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Written by pixabay.
com | edited by Guo Ruidong | Ji Yongsheng ● ● ●When artificial neural networks are learning new concepts, we can see that the connections between their neurons have changed.
So, how does the human brain change during learning? Will there be a neuron to encode newly learned concepts? In order to answer the above questions, a research team from Princeton University in the United States used magnetic resonance imaging to examine the brain images of 24 undergraduate students studying the general part of the computer science course, and found that the neural activity of a specific area of the student's brain is correlated with their test scores.
The results were published in the journal Nature Communications on March 26, 2021 [1].
In the 1960s, neurobiologist Jerome Lightvin proposed the hypothesis of "grandmother cells".
The hypothesis states that there is a certain nerve cell in the human brain, and this cell will be activated when a specific concept, such as your grandmother's portrait, appears.
However, many neuroscientists use experiments to deny this hypothesis.
The mainstream view is that the combined patterns (neural representations) of different neurons, rather than individual neurons, are responsible for discriminating perception in the brain.
It should be noted that the previous research was conducted in a laboratory setting, and the task of the investigation was relatively simple, such as identifying an animal, while Princeton's research was conducted in a real teaching environment and examined undergraduates’ introductory computer science classes.
How much knowledge has been learned, and the concepts involved are nested and abstract.
It is worth mentioning that the research results overturned the "grandmother cell" hypothesis.
Specifically, the study measured the brain activity of 24 undergraduates (11 women) and 5 computer “experts” (postgraduates who have taken this course) after watching some lectures on the course, and a total of 18 were collected.
Test data from students and 4 "experts".
The course is 21 hours in total.
In the first ten weeks of the course, students mainly watch the course videos in installments and answer the test questions in class.
Researchers will perform functional MRI scans of their brains.
Undergraduates will watch the review video in the last week, perform an MRI scan, and then take the exam.
Computer "experts" watched review videos and received brain MRI scans.
So, can students' brain nerve activity be used to predict academic performance? The researchers averaged the brain activity of all students in the class and found that the neural activity of a specific part, especially the neural representation of the hippocampus responsible for memory, has a correlation coefficient of 0.
75 with student performance, the second highest correlation.
It is the angular gyrus of the brain area responsible for vision, with a correlation coefficient of 0.
62.
Figure 1.
Correlation analysis of neural activity in specific parts of the brain and student performance.
(Source: Reference [1]) When a student’s grades were extracted separately, and a specific brain area (anterior cingulate back to Anterior cingulate) activity of a certain student and an "expert" was associated with the analysis, the researchers found that The correlation coefficients are 0.
5 and 0.
43, respectively.
Compared with experts, academic activities are more closely related to students' brain areas.
Figure 2.
The correlation between student performance and neuronal activity in the brain.
(Source: Reference [1]) The above results indicate that the more similar a student's neurological (fMRI-activated brain region sequence) is to the classmate's mean, the higher the test score.
And the accuracy of the prediction is higher than the comparison with the "expert" neural representation.
This may be because although both "experts" and students have mastered a certain concept, they store memories in different ways in their brains.
Another possible explanation is to average the brain activity of 18 students.
Compared with the brain activity of a few "experts", the average value is better to remove the individual specificity, so as to find out that the real memory is related.
Conceptual brain operation mode.
Another finding of the study is that compared with the in-class test, when it comes to specific knowledge points in the last week of the review class, the neural activity of the students is more correlated with the brain activity of the "expert", especially in the review.
As in the exam phase, the students' brain activity is very similar to that of the "expert", that is to say, the neural activity of the student's brain tends to be the same as that of the "expert" after learning.
Figure 3.
The correlation between student brain activity and "expert" brain activity.
(Source: Reference [1]) The results of this study mean that a person’s test scores are highly correlated with their neuroimaging characteristics.
It is worth noting that during the review and final exam phase, the brain area responsible for short-term memory has changed from the hippocampus area responsible for short-term memory to the cognitively-related anterior cingulate gyrus.
The research further proves that the default mode network (the unconscious neural activity, the nervous system that prepares for future events) plays an important role in the learning process.
Previous studies have shown that when watching educational short videos, the higher the correlation between the brain's EEG in time (that is, between different topics), the higher the academic performance [2,3].
The study shows that the average brain activity of ordinary people can predict performance better than "experts".
This finding points out that in previous related studies, excessive attention was paid to the brain activity of "experts".
In the future, when studying the impact of learning on the brain, more attention can be paid to ordinary people, and the average population can be used to understand how the brain changes due to learning.
Of course, the study has certain limitations.
For example, the study did not take into account the impact of students’ cultural differences.
In addition, the content of the experiment was a university introductory course in science and engineering, and did not involve the humanities field, or more abstract concept.
In the future, combined with natural speech processing, and more convenient brain images and EEG data, it will be possible to study how more refined concepts can be learned in a relatively long period of time.
Such research will allow us to understand how learning changes the brain, which can be used as a basis for providing personalized teaching programs.
Reference materials: [1] Meir Meshulam, Liat Hasenfratz, Hanna Hillman, et al,.
Neural alignment predicts learning outcomes in students taking an introduction to computer science course.
Nat Commun 12, 1922 (2021).
https://doi.
org /10.
1038/s41467-021-22202-3[2] Cohen, SS et al.
Neural engagement with online educational videos predicts learning performance for individual students.
Neurobiol.
Learn.
Mem.
155, 60–64 (2018).
[3] Zhu, Y.
, Pan, Y.
& Hu, Y.
Learning desire is predicted by similar neural processing of naturalistic educational materials.
eNeuro Editing | Lucas END
com | edited by Guo Ruidong | Ji Yongsheng ● ● ●When artificial neural networks are learning new concepts, we can see that the connections between their neurons have changed.
So, how does the human brain change during learning? Will there be a neuron to encode newly learned concepts? In order to answer the above questions, a research team from Princeton University in the United States used magnetic resonance imaging to examine the brain images of 24 undergraduate students studying the general part of the computer science course, and found that the neural activity of a specific area of the student's brain is correlated with their test scores.
The results were published in the journal Nature Communications on March 26, 2021 [1].
In the 1960s, neurobiologist Jerome Lightvin proposed the hypothesis of "grandmother cells".
The hypothesis states that there is a certain nerve cell in the human brain, and this cell will be activated when a specific concept, such as your grandmother's portrait, appears.
However, many neuroscientists use experiments to deny this hypothesis.
The mainstream view is that the combined patterns (neural representations) of different neurons, rather than individual neurons, are responsible for discriminating perception in the brain.
It should be noted that the previous research was conducted in a laboratory setting, and the task of the investigation was relatively simple, such as identifying an animal, while Princeton's research was conducted in a real teaching environment and examined undergraduates’ introductory computer science classes.
How much knowledge has been learned, and the concepts involved are nested and abstract.
It is worth mentioning that the research results overturned the "grandmother cell" hypothesis.
Specifically, the study measured the brain activity of 24 undergraduates (11 women) and 5 computer “experts” (postgraduates who have taken this course) after watching some lectures on the course, and a total of 18 were collected.
Test data from students and 4 "experts".
The course is 21 hours in total.
In the first ten weeks of the course, students mainly watch the course videos in installments and answer the test questions in class.
Researchers will perform functional MRI scans of their brains.
Undergraduates will watch the review video in the last week, perform an MRI scan, and then take the exam.
Computer "experts" watched review videos and received brain MRI scans.
So, can students' brain nerve activity be used to predict academic performance? The researchers averaged the brain activity of all students in the class and found that the neural activity of a specific part, especially the neural representation of the hippocampus responsible for memory, has a correlation coefficient of 0.
75 with student performance, the second highest correlation.
It is the angular gyrus of the brain area responsible for vision, with a correlation coefficient of 0.
62.
Figure 1.
Correlation analysis of neural activity in specific parts of the brain and student performance.
(Source: Reference [1]) When a student’s grades were extracted separately, and a specific brain area (anterior cingulate back to Anterior cingulate) activity of a certain student and an "expert" was associated with the analysis, the researchers found that The correlation coefficients are 0.
5 and 0.
43, respectively.
Compared with experts, academic activities are more closely related to students' brain areas.
Figure 2.
The correlation between student performance and neuronal activity in the brain.
(Source: Reference [1]) The above results indicate that the more similar a student's neurological (fMRI-activated brain region sequence) is to the classmate's mean, the higher the test score.
And the accuracy of the prediction is higher than the comparison with the "expert" neural representation.
This may be because although both "experts" and students have mastered a certain concept, they store memories in different ways in their brains.
Another possible explanation is to average the brain activity of 18 students.
Compared with the brain activity of a few "experts", the average value is better to remove the individual specificity, so as to find out that the real memory is related.
Conceptual brain operation mode.
Another finding of the study is that compared with the in-class test, when it comes to specific knowledge points in the last week of the review class, the neural activity of the students is more correlated with the brain activity of the "expert", especially in the review.
As in the exam phase, the students' brain activity is very similar to that of the "expert", that is to say, the neural activity of the student's brain tends to be the same as that of the "expert" after learning.
Figure 3.
The correlation between student brain activity and "expert" brain activity.
(Source: Reference [1]) The results of this study mean that a person’s test scores are highly correlated with their neuroimaging characteristics.
It is worth noting that during the review and final exam phase, the brain area responsible for short-term memory has changed from the hippocampus area responsible for short-term memory to the cognitively-related anterior cingulate gyrus.
The research further proves that the default mode network (the unconscious neural activity, the nervous system that prepares for future events) plays an important role in the learning process.
Previous studies have shown that when watching educational short videos, the higher the correlation between the brain's EEG in time (that is, between different topics), the higher the academic performance [2,3].
The study shows that the average brain activity of ordinary people can predict performance better than "experts".
This finding points out that in previous related studies, excessive attention was paid to the brain activity of "experts".
In the future, when studying the impact of learning on the brain, more attention can be paid to ordinary people, and the average population can be used to understand how the brain changes due to learning.
Of course, the study has certain limitations.
For example, the study did not take into account the impact of students’ cultural differences.
In addition, the content of the experiment was a university introductory course in science and engineering, and did not involve the humanities field, or more abstract concept.
In the future, combined with natural speech processing, and more convenient brain images and EEG data, it will be possible to study how more refined concepts can be learned in a relatively long period of time.
Such research will allow us to understand how learning changes the brain, which can be used as a basis for providing personalized teaching programs.
Reference materials: [1] Meir Meshulam, Liat Hasenfratz, Hanna Hillman, et al,.
Neural alignment predicts learning outcomes in students taking an introduction to computer science course.
Nat Commun 12, 1922 (2021).
https://doi.
org /10.
1038/s41467-021-22202-3[2] Cohen, SS et al.
Neural engagement with online educational videos predicts learning performance for individual students.
Neurobiol.
Learn.
Mem.
155, 60–64 (2018).
[3] Zhu, Y.
, Pan, Y.
& Hu, Y.
Learning desire is predicted by similar neural processing of naturalistic educational materials.
eNeuro Editing | Lucas END
