Models of Sentence Processing

2021 ◽  
pp. 186-275
Author(s):  
Erik D. Reichle
Keyword(s):  
Comparative Analysis ◽  
Eye Movements ◽  
Sentence Processing ◽  
Representative Sample ◽  
Semantic Information ◽  
Linguistic Knowledge ◽  
Subsequent Research ◽  
Large Representative

This chapter first describes what has been learned about how readers process sentences, using information from individual words in combination with linguistic knowledge to generate larger units of meaning corresponding to phrases and sentences. The chapter then reviews what has been learned about sentence processing using various methods, but most notably, the measurement of readers’ eye movements. The chapter then reviews precursor theories and models of sentence processing—models that provide early attempts to explain how readers construct the meanings of phrases and sentences, and that motivate much of the subsequent research to understand the relative contributions of syntactic versus semantic information in sentence processing. The chapter then reviews a large, representative sample of the models that have been used to simulate and understand various facets of sentence processing. These are presented in their order of development to show how the models have evolved to accommodate new empirical findings. The chapter concludes with an explicit comparative analysis of the sentence-processing models and discussion of the empirical findings that each model can and cannot explain.

Models of the Reading Architecture

2021 ◽  
pp. 357-441
Author(s):  
Erik D. Reichle
Keyword(s):  
Comparative Analysis ◽  
Eye Movement ◽  
Sentence Processing ◽  
Representative Sample ◽  
Word Identification ◽  
Movement Control ◽  
Mental Processes ◽  
Eye Movement Control ◽  
Large Representative ◽  
Shed Light

This chapter describes what has been learned about reading architecture, or how the mental processes that support word identification, sentence processing, and discourse representation during reading are coordinated with the systems that support vision, attention, and eye-movement control. The chapter reviews key findings that shed light on the nature of reading architecture, mainly using the results of eye-movement experiments. The chapter then reviews precursor theories and models of the reading architecture—early attempts to explain and simulate reading in its entirety. The chapter goes on to review a large, representative sample of the models that have been used to simulate and understand natural reading. Models are reviewed in their order of development to show how they have evolved to accommodate new empirical findings. The chapter concludes with an explicit comparative analysis of the models and a discussion of the empirical findings that each model can and cannot explain.

Models of Discourse Representation

2021 ◽  
pp. 276-356
Author(s):  
Erik D. Reichle
Keyword(s):  
Comparative Analysis ◽  
Language Processing ◽  
Representative Sample ◽  
Text Processing ◽  
Discourse Processing ◽  
Discourse Representation ◽  
Ongoing Debate ◽  
Large Representative

This chapter first describes what has been learned about how readers represent the meaning of discourse by integrating the meanings to individual sentences to construct the representations needed to understand larger segments of text. The chapter reviews the key findings related to text processing and how this sparked an ongoing debate about the extent to which the making of inferences during reading is obligatory. The chapter reviews precursor theories and models of discourse representation that attempt to explain how discourse representations are generated via the interaction of language processing and memory. The chapter then reviews a large, representative sample of the models that have been used to simulate and understand aspects of discourse processing. They are reviewed in their order of development to show how the models have evolved to accommodate new empirical findings. The chapter concludes with an explicit comparative analysis of the discourse-processing models and discusses the empirical findings that each model can and cannot explain.

Synthesis

2021 ◽  
pp. 442-514
Author(s):  
Erik D. Reichle
Keyword(s):  
Comparative Analysis ◽  
Eye Movements ◽  
Eye Movement ◽  
Sentence Processing ◽  
Word Identification ◽  
Movement Control ◽  
Comprehensive Model ◽  
Eye Movement Control ◽  
Theoretical Assumptions ◽  
Models Of Reading

This chapter opens with a discussion of the limitations of current models of reading, and moves on to the reasons why more comprehensive models of reading are necessary to advance our understanding of the mental, perceptual, and motoric processes that support reading. The chapter then provides a comparative analysis of the various approaches that have been adopted to model reading, and how the theoretical assumptions of models of word identification, sentence processing, discourse representation, and eye-movement control might be combined to build a more comprehensive model of reading in its entirety. The remainder of the chapter then describes one such model, Über-Reader, and a series of simulations to illustrate how the model explains word identification, sentence processing, the encoding and recall of discourse meaning, and the patterns of eye movements that are observed during reading. The final sections of the chapter then address both the limitations and possible future applications of the model.

Models of Word Identification

2021 ◽  
pp. 64-185
Author(s):  
Erik D. Reichle
Keyword(s):  
Comparative Analysis ◽  
Lexical Decision ◽  
Lexical Decision Task ◽  
Empirical Research ◽  
Representative Sample ◽  
Word Identification ◽  
Decision Task ◽  
Correct Order ◽  
New Findings ◽  
Large Representative

This chapter first describes the tasks that are used to study how readers identify printed words (e.g., the lexical-decision task) and then reviews the key empirical findings related to skilled and impaired word identification (i.e., dyslexia). As explained, these findings have both motivated the development of computer models of word identification and been used to evaluate the explanatory adequacy of those models. The chapter then reviews several precursor theories and models of word identification that provide recurring metaphors (e.g., generating word pronunciations via analogy vs. the application of rules) in the development of later, more formally implemented word-identification models. The chapter reviews a large representative sample of these models in the order of their development, to show how the models have evolved in response to empirical research and the need to accommodate new findings (e.g., how the letters in words are perceived in their correct order). The chapter concludes with an explicit comparative analysis of the word-identification models and discussion of the findings that each model can and cannot explain.

Eye-Movement Patterns of Readers With Down Syndrome During Sentence-Processing: An Exploratory Study

2010 ◽  
Vol 115 (3) ◽  
pp. 193-206 ◽  
Author(s):  
Cheryl Frenck-Mestre ◽  
Nathalie Zardan ◽  
Annie Colas ◽  
Alain Ghio
Keyword(s):  
Down Syndrome ◽  
Eye Movements ◽  
Sentence Processing ◽  
Exploratory Study ◽  
Matched Control ◽  
Reading Level ◽  
Control Group ◽  
Future Studies ◽  
Complex Sentences ◽  
Simple Sentences

Abstract Eye movements were examined to determine how readers with Down syndrome process sentences online. Participants were 9 individuals with Down syndrome ranging in reading level from Grades 1 to 3 and a reading-level-matched control group. For syntactically simple sentences, the pattern of reading times was similar for the two groups, with longer reading times found at sentence end. This “wrap-up” effect was also found in the first reading of more complex sentences for the control group, whereas it only emerged later for the readers with Down syndrome. Our results provide evidence that eye movements can be used to investigate reading in individuals with Down syndrome and underline the need for future studies.

The Clustering of Bullying and Cyberbullying Behaviour within Australian Schools

2012 ◽  
Vol 56 (2) ◽  
pp. 142-162 ◽  
Author(s):  
Thérèse Shaw ◽  
Donna Cross
Keyword(s):  
Sample Size ◽  
Academic Outcomes ◽  
Representative Sample ◽  
Intraclass Correlation ◽  
Large Numbers ◽  
Sample Size Calculations ◽  
School Sample ◽  
School Differences ◽  
Bullying Victimisation ◽  
Large Representative

Bullying between students at school can seriously affect students' health and academic outcomes. To date, little is known regarding the extent to which bullying behaviour is clustered within certain schools rather than similarly prevalent across all schools. Additionally, studies of bullying behaviour in schools that do not account for clustering of such behaviour by students within the same school are likely to be underpowered and yield imprecise estimates. This article presents intraclass correlation (ICC) values for bullying victimisation and perpetration measures based on a large representative sample of 106 Australian schools. Results show that bullying is not confined to specific schools and school differences contribute little to explaining students' bullying behaviour. Despite this, seemingly negligible ICC values can substantially affect the sample sizes required to attain sufficiently powered studies, when large numbers of students are sampled per school. Sample size calculations are illustrated.

scholarly journals Deepfakes and Disinformation: Exploring the Impact of Synthetic Political Video on Deception, Uncertainty, and Trust in News

2020 ◽  
Vol 6 (1) ◽  
pp. 205630512090340 ◽  
Author(s):  
Cristian Vaccari ◽  
Andrew Chadwick
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Representative Sample ◽  
Public Discourse ◽  
Civic Culture ◽  
United Kingdom Population ◽  
The United Kingdom ◽  
Experimental Treatments ◽  
The Impact ◽  
Large Representative

Artificial Intelligence (AI) now enables the mass creation of what have become known as “deepfakes”: synthetic videos that closely resemble real videos. Integrating theories about the power of visual communication and the role played by uncertainty in undermining trust in public discourse, we explain the likely contribution of deepfakes to online disinformation. Administering novel experimental treatments to a large representative sample of the United Kingdom population allowed us to compare people’s evaluations of deepfakes. We find that people are more likely to feel uncertain than to be misled by deepfakes, but this resulting uncertainty, in turn, reduces trust in news on social media. We conclude that deepfakes may contribute toward generalized indeterminacy and cynicism, further intensifying recent challenges to online civic culture in democratic societies.

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