Dataset Information

The Influence of Closeness Centrality on Lexical Processing.

ABSTRACT: The present study examined how the network science measure known as closeness centrality (which measures the average distance between a node and all other nodes in the network) influences lexical processing. In the mental lexicon, a word such as CAN has high closeness centrality, because it is close to many other words in the lexicon. Whereas, a word such as CURE has low closeness centrality because it is far from other words in the lexicon. In an auditory lexical decision task (Experiment 1) participants responded more quickly to words with high closeness centrality. In Experiment 2 an auditory lexical decision task was again used, but with a wider range of stimulus characteristics. Although, there was no main effect of closeness centrality in Experiment 2, an interaction between closeness centrality and frequency of occurrence was observed on reaction times. The results are explained in terms of partial activation gradually strengthening over time word-forms that are centrally located in the phonological network.

SUBMITTER: Goldstein R

PROVIDER: S-EPMC5622968 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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The Influence of Closeness Centrality on Lexical Processing.

Goldstein Rutherford R Vitevitch Michael S MS

Frontiers in psychology 20170926

The present study examined how the network science measure known as closeness centrality (which measures the average distance between a node and all other nodes in the network) influences lexical processing. In the mental lexicon, a word such as CAN has high closeness centrality, because it is close to many other words in the lexicon. Whereas, a word such as CURE has low closeness centrality because it is far from other words in the lexicon. In an auditory lexical decision task (Experiment 1) pa ...[more]

PMID: 29018396

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Project description:BackgroundWe examine the accuracy of enzyme catalytic residue predictions from a network representation of protein structure. In this model, amino acid alpha-carbons specify vertices within a graph and edges connect vertices that are proximal in structure. Closeness centrality, which has shown promise in previous investigations, is used to identify important positions within the network. Closeness centrality, a global measure of network centrality, is calculated as the reciprocal of the average distance between vertex i and all other vertices.ResultsWe benchmark the approach against 283 structurally unique proteins within the Catalytic Site Atlas. Our results, which are inline with previous investigations of smaller datasets, indicate closeness centrality predictions are statistically significant. However, unlike previous approaches, we specifically focus on residues with the very best scores. Over the top five closeness centrality scores, we observe an average true to false positive rate ratio of 6.8 to 1. As demonstrated previously, adding a solvent accessibility filter significantly improves predictive power; the average ratio is increased to 15.3 to 1. We also demonstrate (for the first time) that filtering the predictions by residue identity improves the results even more than accessibility filtering. Here, we simply eliminate residues with physiochemical properties unlikely to be compatible with catalytic requirements from consideration. Residue identity filtering improves the average true to false positive rate ratio to 26.3 to 1. Combining the two filters together has little affect on the results. Calculated p-values for the three prediction schemes range from 2.7E-9 to less than 8.8E-134. Finally, the sensitivity of the predictions to structure choice and slight perturbations is examined.ConclusionOur results resolutely confirm that closeness centrality is a viable prediction scheme whose predictions are statistically significant. Simple filtering schemes substantially improve the method's predicted power. Moreover, no clear effect on performance is observed when comparing ligated and unligated structures. Similarly, the CC prediction results are robust to slight structural perturbations from molecular dynamics simulation.

Project description:There is extensive evidence that the segmental (i.e., phonemic) layer of phonology is routinely activated during reading, but little is known about whether phonological activation extends beyond phonemes to subsegmental layers (which include articulatory information, such as voicing) and suprasegmental layers (which include prosodic information, such as lexical stress). In three proofreading experiments, we show that spelling errors are detected more reliably in syllables that are stressed than in syllables that are unstressed if comprehension is a goal of the reader, indicating that suprasegmental phonology is both active during silent reading and can influence orthographic processes. In Experiment 1, participants received instructions to read for both errors and comprehension, and we found that the effect of lexical stress interacted with linguistic predictability, such that detection of errors in more predictable words was aided by stress but detection of errors in less predictable words was not. This finding suggests that lexical stress patterns can be accessed prelexically if an upcoming word is sufficiently predictable from context. Participants with stronger vocabularies showed decreased effects of stress on task performance, which is consistent with previous findings that more skilled readers are less swayed by phonological information in decisions about orthographic form. In two subsequent experiments, participants were instructed to read only for errors (Experiment 2) or only for comprehension (Experiment 3); the effect of stress disappeared when participants read for errors and reappeared when participants read for comprehension, reconfirming our hypothesis that predictability is a driver of lexical stress effects. In all experiments, errors were detected more reliably in words that were difficult to predict from context than in words that were highly predictable. Taken together, this series of experiments contributes two important findings to the field of reading and cognition: (1) The prosodic property of lexical stress can influence orthographic processing, and (2) Predictability inhibits the detection of errors in written language processing.

Dataset Information

The Influence of Closeness Centrality on Lexical Processing.

Publications

The Influence of Closeness Centrality on Lexical Processing.

Similar Datasets

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