Learning to map words onto their referents is difficult, because there are multiple possibilities for forming these mappings. Cross-situational learning studies have shown that word-object mappings can be learned across multiple situations, as can verbs when presented in a syntactic context. However, these previous studies have presented either nouns or verbs in ambiguous contexts and thus bypass much of the complexity of multiple grammatical categories in speech. We show that noun word learning in adults is robust when objects are moving, (...) and that verbs can also be learned from similar scenes without additional syntactic information. Furthermore, we show that both nouns and verbs can be acquired simultaneously, thus resolving category-level as well as individual word-level ambiguity. However, nouns were learned more quickly than verbs, and we discuss this in light of previous studies investigating the noun advantage in word learning. (shrink)

What mechanism implements the mutual exclusivity bias to map novel labels to objects without names? Prominent theoretical accounts of mutual exclusivity (e.g., Markman, 1989, 1990) propose that infants are guided by their knowledge of object names. However, the mutual exclusivity constraint could be implemented via monitoring of object novelty (see Merriman, Marazita, & Jarvis, 1995). We sought to discriminate between these contrasting explanations across two preferential looking experiments with 22-month-olds. In Experiment 1, infants viewed three objects: one name-known, two name-unknown. (...) Of the two name-unknown objects, one was novel, and the other had been previously familiarized. The infants responded to hearing a novel label by increasing attention only to the novel, name-unknown object. In a second experiment in which the name-known object was absent, a novel label increased infants’ attention to a novel object beyond baseline preference for novelty. The experiments provide clear evidence for a novelty-based mechanism. However, differences in the time course of disambiguation across experiments suggest that novelty processing may be influenced by contextual factors. (shrink)

Learning in natural environments is often characterized by a degree of inconsistency from an input. These inconsistencies occur, for example, when learning from more than one source, or when the presence of environmental noise distorts incoming information; as a result, the task faced by the learner becomes ambiguous. In this study, we investigate how learners handle such situations. We focus on the setting where a learner receives and processes a sequence of utterances to master associations between objects and their labels, (...) where the source is inconsistent by design: It uses both “correct” and “incorrect” object‐label pairings. We hypothesize that depending on the order of presentation, the result of the learning may be different. To this end, we consider two types of symbolic learning procedures: the Object‐Label (OL) and the Label‐Object (LO) process. In the OL process, the learner is first exposed to the object, and then the label. In the LO process, this order is reversed. We perform experiments with human subjects, and also construct a computational model that is based on a nonlinear stochastic reinforcement learning algorithm. It is observed experimentally that OL learners are generally better at processing inconsistent input compared to LO learners. We show that the patterns observed in the learning experiments can be reproduced in the simulations if the model includes (a) an ability to regularize the input (and also to do the opposite, i.e., undermatch) and (b) an ability to take account of implicit negative evidence (i.e., interactions among different objects/labels). The model suggests that while both types of learners utilize implicit negative evidence in a similar way, there is a difference in regularization patterns: OL learners regularize the input, whereas LO learners undermatch. As a result, OL learners are able to form a more consistent system of image‐utterance associations, despite the ambiguous learning task. (shrink)

In this study we present a self‐organizing connectionist model of early lexical development. We call this model DevLex‐II, based on the earlier DevLex model. DevLex‐II can simulate a variety of empirical patterns in children's acquisition of words. These include a clear vocabulary spurt, effects of word frequency and length on age of acquisition, and individual differences as a function of phonological short‐term memory and associative capacity. Further results from lesioned models indicate developmental plasticity in the network's recovery from damage, in (...) a non‐monotonic fashion. We attribute the network's abilities in accounting for lexical development to interactive dynamics in the learning process. In particular, variations displayed by the model in the rate and size of early vocabulary development are modulated by (a) input characteristics, such as word frequency and word length, (b) consolidation of lexical‐semantic representation, meaning‐form association, and phonological short‐term memory, and (c) delayed processes due to interactions among timing, severity, and recoverability of lesion. Together, DevLex and DevLex‐II provide an accurate computational account of early lexical development. (shrink)

How does a child rapidly acquire and develop a structured mental organization for the vast number of words in the first years of life? How does a bilingual individual deal with the even more complicated task of learning and organizing two lexicons? It is only until recently have we started to examine the lexicon as a dynamical system with regard to its acquisition, representation, and organization. In this article, I outline a proposal based on our research that takes the dynamical (...) approach to the lexicon, and I discuss how this proposal can be applied to account for lexical organization, structural representation, and competition within and between languages. In particular, I provide computational evidence based on the DevLex model, a self‐organizing neural network model, and neuroimaging evidence based on functional magnetic resonance imaging (fMRI) studies, to illustrate how children and adults learn and represent the lexicon in their first and second languages. In the computational research, our goal has been to identify, through linguistically and developmentally realistic models, detailed cognitive mechanisms underlying the dynamic self‐organizing processes in monolingual and bilingual lexical development; in the neuroimaging research, our goal has been to identify the neural substrates that subserve lexical organization and competition in the monolingual and the bilingual brain. In both cases, our findings lead to a better understanding of the interactive dynamics involved in the acquisition and representation of one or multiple languages. (shrink)

In this study we present a self-organizing connectionist model of early lexical development. We call this model DevLex-II, based on the earlier DevLex model. DevLex-II can simulate a variety of empirical patterns in children's acquisition of words. These include a clear vocabulary spurt, effects of word frequency and length on age of acquisition, and individual differences as a function of phonological short-term memory and associative capacity. Further results from lesioned models indicate developmental plasticity in the network's recovery from damage, in (...) a non-monotonic fashion. We attribute the network's abilities in accounting for lexical development to interactive dynamics in the learning process. In particular, variations displayed by the model in the rate and size of early vocabulary development are modulated by (a) input characteristics, such as word frequency and word length, (b) consolidation of lexical-semantic representation, meaning-form association, and phonological short-term memory, and (c) delayed processes due to interactions among timing, severity, and recoverability of lesion. Together, DevLex and DevLex-II provide an accurate computational account of early lexical development. (shrink)

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

Gleitman and Trueswell’s “The easy words” forms a pair with their earlier paper, “Hard words,” completing a circle in which the authors ask how “easy” words (e.g., concrete nouns) are learned. They take up the hypothesis of “cross‐situational learning,” and argue that accumulating observations actually hinders learning if the mechanism requires holding all exemplars in memory over time. They present an alternative hypothesis, “Propose but Verify,” wherein people use one‐trial learning to confirm or disconfirm their current hypothesis—a mechanism distinctly different (...) from cross‐situational learning. (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)

A method is described for inducing a type-logical grammar from a sample of bare sentence trees which are annotated by lambda terms, called term-labelled trees . Any type logic from a permitted class of multimodal logics may be specified for use with the procedure, which induces the lexicon of the grammar including the grammatical categories. A first stage of semantic bootstrapping is performed, which induces a general form lexicon from the sample of term-labelled trees using Fulop’s (J Log Lang Inf (...) 14(1):49–86, 2005) procedure. Next we present a two-stage procedure for performing distributional learning by unifying the lexical types that are initially discovered. The first structural unification algorithm in essence unifies the initial family of sets of types so that the resulting grammar will generate all term-labelled trees that follow the usage patterns evident from the learning sample. Further altering the lexical categories to generate a recursively extended language can be accomplished by a second unification. The combined unification algorithm is shown to yield a new type-logical lexicon that extends the learning sample to a possibly infinite (and possibly context-sensitive) language in a principled fashion. Finally, the complete learning strategy is analyzed from the perspective of algorithmic learning theory; the range of the procedure is shown to be a class of term-labelled tree languages which is finitely learnable from good examples (Lange et al in Algorithmic learning theory, Vol 872 of lecture notes in artificial intelligence, Springer, Berlin, pp 423–437), and so is identifiable in the limit as a corollary. (shrink)

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals (...) about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the limits of large N and H , which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words. Key words: lexicon bootstrapping; cross-situational learning; supervised learning; random occupancy problems. (shrink)

Intuitively, the accuracy of initial word-referent mappings should be positively correlated with the outcome of learning. Yet recent evidence suggests an inverse effect of initial accuracy in adults, whereby greater accuracy of initial mappings is associated with poorer outcomes in a cross-situational learning task. Here, we examine the impact of initial accuracy on 4-year-olds, 10-year-olds, and adults. For half of the participants most word-referent mappings were initially correct and for the other half most mappings were initially incorrect. Initial accuracy was (...) positively related to learning outcomes in 4-year-olds, had no effect on 10-year-olds' learning, and was inversely related to learning outcomes in adults. Examination of item learning patterns revealed item interdependence for adults and 4-year-olds but not 10-year-olds. These findings point to a qualitative change in language learning processes over development. (shrink)

According to Millian Descriptivism, while the semantic content of a linguistically simple proper name is just its referent, we often use sentences containing such expressions “to make assertions…that are, in part, descriptive” (Soames 2008). Against this view, I show, following Ted Sider and David Braun (2006), that simple sentences containing names are never used to assert descriptively enriched propositions. In addition, I offer a diagnosis as to where the argument for Millian Descriptivism goes wrong. Once we appreciate the distinctive way (...) in which this account fails, we can better appreciate the very modest role that associated descriptive information plays in the pragmatics of proper names. (shrink)