Training Enhances Word Recognition for Efficient Reading

Summary: Researchers developed a training method that enhances the recognition of known versus unknown words, thereby improving reading efficiency. Their study employs a model based on brain behavior that functions like a filter, distinguishing known words from unfamiliar letter combinations.

This model was used to train participants over three days, significantly boosting their reading performance. The training not only aids in learning new languages by streamlining the reading process but also holds potential for addressing reading disorders like dyslexia.

Key Facts:

  1. The training is based on the Lexical Categorization Model, which helps readers efficiently filter and recognize words, thus speeding up the reading process.
  2. Participants showed marked improvement in reading efficiency after just three days of training, which involved tasks that helped distinguish words from non-words.
  3. The study’s findings could lead to new training programs for language learners and individuals with reading disorders, and the approach will be further developed thanks to funding from the German Research Foundation (DFG).

Source: University of Cologne

A team of researchers from the University of Cologne and the University of Würzburg have found in training studies that the distinction between known and unknown words can be trained and leads to more efficient reading.

Recognizing words is necessary to understand the meaning of a text. When we read, we move our eyes very efficiently and quickly from word to word. This reading flow is interrupted when we encounter a word we do not know, a situation common when learning a new language.

This shows a child with a book.
However, when we encounter a new word, we cannot continue reading but would need to look up the word in a lexicon or on the Internet to understand its meaning. Credit: Neuroscience News

The words of the new language might have yet to be comprehended in their entirety, and language-specific peculiarities in spelling still need to be internalized.

The team of psychologists led by junior professor Dr Benjamin Gagl from the University of Cologne’s Faculty of Human Sciences has now found a method to optimize this process.

The current research results were published in npj Science of Learning under the title ‘Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners’.

Starting in May, follow-up studies extending the training programme will be carried out within a project funded by the German Research Foundation (DFG).

“Reading is essential for information processing,” said lead author Benjamin Gagl, who has been studying the cognitive and neural processes of word recognition for years.

Two years ago, he and a team of researchers showed that in our understanding of the processes implemented in word recognition, psychological theories do not make sufficiently precise assumptions about the exact functions of one of the most frequently activated brain areas in the left temporal lobe.

To close this knowledge gap, Gagl and his colleagues developed a model that uses established behavioural findings from psychology to predict the activation of this reading area in the brain; this model serves as the basis for the training programme described in the new study.

Word filters as a building block for efficient reading

The model assumes that this brain region functions like a filter and separates already-known words from irrelevant or not-yet-known letter combinations; only known words are allowed to ‘pass’ to initiate consequential linguistic processing.

However, when we encounter a new word, we cannot continue reading but would need to look up the word in a lexicon or on the Internet to understand its meaning.

The training procedures central to the current study were motivated by the assumptions of the ‘Lexical Categorization Model’. Behavioural studies showed that reading skills improved when participants were trained in this filtering process central to efficient reading.

The training procedure included simple tasks in which readers should distinguish words from non-words (e.g. path vs. poth) by pressing a button. After three training days, reading performance substantially improved in three separate studies.

The team also used a machine learning-based diagnostic procedure that can increase the efficiency of training as it can detect participants who would likely not benefit from further training.

This allows a decision to be made individually for each learner as to whether the lexical categorization training is worth the effort or whether alternative training should be carried out instead.

New ways to compensate for reading problems

As part of a newly acquired project funded by the DFG starting on 1 May, the researchers will further develop the computer models, motivating new training approaches for language learning or for the compensation of other reading disorders.

In addition to the field of German as a foreign language, the training approaches can potentially be used in dyslexia treatment.

“Neuro-cognitive computer models can be used to implement basic scientific findings to be used in individual diagnostic training programmes in educational and clinical settings.

“This enables us to help individual learners to optimize their reading skills and thus significantly improve their information processing skills,” said Gagl.

About this learning and reading research news

Author: Eva Schissler
Source: University of Cologne
Contact: Eva Schissler – University of Cologne
Image: The image is credited to Neuroscience News

Original Research: Open access.
Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners” by Benjamin Gagl et al. npj Science of Learning


Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners

Efficient reading is essential for societal participation, so reading proficiency is a central educational goal.

Here, we use an individualized diagnostics and training framework to investigate processes in visual word recognition and evaluate its usefulness for detecting training responders.

We (i) motivated a training procedure based on the Lexical Categorization Model (LCM) to introduce the framework.

The LCM describes pre-lexical orthographic processing implemented in the left-ventral occipital cortex and is vital to reading.

German language learners trained their lexical categorization abilities while we monitored reading speed change. In three studies, most language learners increased their reading skills.

Next, we (ii) estimated, for each word, the LCM-based features and assessed each reader’s lexical categorization capabilities.

Finally, we (iii) explored machine learning procedures to find the optimal feature selection and regression model to predict the benefit of the lexical categorization training for each individual.

The best-performing pipeline increased reading speed from 23% in the unselected group to 43% in the machine-selected group. This selection process strongly depended on parameters associated with the LCM.

Thus, training in lexical categorization can increase reading skills, and accurate computational descriptions of brain functions that allow the motivation of a training procedure combined with machine learning can be powerful for individualized reading training procedures.

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