scholarly journals A review of computational models of word recognition and pronunciation

2020 ◽  
Author(s):  
M. Alex Kelly
Keyword(s):  
Computational Models ◽  
Distributed Processing ◽  
Model Development ◽  
Dual Processing ◽  
Activation Model ◽  
Development Models ◽  
Dual Route ◽  
Visual Attention Mechanism ◽  
Consistency Effects ◽  
Models Of Reading

How do we recognize words and assign a pronunciation? Computational models provide a formal description of the mechanisms and principles that guide the reading process. I review and evaluate the Interactive-Activation Model (IAM), Dual Route Cascaded (DRC) model, the Parallel Distributed Processing (PDP) model, and the Connectionist Dual Processing (CDP) model, as well as LEX, a variant of the MINERVA model of memory. I evaluate each model’s ability to account for consistency effects, serial effects, syllable effects, and phonological effects. Consistency effects pose a problem for the rule-based pronunciation of the DRC. Serial effects pose a problem for the purely parallel PDP models. Phonological effects pose a problem for all models save CDP. All models suffer from the distribution problem, weakening each model’s ability to learn spelling-to-sound relationships. LEX is the only model that handles polysyllabic words. As none of the models provide a complete answer to the question of ‘how do we read?’, ‘how do we pronounce?’, or ‘how do we recognize words?’, I outline a set of principles as guidelines for future model development. Models of reading should learn, include a visual attention mechanism, be sensitive to phonology, and account for meaning and spelling in addition to recognizing words and pronouncing them.

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.

Models of reading aloud: Dual-route and parallel-distributed-processing approaches.

1993 ◽  
Vol 100 (4) ◽  
pp. 589-608 ◽  
Author(s):  
Max Coltheart ◽  
Brent Curtis ◽  
Paul Atkins ◽  
Micheal Haller
Keyword(s):  
Distributed Processing ◽  
Reading Aloud ◽  
Parallel Distributed Processing ◽  
Dual Route ◽  
Models Of Reading

Assessment of Machine Learning-Based Audiovisual Quality Predictors

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Mythili K. ◽  
Manish Narwaria
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Quality Assessment ◽  
Computational Models ◽  
Model Development ◽  
Multimedia Communication ◽  
Current Approach ◽  
Learning Ability ◽  
Data Uncertainty ◽  
Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

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.

scholarly journals A Comprehensive Review of Low Impact Development Models for Research, Conceptual, Preliminary and Detailed Design Applications

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1541 ◽  
Author(s):  
Sahereh Kaykhosravi ◽  
Usman Khan ◽  
Amaneh Jadidi
Keyword(s):  
Spatial Data ◽  
Spatial Scales ◽  
Low Impact Development ◽  
Model Development ◽  
Point Of View ◽  
Tree Canopy ◽  
Balance Model ◽  
Runoff Generation ◽  
Development Models ◽  
Model Features

This review compares and evaluates eleven Low Impact Development (LID) models on the basis of: (i) general model features including the model application, the temporal resolution, the spatial data visualization, the method of placing LID within catchments; (ii) hydrological modelling aspects including: the type of inbuilt LIDs, water balance model, runoff generation and infiltration; and (iii) hydraulic modelling methods with a focus on the flow routing method. Results show that despite the recent updates of existing LID models, several important features are still missing and need improvement. These features include the ability to model: multi-layer subsurface media, tree canopy and processes associated with vegetation, different spatial scales, snowmelt and runoff calculations. This review provides in-depth insight into existing LID models from a hydrological and hydraulic point of view, which will facilitate in selecting the best-suited model. Recommendations on further studies and LID model development are also presented.

Testing single‐ and dual‐route computational models of auditory repetition with new data from six aphasic patients

Aphasiology ◽  
2008 ◽  
Vol 22 (1) ◽  
pp. 62-76 ◽  
Author(s):  
Rachel Baron ◽  
J. Richard Hanley ◽  
Gary S. Dell ◽  
Janice Kay
Keyword(s):  
Computational Models ◽  
Dual Route

A Computational Study into the Evolution of Dual-Route Dynamics for Affective Processing

2003 ◽  
Vol 15 (2) ◽  
pp. 194-208 ◽  
Author(s):  
Paul den Dulk ◽  
Bram T. Heerebout ◽  
R. Hans Phaf
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cognitive Neuroscience ◽  
Computational Study ◽  
Avoidance Reaction ◽  
Affective Processing ◽  
Dual Processing ◽  
Dual Route ◽  
Dual Route Model ◽  
Artificial World

The evolutionary justification by LeDoux (1996) for his dual-route model of fear processing was analyzed computationally by applying genetic algorithms to artificial neural networks. The evolution was simulated of a neural network controlling an agent that gathered food in an artificial world and that was occasionally menaced by a predator. Connections could not change in the agent's “lifetime,” so there was no learning in the simulations. Only if the smells of food and predator were hard to distinguish and the fitness reflected time pressures in escaping from the predator did the type of dual processing postulated by LeDoux emerge in the surviving agents. Processing in the “quick and dirty” pathway of the fear system ensured avoidance of both predators and food, but a distinction between food and predator was made only in the long pathway. Elaborate processing inhibited the avoidance reaction and reversed it into an approach reaction to food, but strengthened the avoidance reaction to predators (and more finely tuned the direction of escape). It is suggested that “computational neuroethology” (Beer, 1990) may help constrain reasoning in evolutionary psychology, particularly when applied to specific neurobiological models, and in the future may even generate new hypotheses for cognitive neuroscience.

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