the facility with which a person will be able to learn a foreign language. Copyright © 2002 John Wiley & Sons, Ltd.     

Knowing who knows: Metacognitive and causal learning abilities guide infants’ selective social learning

Given the widespread interest in the development of children's selective social learning, there is mounting evidence suggesting that infants prefer to learn from competent informants (Poulin‐Dubois & Brosseau‐Liard, Current Directions in Psychological Science, 2016, 25). However, little research has been dedicated to understanding how this selectivity develops. The present study investigated whether causal learning and precursor metacognitive abilities govern discriminant learning in a classic word‐learning paradigm. Infants were exposed to a speaker who accurately (reliable condition) or inaccurately (unreliable condition) labeled familiar objects and were subsequently tested on their ability to learn a novel word from the informant. The predictive power of causal learning skills and precursor metacognition (as measured through decision confidence) on infants' word learning was examined across both reliable and unreliable conditions. Results suggest that infants are more inclined to accept an unreliable speaker's testimony on a word learning task when they also lack confidence in their own knowledge on a task measuring their metacognitive ability. Additionally, when uncertain, infants draw on causal learning abilities to better learn the association between a label and a novel toy. This study is the first to shed light on the role of causal learning and precursor metacognitive judgments in infants' abilities to engage in selective trust.