How infants learn word meanings and propositional attitudes: a neural network model

نویسنده

  • Alistair Knott
چکیده

Michael Tomasello’s influential model of language development (Tomasello, 2000; Tomasello, 2003) emphasises the role of infants’ pragmatic understanding of the world in supporting their learning of language. For all humans, whether adult or child, language has a pragmatic function: we communicate linguistically in order to further social goals (for instance to share our beliefs and desires with others, or to ascertain the beliefs and desires of others) or to pursue joint undertakings (for instance to collaborate in a shared task). Tomasello imagines an infant observing a speaker producing an utterance directed at a hearer (possibly the infant herself). He proposes that the infant’s understanding of the intentions of the speaker and hearer in this context plays a crucial role in her ability to learn an association between the linguistic form of the utterance and its meaning. The infant interprets the meaning of the utterance in the light of her understanding of the speaker’s current goals, and of how the hearer features in these goals. At some point during development, the infant becomes aware of the general fact that human agents perform actions in service of goals. In Tomasello’s account, learning this fact is a precondition for learning language. When the pragmaticallyaware infant observes a speaker producing an utterance, she attempts to infer the speaker’s goals, and then forms hypotheses about how the words in the utterance further these goals. In Tomasello’s model, an early instance of pragmatic learning in language development concerns the learning of the meanings of individual words. Words are symbols that denote concepts. While some theories of language development assume the relationship between words and concepts is founded simply in the existence of regular associations or co-occurrences between words and concepts in the infant’s mind, for Tomasello it is fundamentally pragmatic in origin, and has to be learned by infants in pragmatic analyses of speech events. What infants must learn is that that uttering words can serve to evoke representations in the mind of the hearer. It is only after learning this general fact that infants can properly begin to learn the meanings of particular words. Tomasello sees two pragmatic ablities as prerequisites for learning word meanings. One is the ability to establish joint attention with an observed agent, i.e. to attend to the same object the observed agent is attending to. When an infant has acquired this ability, a speaker’s gaze will direct her attention to particular objects. Since speakers often visually attend to the situations they describe linguistically, the infant can learn that words can likewise serve to direct attention to arbitrary concepts. The other important pragmatic ability is the ability to infer the communicative intentions of an observed agent, i.e. to infer the goal underlying the agent’s communicative actions. The infant must learn that there is a special class of actions (communicative actions) which have communicative effects rather than physical effects. Communicative actions are physical actions, which are directed at another agent, who is physically present in the communicative situation. But unlike regular physical actions, their effect is on the agent’s mental state rather than on his physical state: specifically, they evoke representations in the agent’s mind. In spoken language, these actions are articulatory gestures that realise phonological word forms. Tomasello argues that the infant

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Semantic Compositionality through Recursive Matrix-Vector Spaces

Single-word vector space models have been very successful at learning lexical information. However, they cannot capture the compositional meaning of longer phrases, preventing them from a deeper understanding of language. We introduce a recursive neural network (RNN) model that learns compositional vector representations for phrases and sentences of arbitrary syntactic type and length. Our mode...

متن کامل

Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding

Neural network based approaches for sentence relation modeling automatically generate hidden matching features from raw sentence pairs. However, the quality of matching feature representation may not be satisfied due to complex semantic relations such as entailment or contradiction. To address this challenge, we propose a new deep neural network architecture that jointly leverage pre-trained wo...

متن کامل

A Probabilistic Model for Semantic Word Vectors

Vector representations of words capture relationships in words’ functions and meanings. Many existing techniques for inducing such representations from data use a pipeline of hand-coded processing techniques. Neural language models offer principled techniques to learn word vectors using a probabilistic modeling approach. However, learning word vectors via language modeling produces representati...

متن کامل

Investigating the semantic representation of Chinese emotion words with co- occurrence data and self-organizing maps neural networks

Introduction Regarding the investigation of the word representations, previous researchers often asked participants to rate the similarities between emotion words (Barrett, 2004; Cheng, Cheng, Cho, & Chen, 2013; Romney, Moore, & Rusch, 1997), or to give the scores upon certain psychological dimensions (e.g. valence, arousal, et. al) (Bradley & Lang, 1999; Cho, Chen, & Cheng, 2013; Morgan & Heis...

متن کامل

Control over Power Conversion Efficiency of BHJ Solar Cells: Learn more from Less, with Artificial Intelligence

Harvesting the energy from the sun through the bulk heterojunction (BHJ) solar cells need materials with specific electronic characteristics. However, any promising material if cast improperly in cells will end into low or even null power conversion efficiency (PCE). Cell casting optimization is a time/material consumable step in any photovoltaic manufacturing practice. In this study, we sh...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013