AoA effects in reading aloud and lexical decision: Locating the (semantic) locus in terms of the number of backward semantic associations

2020 ◽  
Vol 73 (11) ◽  
pp. 2036-2044
Author(s):  
Michael J Cortese ◽  
Sean Toppi ◽  
Maya M Khanna ◽  
Jonathan B Santo
Keyword(s):  
Lexical Decision ◽  
Word Processing ◽  
Computational Models ◽  
Reading Aloud ◽  
Free Association ◽  
Age Of Acquisition ◽  
Semantic Associations ◽  
Network Plasticity ◽  
Analyse Data ◽  
Subject Ratings

In the present study, we analyse data from the English Lexicon Project to assess the extent to which age of acquisition (AoA) effects on word processing stem from the number of semantic associations tied to a word. We show that the backward number of associates (bNoA; that is, the log transformed number of words that produce the target word in free association) is an important predictor of both lexical decision and reading aloud performance, and reduces the typical AoA effect as represented by subject ratings in both tasks. Although the AoA effect is reduced, it remains a significant predictor of performance above and beyond bNoA. We conclude that the semantic locus of AoA effects can be found in the number of backward connections to the word, and that the independent AoA effect is due to network plasticity. We discuss how computational models currently explain AoA effects, and how bNoA may affect their processing.

Examining word processing via a megastudy of conditional reading aloud

2018 ◽  
Vol 71 (11) ◽  
pp. 2295-2313 ◽  
Author(s):  
Michael J Cortese ◽  
Mark Yates ◽  
Jocelyn Schock ◽  
Lizete Vilks
Keyword(s):  
Lexical Decision ◽  
Word Processing ◽  
Stepwise Regression ◽  
Semantic Information ◽  
Reading Aloud ◽  
Regression Analyses ◽  
Mixed Effect ◽  
Decision Performance ◽  
Pairwise Correlations ◽  
Monosyllabic Words

Results from a megastudy on conditional reading aloud for 2,145 monosyllabic words are reported. In stepwise regression analyses, the predictor variables accounted for over 66% of the reaction time (RT) variance. Linear mixed effect modelling on log RT indicated that every variable that related to RT in either reading aloud or lexical decision also related to RT in conditional reading aloud. Notably, differences among tasks were observed. Specifically, lexical decision showed stronger reliance on semantic information than the other two tasks, but conditional reading aloud also showed strong reliance on semantic information. Interestingly, feedback consistency affected reading aloud and conditional reading but not lexical decision. Pairwise correlations revealed that conditional reading aloud performance showed moderately strong relationships to lexical decision and reading aloud performance, whereas reading aloud and lexical decision performance were weakly related to each other. Conditional reading aloud produces reliable data that can be used to examine word processing. Theoretical challenges moving forward include how to best conceptualise and model processes involved in this task.

The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

2020 ◽  
Author(s):  
Kun Sun
Keyword(s):  
Eye Tracking ◽  
Semantic Similarity ◽  
Cognitive Processing ◽  
Language Processing ◽  
Language Comprehension ◽  
Word Processing ◽  
Reading Time ◽  
Computational Models ◽  
Tracking Data ◽  
Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

scholarly journals Lateralızatıon of Non-Word Processıng in Monolınguals

2020 ◽  
Vol 15 ◽  
pp. 185-190
Author(s):  
Filiz Mergen ◽  
Gulmira Kuruoglu
Keyword(s):  
Lexical Decision ◽  
Lexical Decision Task ◽  
Left Hemisphere ◽  
Word Processing ◽  
Lexical Processing ◽  
Response Times ◽  
Hemispheric Dominance ◽  
Decision Task ◽  
Visual Hemifield ◽  
Overwhelming Evidence

A great bulk of research in the psycholinguistic literature has been dedicated to hemispheric organization of words. An overwhelming evidence suggests that the left hemisphere is primarily responsible for lexical processing. However, non-words, which look similar to real words but lack meaningful associations, is underrepresented in the laterality literature. This study investigated the lateralization of Turkish non-words. Fifty-three Turkish monolinguals performed a lexical decision task in a visual hemifield paradigm. An analysis of their response times revealed left-hemispheric dominance for non-words, adding further support to the literature. The accuracy of their answers, however, were comparable regardless of the field of presentation. The results were discussed in light of the psycholinguistic word processing views.

A case study of the effect of age-of-acquisition on reading aloud in Chinese dyslexia

Neurocase ◽  
2008 ◽  
Vol 14 (3) ◽  
pp. 276-289 ◽  
Author(s):  
Sam-Po Law ◽  
Winsy Wong ◽  
Olivia Yeung ◽  
Brendan S. Weekes
Keyword(s):  
Reading Aloud ◽  
Age Of Acquisition ◽  
Effect Of Age

Multilink for bilingual language production

2018 ◽  
Vol 22 (04) ◽  
pp. 687-688 ◽  
Author(s):  
IVA IVANOVA ◽  
DANIEL KLEINMAN
Keyword(s):  
Lexical Decision ◽  
Language Production ◽  
Computational Models ◽  
Current Model ◽  
Word Naming ◽  
Major Benefit ◽  
Theoretical Assumptions

A major benefit of computational models is their ability to demonstrate which theoretical assumptions are truly necessary to explain a pattern of data. Dijkstra, Wahl, Buytenhuijs, van Halem, Al-jibouri, de Korte, and Rekké (in press) have impressively shown with Multilink that it is possible to account for a range of findings from bilingual lexical decision, word naming, and forward and backward translation tasks with an integrated lexicon, without lateral connections between translation equivalents, and without inhibition. In this commentary, we consider the applicability of the current model to other multilingual language production tasks, and note where the model's assumptions might need revision as its scope is expanded.

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