Supplemental Material for Retuning of Lexical-Semantic Representations: Repetition and Spacing Effects in Word-Meaning Priming

2017 ◽  
Keyword(s):  
Word Meaning ◽  
Semantic Representations ◽  
Lexical Semantic ◽  
Spacing Effects

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

2018 ◽  
Vol 44 (7) ◽  
pp. 1130-1150 ◽  
Author(s):  
Hannah N. Betts ◽  
Rebecca A. Gilbert ◽  
Zhenguang G. Cai ◽  
Zainab B. Okedara ◽  
Jennifer M. Rodd
Keyword(s):  
Word Meaning ◽  
Semantic Representations ◽  
Lexical Semantic ◽  
Spacing Effects

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.

The effect of a bilingual learning mode on the establishment of lexical semantic representations in the L2

2013 ◽  
Vol 17 (3) ◽  
pp. 572-588 ◽  
Author(s):  
IRINA ELGORT ◽  
ANNA E. PIASECKI
Keyword(s):  
Word Learning ◽  
Alternative Explanation ◽  
Learning Approach ◽  
Semantic Features ◽  
Semantic Representations ◽  
Lexical Semantic ◽  
Lexical Representations ◽  
Orthographic Representations ◽  
Bilingual Learning

Deliberate vocabulary learning is common in the L2, however, questions remain about most efficient and effective forms of this learning approach. Bilingual models of L2 word learning and processing can be used to make predictions about outcomes of learning new vocabulary from bilingual (L2–L1) flashcards, and these predictions can be tested experimentally. In the present study, 41 late adult German–English bilinguals learned 48 English pseudowords using bilingual flashcards. Quality of component lexical representations established for the studied items was probed using form priming and semantic priming. The results show that, although all participants were able to establish robust orthographic representations of the studied items, only bilinguals with large L2 vocabularies established high-quality lexical semantic representations. With neither the Revised Hierarchical Model (RHM) nor the Sense Model able to fully account for these findings, an alternative explanation based on a distributed semantic features view of word learning is proposed. Learning implications of the findings are discussed.

scholarly journals Semantic diversity is best measured with unscaled vectors: Reply to Cevoli, Watkins and Rastle (2020)

2021 ◽  
Author(s):  
Paul Hoffman ◽  
Matthew A. Lambon Ralph ◽  
Timothy T. Rogers
Keyword(s):  
Word Recognition ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Word Meaning ◽  
Singular Values ◽  
Semantic Representations ◽  
Diversity Measure ◽  
Contextual Variability ◽  
Original Measure ◽  
Special Case

AbstractSemantic diversity refers to the degree of semantic variability in the contexts in which a particular word is used. We have previously proposed a method for measuring semantic diversity based on latent semantic analysis (LSA). In a recent paper, Cevoli et al. (2020) attempted to replicate our method and obtained different semantic diversity values. They suggested that this discrepancy occurred because they scaled their LSA vectors by their singular values, while we did not. Using their new results, they argued that semantic diversity is not related to ambiguity in word meaning, as we originally proposed. In this reply, we demonstrate that the use of unscaled vectors provides better fits to human semantic judgements than scaled ones. Thus we argue that our original semantic diversity measure should be preferred over the Cevoli et al. version. We replicate Cevoli et al.’s analysis using the original semantic diversity measure and find (a) our original measure is a better predictor of word recognition latencies than the Cevoli et al. equivalent and (b) that, unlike Cevoli et al.’s measure, our semantic diversity is reliably associated with a measure of polysemy based on dictionary definitions. We conclude that the Hoffman et al. semantic diversity measure is better-suited to capturing the contextual variability among words and that words appearing in a more diverse set of contexts have more variable semantic representations. However, we found that homonyms did not have higher semantic diversity values than non-homonyms, suggesting that the measure does not capture this special case of ambiguity.

scholarly journals Predictive pre-activation of orthographic and lexical-semantic representations facilitates visual word recognition

2020 ◽  
Author(s):  
Susanne Eisenhauer ◽  
Benjamin Gagl ◽  
Christian J. Fiebach
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Predictive Coding ◽  
Visual Word ◽  
Semantic Representations ◽  
Lexical Semantic ◽  
Brain Responses ◽  
Linguistic Representations ◽  
Context Dependent ◽  
Stimulus Features

AbstractTo a crucial extent, the efficiency of reading results from the fact that visual word recognition is faster in predictive contexts. Predictive coding models suggest that this facilitation results from pre-activation of predictable stimulus features across multiple representational levels before stimulus onset. Still, it is not sufficiently understood which aspects of the rich set of linguistic representations that are activated during reading – visual, orthographic, phonological, and/or lexical-semantic – contribute to context-dependent facilitation. To investigate in detail which linguistic representations are pre-activated in a predictive context and how they affect subsequent stimulus processing, we combined a well-controlled repetition priming paradigm, including words and pseudowords (i.e., pronounceable nonwords), with behavioral and magnetoencephalography measurements. For statistical analysis, we used linear mixed modeling, which we found had a higher statistical power compared to conventional multivariate pattern decoding analysis. Behavioral data from 49 participants indicate that word predictability (i.e., context present vs. absent) facilitated orthographic and lexical-semantic, but not visual or phonological processes. Magnetoencephalography data from 38 participants show sustained activation of orthographic and lexical-semantic representations in the interval before processing the predicted stimulus, suggesting selective pre-activation at multiple levels of linguistic representation as proposed by predictive coding. However, we found more robust lexical-semantic representations when processing predictable in contrast to unpredictable letter strings, and pre-activation effects mainly resembled brain responses elicited when processing the expected letter string. This finding suggests that pre-activation did not result in ‘explaining away’ predictable stimulus features, but rather in a ‘sharpening’ of brain responses involved in word processing.Impact StatementVisual word recognition is facilitated in predictive contexts. Predictive coding postulates that context-dependent facilitation involves the pre-activation of expected stimulus features, but it is not clear on which linguistic representations this mechanism relies during word recognition. Combining magnetoencephalography with high-powered linear mixed modeling, we show that context-dependent facilitation relies on pre-activation of orthographic and lexical-semantic representations in neuronal signals before actually perceiving an expected word.

scholarly journals Settling into Semantic Space: An Ambiguity-Focused Account of Word-Meaning Access

2019 ◽  
Author(s):  
Jennifer M Rodd
Keyword(s):  
Dimensional Space ◽  
Word Meaning ◽  
Semantic Space ◽  
Semantic Knowledge ◽  
Vital Role ◽  
Future Research ◽  
Learning Mechanisms ◽  
Stable States ◽  
Lexical Semantic ◽  
Lexical Semantic Knowledge

Most words are ambiguous: individual wordforms (e.g., “run”) can map onto multiple different interpretations depending on their sentence context (e.g., “the athlete/politician/river runs”). Models of word-meaning access must therefore explain how listeners and readers are able to rapidly settle on a single, contextually appropriate meaning for each word that they encounter. This article presents a new account of word meaning that places semantic disambiguation at its core, and integrates evidence from a wide variety of experimental approaches to explain this key aspect of language comprehension. The model has three key characteristics. (i) Lexical-semantic knowledge is viewed as a high-dimensional space; familiar word meanings correspond to stable states within this lexical-semantic space. (ii) Multiple linguistic and paralinguistic cues can influence the settling process by which the system resolves on one of these familiar meanings. (iii) Learning mechanisms play a vital role in facilitating rapid word-meaning access by shaping and maintaining high quality lexical-semantic knowledge. Several key areas for future research are identified.

scholarly journals Insights into Early Word Comprehension: Tracking the Neural Representations of Word Semantics in Infants.

2021 ◽  
Author(s):  
Rohan Saha ◽  
Jennifer Campbell ◽  
Janet F. Werker ◽  
Alona Fyshe
Keyword(s):  
Brain Activity ◽  
Word Meaning ◽  
Age Groups ◽  
Event Related Potential ◽  
Neural Representation ◽  
Machine Learning Techniques ◽  
Semantic Representations ◽  
Word Comprehension ◽  
Neural Representations ◽  
The Brain

Infants start developing rudimentary language skills and can start understanding simple words well before their first birthday. This development has also been shown primarily using Event Related Potential (ERP) techniques to find evidence of word comprehension in the infant brain. While these works validate the presence of semantic representations of words (word meaning) in infants, they do not tell us about the mental processes involved in the manifestation of these semantic representations or the content of the representations. To this end, we use a decoding approach where we employ machine learning techniques on Electroencephalography (EEG) data to predict the semantic representations of words found in the brain activity of infants. We perform multiple analyses to explore word semantic representations in two groups of infants (9-month-old and 12-month-old). Our analyses show significantly above chance decodability of overall word semantics, word animacy, and word phonetics. As we analyze brain activity, we observe that participants in both age groups show signs of word comprehension immediately after word onset, marked by our model's significantly above chance word prediction accuracy. We also observed strong neural representations of word phonetics in the brain data for both age groups, some likely correlated to word decoding accuracy and others not. Lastly, we discover that the neural representations of word semantics are similar in both infant age groups. Our results on word semantics, phonetics, and animacy decodability, give us insights into the evolution of neural representation of word meaning in infants.

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