(Photo by Lijphoto on Shutterstock)
COLOGNE, Germany — Learning a new language is no easy feat, especially when it comes to reading. Even if you've mastered the basics of grammar and vocabulary, the process of visually recognizing and understanding words can still feel slow and cumbersome. But what if there was a way to train your brain to read more efficiently in your new language? A recent study by researchers Benjamin Gagl and Klara Gregorová suggests that a computer model of how the brain processes words could hold the key.
To understand their approach, let's start with a quick crash course on how reading works in the brain. When your eyes land on a word, the visual information is sent to a specific area in the back of your brain called the left ventral occipito-temporal cortex, or lvOT for short. This region is sometimes referred to as the “visual word form area” because it seems to specialize in recognizing and categorizing words.
The lvOT essentially acts as a gatekeeper, quickly determining if a string of letters is a real word that's worth sending along for further processing or a nonsense jumble that can be ignored. This “lexical categorization” process is thought to be a crucial early step in efficient reading.
Here's where Gagl and Gregorová's work, published in the journal npj: Science of Learning, comes into play. They developed a computational model called the Lexical Categorization Model (LCM) that simulates how the lvOT makes these word/nonword decisions. The model assumes that the lvOT gauges the “word-likeness” of a letter string based on its similarity to all the words a reader knows.
Real words and totally unfamiliar jumbles are easy for the lvOT to categorize. But letter strings that fall in the fuzzy middle ground are trickier. Think of a word you've never seen before, but that follows the general spelling patterns of your language. The LCM captures this, showing higher uncertainty for these “hard-to-categorize” items.
The researchers reasoned that if the LCM accurately reflects how lexical categorization works in the brain, then training people to get better at this process should lead to more efficient reading. To test this, they recruited 76 German language learners and had them practice speedy word/nonword decisions using a task based on the LCM.
The results were promising: after just three 45 to 60-minute training sessions, the majority of participants showed significant improvements in their overall reading speed as measured by a standardized test. The benefits seemed to be directly related to lexical categorization, too. Those who showed the biggest reductions in uncertainty for hard-to-categorize items on the training task also tended to have the largest reading speed boosts.
The researchers didn't stop there. They wanted to see if they could predict who would benefit most from the lexical categorization training. So they fed a machine learning algorithm a wide range of data from each participant's initial performance - things like their baseline reading speed, their accuracy and reaction times on different types of words in the training task, and the “word-likeness” values for each item from the LCM.
The algorithm successfully predicted participants' ultimate reading speed improvements with about 50 percent accuracy. Intriguingly, the most important predictors weren't just things like starting reading level — they also included key parameters from the LCM, like the degree of lexical categorization uncertainty a reader showed for different items. This suggests that the model is truly capturing something meaningful about how individual brains process words.
The researchers see this as a proof-of-concept for using cognitive models to design and optimize literacy training. If you can build an accurate model of a mental process, you can target that process with training, then use the model to predict who will respond best. The LCM-based training could be a boon for language learners, who often struggle with slow, effortful reading even when they've mastered other aspects of their new tongue.
But the implications could extend even further. The researchers note that many types of reading difficulties, from developmental dyslexia to the challenges faced by adults learning to read for the first time, are associated with reduced lvOT activation during word recognition. Tailoring lexical categorization training to these populations could potentially be a game-changer.
Of course, there's still work to be done to refine and validate the approach. The current study relied on fancy cross-validation techniques to avoid overfitting the prediction model to the data, but testing it on a totally independent sample will be an important next step. And integrating lexical categorization training with other evidence-based approaches, like phonics instruction, could make it even more effective.
Still, this research represents an exciting melding of computational modeling, neuroimaging insights, and educational application. It's a reminder that the better we understand the brain bases of complex skills like reading, the more powerfully we can intervene when those skills are lagging. So the next time you're struggling through a page of text in an unfamiliar language, take heart — a little bit of LCM-inspired practice might be just what your visual word form area needs.
EdNews Editor-in-Chief Steve Fink contributed to this report.
