1,155 research outputs found
A broad-coverage distributed connectionist model of visual word recognition
In this study we describe a distributed connectionist model of morphological processing, covering a realistically sized sample of the English language. The purpose of this model is to explore how effects of discrete, hierarchically structured morphological paradigms, can arise as a result of the statistical sub-regularities in the mapping between
word forms and word meanings. We present a model that learns to produce at its output a realistic semantic representation of a word, on presentation of a distributed representation of its orthography. After training, in three experiments, we compare the outputs of the model with the lexical decision latencies for large sets of English nouns and verbs. We show that the model has developed detailed representations of morphological structure, giving rise to effects analogous to those observed in visual lexical decision experiments. In addition, we show how the association between word form and word meaning also
give rise to recently reported differences between regular and irregular verbs, even in their completely regular present-tense forms. We interpret these results as underlining the key importance for lexical processing of the statistical regularities in the mappings between form and meaning
The Missing Link between Morphemic Assemblies and Behavioral Responses:a Bayesian Information-Theoretical model of lexical processing
We present the Bayesian Information-Theoretical (BIT) model of lexical processing: A mathematical model illustrating a novel approach to the modelling of language processes. The model shows how a neurophysiological theory of lexical processing relying on Hebbian association and neural assemblies can directly account for a variety of effects previously observed in behavioural experiments. We develop two information-theoretical measures of the distribution of usages of a morpheme or word, and use them to predict responses in three visual lexical decision datasets investigating inflectional morphology and polysemy. Our model offers a neurophysiological basis for the effects of
morpho-semantic neighbourhoods. These results demonstrate how distributed patterns of activation naturally result in the arisal of symbolic structures. We conclude by arguing that the modelling framework exemplified here, is
a powerful tool for integrating behavioural and neurophysiological results
Computational explorations of semantic cognition
Motivated by the widespread use of distributional models of semantics within the cognitive science community, we follow a computational modelling approach in order to better understand and expand the applicability of such models, as well as to test potential ways in which they can be improved and extended. We review evidence in favour of the assumption that distributional models capture important aspects of semantic cognition. We look at the modelsâ ability to account for behavioural data and fMRI patterns of brain activity, and investigate the structure of model-based, semantic networks. We test whether introducing affective information, obtained from a neural network model designed to predict emojis from co-occurring text, can improve the performance of linguistic and linguistic-visual models of semantics, in accounting for similarity/relatedness ratings. We find that adding visual and affective representations improves performance, especially for concrete and abstract words, respectively. We describe a processing model based on distributional semantics, in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can account for response time and accuracies in lexical and semantic decision tasks, as well as for concreteness/imageability and similarity/relatedness ratings. We evaluate the differences between concrete and abstract words, in terms of the structure of the semantic networks derived from distributional models of semantics. We examine how the structure is related to a number of factors that have been argued to differ between concrete and abstract words, namely imageability, age of acquisition, hedonic valence, contextual diversity, and semantic diversity. We use distributional models to explore factors that might be responsible for the poor linguistic performance of children suffering from Developmental Language Disorder. Based on the assumption that certain model parameters can be given a psychological interpretation, we start from âhealthyâ models, and generate âlesionedâ models, by manipulating the parameters. This allows us to determine the importance of each factor, and their effects with respect to learning concrete vs abstract words
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From Words to Behaviour via Semantic Networks
The contents and structure of semantic networks have
been the focus of much recent research, with major
advances in the development of distributional models. In
parallel, connectionist modeling has extended our
knowledge of the processes engaged in semantic
activation. However, these two lines of investigation have
rarely brought together. Here, starting from a standard
textual model of semantics, we allow activation to spread
throughout its associated semantic network, as dictated by
the patterns of semantic similarity between words. We
find that the activation profile of the network, measured
at various time points, can successfully account for
response times in the lexical decision task, as well as for
subjective concreteness and imageability ratings
A distributional model of semantic context effects in lexical processinga
One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence prob ability distributions, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. In our simulations the effects of semantic context are mod eled using general-purpose techniques and representations from multivariate statistics, augmented with simple assumptions reflecting the inherently incremental nature of speech understanding. The contribution of our study is to show that special-purpose m echanisms are not necessary in order to capture the general pattern of the experimental results, and that a range of semantic context effects can be subsumed under the same principled account.âș
Effects of experience in a developmental model of reading
There is considerable evidence showing that age of acquisition (AoA) is an important factor influencing lexical processing. Early-learned words tend to be processed more quickly compared to later-learned words. The effect could be due to the gradual reduction in plasticity as more words are learned. Alternatively, it could originate from differences within semantic representations. We implemented the triangle model of reading including orthographic, phonological and semantic processing layers, and trained it according to experience of a language learner to explore the AoA effects in both naming and lexical decision. Regression analyses on the modelâs performance showed that AoA was a reliable predictor of naming and lexical decision performance, and the effect size was larger for lexical decision than for naming. The modelling results demonstrate that AoA operates differentially on concrete and abstract words, indicating that both the mapping and the representation accounts of AoA were contributing to the modelâs performance
Shades of meaning: Uncovering the geometry of ambiguous word representations through contextualised language models
Lexical ambiguity presents a profound and enduring challenge to the language
sciences. Researchers for decades have grappled with the problem of how
language users learn, represent and process words with more than one meaning.
Our work offers new insight into psychological understanding of lexical
ambiguity through a series of simulations that capitalise on recent advances in
contextual language models. These models have no grounded understanding of the
meanings of words at all; they simply learn to predict words based on the
surrounding context provided by other words. Yet, our analyses show that their
representations capture fine-grained meaningful distinctions between
unambiguous, homonymous, and polysemous words that align with lexicographic
classifications and psychological theorising. These findings provide
quantitative support for modern psychological conceptualisations of lexical
ambiguity and raise new challenges for understanding of the way that contextual
information shapes the meanings of words across different timescales
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