The E-Z Reader model of eye movement control in reading

The E-Z Reader model (Reichle et al. 1998; 1999) provides a theoretical framework for understanding how word identification, visual processing, attention, and oculomotor control jointly determine when and where the eyes move during reading. In this article, we first review what is known about eye movements during reading. Then we provide an updated version of the model (E-Z Reader 7) and describe how it accounts for basic findings about eye movement control in reading. We then review several alternative models of eye movement control in reading, discussing both their core assumptions and their theoretical scope. On the basis of this discussion, we conclude that E-Z Reader provides the most comprehensive account of eye movement control during reading. Finally, we provide a brief overview of what is known about the neural systems that support the various components of reading, and suggest how the cognitive constructs of our model might map onto this neural architecture.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0140525X03000104

The E-Z reader model of eye-movement control in reading: comparisons to other models

http://www.unicog.org/publications/CohenDehaene_visualwordform_NeuroimagePreprint.pdf

Specialization within the ventral stream: the case for the visual word form area.

Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and easily falter when presented with noisy data. In this paper, we confront NMT models with synthetic and natural sources of noise. We find that state-of-the-art models fail to translate even moderately noisy texts that humans have no trouble comprehending. We explore two approaches to increase model robustness: structure-invariant word representations and robust training on noisy texts. We find that a model based on a character convolutional neural network is able to simultaneously learn representations robust to multiple kinds of noise.

/pdf/synthetic-and-natural-noise-both-break-neural-machine-41z8rg2kva.pdf

Synthetic and Natural Noise Both Break Neural Machine Translation

http://www.unicog.org/publications/GaillardCohen_IntracranialfMRIreadingAlexia_Neuron2006.pdf

Direct Intracranial, fMRI, and Lesion Evidence for the Causal Role of Left Inferotemporal Cortex in Reading

MiXeD-cAsE stimuli have long been used to test whether word recognition is based on holistic visual information or preliminary letter identification. However, without knowing which properties of mixed-case stimuli disrupt processing, it is not possible to determine which visual units mediate word recognition. The present studies examined the effects of case mixing on word and nonword naming as a function of (a) whether spaces were inserted between letters and (b) whether letter size was alternated independent of letter case. The results suggest that case-mixing disruption effects are due to at least 2 factors: the introduction of inappropriate grouping between letters with the same size and case, and the disruption of transletter features. The data support a model of visual lexical access based on the input from multiple visually based units.

Disruption to word or letter processing? The origins of case-mixing effects

A connectionist model was developed to simulate the production of phonological, semantic and orthographic lexical representations from an orthographic input code. When lesioned, the network displayed many of the characteristics of the neurological disorder, pure alexia. These included: greater accuracy in the recognition of letters compared to words, and (in some cases) spared semantic categorization and lexical decision ability relative to naming. Performance was superior for high relative to low frequency words and (for some lesions) for ‘high imageability’ words with more semantic referents. Similar to alexic patients, errors in the model tended to be visually rather than semantically related to target words and disrupted input had a greater effect on naming than on lexical decision performance. There were also tendencies apparent in the model which have not so far been reported for alexic patients but are found in other neurological patients, including category specificity effects and superior performance on superordinate relative to subordinate semantic categorization.



Noise was added to the input units of the unlesioned and lesioned model in an attempt to simulate case mixing effects on both normal and alexic reading. The unlesioned model demonstrated similar effects to normal readers with mixed case stimuli (Besner & McCann, 1987; Mayall & Humphreys, 1996). Further, effects on the lesioned model were comparable to those found with alexic patients (Bub & Arguin, 1995; Mayall & Humphreys, submitted).



The similarity of the performance characteristics of the model to those of pure alexic patients suggests that the architectures of the modelled and the human reading system encompass common properties, and that covert recognition in pure alexia can be attributed to the architecture of the word processing system.

A connectionist model of alexia: Covert recognition and case mixing effects

Methodology and validity in the construction of computational models of cognitive deficits following brain damage

This study investigates the visual representations used in the early stages of word recognition. In particular we look at the role of letter position information and the possible existence of visual units which are larger than single letters. Simulations here are intended to model the early stages of visual word recognition in both normal reading and in cases of peripheral dyslexia. In a feedforward network, letter units are coarse coded for position across words. The model uses separate sets of units to code upper and lower case letters. Exposure to all upper and all lower case words trains the letter units to activate a common set of local phonological units

Kate Mayall

Papers

Disruption to word or letter processing? The origins of case-mixing effects

A connectionist model of alexia: Covert recognition and case mixing effects

Methodology and validity in the construction of computational models of cognitive deficits following brain damage

Modelling Early Stages of Word Recognition: Case and the Peripheral Dyslexias