Comprehending Computational Language Comprehension. Review of Understanding Language Understanding: Computational Models of Reading, by A. Ram and K. Moorman

2000 ◽  
Vol 44 (4) ◽  
pp. 660-668
Author(s):  
Holly A Taylor
Keyword(s):  
Language Comprehension ◽  
Computational Models ◽  
Language Understanding ◽  
Models Of Reading

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 Neural Components of Reading Revealed by Distributed and Symbolic Computational Models

2020 ◽  
Vol 1 (4) ◽  
pp. 381-401
Author(s):  
Ryan Staples ◽  
William W. Graves
Keyword(s):  
Neural Activity ◽  
Neural Coding ◽  
Computational Models ◽  
Ann Model ◽  
Distributed Representations ◽  
Neural Data ◽  
Cognitive Components ◽  
Components Of Reading ◽  
Artificial Neural Network Ann ◽  
Models Of Reading

Determining how the cognitive components of reading—orthographic, phonological, and semantic representations—are instantiated in the brain has been a long-standing goal of psychology and human cognitive neuroscience. The two most prominent computational models of reading instantiate different cognitive processes, implying different neural processes. Artificial neural network (ANN) models of reading posit nonsymbolic, distributed representations. The dual-route cascaded (DRC) model instead suggests two routes of processing, one representing symbolic rules of spelling–to–sound correspondence, the other representing orthographic and phonological lexicons. These models are not adjudicated by behavioral data and have never before been directly compared in terms of neural plausibility. We used representational similarity analysis to compare the predictions of these models to neural data from participants reading aloud. Both the ANN and DRC model representations corresponded to neural activity. However, the ANN model representations correlated to more reading-relevant areas of cortex. When contributions from the DRC model were statistically controlled, partial correlations revealed that the ANN model accounted for significant variance in the neural data. The opposite analysis, examining the variance explained by the DRC model with contributions from the ANN model factored out, revealed no correspondence to neural activity. Our results suggest that ANNs trained using distributed representations provide a better correspondence between cognitive and neural coding. Additionally, this framework provides a principled approach for comparing computational models of cognitive function to gain insight into neural representations.

Connectionist models of reading

2007 ◽  
pp. 234-250 ◽  
Author(s):  
Mark S. Seidenberg
Keyword(s):  
Computational Models ◽  
Distributed Processing ◽  
Causal Effects ◽  
Task Orientation ◽  
Connectionist Models ◽  
Grain Sizes ◽  
Skilled Reading ◽  
Simple Processing ◽  
Reading Impairments ◽  
Models Of Reading

Connectionist computational models have been extensively used in the study of reading: how children learn to read, skilled reading, and reading impairments (dyslexia). The models are computer programs that simulate detailed aspects of behaviour. This article provides an overview of connectionist models of reading, with an emphasis on the “triangle” framework. The term “connectionism” refers to a broad, varied set of ideas, loosely connected by an emphasis on the notion that complexity, at different grain sizes or scales ranging from neurons to overt behaviour, emerges from the aggregate behaviour of large networks of simple processing units. This article focuses on the parallel distributed processing variety developed by Rumelhart, McClelland, and Hinton (1986). First, it describes basic elements of connectionist models of reading: task orientation, distributed representations, learning, hidden units, and experience. The article then looks at how models are used to establish causal effects, along with quasiregularity and division of labor.

scholarly journals PENGARUH PERMAINAN BINGO DALAM KEMAMPUAN MEMAHAMI BAHASA PADA ANAK USIA 4-5 TAHUN DI KELURAHAN KAMPUNG JAWA 1 KOTA PARIAMAN

2020 ◽  
Vol 8 (3) ◽  
pp. 273
Author(s):  
Mutiara Syafni
Keyword(s):  
Data Analysis ◽  
Language Comprehension ◽  
Quantitative Research ◽  
Language Skills ◽  
Language Understanding ◽  
Paired Sample ◽  
Comprehension Skills ◽  
The Family ◽  
Quasi Experimental ◽  
The Village

Education has a role, namely to develop all the potential of students, the language skills of a child can be observed through language understanding. The bingo game is an activity that can be used according to a theme or subject. This study aims to determine the effect of bingo on the ability to understand language in children aged 4-5 years in Kampung Jawa 1 Village, Pariaman City.This type quantitative research quasi-experimental method with a prettest and postest design. The population were children aged 4-5 years in the family of the village of Kampung Jawa 1 with 15 populations. The sample of this study took 5 children selected from RT 03, the technique sampling was purposive sampling. Data analysis used paired sample t-test. Based on the results of research before being given action (pretest) the ability to understand children's language is dominated by sufficiently capable catogy, namely (60%), after being given action (posttest) the ability to understand children's language is dominated by capable and very capable catogy, namely (60%) and there is an effect of play. bingo in the ability to understand language in children. Sig 0.009 <0.05. It is recommended that parents and AUD educators be able to use bingo games and develop language comprehension skills in children Keywords: Bingo Games, Language Skills, 4-5 Years Old Children

Computational Models of Reading

2021 ◽  
Author(s):  
Erik D. Reichle
Keyword(s):  
Sentence Processing ◽  
Visual Processing ◽  
Computational Models ◽  
Word Identification ◽  
Movement Control ◽  
Theoretical Work ◽  
Computer Models ◽  
Reading Processes ◽  
Core Reading ◽  
Models Of Reading

This book describes computational models of reading, or models that simulate and explain the mental processes that support the reading of text. The book provides introductory chapters on both reading research and computer models. The central chapters of the book then review what has been learned about reading from empirical research on four core reading processes: word identification, sentence processing, discourse representation, and how these three processes are coordinated with visual processing, attention, and eye-movement control. These central chapters also review an influential sample of computer models that have been developed to explain these key empirical findings, as well as comparative analyses of those models. The final chapter attempts to integrate this empirical and theoretical work by both describing a new comprehensive model of reading, Über-Reader, and reporting several simulations to illustrate how the model accounts for many of the basic phenomena related to reading.

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