Semantic Representations of Word Meanings by the Cerebral Hemispheres

2002 ◽  
Vol 80 (3) ◽  
pp. 393-420 ◽  
Author(s):  
Elizabeth Ince ◽  
Stephen D. Christman
Keyword(s):  
Cerebral Hemispheres ◽  
Semantic Representations ◽  
Word Meanings

Suppression of irrelevant ambiguous word meanings in the cerebral hemispheres

1991 ◽  
Author(s):  
Christine Chiarello ◽  
Kim Cannon ◽  
Lorie Richards ◽  
Lisa Maxfield
Keyword(s):  
Ambiguous Word ◽  
Cerebral Hemispheres ◽  
Word Meanings

Prototype semantics: syntaphor in polysemy and beyond

2021 ◽  
Vol 17 ◽  
pp. 11-29
Author(s):  
Bogusław Bierwiaczonek
Keyword(s):  
Cognitive Processes ◽  
Semantic Representations ◽  
Word Meanings

The paper shows how a recently introduced concept of syntaphor, which blends synecdoche with metaphor, may be used to represent semantic extensions of polysemous lexemes like paint or pin. The network semantic representations using syntaphor are both more descriptively adequate and precise. They also account for the fact that the word meanings differ in their use from vague general senses to rather specific, and yet entrenched senses. Finally, it is demonstrated that syntaphor is one of the crucial cognitive processes resulting in lexical differences between languages.

scholarly journals Retuning of lexical-semantic representations: Repetition and spacing effects in word-meaning priming.

2017 ◽  
Author(s):  
Hannah Betts ◽  
Rebecca Anne Gilbert ◽  
Zhenguang Garry Cai ◽  
Zainab B. Okedara ◽  
Jennifer M Rodd
Keyword(s):  
Semantic Network ◽  
Word Meaning ◽  
Stable State ◽  
Ambiguous Word ◽  
Recent Experience ◽  
Semantic Representations ◽  
Word Meanings ◽  
Lexical Semantic ◽  
Spacing Effects ◽  
Ambiguous Words

Current models of word-meaning access typically assume that lexical-semantic representations of ambiguous words (e.g. ‘bark of the dog/tree’) reach a relatively stable state in adulthood, with only the relative frequencies of the meanings in the language and immediate sentence context determining meaning preference. However, recent experience also affects interpretation: recently-encountered word-meanings become more readily available (Rodd et al., 2016; 2013). Here, three experiments investigated how multiple encounters with word-meanings influence the subsequent interpretation of these words. Participants heard ambiguous words contextually-disambiguated towards a particular meaning and, after a 20-30 minute delay, interpretations of the words were tested in isolation. We replicate the finding that one encounter with an ambiguous word biased later interpretation of this word towards the primed meaning for both subordinate (Experiments 1, 2, 3) and dominant meanings (Experiment 1). In addition, for the first time, we show cumulative effects of multiple repetitions of both the same and different meanings. The effect of a single subordinate exposure persisted after a subsequent encounter with the dominant meaning, compared to a dominant exposure alone (Experiment 1). Furthermore, three subordinate word-meaning repetitions provided an additional boost to priming compared to one, although only when their presentation was spaced (Experiments 2, 3); massed repetitions provided no such boost (Experiments 1, 3). These findings indicate that comprehension is guided by the collective effect of multiple recently activated meanings and that the spacing of these activations is key to producing lasting updates to the lexical-semantic network.

Rich Lexical Represe ntations and Conflicting Features

2012 ◽  
Vol 4 (2) ◽  
pp. 209-231 ◽  
Author(s):  
Lotte Hogeweg
Keyword(s):  
Optimality Theory ◽  
Optimization Process ◽  
Semantic Representations ◽  
Full Understanding ◽  
Word Meanings ◽  
Common View ◽  
Lexical Representations ◽  
Fine Grained ◽  
The Common ◽  
To Come

This paper argues that interpretations are fine-grained and that, to come to a full understanding of meaning, it is important to find out more about how such detailed interpretations are derived. As a first step towards answering this question it is insightful to look at the interpretation of metaphors. Psycholinguistic experiments have shown that the interpretation of metaphors involves the suppression of irrelevant or incompatible features. These studies could be taken as an indication against the common view that word meanings are underspecified and enriched in a context. In contrast with this underspecification view, this paper suggests a view of the lexicon in which words come with very rich semantic representations. When two representations are combined, a conflict may arise when elements of the representations are incompatible. This paper argues that such a conflict is best analyzed in Optimality Theory. The optimization process of combining rich lexical representations is illustrated with an analysis of the adjective-noun combination stone lion.

scholarly journals Behavioral correlates of cortical semantic representations modeled by word vectors

2020 ◽  
Author(s):  
Satoshi Nishida ◽  
Antione Blanc ◽  
Naoya Maeda ◽  
Masataka Kado ◽  
Shinji Nishimoto
Keyword(s):  
Language Processing ◽  
Semantic Processing ◽  
Semantic Information ◽  
Semantic Representations ◽  
Behavioral Correlates ◽  
Word Meanings ◽  
Brain Models ◽  
And Behavior ◽  
The Brain ◽  
Psychological Experiments

AbstractThe quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field of natural language processing, provide a powerful tool for such quantitative modeling. However, whether semantic representations revealed by the word vector-based models actually capture our perception of semantic information remains unclear, as there has been no study explicitly examining the behavioral correlates of the modeled semantic representations. To address this issue, we compared the semantic structure of nouns and adjectives estimated from word vector-based brain models with that evaluated from human behavior. The brain models were constructed using voxelwise modeling to predict the functional magnetic resonance imaging (fMRI) response to natural movies from semantic contents in each movie scene through a word vector space. The semantic dissimilarity of word representations was then evaluated using the brain models. Meanwhile, data on human behavior reflecting the perception of semantic dissimilarity between words were collected in psychological experiments. We found a significant correlation between brain model- and behavior-derived semantic dissimilarities of words. This finding suggests that semantic representations in the brain modeled via word vectors appropriately capture our perception of word meanings.Author summeryWord vectors, which have been originally developed in the field of engineering (natural language processing), have been extensively leveraged in neuroscience studies to model semantic representations in the human brain. These studies have attempted to model brain semantic representations by associating them with the meanings of thousands of words via a word vector space. However, there has been no study explicitly examining whether the modeled semantic representations actually capture our perception of semantic information. To address this issue, we compared the semantic representational structure of words estimated from word vector-based brain models with that evaluated from behavioral data in psychological experiments. The results revealed a significant correlation between these model- and behavior-derived semantic representational structures of words. This indicates that the brain semantic representations modeled using word vectors actually reflect the human perception of word meanings. Our findings contribute to the establishment of word vector-based brain modeling as a useful tool in studying human semantic processing.

scholarly journals Behavioral correlates of cortical semantic representations modeled by word vectors

2021 ◽  
Vol 17 (6) ◽  
pp. e1009138
Author(s):  
Satoshi Nishida ◽  
Antione Blanc ◽  
Naoya Maeda ◽  
Masataka Kado ◽  
Shinji Nishimoto
Keyword(s):  
Human Behavior ◽  
Language Processing ◽  
Semantic Processing ◽  
Quantitative Modeling ◽  
Semantic Representations ◽  
Neural Basis ◽  
Behavioral Correlates ◽  
Word Meanings ◽  
Brain Models ◽  
The Brain

The quantitative modeling of semantic representations in the brain plays a key role in understanding the neural basis of semantic processing. Previous studies have demonstrated that word vectors, which were originally developed for use in the field of natural language processing, provide a powerful tool for such quantitative modeling. However, whether semantic representations in the brain revealed by the word vector-based models actually capture our perception of semantic information remains unclear, as there has been no study explicitly examining the behavioral correlates of the modeled brain semantic representations. To address this issue, we compared the semantic structure of nouns and adjectives in the brain estimated from word vector-based brain models with that evaluated from human behavior. The brain models were constructed using voxelwise modeling to predict the functional magnetic resonance imaging (fMRI) response to natural movies from semantic contents in each movie scene through a word vector space. The semantic dissimilarity of brain word representations was then evaluated using the brain models. Meanwhile, data on human behavior reflecting the perception of semantic dissimilarity between words were collected in psychological experiments. We found a significant correlation between brain model- and behavior-derived semantic dissimilarities of words. This finding suggests that semantic representations in the brain modeled via word vectors appropriately capture our perception of word meanings.

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