Rikei and Bunkei Are Not Brain Types, but Words for Institutions and Self-Understanding

Rikei and Bunkei Are Not Brain Types, but Words for Institutions and Self-Understanding

1. Rikei and bunkei as classifications

In Japan, the words rikei and bunkei are often used in everyday conversation as if they described types of people. If someone is good at mathematics and science, they are called rikei. If they are good at Japanese and social studies, they are called bunkei. Someone who likes reading is bunkei, while someone who likes machines or numbers is rikei. This usage is convenient, but it is quite rough if taken strictly. At the very least, it is not a scientific classification that divides human brains into two kinds.

At the institutional level, however, the choice between humanities and sciences has a strong effect. According to materials from Japan's Ministry of Education, Culture, Sports, Science and Technology, two out of three high schools, or 66 percent, divide students into humanities and science courses. Among third-year high school students enrolled in those tracks, 32 percent are in science courses and 68 percent are in humanities courses. The completion rate for Mathematics III is also limited: 29.5 percent in ordinary-course schools and 21.6 percent overall. In other words, rikei and bunkei are not merely impressionistic labels. They are practical forks in the road that affect which subjects students take, which entrance examinations they can sit for, and which university faculties become easier to apply to.

Yet an institutional fork and the structure of human ability are not the same thing. The humanities-science distinction can be useful for organizing subjects and entrance exams, but it becomes risky when used to fix an individual's possibilities. A person who is relatively strong at mathematics is not necessarily weak in reading comprehension or historical thinking. A person who loves novels is not necessarily unsuited to statistics or programming. In reality, abilities are continuous, overlapping, and shaped by learning time and environment.

For Pochang Lab, the important point is not to treat rikei and bunkei like a personality diagnosis. They are labels for choices that emerged within a particular education system. They are not names that reveal the essence of the brain.

2. Scholarship was not originally divided into humanities and sciences

If we look back historically, philosophy and mathematics, natural inquiry and political thought, logic and literature were not separated as sharply as they are now. Pythagoras is known as a mathematician, but he was also the central figure of a religious and philosophical community. Descartes is discussed as a representative of modern philosophy, yet he also contributed greatly to analytic geometry and influenced the methods of natural science. Leibniz was a scholar across philosophy, logic, mathematics, law, and history, and he was also involved in the development of calculus. Bertrand Russell was a philosopher, but also a major figure in mathematical logic and the foundations of mathematics.

This is not just trivia. For intellectuals before the modern period, understanding the world meant treating number, language, logic, nature, and society as connected. The present classification in which philosophy is bunkei and mathematics is rikei is less a universal boundary inside knowledge itself than a classification strengthened by modern university systems, specialization, entrance examinations, and professional qualifications.

Of course, specialization has enormous benefits. Medicine, engineering, physics, law, literature, and economics each require long training. No one can study everything at the same depth at the same time. But if a convenient distinction for specialization is mistaken for the limit of human talent, we lose sight of the original connections among disciplines. Philosophical questions require logic. Thinking about economics and society requires statistics. Implementing science and technology in society requires ethics, law, and communication.

3. Test deviation scores show how relative the labels are

The ambiguity of rikei and bunkei becomes easier to see through hensachi, Japan's deviation-score system. A deviation score is a relative index with an average of 50 and a standard deviation of 10. If scores are assumed to be close to a normal distribution, a score of 70 is two standard deviations above the mean, or roughly the top 2.3 percent. A score of 75 is 2.5 standard deviations above the mean, close to the top 0.6 percent. A score of 55 is 0.5 standard deviations above the mean, or around the top 31 percent.

The important point is that being strongest at mathematics within oneself and being strong at mathematics in society as a whole are different matters. Suppose a humanities student at a highly selective university has a deviation score of 68 in mathematics and 75 in Japanese. This person may call themselves bunkei. Now suppose a science student at another university has a deviation score of 56 in mathematics and 45 in Japanese. This person may call themselves rikei. Both labels may make sense as self-understanding, but in an absolute comparison, the first person's mathematics ability may exceed the second person's mathematics ability.

This example shows that bunkei and rikei often refer not to one's level within society as a whole, but to one's relative strengths within oneself. If mathematics is stronger than Japanese inside one's own profile, one may call oneself rikei. If Japanese is stronger than mathematics, one may call oneself bunkei. That kind of classification can help with course choice, but it does not accurately express total ability or future potential.

In entrance examinations, it is rational to use strong subjects. Strategies exist in which students avoid weak subjects and compete through subjects with higher score efficiency. The reason switching from science to humanities is discussed relatively often is that mathematics and science are cumulative, so the learning cost of returning after leaving them is high. The reverse switch, from humanities to science, also exists, but it is generally more burdensome. Subjects such as Mathematics III, physics, and chemistry require several years of prerequisite knowledge, so the later the change in direction, the more disadvantageous it tends to become.

However, none of this implies that mathematics is inherently higher-level while Japanese or social studies are easy. Japanese and social studies also involve advanced training. Reading academic papers, defining concepts, building claims in a falsifiable form, and critically handling historical materials and statistics all require long practice. The difference lies in the way subjects are accumulated in elementary and secondary education and in the structure of entrance-exam subjects. In mathematics, a gap in one unit makes it difficult to proceed to the next. Japanese and social studies may look easier to rejoin midway, but at the top level, differences in reading speed, vocabulary, background knowledge, and argumentative writing become very clear.

4. Cognitive science does not support two kinds of brains

Human beings have strengths and weaknesses. That is a fact. But explaining those strengths and weaknesses through a binary idea of a science brain and a humanities brain is scientifically too simple.

In the history of psychometrics, since Charles Spearman in the early twentieth century, it has been known that performance across many cognitive tasks tends to show positive correlations. The idea known as the general intelligence factor suggests that abilities such as mathematics, vocabulary, spatial reasoning, and memory are not completely independent, but share some common basis. Of course, that alone cannot explain all differences among specific abilities. In the Cattell-Horn-Carroll theory, which became widely used in the later twentieth century, multiple broad abilities are distinguished, including fluid reasoning, crystallized knowledge, quantitative knowledge, reading and writing, short-term memory, and visual-spatial processing. In this framework, being good at mathematics or writing is not a single axis. It is understood as a combination of multiple abilities.

The idea of ability tilt is also useful. In a 2014 study by Thomas Coyle and colleagues, differences between mathematical and verbal scores on the American SAT and ACT, that is, within-person tilts, were shown to be related to college-major choice. A tilt in which mathematics is relatively higher than verbal ability tends to be associated with STEM majors, while a tilt in which verbal ability is relatively higher than mathematics tends to be associated with humanities majors. But what this research shows is that ability tilt relates to choice of major. It does not mean that people who can do one side cannot do the other.

Neuroscience also does not support a simple left-brain/right-brain type theory. In 2013, Jared Nielsen and colleagues at the University of Utah analyzed resting-state brain images of 1,011 people aged 7 to 29 and examined functional lateralization across 7,266 regions. There are local left-right differences related to functions such as language and attention. But the study did not find evidence that individuals as a whole divide into left-brained and right-brained types. This is relevant to the popular idea of rikei brains and bunkei brains. Networks involved in particular tasks may differ, but human beings are not simple enough to be divided into two kinds.

Advanced mathematical processing and language processing are not completely identical. Number, space, and symbolic manipulation have their own neural bases. But when a person reads a research paper, forms a hypothesis, interprets experimental results, and explains them to others, language, memory, attention, and social understanding are also involved. Science-oriented and humanities-oriented abilities do not sit in two separate rooms inside the brain. Depending on the task, networks cooperate.

5. A sense of weakness can widen ability gaps

The rikei-bunkei label becomes dangerous when it fixes a person's sense of weakness. Saying "I am bunkei because I am bad at math" or "I am rikei because I am bad at writing" may provide short-term relief. Over the long term, however, it can reduce practice, encourage avoidance, and as a result actually widen achievement gaps.

Research on mathematics anxiety is a typical example. In 2007, Mark Ashcraft and Jeremy Krause argued that mathematics performance depends heavily on working memory, and that people with high mathematics anxiety spend part of that working memory on the anxiety itself. Working memory is the mental workbench used to temporarily hold intermediate calculations and conditions while processing them. People with high mathematics anxiety tend to avoid mathematics courses, college majors, and occupations that use mathematics. This is not simply because they have low ability. It is also because anxiety reduces learning opportunities themselves.

Douglas Hembree's 1990 meta-analysis also found strong relationships between mathematics anxiety and mathematics achievement, liking for mathematics, and willingness to study more mathematics. In summaries by Ashcraft and others, the correlation between mathematics anxiety and high-school mathematics performance is around minus 0.30, the correlation with enjoyment of mathematics is around minus 0.75, and the correlation with motivation to study additional mathematics is around minus 0.64. Correlation is not causation itself, but these figures show that weakness, avoidance, and lower performance can easily form a cycle.

Stereotype threat is another important issue. In 1999, Steven Spencer, Claude Steele, and Diane Quinn showed that when women were made aware of the stereotype that women are disadvantaged on difficult mathematics tests, they performed worse than equally capable men. Conversely, when the test was described as one that does not produce gender differences, the gap disappeared. This indicates that psychological pressure in an evaluative situation can affect performance, apart from ability itself.

Japan has related problems. A 2022 paper in the Japan Society for Educational Technology pointed out the possibility that an order in which girls are seen as humanities-oriented and boys as science-oriented forms from elementary and junior high school stages and is reflected in achievement and motivation. It also showed that words from teachers, friends, and family interact with students' values and influence humanities-science choices. This is not only a problem for women. When boys come to believe that they are bad at Japanese or the arts, the same structure can operate.

A 2015 survey by Japan's Ministry of Economy, Trade and Industry, which gathered responses from 10,000 workers under the age of 40, asked 5,941 people who ultimately chose humanities what conditions might have increased the likelihood that they would have chosen science. The largest item was: if they had not been weak at mathematics or science. This suggests that avoiding science is not tied only to lack of interest, but strongly tied to a sense of weakness.

What matters here is that the feeling of being weak does not necessarily mean an actual limit of talent. Mathematics is strongly cumulative. If a student stumbles at linear functions in junior high school, calculus in high school can look remote. If there is a gap in fractions, equations, functions, geometry, or probability, later classes can feel entirely incomprehensible. But in many cases this is not lack of talent. It is the result of moving forward while prerequisite knowledge is missing. Conversely, in Japanese, unless one trains vocabulary, background knowledge, summarization, and recognition of logical structure, difficult criticism and legal documents cannot be read well.

6. Japanese achievement data puts the binary in perspective

Japan's 15-year-olds perform at a high level internationally in mathematics, reading, and science. In OECD PISA 2022, Japanese students exceeded the OECD average in all three areas. In mathematics, 88 percent reached at least the minimum proficiency level, far above the OECD average of 69 percent. The share of top performers in mathematics was 23 percent, compared with the OECD average of 9 percent. In reading, 86 percent reached the minimum proficiency level, and in science 92 percent did so.

This shows that, looking at Japan as a whole, the structure is not that students can do either mathematics or Japanese but not both. Rather, many students handle multiple areas above a certain level. Nevertheless, high-school humanities-science choice produces a 32-to-68 split. That means the choice cannot be explained by ability distribution alone. Course systems, entrance-exam subjects, expectations from others, self-efficacy, images of the future, and gender-role assumptions overlap to produce a biased distribution of paths.

The rate at which women enter science and engineering is also difficult to explain by ability alone. OECD education statistics indicate that in 2021 women made up 58 percent of first-time tertiary graduates on average, yet they accounted for 33 percent of STEM graduates on the OECD average, and 20 percent or less in Japan and Chile. Materials from Japan's Council for the Creation of Future Education also show that although a certain number of first-year high-school girls have high mathematical and scientific literacy, only 7 percent of women major in science and engineering at university. The issue here is not ability, but the environment of choice.

When we look at this data, rikei and bunkei are not simply the result of natural talent distribution. Choices change depending on what paths society shows students, which subjects it encourages them to give up early, and what kinds of people it says belong in which fields.

7. Top overseas universities combine specialization with breadth

The world's leading universities have not eliminated specialization. Medicine, engineering, physics, literature, political science, economics, and other fields all exist. But before and after students enter specializations, many top universities emphasize broad foundational education.

At Harvard University, undergraduates satisfy requirements in general education, distribution areas, language, writing, and quantitative reasoning with data before deciding and completing their concentration. At MIT, all undergraduates are required to take eight subjects in the humanities, arts, and social sciences. At Stanford University, students have general education requirements across eight Ways of Thinking and Doing, totaling eleven courses, to broaden thought and practice. None of these systems is designed to make science students study only science or humanities students study only humanities.

The University of Tokyo also has students spend the first two years after admission in the Junior Division of the College of Arts and Sciences. Students are divided into humanities and sciences streams, but they receive broad liberal arts education. The first two years at Komaba are positioned not only as preparation for specialization, but also as the creation of an integrated foundation that supports later specialization. Behind this is the idea of avoiding premature fixation and giving students a map of knowledge as a whole.

In recent years, Japan's Ministry of Education has also moved away from treating the conventional humanities-science division as universal. It has promoted high-school education that uses inquiry as an axis and lets students encounter diverse fields. Since the 2022 school year, it has also become possible to establish departments such as interdisciplinary studies within the ordinary course. This reflects the fact that social problems can no longer be solved by one discipline alone. Climate change, medicine, AI, declining birthrates, disaster prevention, and regional economies cross natural science, statistics, legal systems, ethics, psychology, culture, and public administration.

8. In employment, combinations of abilities matter more than faculty names

There are certainly areas in employment where a science background is advantageous. In occupations with strong professional-course or qualification requirements, such as physician, pharmacist, architect, research and development, semiconductors, chemistry, machinery, electricity, and information engineering, training in a science faculty or graduate school has major significance. In research and advanced technical roles, not only the faculty name but also the laboratory, papers, experimental experience, mathematical ability, and programming experience are evaluated.

On the other hand, in IT companies and generalist hiring, it is not simple to say that humanities students are disadvantaged and science students are advantaged. A 2020 survey by the Japan Information Technology Services Industry Association of 741 new employees at JISA member companies found that the fields studied as students were information-related fields at 26.2 percent, non-information science fields at 24.7 percent, and humanities at 47.8 percent. At least in this survey, almost half of new employees at IT companies came from humanities backgrounds. If a person has learned programming independently and can show project experience, a humanities background can still be evaluated sufficiently.

Estimates by the Ministry of Economy, Trade and Industry on IT human-resource supply and demand indicate that by 2030 Japan may still face an IT talent gap ranging from 164,000 to 787,000 people. AI and data science are also becoming more important. The World Economic Forum's 2025 Future of Jobs Report identifies AI and big data, analytical thinking, creative thinking, resilience, flexibility, and technological literacy as skills that are already important and will continue to grow in importance. At the same time, leadership, social influence, curiosity, lifelong learning, and systems thinking are also emphasized. The person described here is not only science-oriented or only humanities-oriented.

Keidanren's 2024 proposal also notes that although placement destinations for doctoral talent differ between science and humanities, there is an opinion that because integration across humanities and sciences will advance, the two should not be discussed as if they must be sharply separated. Companies look at specialization, problem-setting ability, research and analytical ability, data-analysis ability, presentation ability, communication ability, initiative, and teamwork. In research and development, sales, and corporate planning alike, words that cannot read numbers are weak, and numbers that cannot be put into words are also weak.

University names and faculty names are not irrelevant. Humanities students at highly selective universities may be highly evaluated in generalist roles, consulting, finance, and IT business positions. Conversely, even science graduates may struggle in hiring if they cannot explain their expertise or show experience and interpersonal ability that connect to practice. Still, in areas such as professional qualifications and research roles, a high humanities deviation score cannot replace specialized training. Therefore, there is no single faculty that is advantageous for employment. The answer changes depending on the industry, the occupation, and which abilities a person can prove.

9. What high school students should consider when choosing a track

The humanities-science choice in high school is both an act of self-determination and a mechanism that can narrow future options. Therefore, the question should not be whether one is a science person or a humanities person. The necessary task is to check which subjects are prerequisites for future options.

To study medicine, pharmacy, science and engineering, agriculture, information engineering, architecture, physics, chemistry, or life science in depth, mathematics and science coursework is a strong prerequisite. If a student avoids that area, the cost of returning later is large. On the other hand, law, political science, literature, history, international relations, sociology, business administration, and education mainly require reading comprehension, argumentation, historical background, and understanding of social institutions. But economics, psychology, sociology, business administration, and public policy increasingly place weight on statistics and data analysis. The idea that mathematics is unnecessary because one is bunkei is already outdated.

The criteria for choice can be organized into three points. First, which prerequisite subjects are required for the future specialization. Second, which areas can sustain interest even after long hours of study. Third, whether one is using weakness as a reason to escape. Some weak subjects grow if the prerequisites are repaired. Conversely, some fields may be strong but lack enough interest to withstand long training.

It is important to extend strengths. But it is better not to decide too early that weaknesses are personal limits. Students who lean toward science become stronger the more they gain the ability to write, present, and understand history and ethics. Students who lean toward the humanities become stronger the more they acquire at least basic mathematics, statistics, spreadsheets, programming, and scientific literacy. A person with a deviation score of 80 in a strong subject and 75 in a weak subject is still at an extremely high level in the so-called weak subject across society as a whole. It is necessary not to confuse one's internal ranking with one's level in society as a whole.

10. What will matter from now on is ambidextrous intelligence

People who are strong in modern society do not remain closed inside either mathematics or language. They read data, form hypotheses, distinguish causation from correlation, and understand the limits of numbers. At the same time, they explain the meaning in words, persuade stakeholders, and consider ethical and institutional effects. This is not the simple addition of science ability and humanities ability. It is an integrated ability for handling complex reality.

In the age of AI, programming and statistical knowledge become important. But to decide what AI should be used for, whom it affects and how, and whether flawed data may create discrimination or disadvantage, perspectives from the humanities and social sciences are necessary. Conversely, if one only talks about social problems and cannot handle data or mathematical models, policy and management judgments become weak. Economic indicators, infection trends, advertising effects, wages, demographics, and climate risk cannot be understood unless numbers and context are read together.

The benefit of the words rikei and bunkei is that they make learning plans easier to build. Time in high school and university is limited, and entrance-exam subjects and specialized courses differ. Without classification, education systems would be difficult to operate. But the disadvantage is that the words can make human possibilities look small at an early stage. "I am bunkei, so I cannot do mathematics" and "I am rikei, so I am bad at writing" are convenient explanations, but they can also become reasons not to practice.

In conclusion, rikei and bunkei do exist. But they are not kinds of human beings. They are names for education systems and choice strategies. Ability is not a single block. It is formed through overlapping common cognitive foundations, individual strengths, learning experiences, environments, and self-beliefs. In course choice, it is rational to use one's strengths. At the same time, it is important not to close future options with the word weakness.

Ultimately, people who do large work in society have expertise while also being able to speak with people outside that expertise. They can understand formulas and explain them in prose. They know history and can question data. They create technology and consider its effects on society. This ambidextrous intelligence is the way to use the old boundary between humanities and sciences without being trapped inside it.