Cross-situational Word Learning Research Articles

Competition between multiple words for a referent in cross-situational word learning.

Three experiments investigated competition between word-object pairings in a cross-situational word-learning paradigm. Adults were presented with One-Word pairings, where a single word labeled a single object, and Two-Word pairings, where two words labeled a single object. In addition to measuring learning of these two pairing types, we measured competition between words that refer to the same object. When the word-object co-occurrences were presented intermixed in training (Experiment 1), we found evidence for direct competition between words that label the same referent. Separating the two words for an object in time eliminated any evidence for this competition (Experiment 2). Experiment 3 demonstrated that adding a linguistic cue to the second label for a referent led to different competition effects between adults who self-reported different language learning histories, suggesting both distinctiveness and language learning history affect competition. Finally, in all experiments, competition effects were unrelated to participants' explicit judgments of learning, suggesting that competition reflects the operating characteristics of implicit learning processes. Together, these results demonstrate that the role of competition between overlapping associations in statistical word-referent learning depends on time, the distinctiveness of word-object pairings, and language learning history.

The Effects of Learning and Retrieval Contexts on Cross-situational Word Learning.

Natural linguistic environments usually provide structured input, in that words that are semantically-related are likely to occur in the same situation. The current study examined whether this kind of semantically-themed structure facilitated cross-situational word learning. Results from two experiments consistently showed that participants had higher performance in semantically-themed learning contexts. In contrast, themed retrieval contexts did not affect performance. Our work suggests that learners' ability to use statistical information to acquire word-to-object mappings is enhanced when learning is put in a more naturalistic and structured context.

The interplay of cross-situational word learning and sentence-level constraints.

A variety of mechanisms contribute to word learning. Learners can track co-occurring words and referents across situations in a bottom-up manner (cross-situational word learning, CSWL). Equally, they can exploit sentential contexts, relying on top-down information such as verb-argument relations and world knowledge, offering immediate constraints on meaning (word learning based on sentence-level constraints, SLCL). 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.

Cross-situational statistical word learning in young children

Recent empirical work has highlighted the potential role of cross-situational statistical word learning in children’s early vocabulary development. In the current study, we tested 5- to 7-year-old children’s cross-situational learning by presenting children with a series of ambiguous naming events containing multiple words and multiple referents. Children rapidly learned word-to-object mappings by attending to the co-occurrence regularities across these ambiguous naming events. The current study begins to address the mechanisms underlying children’s learning by demonstrating that the diversity of learning contexts affects performance. The implications of the current findings for the role of cross-situational word learning at different points in development are discussed along with the methodological implications of employing school-aged children to test hypotheses regarding the mechanisms supporting early word learning.

Cross-situational word learning is both implicit and strategic.

For decades, implicit learning researchers have examined a variety of cognitive tasks in which people seem to automatically extract structure from the environment. Similarly, recent statistical learning studies have shown that people can learn word-object mappings from the repeated co-occurrence of words and objects in individually ambiguous situations. In light of this, the goal of the present paper is to investigate whether adult cross-situational learners require an explicit effort to learn word-object mappings, or if it may take place incidentally, only requiring attention to the stimuli. In two implicit learning experiments with incidental tasks directing participants' attention to different aspects of the stimuli, we found evidence of learning, suggesting that cross-situational learning mechanisms can operate incidentally, without explicit effort. However, performance was superior under explicit study instructions, indicating that strategic processes also play a role. Moreover, performance under instruction to learn word meanings did not differ from performance at counting co-occurrences, which may indicate these tasks engage similar strategies.

Cross-situational word learning in the right situations.

Upon hearing a novel word, language learners must identify its correct meaning from a diverse set of situationally relevant options. Such referential ambiguity could be reduced through repetitive exposure to the novel word across diverging learning situations, a learning mechanism referred to as cross-situational learning. Previous research has focused on the amount of information learners carry over from 1 learning instance to the next. In the present article, we investigate how context can modulate the learning strategy and its efficiency. Results from 4 cross-situational learning experiments with adults suggest the following: (a) Learners encode more than the specific hypotheses they form about the meaning of a word, providing evidence against the recent view referred to as single hypothesis testing. (b) Learning is faster when learning situations consistently contain members from a given group, regardless of whether this group is a semantically coherent group (e.g., animals) or induced through repetition (objects being presented together repetitively, just like a fork and a door may occur together repetitively in a kitchen). (c) Learners are subject to memory illusions, in a way that suggests that the learning situation itself appears to be encoded in memory during learning. Overall, our findings demonstrate that realistic contexts (such as the situation in which a given word has occurred; e.g., in the zoo or in the kitchen) help learners retrieve or discard potential referents for a word, because such contexts can be memorized and associated with a to-be-learned word.

Retrieval Dynamics and Retention in Cross‐Situational Statistical Word Learning

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.

Competitive Processes in Cross‐Situational Word Learning

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.

Statistical word learning at scale: the baby's view is better.

A key question in early word learning is how children cope with the uncertainty in natural naming events. One potential mechanism for uncertainty reduction is cross-situational word learning - tracking word/object co-occurrence statistics across naming events. But empirical and computational analyses of cross-situational learning have made strong assumptions about the nature of naming event ambiguity, assumptions that have been challenged by recent analyses of natural naming events. This paper shows that learning from ambiguous natural naming events depends on perspective. Natural naming events from parent-child interactions were recorded from both a third-person tripod-mounted camera and from a head-mounted camera that produced a 'child's-eye' view. Following the human simulation paradigm, adults were asked to learn artificial language labels by integrating across the most ambiguous of these naming events. Significant learning was found only from the child's perspective, pointing to the importance of considering statistical learning from an embodied perspective.

Reinforcement and inference in cross-situational word learning

Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.

Social and Discourse Contributions to the Determination of Reference in Cross-Situational Word Learning

How do children infer the meanings of their first words? Even in infant-directed speech, object nouns are often used in complex contexts with many possible referents and in sentences with many other words. Previous work has argued that children can learn word meanings via cross-situational observation of correlations between words and their referents. While cross-situational associations can sometimes be informative, social cues to what a speaker is talking about can provide a powerful shortcut to word meaning. The current study takes steps toward quantifying the informativeness of cues that signal speakers' chosen referent, including their eye-gaze, the position of their hands, and the referents of their previous utterances. We present results based on a hand-annotated corpus of 24 videos of child-caregiver play sessions with children from 6 to 18 months old, which we make available to researchers interested in similar issues. Our analyses suggest that although they can be more useful than cross-situational information in some contexts, social and discourse information must also be combined probabilistically to be effective in determining reference.

Actively Learning Object Names Across Ambiguous Situations

Previous research shows that people can use the co-occurrence of words and objects in ambiguous situations (i.e., containing multiple words and objects) to learn word meanings during a brief passive training period (Yu & Smith, 2007). However, learners in the world are not completely passive but can affect how their environment is structured by moving their heads, eyes, and even objects. These actions can indicate attention to a language teacher, who may then be more likely to name the attended objects. Using a novel active learning paradigm in which learners choose which four objects they would like to see named on each successive trial, this study asks whether active learning is superior to passive learning in a cross-situational word learning context. Finding that learners perform better in active learning, we investigate the strategies and discover that most learners use immediate repetition to disambiguate pairings. Unexpectedly, we find that learners who repeat only one pair per trial--an easy way to infer this pair-perform worse than those who repeat multiple pairs per trial. Using a working memory extension to an associative model of word learning with uncertainty and familiarity biases, we investigate individual differences that correlate with these assorted strategies.

Propose but verify: Fast mapping meets cross-situational word learning

We report three eyetracking experiments that examine the learning procedure used by adults as they pair novel words and visually presented referents over a sequence of referentially ambiguous trials. Successful learning under such conditions has been argued to be the product of a learning procedure in which participants provisionally pair each novel word with several possible referents and use a statistical-associative learning mechanism to gradually converge on a single mapping across learning instances [e.g., Yu, C., & Smith, L. B. (2007). Rapid word learning under uncertainty via cross-situational statistics. Psychological Science, 18(5), 414–420]. We argue here that successful learning in this setting is instead the product of a one-trial procedure in which a single hypothesized word-referent pairing is retained across learning instances, abandoned only if the subsequent instance fails to confirm the pairing – more a ‘fast mapping’ procedure than a gradual statistical one. We provide experimental evidence for this propose-but-verify learning procedure via three experiments in which adult participants attempted to learn the meanings of nonce words cross-situationally under varying degrees of referential uncertainty. The findings, using both explicit (referent selection) and implicit (eye movement) measures, show that even in these artificial learning contexts, which are far simpler than those encountered by a language learner in a natural environment, participants do not retain multiple meaning hypotheses across learning instances. As we discuss, these findings challenge ‘gradualist’ accounts of word learning and are consistent with the known rapid course of vocabulary learning in a first language.

Detailed Behavioral Analysis as a Window Into Cross‐Situational Word Learning

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.

2.5-Year-olds use cross-situational consistency to learn verbs under referential uncertainty

Recent evidence shows that children can use cross-situational statistics to learn new object labels under referential ambiguity (e.g., Smith & Yu, 2008). Such evidence has been interpreted as support for proposals that statistical information about word-referent co-occurrence plays a powerful role in word learning. But object labels represent only a fraction of the vocabulary children acquire, and arguably represent the simplest case of word learning based on observations of world scenes. Here we extended the study of cross-situational word learning to a new segment of the vocabulary, action verbs, to permit a stronger test of the role of statistical information in word learning. In two experiments, on each trial 2.5-year-olds encountered two novel intransitive (e.g., “She’s pimming!”; Experiment 1) or transitive verbs (e.g., “She’s pimming her toy!”; Experiment 2) while viewing two action events. The consistency with which each verb accompanied each action provided the only source of information about the intended referent of each verb. The 2.5-year-olds used cross-situational consistency in verb learning, but also showed significant limits on their ability to do so as the sentences and scenes became slightly more complex. These findings help to define the role of cross-situational observation in word learning.

What you learn is what you see: using eye movements to study infant cross-situational word learning.

Recent studies show that both adults and young children possess powerful statistical learning capabilities to solve the word-to-world mapping problem. However, the underlying mechanisms that make statistical learning possible and powerful are not yet known. With the goal of providing new insights into this issue, the research reported in this paper used an eye tracker to record the moment-by-moment eye movement data of 14-month-old babies in statistical learning tasks. Various measures are applied to such fine-grained temporal data, such as looking duration and shift rate (the number of shifts in gaze from one visual object to the other) trial by trial, showing different eye movement patterns between strong and weak statistical learners. Moreover, an information-theoretic measure is developed and applied to gaze data to quantify the degree of learning uncertainty trial by trial. Next, a simple associative statistical learning model is applied to eye movement data and these simulation results are compared with empirical results from young children, showing strong correlations between these two. This suggests that an associative learning mechanism with selective attention can provide a cognitively plausible model of cross-situational statistical learning. The work represents the first steps in using eye movement data to infer underlying real-time processes in statistical word learning.

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.

Using Speakers' Referential Intentions to Model Early Cross-Situational Word Learning

Word learning is a "chicken and egg" problem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.