The distinction between words that are known and unknown can be trained and leads to more efficient reading. To understand the meaning of a text, it is necessary to recognize the words. As we are reading, we are jumping from word to word efficiently and quickly. But when we encounter a word that we don’t understand, the flow is interrupted. Usually, to understand the meaning of that word, we need to look it up. This usually happens when we are learning a new language.
How the model works
Dr. Benjamin Gagl from the University of Cologne’s Faculty of Human Sciences has found a way to optimize this procedure.
Reading is essential for information processing, Benjamin Gagl said.
Gagl and his colleagues created a model that uses established behavioral findings in order to predict the activation of this specific reading area in the brain. This model forms the foundation of the training program that is described in this new study. This model thinks that this brain region works similarly to a filter, and it separates the already known words from words that are either irrelevant or not yet known letter combinations. The words that are allowed to pass are the ones that are already known in order to initiate consequential linguistic processing. The studies show that reading skills improved when participants were trained in this study. This procedure included simple tasks where readers should be able to distinguish words from non-words(e.g., cake vs. like) by pressing a button.
The results of the study
After only three days of training, reading performance improved in three separate studies.
This enables us to help individual learners to optimize their reading skills and thus significantly improve their information processing skills, said Gagl.
The researchers will continue to develop the computer models, encouraging new training approaches for language learning.