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)

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

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

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. (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)

It is well-established that toddlers can correctly select a novel referent from an ambiguous array in response to a novel label. There is also a growing consensus that robust word learning requires repeated label-object encounters. However, the effect of the context in which a novel object is encountered is less well-understood. We present two embodied neural network replications of recent empirical tasks, which demonstrated that the context in which a target object is encountered is fundamental to referent selection and word (...) learning. Our model offers an explicit account of the bottom-up associative and embodied mechanisms which could support children’s early word learning and emphasises the importance of viewing behaviour as the interaction of learning at multiple timescales. (shrink)

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

Although the language we encounter is typically embedded in rich discourse contexts, many existing models of processing focus largely on phenomena that occur sentence-internally. Similarly, most work on children's language learning does not consider how information can accumulate as a discourse progresses. Research in pragmatics, however, points to ways in which each subsequent utterance provides new opportunities for listeners to infer speaker meaning. Such inferences allow the listener to build up a representation of the speakers' intended topic and more generally (...) to identify relationships, structures, and messages that extend across multiple utterances. We address this issue by analyzing a video corpus of child–caregiver interactions. We use topic continuity as an index of discourse structure, examining how caregivers introduce and discuss objects across utterances. For the analysis, utterances are grouped into topical discourse sequences using three annotation strategies: raw annotations of speakers' referents, the output of a model that groups utterances based on those annotations, and the judgments of human coders. We analyze how the lexical, syntactic, and social properties of caregiver–child interaction change over the course of a sequence of topically related utterances. Our findings suggest that many cues used to signal topicality in adult discourse are also available in child-directed speech. (shrink)

Prior research has shown that people can learn many nouns from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a recent associative model, (...) we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings. (shrink)

Given that there is referential uncertainty (noise) when learning words, to what extent can forgetting filter some of that noise out, and be an aid to learning? Using a Cross Situational Learning model we find a U-shaped function of errors indicative of a “Goldilocks” zone of forgetting: an optimum store-loss ratio that is neither too aggressive or too weak, but just the right amount to produce better learning outcomes. Forgetting acts as a high-pass filter that actively deletes (part of) the (...) referential ambiguity noise, retains intended referents, and effectively amplifies the signal. The model achieves this performance without incorporating any specific cognitive biases of the type proposed in the constraints and principles account, and without any prescribed developmental changes in the underlying learning mechanism. Instead we interpret the model performance as more of a by-product of exposure to input, where the associative strengths in the lexicon grow as a function of linguistic experience in combination with memory limitations. The result adds a mechanistic explanation for the experimental evidence on spaced learning and, more generally, advocates integrating domain-general aspects of cognition, such as memory, in to the language acquisition process. (shrink)

According to usage‐based approaches to language acquisition, linguistic knowledge is represented in the form of constructions—form‐meaning pairings—at multiple levels of abstraction and complexity. The emergence of syntactic knowledge is assumed to be a result of the gradual abstraction of lexically specific and item‐based linguistic knowledge. In this article, we explore how the gradual emergence of a network consisting of constructions at varying degrees of complexity can be modeled computationally. Linguistic knowledge is learned by observing natural language utterances in an ambiguous (...) context. To determine meanings of constructions starting from ambiguous contexts, we rely on the principle of cross‐situational learning. While this mechanism has been implemented in several computational models, these models typically focus on learning mappings between words and referents. In contrast, in our model, we show how cross‐situational learning can be applied consistently to learn correspondences between form and meaning beyond such simple correspondences. (shrink)

Cross-situational statistical learning of words involves tracking co-occurrences of auditory words and objects across time to infer word-referent mappings. Previous research has demonstrated that learners can infer referents across sets of very phonologically distinct words, but it remains unknown whether learners can encode fine phonological differences during cross-situational statistical learning. This study examined learners’ cross-situational statistical learning of minimal pairs that differed on one consonant segment, minimal pairs that differed on one vowel segment, and non-minimal pairs that differed on two (...) or three segments. Learners performed above chance for all pairs, but performed worse on vowel minimal pairs than on consonant minimal pairs or non-minimal pairs. These findings demonstrate that learners can encode fine phonetic detail while tracking word-referent co-occurrence probabilities, but they suggest that phonological encoding may be weaker for vowels than for consonants. (shrink)