Continuous measure of word learning supports associative model

ICDL/EpiRob, 2012

Research has shown that people can learn many nouns (i.e., word-referent mappings) from a short series of ambiguous situations containing multiple word-referent pairs. Associative models assume that people accomplish such crosssituational learning by approximately tracking which words and referents co-occur. However, some researchers posit that learners hypothesize only a single referent for each word, and retain and test this hypothesis unless it is disconfirmed. To compare these two views, we fit two models to individual learning trajectories in a cross-situational word-learning task, in which each trial presents four objects and four spoken words-16 possible wordobject pairings per trial. The model that maintains a single hypothesis for each word does not fit as well as the associative model that roughly learns the co-occurrence structure of the data using competing attentional biases for familiar pairings and uncertain stimuli. We conclude that language acquisition is likely supported by memory, not sparse hypotheses.

Cognitive Science, 2011

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.

Cognitive Science, 2010

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.

2007

Abstract Recent studies (eg Yu & Smith, in press; Smith & Yu, submitted) show that both adults and young children possess powerful statistical computation capabilities--they can infer the referent of a word from highly ambiguous contexts involving many words and many referents. This paper goes beyond demonstrating empirical behavioral evidence--we seek to systematically investigate the nature of the underlying learning mechanisms.

2010

Abstract A number of modern word learning theories posit statistical processes in which knowledge is accumulated across many exposures to a word and its potential referents. Accordingly, words do not go directly from unknown to known, but rather pass through intermediate stages of partial knowledge. This work presents empirical evidence for the existence of such partial knowledge, and further demonstrates its active driving role in cross-situational word learning.

2016

We test the predictions of different computational models of cross-situational word learning that have been proposed in the literature by comparing their behavior to that of young children and adults in the word learning task conducted by Ramscar, Dye, and Klein (2013). Our experimental results show that a Hebbian learner and a model that relies on hypothesis testing fail to account for the behavioral data obtained from both populations. Ruling out such accounts might help reducing the search space and better focus on the most relevant aspects of the problem, in order to disentangle the mechanisms used during language acquisition to map words and referents in a highly noisy environment.

2012

Abstract 1. Both adults and young children possess powerful statistical computation capabilities—they can infer the referent of a word from highly ambiguous contexts involving many words and many referents by aggregating cross-situational statistical information across contexts. This ability has been explained by models of hypothesis testing and by models of associative learning.

2012

PLoS ONE, 2012

Despite a growing number of studies, the neurophysiology of adult vocabulary acquisition is still poorly understood. One reason is that paradigms that can easily be combined with neuroscientfic methods are rare. Here, we tested the efficiency of two paradigms for vocabulary (re-) acquisition, and compared the learning of novel words for actions and objects. Cortical networks involved in adult native-language word processing are widespread, with differences postulated between words for objects and actions. Words and what they stand for are supposed to be grounded in perceptual and sensorimotor brain circuits depending on their meaning. If there are specific brain representations for different word categories, we hypothesized behavioural differences in the learning of action-related and object-related words. Paradigm A, with the learning of novel words for body-related actions spread out over a number of days, revealed fast learning of these new action words, and stable retention up to 4 weeks after training. The single-session Paradigm B employed objects and actions. Performance during acquisition did not differ between action-related and object-related words (time*word category: p = 0.01), but the translation rate was clearly better for object-related (79%) than for action-related words (53%, p = 0.002). Both paradigms yielded robust associative learning of novel action-related words, as previously demonstrated for object-related words. Translation success differed for action-and object-related words, which may indicate different neural mechanisms. The paradigms tested here are well suited to investigate such differences with neuroscientific means. Given the stable retention and minimal requirements for conscious effort, these learning paradigms are promising for vocabulary re-learning in brain-lesioned people. In combination with neuroimaging, neuro-stimulation or pharmacological intervention, they may well advance the understanding of language learning to optimize therapeutic strategies.