Trends in Cognitive Sciences
Emergent constraints on word-learning: a computational perspective
Section snippets
Explaining word-learning through general learning processes
Many models of word-learning are grounded in general learning processes, rather than language-specific ones. Either implicitly or explicitly, these models suggest that although word-learning constraints are linguistic in nature, the learning mechanisms they spring from might not be. Instead, these linguistic constraints might emerge from general learning processes as they operate on linguistic experience.
Accelerating representations
In some word-learning models, early learning produces expectations that enable faster subsequent learning – which further strengthens the expectations, leading to yet faster learning. We may think of these expectations as ‘accelerating representations’: they permit a slow entry into word-learning to give way to accelerated learning as the expectations gradually become more accurate (cf. ‘autonomous bootstrapping’ [29]). This concept could help to explain the vocabulary spurt – a sometimes
Grounding meaning in the world and in words
Several computational models ground words in perceptual representations of objects and events in the world 11, 17, 18, 43, 44, 45. Most others ground word meaning in more abstract featural representations, but still on the assumption that there is some concrete element of experience to which the word is being linked.
However, much word-learning does not occur in this fashion – people eventually learn words for things that are not grounded in their personal experience at all (e.g. ‘prehistoric’).
Constraints and semantic universals
Word-learning constraints are a possible source of cross-linguistic semantic universals. For if words are learned in the same constrained manner across languages, the meanings of words in different languages should bear some mark of the constraints that produced them.
Regier's connectionist model of spatial term learning [11] illustrates this idea. The model learns to categorize spatial events according to the spatial system of a given target language. Because languages differ in their spatial
Objections and limitations
Many of the models discussed in this review assume an associative basis of some sort for word-learning. This basic assumption is one that has encountered two broad sorts of objection.
The first objection is that word-learning is too fast to be a reflection of an associative or statistical process 8, 30, 31. As we have seen, children can eventually learn a new word given only a very few exposures. But is this really a problem? It is true that one often thinks of associative learning as requiring
Conclusions
Word-learning is generally thought to be an underdetermined inductive problem, such that children require a set of constraints to tackle it. This view has been bolstered by the considerable empirical evidence for such constraints. However, several recent computational models of word-learning suggest that these constraints need not spring from language-specific forces. General-purpose learning mechanisms, accelerating representations, and perceptual and textual forces might all combine to
Acknowledgements
This work was supported by NIH grant DC03384. I thank Susanne Gahl and the anonymous reviewers for helpful comments on an earlier draft of this paper.
References (51)
- et al.
Vocabulary acquisition and verbal short-term memory: computational and neural bases
Brain Lang.
(1997) Dyslexic and category-specific aphasic impairments in a self-organizing feature map model of the lexicon
Brain Lang.
(1997)A modular neural network model of concept acquisition
Cogn. Sci.
(1991)Advances in the computational study of language acquisition
Cognition
(1996)A computational study of cross-situational techniques for learning word-to-meaning mappings
Cognition
(1996)- et al.
Learning words from sights and sounds: a computational model
Cogn. Sci.
(2002) Word and Object
(1960)Categorization and Naming in Children: Problems of Induction
(1989)Young children and adults use lexical principles to learn new nouns
Dev. Psychol.
(1992)- et al.
The mutual exclusivity bias in children's word learning
Monogr. Soc. Res. Child Dev. No. 54
(1989)
Acquisition of the novel name-nameless category (N3C) principle
Child Dev.
Knowledge of Language: Its Nature, Origin and Use
Evidence against a dedicated system for word-learning in children
Nature
How Children Learn the Meanings of Words
Competition, attention, and young children's lexical processing
The Human Semantic Potential: Spatial Language and Constrained Connectionism
Competition and lexical categorization
The emergence of words
Acquiring the mapping from meaning to sounds
Connect. Sci.
Symbol grounding or the emergence of symbols? Vocabulary growth in children and a connectionist net
Connect. Sci.
Perceptually grounded language learning: II. Dete: a neural/procedural model
Connect. Sci.
Understanding normal and impaired word reading: computational principles in quasi-regular domains
Psychol. Rev.
Cryptotype, overgeneralization and competition: a connectionist model of the learning of english reversive prefixes
Connect. Sci.
A solution to Plato's problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge
Psychol. Rev.
Cited by (51)
Building human-like communicative intelligence: A grounded perspective
2022, Cognitive Systems ResearchLexicon structure and the disambiguation of novel words: Evidence from bilingual infants
2013, CognitionCitation Excerpt :For example, under Markman & Wachtel’s (1988) mutual exclusivity account, children operate under the default assumption that object labels denote mutually exclusive categories, and use this assumption to infer that a novel label could not go with an object that already has a label. It has also been suggested that mutual exclusivity is an emergent property of computational processes that support word learning (Frank, Goodman, & Tenenbaum, 2009; McMurray, Horst, & Samuelson, 2012; Merriman, 1999; Regier, 1996, 2003), or that it might be founded in children’s preference for novelty (Horst, Samuelson, Kucker, & McMurray, 2011). An alternative view is that the development of disambiguation is driven by experience, and that it emerges only once a child has established that each object should have a basic-level label (Mervis & Bertrand, 1994), or has ascertained that adults use different words to refer to different kinds of objects (Diesendruck & Markson, 2001).
Probabilistic Inference in Human Infants
2012, Advances in Child Development and BehaviorCitation Excerpt :Take, for example, the domain of word learning, where a satisfying theory must account for a number of known phenomena. Empiricist accounts of word learning (e.g. Colunga & Smith, 2005; Regier, 2003, 2005) account reasonably well for the fact that children are capable of learning words at multiple levels of taxonomic hierarchies (e.g. they learn words such as animal, dog, and poodle). However, they have difficulty dealing with the fact that children are able to learn the meaning of new words after observing very small numbers of exemplars, a phenomenon called fast mapping, as the learning mechanisms typically posited by these accounts require a large number of object and label pairings to acquire new words.
Young children's use of statistical sampling evidence to infer the subjectivity of preferences
2011, CognitionCitation Excerpt :This rational learning mechanism differs from existing associative models that have been used to explain how children learn the meanings of words. Associative learning models assume that children pick up on the statistical regularities among early lexical categories – they keep track of word-referent pairings, adjust the strengths of these associations based on repeated exposures, and form expectations about how to generalize novel words (e.g., Colunga & Smith, 2005; Regier, 2003). Such associative models might explain how children form prior beliefs about the preference of others (e.g., through repeated exposures to people favoring objects that are intrinsically interesting) and bring generalized expectations to the current situation.
Mutual exclusivity in autism spectrum disorders: Testing the pragmatic hypothesis
2011, CognitionCitation Excerpt :On such accounts, word learning constraints are either a direct reflection of the structure of domain-general learning mechanisms or are the result of applying these learning mechanisms to input which has an underlying structure that gives rise to the relevant constraint (Regier, 2005; Smith et al., 2002).1 For example, Regier (2003) proposes that mutual exclusivity arises from general mechanisms of competition in a connectionist network. As a word becomes more associated with one referent, the probability that the same word will be used with another referent declines sharply.