While the studies of nouns and their connection to brain function are all interesting, they are hardly groundbreaking. There have been many studies such as this performed prior to 2008. It has been discovered already that humans have a general understanding and shared regard for words, as well as images, using fMRIs. It would be interesting to see how people of different languages relate to the same words in their native language, as well as foreign languages. Do English speakers respond to the English “book” in the same way that they do the Spanish word “libro?” Would there be a discrepancy in time? Is there an entirely different framework for foreign languages? If pictures are included, does nothing change? If a framework is created for foreign languages, could this be the key to creating an easier method to learning foreign languages? The study could be performed in many different, interesting ways that the study could be performed.

Another article I have read was written by John R. Anderson, and titled, “Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms.” It was published It was published in Neuropsychologia in 2011. I found several interesting primary ideas about the article. For instance I was unaware that second-by-second thinking could be tracked by the human brain, using the Markov Model Algorithms in order to deduce problem solving. I was fascinated by the idea that an fMRI system could deduce a child’s thought process, reading the child’s mind, as they solved a relatively complicated algebra problem. This first methodology was so accurate it was able to infer which problem-solving technique the child was using. The second methodology was interesting in the way that it was able to follow the steps taken in order to deduce the child’s thought process.

The second method of evaluation outlined in the article involves “model discovery.” It is not as elegant as Markov’s algorithms, nor is it as useful. However, it does show the steps of algebraic equations as well as the method involved in performing each step of the equation. The system is called model evaluation, and it sounds much like how teachers go about teaching students today. Researchers observed students using model discovery and found that they are able to detect how many steps an equation takes, as well as what method is needed to perform the step, but they are unable to deduce which step is being performed incorrectly. The method used to complete the steps of the algebraic equations prove to be the most useful points of reference in the model discovery because it allowed researchers to determine how fluent students were in solving algebraic equations.

This study, especially for math, sounds revolutionary. Understanding a student’s thought process for any problem-solving activity could be a valuable tool in any teaching situation. Teachers would have the capacity to understand exactly where a child needs help. They could comprehend what a child misunderstood about lessons and easily cater lesson plans specifically for the child. Reading a child’s problem-solving techniques could effectively revolutionize the teaching profession, making learning easier than ever before. Children who struggle with learning disorders may finally get the tailored help they need, as well. This mind-reading methodology could also raise test scores, securing more funding for schools.

While I understand performing studies such as this, I cannot understand why, after the fMRI methodology was used, there was a necessity for any other studies to be performed. Why were the Hidden Markov Model algorithms not explored further in an educational atmosphere? Studies are allowed to change, and while it may have seemed prudent to end the original study, it appears that understanding how this technique could presume where children need the most help during problem-solving activities would have produced more of a benefit. Anderson does not overstate the results, but perhaps understates them. The study could expand into education, or perhaps be modified and used as a type of lie detector. Lying is a form of problem solving. If Markov’s algorithm was modified to sense a fraction of adjustment in thought process while under pressure it could be used as an inarguable lie detector.