Paper Reading | Dylan Chiang

Paper reading: Capsaicin diet drives gut inflammation and exosomal miR-17-3p elevation in idiopathic short stature

Sun, 18 Jan 2026 00:00:00 +0000

#paper information

Title: Capsaicin diet drives gut inflammation and exosomal miR-17-3p elevation in idiopathic short stature Journal: Nature Communications Year: 2025 Date: 2025-12-30

Research background

Idiopathic Short Stature (ISS) refers to a condition in which the height is two standard deviations lower than the person of the same age, but growth hormone (GH) levels are normal and there is no obvious pathological cause. The cause of ISS has long been a mystery.

Although genetics is the main factor in determining height, the influence of environmental factors such as diet cannot be ignored. Past research has mostly focused on nutritional deficiencies (such as calcium, protein), but how specific dietary components affect bone growth through molecular mechanisms is still poorly understood. This study focuses on capsaicin, the main component of spicy food, and explores whether and how it affects children’s growth and development.

Research methods

The research team adopted a systematic approach from clinical correlation analysis to molecular mechanism verification:

Clinical correlation analysis: Analyze the relationship between children’s eating habits and height, especially the intake of capsaicin.
Animal model: Establish a rat model of long-term ingestion of capsaicin and observe its effects on body length, femur length and growth plate structure.
Exosome tracking: Isolate exosomes (Exosomes) in serum, analyze their miRNA content, and track whether these exosomes can reach the bone growth plate.
Molecular mechanism exploration: Verify the regulatory effect of candidate miRNA on growth signaling pathways (such as ZNF148/SOS1/RAS/ERK and PI3K/AKT) in chondrocytes cultured in vitro.

Main findings

1. Capsaicin intake causes growth retardation and intestinal inflammation

In animal experiments, rats that long-term consumed a diet rich in capsaicin had significantly lower body weight and length than those in the control group. Histological examination found that these rats had thinner growth plates** and reduced new bone formation. At the same time, these rats showed significant intestinal inflammation. Notably, their growth hormone (GH) and insulin-like growth factor-1 (IGF-1) levels were normal, fully consistent with the clinical features of ISS.

2. Exosomal miR-17-3p is a key messenger

The study found that capsaicin-induced intestinal inflammation led to a significant increase in the levels of miR-17-3p carried by exosomes in the serum. These exosomes act as “long-range messengers” and are transported from the intestine to the growth plate cartilage of the bone.

3. Molecular mechanism: inhibiting chondrocyte proliferation

After reaching the growth plate, miR-17-3p enters chondrocytes, targeting and inhibiting the expression of the ZNF148 gene. Downregulation of ZNF148 further blocked SOS1 and the downstream RAS/ERK and PI3K/AKT signaling pathways. These pathways are critical for chondrocyte proliferation and hypertrophy, key processes in longitudinal bone growth. Therefore, inhibition of this pathway directly results in stunted bone growth.

Research significance

1. Revealed the new mechanism of the “gut-bone axis”

This study established a specific “diet-intestinal inflammation-exosomes-bone growth” cross-organ regulation model, proving that intestinal microorganisms and inflammatory status can directly affect the development of distal organs (bones) through non-hormonal pathways (exosomal miRNAs).

2. Provides a new etiological explanation for ISS

For many short children with normal growth hormone levels, this study offers a possible explanation: Perhaps it’s not a lack of hormones, but chronic inflammatory signals triggered by specific dietary habits that inhibit the bone’s response to growth.

3. Potential therapeutic targets

The findings suggest that, in addition to growth hormone therapy, intervention targeting miR-17-3p (such as the use of antisense oligonucleotides) or improving intestinal inflammation may become a new strategy for the treatment of some children with ISS. Of course, the simplest advice may be this: Avoid excessive consumption of spicy foods in developing children.

My understanding

1. Molecular-level interpretation of “You are what you eat”

This statement is usually understood to mean that nutrients make up body tissues. But the study shows a deeper meaning: Food is not just fuel, it’s a signal. Capsaicin itself is not a component of bone, but the biological response it triggers (inflammation -> release of exosomes) is like an “instruction” that rewrites the growth program of bone cells.

2. Growth hormone is not a panacea

When encountering children with ISS clinically, parents often anxiously seek growth hormone injections. However, if the cause is that the downstream cell signaling pathway is “stuck” by inflammatory factors (such as miR-17-3p), simply increasing the upstream growth hormone may have limited effect. This reminds us that medicine needs to shift from “replenishing deficiency” to “relieving inhibition.”

3. The collision of traditional wisdom and modern science

There is a saying in many cultures that “children who eat too much spicy food will not grow taller” (although it is more about eating spicy food that will “get angry” or affect digestion). This study unexpectedly provides rigorous molecular biology evidence for this folk observation. It reminds us that in developmental biology, gut health and systemic immune status may be more critical than we thought.

(Note: Please search for the paper published by Wang et al. in the December 2025 issue)

Reading date: 2026-01-18 Notes organized: Dylan Chiang

Paper reading: Exercise is good for the brain but exercising outside is potentially better

Sun, 18 Jan 2026 00:00:00 +0000

#paper information

Title: Exercising is good for the brain but exercising outside is potentially better Journal: Scientific Reports Year: 2023 Link:

Research background

We all know exercise is good for the brain, but exposure to nature (green surroundings) can also help with mental recovery. This is known in psychology as Attention Restoration Theory (ART), the idea that nature can help us recover from the sensory overload of the city.

However, most past studies have examined “movement” and “environment” separately. This study sought to answer a specific question: **Does exercise produce better cognitive gains if performed outdoors than if the same exercise were performed indoors? **

Research methods

The research team recruited 30 college students (average age approximately 21 years old) and conducted a within-subject design experiment:

Experimental Conditions: Each participant completed two 15-minute walking tasks, one indoor (a laboratory environment) and one outdoors (a green path on campus).
Measurement indicators:
- Measure brain activity using electroencephalography (EEG).
- Focus on P300 Event Related Potential (ERP). P300 is an important neural index reflecting the allocation of attention and working memory resources (the larger the amplitude, the more effective the cognitive resource deployment is).
- Participants took a standard cognitive test (Oddball task) before and after exercise.

Main findings

1. Outdoor sports significantly increase the P300 amplitude

The study results showed that participants’ P300 amplitude increased significantly after walking for 15 minutes outdoors**. This means that their attention allocation ability and cognitive processing efficiency have been significantly improved.

2. The effect of indoor exercise is not as good as expected

Surprisingly, no significant increase in P300 amplitude was observed after indoor walking at the same intensity**. This suggests that mere short-term “physical exercise” may not be enough to immediately trigger this level of cognitive improvement, and that environmental factors play a key amplifier role.

3. No significant difference in reaction time

Although the brain’s neural signals (P300) showed significant differences, at the behavioral level (reaction speed on cognitive tests), the indoor and outdoor groups did not show significant differences. This could mean that changes at the neural level precede behavioral changes, or that the task is less sensitive to behavioral changes.

Research significance and inspiration

This research supports the concept of Green Exercise. It tells us that the environmental context of movement is crucial.

1 + 1 > 2: The combination of exercise itself increases blood flow and the outdoor environment provides mental recovery creates a synergistic effect.
The Antidote to City Life: In modern city life, our brains are often in constant cognitive fatigue. The study suggests that instead of walking on a treadmill at the gym for 15 minutes, a 15-minute walk in the park may have a better “reset” effect on the brain.

My understanding

1. Environment is an extension of cognition

This paper made me think that our brain is not an isolated processor, but is deeply coupled with the environment. Indoor environments (especially boring labs or gyms) may lack enough sensory stimulation or be filled with distracting artificial signals, preventing the brain from entering the state of “relaxed focus” that it would in a natural environment.

2. Redefinition of “efficiency”

We often choose to exercise indoors for the sake of “efficiency” (not affected by weather, controllable). But if the goal is to recover mentally after work, this study shows that sacrificing a little convenience and going outdoors is truly “efficient” from a neuroscience perspective.

3. Simple and easy

15 minutes is not a very long threshold. This means that taking a walk in the park downstairs during your lunch break at work may be the most cost-effective way to recharge your brain.

Reading date: 2026-01-18 Notes organized: Dylan Chiang

Paper reading: How people think about being alone shapes their experience of loneliness

Sun, 04 Jan 2026 00:00:00 +0000

#paper information

Title: How people think about being alone shapes their experience of loneliness Journal: Nature Communications Year: 2025 DOI:

Research background

Loneliness is widely regarded as a global health challenge, closely related to depression, cardiovascular disease and even premature death. In response to this crisis, media and public health campaigns have sprung up in various countries to warn the public about the dangers of social isolation.

However, this study raises a paradoxical point of view: **Do these well-intentioned propaganda actually exacerbate people’s loneliness because they overemphasize the negative consequences of “being alone”? **

The core theory is that “being alone” (objective state) and “loneliness” (subjective feeling) are completely different. The research team hypothesized that people’s weighing beliefs (Beliefs) about being alone are the key to determining whether being alone causes pain or recovery.

Research methods

This study constructed a complete chain of arguments through 5 complementary experiments and surveys (total sample size N = 3,547):

Media Discourse Analysis (Study 1): Analyze the top ten newspapers in the United States from 2020 to 2022 to quantify their tendency to depict solitude.
Cause and Effect Experiment (Study 2): Randomly assign participants to read reports about the “benefits” or “risks” of being alone, and observe real-time changes in beliefs.
Experience Sampling (Study 3): 161 young people were tracked five times a day for two weeks to capture solitary behavior and psychological changes in daily life.
Cross-cultural verification (Study 4 & 5): Across 9 countries on six continents, verify whether this psychological mechanism is universal.

Main findings

1. The media’s “high stigma” against solitude

The study found that U.S. news headlines were 10 times more likely to describe solitude as “harmful” than as “beneficial.” In the main text, risks are mentioned 5 times more frequently than benefits are mentioned. The mass media almost equates “being alone” with “danger.”

2. The causal impact of beliefs on loneliness

Experiments have shown that even brief exposure to information about the benefits of solitude can significantly improve people’s beliefs about solitude. And this belief directly determines subsequent behavioral responses.

3. The “buffering and catalyzing” effect in daily life

This is the best part of the study (Study 3):

Negative Belief Holder: The longer you spend alone, the more intense your loneliness will rise. To them, being alone is a threat from which they need to escape.
Positive Belief Holders: After being alone, loneliness is significantly reduced. They see solitude as an opportunity to “recharge” and “repair themselves.”
Generalizability: This effect has been proven across different cultures, including collectivistic and individualistic cultures.

Research significance and inspiration

1. The “echo effect” of public health propaganda

The study raises a red flag for public health experts: campaigns that emphasize only the dangers of loneliness (such as “Loneliness is equivalent to smoking 15 cigarettes a day”) may inadvertently reinforce the public’s fear of being alone. When people have to be alone, which is almost inevitable in modern life, this fear translates into deeper psychological distress.

2. New approaches to cognitive intervention

Rather than forcing everyone to socialize, help those who feel lonely re-examine the value of being alone. Cultivating positive beliefs about being alone may be more effective in alleviating subjective loneliness than increasing social interaction.

My understanding

This paper provides a very insightful perspective: **Part of the reason why we feel lonely is because we are told that “being alone is not normal.” **

1. The self-fulfilling prophecy of cognitive frameworks

From the perspective of cognitive evaluation theory, being alone is a neutral stimulus. If we assume that being alone represents a social failure or a health threat (negative beliefs), then when we are alone, the brain will activate the threat detection mechanism, producing feelings of anxiety and loneliness. This creates a vicious cycle: the more lonely you feel, the more you fear being alone; the more you fear being alone, the more painful it is when you are alone.

2. Media Ecology and Mental Toughness

In the current era of digital media, social media constantly showcases the lively scenes of others, while traditional media constantly warns of the dangers of isolation. This double attack has greatly compressed our psychological space to face ourselves calmly. This study reminds us that building cognitive resilience (Belief Resilience) to “the benefits of being alone” may be an essential skill for modern people.

3. A “moderate turn” in policy

Public health policies should shift from simply “anti-loneliness” to “supporting healthy solitude.” Propaganda should include how to spend quality time alone and how to use alone time for creative thinking or emotional regulation, rather than just treating it as a defective product after a lack of social interaction.

4. Conclusion: Solitude is a kind of self-healing ability

Loneliness is not caused by spatial walls, but by psychological frameworks. Perhaps the biggest lesson from this research is that in an age where everyone is connected, being able to enjoy alone time is an important form of mental toughness.

It means that you no longer passively endure loneliness, but actively use this time to carry out self-repair (Rejuvenation) and recharge. When being alone is no longer a threat, but an opportunity, you have a powerful anti-loneliness mechanism.

Reading date: 2026-01-04 Notes organized: Dylan Chiang

Paper reading: Topological turning points across the human lifespan

Sun, 04 Jan 2026 00:00:00 +0000

#paper information

Title: Topological turning points across the human lifespan Journal: Nature Communications Year: 2025 DOI:

Research background

The structural topology of the human brain develops nonlinearly with age and is closely related to cognitive trajectories. Past research has tended to focus on specific age ranges (such as childhood or old age) but lacks a global perspective across the entire life cycle.

This study raises a core question: ** Are there key “turning points” in brain organization that mark the brain entering different stages of development? **

To answer this question, the research team collected nine data sets ranging in age from 0 to 90 years, with a total sample size of 4,216 people.

Research methods

1. Data analysis scale

Number of samples: N = 4,216 (neurotypical subset n = 3,802)
Technology: Diffusion Imaging
Metrics: 12 Graph Theory Metrics, used to quantify brain tissue architecture.

2. Manifold Learning

Use UMAP (Uniform Manifold Approximation and Projection) to project high-dimensional topological data into a three-dimensional manifold space.
The aim is to capture the non-linear dynamics of age-related changes in brain topology.

Key findings: Five life stages and four turning points

The study found that there are four major “turning points” in the development of brain topology, which occur around the ages of 9, 32, 66 and 83 years old. These turning points divide life into five unique Epochs:

Stage	Age range	Name	Characteristic description
Epoch 1	0–9 years old	Infancy to childhood	Global integration decreases and local preference (Segregation) increases.
Epoch 2	9–32 years old	Adolescence	The degree of brain network integration increases, and the small-world property (Small-worldness) is significantly improved.
Epoch 3	Ages 32–66	Adulthood	Developmental trajectories stabilize, with a slow decline in integration and an increase in local efficiency.
Epoch 4	Age 66–83	Early aging	Changes in modularity become the dominant feature.
Epoch 5	83–90 years old	Late aging	The relationship between age and topology weakens, and subgraph centrality becomes the main feature.

Detailed description of key findings

32: The most powerful turning point in your life
- Research shows that age 32 is the most dramatic time for brain topological rewiring.
- This is consistent with the finding that White Matter Volume and Fractional Anisotropy peak around age 29.
- This marks the brain’s shift from the “increased efficiency and integration” stage to the “increased isolation and stability” stage.
Age 9: The End of Childhood -The turning point coincides with the onset of puberty, peak cortical thickness, and substantial improvements in cognitive abilities.
- Marks a structural shift in brain structure from a single pattern of growth to a more complex pattern of maturation.
Ageing does not proceed at a uniform speed
- The turning point between ages 66 and 83 heralds the onset of a “simplified” mode of the brain, which is associated with an accelerated decline in white matter integrity and an increased risk of health problems such as dementia and high blood pressure.

Research significance and inspiration

1. Redefine “adolescence”

The study found that topological developmental trajectories during adolescence continue until age 32, well beyond what is traditionally thought to be around age 20. This supports the modern neuroscientific view that the brain matures much later than we thought.

2. The guiding value of non-linear development

The development of the brain does not rise in a straight line and then decline, but is a “turn” full of qualitative changes. This has important implications for clinical diagnosis, helping to distinguish normal staged changes from pathological cognitive decline.

My understanding

This study uses large-scale data to demonstrate the nonlinear dynamics of human brain structural topology during the life cycle, the most noteworthy of which is the turning point of 32 years old. Examined from the perspective of phylogeny and developmental biology, this finding provides several profound implications:

1. Trade-off between structure and function

Before the age of 30, the main axis of brain development is “increased integration.” This is a structured support period for extensive exploration and learning of the external environment. The brain improves the efficiency of information exchange throughout the brain by strengthening long-distance connections. However, this high level of integration is also accompanied by higher energy consumption and signal interference.

The turning point at age 32 marks the brain’s shift from “comprehensive expansion” to “precision optimization.” The decline in overall integration is not equivalent to a decline in functionality, but more like a structural “specialization.” Specific modules (Sub-networks) become more independent. This enhancement in modularity is beneficial to the stability of performing specific tasks and reduces noise interference in irrelevant areas. In cognitive psychology, this corresponds to the transition from “divergent flexibility” to “professional proficiency.”

2. Synergy of biological markers

The age of 32 does not exist in isolation. It is highly synchronized with peak white matter integrity, completion of the myelination process, and stabilization of neurotransmitter systems. This shows that during the course of evolution, the human body has set a biological clock that closes the “extensive remodeling window” at the beginning of the fourth decade. This synchronicity implies that the brain structure is shifted to better adapt to the long-term social division of labor and survival challenges in adulthood, and to invest limited metabolic resources into more productive specialized modules.

3. Reconstruction of the definition of “adolescence”

If the activity of topological rewiring is considered an indicator of adolescence, this study clearly shows that our definition of brain maturity (often pegged at around age 20) may be too dependent on legal and cultural labels rather than biological facts. From a brain structure perspective, the aftermath of adolescence actually extends into age 32. During this period, the brain still has strong potential for global integration, which explains why this period is the visual window for the final consolidation of professional skills shaping and worldview.

4. Precursors and nonlinear characteristics of aging

The study’s description of the turning points at ages 66 and 83 reveals that aging is not a slow wear-and-tear process but a staged evolution involving specific topological features. Especially after the age of 80, the relationship between age and topology weakens, suggesting that individual differences (such as genes, lifestyle, reserve capacity) may exceed the influence of basic developmental laws at extremely advanced ages.

In summary, this study reminds us that the brain executes different optimization logics at different stages of life: when young, it pursues “connecting everything” to gain possibilities, in middle age, it pursues “module independence” to ensure efficiency, and in old age, it tries to maintain core connections to cope with inevitable hardware degradation. This structural shift is not a negative degeneration, but a manifestation of life’s constant search for optimal adaptive solutions as time goes by.

Reading date: 2026-01-04 Notes organized: Dylan Chiang

Paper reading: Video game play is positively correlated with well-being

Sat, 27 Dec 2025 00:00:00 +0000

#paper information

Title: Video game play is positively correlated with well-being Journal: Royal Society Open Science Year: 2021 DOI:

Research background

The impact of video games on mental health has long been controversial. Many policymakers and parents worry that excessive gaming will lead to psychological problems (such as addiction, anxiety), but past studies often have a fatal flaw: heavy reliance on participants’ “self-reported” gaming time.

Psychological research shows that humans tend to be very inaccurate in their estimates of their own behavior. In order to break through this limitation, the Oxford Internet Institute collaborated with game companies (Electronic Arts and Nintendo of America) to obtain actual game telemetry data from players.

Research methods

The study was one of the largest and most methodologically rigorous of its kind at the time:

Sample Data:
- Plants vs. Zombies: Battle for Neighborville (Plants vs. Zombies: Battle for Neighborville)
- Assemble! Animal Crossing: New Horizons (Animal Crossing: New Horizons) *Total sample size N = 3,274 adult gamers.
Data type:
- Objective Behavioral Data: Record of actual game play time accurate to the second.
- Subjective Psychological Questionnaire: Includes positive/negative emotional experience (SPANE) and psychological need satisfaction (PENS, such as autonomy, competence, and belonging).
Analysis model: Use the original pre-registered analysis process to conduct correlation analysis between objective game time and subjective well-being.

Main findings

1. Game time is positively correlated with happiness

Contrary to the stereotype that “the more you play games, the less happy you are”, the study found that there is a significant and small positive correlation between objective game time and player happiness. That is, in this sample, people who played games for longer periods of time tended to report higher levels of happiness.

2. “Quality of experience” is more important than “length of time”

From the perspective of explanatory power, players’ psychological needs in the game are not fully satisfied (such as whether they feel free and dominant, whether they feel connected to others), which is more predictive of happiness than simply the length of the game.

3. Self-report bias

The study reconfirms that self-reported gaming time is only moderately correlated with actual recorded gaming time, confirming that past studies that relied on self-reporting may have had larger errors.

Research significance and inspiration

1. Challenging the foundations of policymaking

Many restrictions on games (such as time limits) are based on the assumption that “the longer the time, the greater the harm.” The results of this study raise doubts about this and suggest that policymakers should pay more attention to the content and experience quality of games rather than simply one-size-fits-all duration.

2. A new paradigm for industry cooperation

This research demonstrates the huge potential for data sharing between academia and industry. By analyzing telemetry data that cannot be faked, psychological research can move from “memoir” to “empirical science.”

My understanding

Although this paper was published in 2021, its methodological significance is greater than the conclusion itself.

1. The power of “data honesty”

We often unconsciously glorify or demonize our own behavior. “I played maybe 2 hours” in the questionnaire might actually be 4 hours. The greatest value of this Oxford University study is that it uses cold but honest server logs to burst the bubble of subjective recall.

2. Correlation\Causality

“Positive correlation” must be interpreted with caution. This does not necessarily mean that “playing games makes people happy”, it may also mean that “happy people have more time and mood to play games.” But at least it effectively refutes the determinism that “playing games will inevitably lead to unhappiness.”

3. Games as the “Third Space” in the Digital Age

Especially in games like “Animal Crossing”, players socialize, build, and express themselves in the virtual world. This is not just entertainment, but a digital social and emotional habitat. When the real world is full of uncertainty, games provide a safe haven that is controllable, feedback-laden, and can gain a sense of belonging, which in itself is a manifestation of psychological resilience.

Reading date: 2025-12-27 Notes organized: Dylan Chiang

Paper reading: The Savanna Theory of Happiness

Tue, 25 Nov 2025 00:00:00 +0000

#paper information

Title: The Savanna Theory of Happiness Source: Journal of Personality and Social Psychology (or similar, based on context) Paper ID: N/A Link:

Research background

This paper proposed “The Savanna Theory of Happiness”. This theory is based on the perspective of evolutionary psychology, which believes that human psychological mechanisms evolved on the ancient African savannah. Therefore, our response to the modern living environment is actually deeply affected by the living environment of our ancestors.

Specifically, the authors try to explain why certain factors in modern life (such as population density, frequency of social interaction) affect our happiness, and why these effects differ among people with different IQs.

Research methods

Core concepts

The Savanna Principle: The human brain is adapted to the ancestral environment, not the modern environment. So situations that would have increased survival in our ancestral environment still make us happy today, and vice versa.
The moderating role of IQ: Intelligence (General Intelligence) is defined as the ability to solve evolutionarily “novel” problems. Smart people are better able to adapt to the modern lifestyle, which is completely different from their ancestral environment, and are therefore less constrained by the “savanna principle.”

Experimental design

The study used data from the U.S. National Longitudinal Study of Adolescent Health (Add Health) and analyzed the subjects’ IQ, population density where they lived, frequency of socializing with friends, and their self-reported life satisfaction (happiness).

Main findings

Overall, people are less happy when they live in places with higher population density. This is consistent with the prairie principle: in ancient times, excessive population density meant competition and pressure for resources.

Key Difference: This negative correlation is more pronounced among low IQ individuals; while high IQ individuals are less negatively affected by population density. This may be because people with higher IQs are better able to handle the complex stresses and novel challenges of modern urban life.

For most people, the more time they spend with friends, the happier they feel. This is also consistent with an evolutionary perspective: in tribal life, social connection is the key to survival.

Amazing reversal: For people with extremely high IQs, the opposite is true. More frequent social activities are associated with lower life satisfaction.

Research significance and inspiration

Why do smart people like to be alone?

This study provides an evolutionary psychology explanation for the stereotype that smart people are more lonely. People with high IQs may be more focused on long-term, modern goals (such as career, academic breakthroughs) that are “novel” in evolutionary history. Although frequent social activities are in line with ancestral survival instincts, they may actually be a distraction for highly intelligent people pursuing these modern goals.

In addition, the brains of people with high IQs are better able to adapt to “unnatural” modern life (such as solitude and high-density living), so they do not need to rely on close tribal social interaction to gain a sense of security like their ancestors did.

My understanding

This paper is very interesting. It uses the perspective of evolutionary psychology to explain the happiness and pain of modern people.

1. “Primitive Man” lives in modern cities

We often feel anxious or unhappy, often because our “primitive brains” have not adapted to modern society. For example, feeling irritable on a crowded subway may be because our genes are screaming: “There are too many people here and not enough resources. Run!”

2. IQ as an “adapter”

The author views IQ as a tool to deal with “evolutionary novelty”, which is a unique perspective. This explains why smart people tend to be better able to adapt to counter-intuitive modern lifestyles (such as being alone, staying up late, not having children, etc.). They are more able to suppress primitive impulses and pursue goals that are evolutionarily “meaningless” but are respected by modern society.

The mainstream values of society usually encourage social interaction and believe that “having more friends is better.” But this study tells us that for some people, especially those with high IQs, being oversocialized can be a burden. Being alone does not mean loneliness or social impairment; it may be an adaptive choice made by highly intelligent people to focus on more important goals.

Extended thinking

1. Definition of happiness

If happiness is a product of evolution, does that mean we should live by our instincts (move to the country, make more friends)? Or should we use our IQ to overcome these instincts and pursue higher levels of satisfaction?

The data for this paper come from before the popularity of social media. Does online socializing count as “socializing” today? Does its effect on happiness also follow the Savannah Principle?

Paper reading: Cognitive processes of ingroup favoritism across 20 countries

Sat, 22 Nov 2025 00:00:00 +0000

#paper information

Title: Cognitive processes of ingroup favoritism across 20 countries: An eye-tracking investigation of culture, behavior, and cognition Author: Rima-Maria Rahal & Frederik Schulze Spüntrup Journal: PNAS (Proceedings of the National Academy of Sciences) Year: 2025 (Vol. 122, No. 32) DOI:

Research background

In-group favoritism is a common social psychological phenomenon - we tend to favor “one of our own” even if the definition of “one of our own” may only be based on a trivial criterion (such as a different favorite painter).

This preference has serious consequences in the era of globalization:

COVID-19 Vaccine Distribution: Western countries hoard vaccines, developing countries can only wait
Climate Crisis: Countries shirking responsibility and prioritizing their own short-term interests
Xenophobia on the rise: Right-wing populism spreads around the world

In the past, most studies could only be conducted in laboratories, with samples restricted to specific areas. This study breaks through this limitation and uses webcam-based eye-tracking, allowing researchers to collect high-quality cognitive data in participants’ homes, achieving unprecedented cultural and geographical diversity.

Research methods

1. Innovative research design

Sample:

1,850 participants
20 countries (covering Europe, Asia, America, etc.)
Eye tracking using participants’ own webcams

Experimental Task:

Group allocation: based on color recognition task (minimum group paradigm)
Group Reinforcement: Compete with out-group members for bonuses
Decompose the Dictator Game: 80 decisions, each time choosing between the “self-interest” and “pro-social” options
- Self-interest option: you get the most and the other side gets very little
- Prosocial option: take less for yourself, and both the other party and the total will increase

2. Measurement indicators

Behavioral level:

Differences in generosity toward in-group vs. out-group members

Cognitive Level (Eye Tracking):

Decision time
fixation count
Number of messages checked
Allocation of attention to self vs. other’s gains

Cultural level:

Individualism vs. Collectivism
Government effectiveness
Religious importance
Historical disease burden & COVID-19 impact

Main findings

1. In-group preference is universal, but the degree varies widely

Core findings:

✅ All 20 countries show in-group preference (OR = 4.57)
❗ But effect sizes vary widely across countries
📊 The stronger the prosocial tendency of people, the more serious the discrimination against out-groups.

**What does this mean? **“Good people” do not necessarily do not discriminate, they may just leave kindness to “their own people”.

The study tested the Material Security Hypothesis: the more unstable a society is, the more people rely on their in-groups.

Evidence Support:

✅ The lower the government effectiveness** → the stronger the in-group preference**
✅ COVID-19 death toll higher → in-group preference stronger
✅ The lower the historical disease burden → the lower the in-group preference

Unexpected Discovery:

❌ Religion more important → In-group preference stronger (contrary to prediction!)
- Null hypothesis: Religion as a stable institution should reduce preferences
- Practical: Religion may reinforce in-group and out-group boundaries

3. Is an individualistic culture more discriminatory?

Cultural Dimensions Theory predicts: Collectivist cultures should place greater emphasis on ingroups.

Actual result: Completely opposite!

The higher the individualism** → the stronger the in-group preference**

Possible explanation: -In individualistic cultures, people are more eager for group identification

Lacking a fixed social structure, it is more necessary to create boundaries for “one’s own people”

4. Cross-cultural differences in cognitive processes are more complex than behavior

Overall Trend (shown by meta-analysis):

Decision-making becomes more laborious when facing in-group members:
- Longer decision-making time
- More views
- Check more information

But:

⚠️ The direction of this effect varies greatly between countries
In some countries: in-group decision-making is more laborious
In other countries: out-group decisions are more laborious (reversed effect!)

The role of prosocial tendencies also varies across cultures:

In some countries: more prosocial → more effortful in-group decision-making
In other countries: the effect is reversed

5. “Color blindness”: More than half of people choose not to read group information

Amazing discovery:

In 53.49% of decisions, participants did not look at the group information at all (even if it was free)
54.79% of people clearly stated that they did not want to know the other party’s group identity

**Who chooses to remain “color blind”? **

People with strong self-interest (low SVO)
People who have no favorable impressions of either group
People who think others will not view group information

Consequences of color blindness:

People who choose color blindness: No discrimination
People who choose to view: 10% less for external groups

**What does this mean? ** The average discrimination effect may be driven by the few people who “want to know” the group identity!

Research significance and inspiration

Methodological Revolution

Breakthrough in web camera eye tracking:

💻 Participants can complete it at home, no professional laboratory required
🌍 Achieve unprecedented geographical diversity
💰 Lower cost and higher efficiency
📊 Sufficient data quality (spatial and temporal resolution)

This democratizes cognitive research—it’s no longer just university labs in wealthy areas.

Theoretical contribution

Challenges the cultural dimension theory
- Individualism vs. collectivism effects are opposite to predicted
- A single cultural dimension is not enough to explain complex social cognition
Supports the material security hypothesis (partially)
- Social uncertainty does increase discrimination
- But the role of religion is more complex
Reveal the cultural specificity of cognitive processes
- Cognitive mechanisms cannot be assumed to be consistent across cultures
- Even if the behavioral patterns are similar, the cognitive processes behind them may be completely different

Practical inspiration

A warning for global policymaking:

❌ “One size fits all” anti-discrimination policies may be counterproductive in some cultures
✅ Requires culturally appropriate interventions
🎯 “Color-blind” strategies may be effective: de-emphasize group differences instead of emphasizing equal treatment

Implications for reducing discrimination:

🔍 Understand who chooses to view group information (and why)
🎓 Educational focus: reduce focus on group boundaries
🌐 In a society with high uncertainty, stronger intervention is needed

My understanding

After reading this paper, my biggest shock was: it turns out that our understanding of “discrimination” may be based on a very narrow sample. If you studied ingroup preference only in a sample of Western college students, you might come to one conclusion about the cognitive mechanisms. But this study tells us that the same behavior (discrimination against outgroups) may come from completely different cognitive processes in different cultures.

1. Are “good people” more discriminatory? This is counter-intuitive

I have always thought that people with strong prosocial tendencies (people who are willing to sacrifice for others) should be less likely to discriminate. But this study says: On the contrary, these “good people” are more likely to reserve kindness only for “their own people.”

This reminds me of some phenomena in reality. Many people who claim to be “patriotic” may be generous to their fellow countrymen, but are extremely repulsive to foreigners or immigrants. Or some religious groups are very united and supportive within themselves, but are indifferent or even hostile towards “infidels”. This kind of “parochial altruism” may be more common and more persistent than we think.

The explanation proposed in the paper is that prosocial people care more about “fairness” and “justice.” However, when they think that the out-group is “not one of their own,” this sense of fairness does not apply. This made me reflect deeply: It is not enough to simply promote “kindness” or “altruism”. What is more important is to expand the scope of “us” and allow more people to be included in the circle of “ours”.

2. Are individualistic cultures more discriminatory? This overturned my understanding

I have always felt that collectivistic cultures (such as Asia) should emphasize the distinction between inside and outside more, while individualistic cultures (such as Europe and the United States) should treat everyone more equally. But this study found the exact opposite!

The explanation proposed by the author is very interesting: in individualistic cultures, people are more eager for group identification. Because there are no mandatory family or community ties, people need to find their own “sense of belonging,” which makes them more sensitive to “who is one of their own” and “who is an outsider.”

This reminds me of racial issues in the United States, immigrant rejection in Europe, etc. These highly individualistic societies are particularly prone to group conflicts. Perhaps it is precisely because in a society where “everyone is for themselves”, finding “one’s own team” has become more important and precious.

The most surprising finding from this study is that in more than half of the decisions, the participants did not look at the other party’s group information at all. What’s even more astonishing is that those who clearly chose “not to know” showed no discrimination at all!

This gave me an important revelation: **Perhaps the key to reducing discrimination is not to educate people to “treat all groups equally,” but to make people “not pay attention to group differences.” **

Think about it, in real life, many times we are forced or led to pay attention to group differences-the “race” option on the form, “immigrant crime” in news reports, the “us vs. them” narrative on social media. If we can create more “color-blind” environments and systems, perhaps discrimination will naturally decrease.

Of course, this also raises a philosophical question: Does the “color-blind” strategy ignore real group inequalities? Does not recognizing differences mean evading the responsibility to address inequality? This is an issue that needs to be weighed carefully.

4. Cultural specificity of cognitive processes: an unsolved mystery

The biggest mystery of this study is: Why is the relationship between prosocial tendencies and decision-making effort completely reversed in different countries?

In some countries, more prosocial people make more effortful decisions when faced with their ingroup (perhaps weighing how to maximize the interests of the ingroup). But in other countries, the effect is reversed. The papers acknowledge that they cannot explain this difference with pre-registered cultural variables (individualism, government effectiveness, religion, etc.).

This is actually the most honest and valuable part of this study: it reveals our ignorance. We thought we understood the mechanics of in-group preference, but in fact we couldn’t even explain why cognitive processes differ across cultures.

This reminds us: psychological theories cannot only be based on WEIRD (Western, Educated, Industrialized, Rich, Democratic) samples. The human mind is far more complex and culturally shaped than we imagine.

5. Democratizing Methodology: The Webcam Revolution

From a broader perspective, this study shows how technology is changing research possibilities. In the past, eye tracking required expensive equipment and specialized labs that only a few wealthy universities could afford. Data of sufficient quality can now be collected using participants’ own webcams.

what does that mean? Democratizing Research. Researchers from developing countries no longer need multimillion-dollar equipment to conduct cutting-edge cognitive research. Participants are no longer limited to college students living near college towns.

This technological advancement may be more important than any single research discovery. It changes the game of “who can do research” and “who can be researched.”

6. Warning on policy: Beware of “one size fits all”

Finally, this study has an important warning for policymakers: Do not assume that anti-discrimination policies that work in one place will work in another.

If cognitive processes are so different across cultures, then interventions also need to be context-specific. An education program that emphasizes “treating all people equally” may be effective in some cultures but may be counterproductive in others (because it reinforces perceptions of group boundaries).

7. Limitations: The Puzzle of Causation

Of course, this study also has obvious limitations. Cultural variables cannot be manipulated experimentally, so we cannot determine the direction of causation. Does social uncertainty lead to discrimination, or does discrimination lead to social instability? This knot of cause and effect cannot be untied.

Furthermore, the paper acknowledges that many unobserved variables (language, personal wealth, migration history, etc.) may confound the results. These are challenges inherent in cross-cultural research.

Extended thinking

1. What if discrimination is the “default mode” of cognition?

This research got me thinking: maybe the human brain was evolved to distinguish between in- and out-groups. If so, reducing discrimination is not about telling people “don’t discriminate”, but about redefining “who are our own people”.

The ultimate goal of globalization and multiculturalism may not be to eliminate group boundaries, but to expand the scope of “in-groups” until “all humanity” becomes “us”.

2. Tribalism in the Internet Age

Social media and recommendation algorithms make it easier for us to find our “strata”—those “people” who think the same way. This may exacerbate ingroup bias.

If people from individualistic cultures particularly crave group identification, and the Internet makes this identification extremely easy (just join a Facebook group), then we may be witnessing an era of “Tribalism 2.0.”

3. New challenges in the AI era

When AI systems start making resource allocation decisions (such as loan approval, job recommendation), will they also learn “in-group preferences”? If the training data is filled with human discrimination patterns, AI may amplify these biases.

And even more troubling is that AI will not “select color blindness” - it will process all available data, including group information. This can make discrimination more systematic and harder to detect.

Reading date: 2025-11-22 Notes organized: Dylan Chiang

Paper reading: Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Sat, 22 Nov 2025 00:00:00 +0000

#paper information

Title: Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? Source: arXiv Paper ID: 2504.13837 Link: |

Research background

Reinforcement Learning with Verifiable Rewards (RLVR) has recently achieved significant success in improving the inference performance of large language models, especially on mathematics and programming tasks.

Conventional wisdom holds that RLVR enables LLMs to continuously improve themselves, thereby acquiring new reasoning capabilities beyond the base model—just as traditional RL helps agents explore and learn new strategies.

But is this assumption really true?

This study attempts to answer a key question by systematically exploring the boundaries of the reasoning capabilities of RLVR-trained LLMs in various scenarios:

**Does RLVR training actually allow LLMs to acquire new reasoning capabilities beyond the base model? **

Research methods

Evaluate Design

The research team uses pass@k (at large k values) as an evaluation metric across multiple dimensions:

Model Family: multiple different LLM architectures
RL Algorithms: six popular RLVR algorithms
Benchmarks: Mathematics, Programming, Visual Reasoning

Analysis method

Pass@k Evaluation: Use large k values to detect the upper bound on the model’s capabilities
Coverage Analysis (Coverage Analysis): Measure whether the RLVR training model can produce solutions that the basic model cannot produce
Perplexity Analysis (Perplexity Analysis): Evaluate whether the output of the RLVR training model is still within the distribution of the basic model

Main findings

1. Current training settings fail to inspire fundamentally new inference patterns

Surprising core findings:

✅ Small k value (such as k=1): RLVR training model is better than the base model
❌ Large k value: The basic model instead obtains a higher pass@k score

**What does this mean? **

RLVR training mainly focuses on “selecting” the existing capabilities of the basic model, rather than creating new reasoning models. It’s like learning to pick the right tool faster from a toolbox already full of tools, but it doesn’t create new tools.

2. Inference capabilities are limited by the basic model

Through coverage analysis and perplexity analysis, the study found:

Observed reasoning ability derived from** and limited by the underlying model
Treating the basic model as an upper bound of capability, six popular RLVR algorithms perform similarly
All methods are far from optimal in exploiting the full potential of the underlying model

Basic model is the ceiling - No matter how trained, RLVR cannot make the model jump out of the capability boundary defined by the basic model.

3. Distillation methods show different advantages

In contrast, Knowledge Distillation (Distillation) exhibits different characteristics:

Ability to introduce new reasoning modes from the teacher model
Truly expands the reasoning capabilities of your model
Don’t just rearrange existing knowledge

This hints at an important methodological difference: distillation can “teach” the model new reasoning paths, while RLVR is only “optimizing choices.”

Research significance and inspiration

Reflections on RL in the field of LLM

This study reveals a fundamental limitation of current RLVR methods:

**Current RLVR training paradigms have yet to realize the potential of RL to inspire truly novel reasoning capabilities in LLMs. **

This is a far cry from what we’ve come to expect from RL. In traditional RL domains (such as game AI), RL can discover strategies that humans have never thought of. But in the field of LLM, the current RLVR method seems to be only “mining” rather than “creating”.

Future research directions

Research highlights the need to improve the RL paradigm:

Continuous Scaling (Continual Scaling)
- Long-term, continuous training process
- Rather than a one-time optimization
Multi-turn Agent-Environment Interaction (Multi-turn Agent-Environment Interaction)
- Truly interactive learning
- instead of training on static data sets
New Assessment Paradigm
- Pass@k (big k) instead of just looking at pass@1
- A more comprehensive assessment of capability boundaries

My understanding

After reading this paper, my biggest feeling is: this is really a “slap in the face” paper. In the past year, RLVR has been highly praised in the LLM field. OpenAI’s o1 and DeepSeek’s R1 both claim to use reinforcement learning to make the model “learn to think.” But this study tells us calmly: Wait, are you sure the model really learned something new? Or have you simply learned to pick the right answers faster from the toolbox you already have?

1. “Selector” vs “Creator”: A cruel truth

I think the core insight of this paper is to position RLVR as a “selector” rather than a “capability expander”. This may sound a bit abstract, but it’s actually pretty easy to understand. Imagine you have a student who already has many ways to solve the problem (basic model) in his mind, but he is not sure which method is best, so he tries randomly. RLVR training is like giving him a lot of practice questions, allowing him to learn “use this method when he sees this kind of question”, which improves his answering accuracy (pass@1).

But the problem is, if you give this student many opportunities to try (pass@k, big k), he will be able to try out the correct answer by “trial and error”. The RLVR training didn’t teach him any new problem-solving methods, it just made him find the right one faster. This is why when pass@k is large, the basic model performs better - because it still retains more diverse trial possibilities, and the model after RLVR training has been “finalized” and will only give priority to those paths with high rewards during the training process.

This finding is actually cruel, because it means that many of the “improvements in reasoning” we have seen in the past year may be just superficial.

2. The basic model is the ceiling: where should investments be placed?

This paper made me rethink resource allocation. If the basic model is the ceiling of capabilities, then should the industry invest more resources in pre-training instead of various fancy parameter adjustments for RLVR?

The current trend is that everyone is desperately thinking about how to use less data and more clever reward shaping to do RLVR. But this research tells us that no matter how optimized you are, you cannot escape the capabilities of the basic model. So instead of finessing on RLVR, wouldn’t it be more practical to directly train a stronger basic model?

Of course, this is not to say that RLVR is completely useless. In the pass@1 scenario (which is the most common situation in practical applications), RLVR can indeed significantly improve performance. But we have to clearly understand its essence: it is an “optimizer” rather than an “expander”.

3. Enlightenment of knowledge distillation: Why can it do what RLVR cannot?

The paper mentioned that knowledge distillation can introduce new reasoning patterns, which made me very curious. Why does distillation work but RLVR not?

My understanding is that distillation is essentially “knowledge transfer” - you learn “new ways of thinking” from a stronger teacher model. This is true expansion of capabilities. RLVR only “self-optimizes” within its own capabilities without the injection of external knowledge.

This makes me think that perhaps the truly effective training paradigm in the future should be a combination of “distillation + RLVR”: first use distillation to expand the boundaries of capabilities, and then use RLVR to optimize the quality of decision-making. Relying on RLVR alone to allow the model to “emerge” new capabilities may be an unrealistic expectation.

4. Pass@k’s philosophy: What exactly are we evaluating?

This paper also reminded me of the importance of evaluation methods. We are accustomed to using pass@1 to evaluate the model because it is closest to the real application scenario. But pass@1 may give us an overly optimistic illusion.

Pass@k (big k) reveals the “capability boundary” of the model - the upper limit of what it “might” get right. And pass@1 just tells us what the model would “normally” do. The gap between the two is what RLVR is doing: narrowing the distance between “possible” and “usual”.

But if we only look at pass@1, we will mistakenly think that RLVR really makes the model smarter. In fact, the “ceiling” of the model has not increased, only the “average performance” has increased. This distinction is important.

5. Thinking about the future: What does true reasoning require?

After reading this paper, I feel that the current RLVR paradigm may have been designed wrong from the beginning. We treat LLM as a “static policy space” and then use RL to optimize policy selection. But real reasoning shouldn’t be like this. Real reasoning should be “dynamic, interactive, and continuous learning.” Just like humans do not learn to think through problem solving exercises, but develop reasoning abilities through long-term interaction with the world, trial and error, and reflection. The “multiple rounds of agent-environment interaction” mentioned in the paper may be the right direction.

Maybe what we need is not to use RL to train the model, but to use RL to let the model “live” in an environment and truly explore, make mistakes, learn, and grow. That is the essence of RL, not the current “pseudo RL” done on static data sets.

6. A disturbing question

Finally, this paper made me think of a deeper question: If RLVR cannot make the model truly smart, then what is going on with these o1 models that claim to have “reasoning capabilities”?

There are two possibilities: First, their basic models are already very powerful, and RLVR just exploits this potential; second, they use some techniques that are not covered in this paper (such as chain-of-thought, test-time compute scaling).

In any case, this study reminds us: Don’t be fooled by superficial performance improvements, but think deeply about the essential source of capabilities. This is important for both doing research and making products.

Extended thinking

1. RLVR vs. Knowledge Distillation: What is the essential difference?

Why can distillation introduce new patterns?
What is the fundamental difference between the two in the training process?
Can the advantages of both be combined?

2. The importance of evaluation methods

What does the difference between Pass@1 vs. Pass@k (big k) reveal?
Are we overly reliant on a single metric to assess model capability?
What other dimensions of assessment can be explored?

3. The role of the basic model

Is it more worthwhile to invest in better base model training vs. more sophisticated RLVR methods?
Where can we improve Pre-training?
How is the “capability boundary” of the basic model formed?

Reading date: 2025-11-22 Notes organized: Dylan Chiang

Paper reading: Genome-wide study association of sexlessness

Thu, 20 Nov 2025 00:00:00 +0000

#paper information

Title: Genome-wide association study of sexlessness Journal: PNAS 2025 Vol. 122 No. 38 DOI: Research Number: e2418257122

Research background

This study examines the genetic and environmental correlates of lifelong sexlessness. The study used large-scale data from UK Biobank to analyze genotypic and phenotypic profiles of more than 400,000 participants.

Main findings

1. Relationship between regional factors and Sexlessness

Sex Ratio

Sex ratio at birthplace: no significant association with sexlessness
Gender ratio in current place of residence:
- Males showed a small but significant negative correlation (r = -0.07, P = 0.0004)
- Areas with fewer women have higher rates of male sexlessness

Income Inequality via Gini Coefficient

Income inequality based on current place of residence is significantly and positively related to sexlessness:

Male: r = 0.09, P = 2.8 × 10⁻¹⁵
Female: r = 0.14, P = 3.6 × 10⁻⁶
Overall: r = 0.15, P = 9.5 × 10⁻¹³

Explanation: Areas with higher income inequality also have higher rates of sexlessness

2. Genome-wide association study (GWAS) results

Samples and methods

Number of participants: 404,470 people of European ancestry
Sexless individuals: 3,897 people (~1%)
Number of SNPs: 10.6 million single nucleotide polymorphisms

Main findings

Significant site: A site on chromosome 1 (top SNP: rs912773)
- This site is located in an intergenic region, the nearest gene is LOC107984933
- Single SNP effect size is extremely small

SNP-based Heritability

Population	Heritability	Standard error	P value
Men	17%	4%	2.3 × 10⁻⁵
Women	14%	3%	3.1 × 10⁻⁶
Overall	12%	-	1.9 × 10⁻⁹

IMPORTANT OBSERVATION:

Genetic correlation between males and females is 0.56 (SE = 0.17, P = 0.0007) -Suggests that SNP associations are directionally consistent between men and women but only partially overlap

Evolutionary evidence

The study examined changes in allele frequency of rs4654352, one of two genome-wide significant SNPs, in the Ancient DNA Database (AADR):

Time Span: Past 12,000 years
Sample: 3,458 ancient individuals from Europe
Trend: A continued decrease in frequency of the A allele associated with higher sexlessness
- 10,000-12,000 years ago: 87%
- 0-2,000 years ago: 56%

Conclusion: Consistent with negative selection pressure targeting sexlessness-related alleles

3. Polygenic Score (PGS) analysis

UK Biobank Internal Validation

PGS significantly predicted sexlessness in an independent sibling sample (Beta = 0.016, SE = 0.005, P = 0.002)
Gene-Environment Correlation Test:
- Only 7.4% of the effect can be attributed to gene-environment correlations among families
- This compares to 49% for education, 48% for IQ, and 15% for BMI
- Conclusion: GWAS signals are minimally affected by gene-environment correlations

Australian independent sample verification

PGS was significantly associated with multiple relevant phenotypes in Australian samples (N = 1,354-13,532):

Phenotype	Direction of Association	Significance
Number of relationships lasting ≥3 months	Negative correlation	✓✓ (overall)
Never had a sexual partner	Positive correlation	✓✓ (female)
Number of sexual partners	Negative correlation	✓✓ (overall)
Age at first sexual intercourse	Positive correlation	✓✓ (overall, men and women)

Religious Impact (N = 6,764):

Religious people are more likely to have no sexual experience in their lives (P < 0.0001)
Gender analysis: significant for women (P = 0.002), not significant for men (P = 0.06)

4. Genetic overlap with other complex traits

Genetic correlation with childlessness

Gender	Genetic correlation	Standard error	P value
Female	0.68	0.10	7.0 × 10⁻¹¹
Male	0.65	0.09	7.0 × 10⁻¹⁴

Showing high genetic overlap between sexlessness and childlessness

Other important genetic correlates

Positive correlation (genes associated with sexlessness are also associated with the following traits):

Cognitive and Socioeconomic Status:
- Child vs. Adult IQ
- Education level
- Income and socioeconomic status
Mental Illness:
- Autism Spectrum Disorder
- Anorexia
Lifestyle:
- Less substance use (alcohol, cigarettes, marijuana)

Negative correlation:

Mental Illness: -ADHD
- Post-traumatic stress disorder
Psychosocial Traits:
- Extraversion
- Relationships with friends and family -Subjective well-being

Phenotypic correlation analysis

Mental health indicators (especially men)

Sexless men were significantly more likely to:

Suffering from neurosis
feel unhappy
feeling lonely
Believe that life lacks meaning

More likely to live alone
Social visits are less frequent
Lack of intimacy or someone to confide in

Physical characteristics

MEN: Lower grip strength and lean body mass (arm muscle mass)
Overall: Start wearing glasses earlier

Substance Use

Associated with less use of:

alcohol
cigarettes
Marijuana

Education and socioeconomic status

Phenotype related: -Positively associated with higher educational attainment (for both men and women)
- Negatively associated with lower household income
- Place of residence with high levels of economic deprivation (Townsend index)
Genetic correlation (opposite direction):
- Positive genetic correlation (~0.5) with higher IQ, education, income, and socioeconomic status

Research limitations and precautions (Box 1: Cautionary Notes)

1. Culture Specificity

The association between genetics and behavior reflects the interaction between genetics and the environment within a specific cultural context
Findings from contemporary Western societies should not be generalized to other ancestries or cultural backgrounds

2. Individual results cannot be predicted

Polygenic scores are not suitable for predicting sexlessness at the individual level
Approximations based on large samples and small effects
It is inevitable that there will be people with high scores but low phenotypes, and vice versa

3. Complexity

Heredity is just one aspect of a complex network that influences behavior
Environmental, social, cultural and personal factors all play an important role
This study explores correlation, not causation

4. Measurement Error

-Sexlessness is a self-report variable

There may be under-reporting or over-reporting, leading to classification errors
Social norms may influence reporting behavior (men may over-report, women may under-report)

Discussion and Interpretation

1. Scientific evidence for the “nerd” stereotype?

The study found a combination of characteristics that closely aligned with the “nerd” stereotype:

Introversion
Wearing glasses when young
Smart and academically successful
Physically weak
Social isolation, loneliness
Higher autism spectrum traits
Nervous
Less use of drugs and alcohol

2. Complexity of Causal Direction

Sexual inexperience → Mental health issues: Sex is a basic human drive, and its lack may affect happiness
Mental Health Issues → Sexual Inexperience: Psychological issues may make it more difficult to approach or attract a partner
Third Variable: Other factors may increase the likelihood of both

Genetic correlations provide some clues:

The genetic correlation between sexlessness and depression, anxiety, and loneliness is negative (not positive)
But there is a positive genetic correlation with autism spectrum disorder, consistent with the characteristics of interpersonal difficulties

3. The Paradox of Intelligence and Reproductive Success

Evolutionary Expectation: Intelligence and resources should be attractive traits
Research Finding: Positive genetic correlation (~0.5) with higher IQ, education, and socioeconomic status

Possible explanation:

Young adults with high educational/socioeconomic potential may avoid intimate relationships in order to pursue career plans
This pattern may continue into later life

Challenges to the Sexual Selection Hypothesis:

The sexual selection hypothesis of human intelligence proposes that intelligence evolves due to its contribution to mating success.
This study finds that intelligence is positively related to sexlessness, strengthening the evidence against this hypothesis

4. Sexlessness vs. Asexuality

-Asexuals account for about 1% (similar to the sexless proportion in this study)

But more than half of asexuals are not virgins
Asexuals have lower average education and socioeconomic status (contrary to this study’s findings)
The correlation between male sexlessness and regional female scarcity & income inequality difficult to explain by asexuality

Conclusion: Asexuality may contribute to part of the phenotype, but it is not the main factor

Evolutionary meaning

1. Sexlessness as a fitness proxy variable

Significant SNP heritability allows genetic correlation analysis
Significant negative genetic correlation with number of heterosexual sexual partners and number of children
May be a more “pure” ancestral fitness surrogate than other variables
- Less affected by social sexual orientation, pickiness in mate selection, family planning, career orientation, etc.

2. Selected Evidence from Ancient DNA

The A allele (associated with sexlessness) has continued to decline over the past 12,000 years
Expected negative selection pressure
but need to be interpreted with caution:
- The exact function of this variant is unknown
- Observed effect sizes are small

3. Maintenance of genetic variation

Genetic variation may be maintained through mutation-selection balance:

Negative selection acts on this trait
New mutations continue to arise in the population

Methodological Highlights

1. Large-Scale GWAS

404,470 participants, 10.6 million SNPs
Corrected genetic correlation matrix and 25 genetic principal components using linear mixed models

2. Multi-sample verification

UK Biobank internal validation of sibling samples
External verification by Australian independent samples

3. Gene-Environment Correlation Test

Comparison of within-family vs. between-family effects
Only 7.4% can be attributed to family-level gene-environment correlations (much lower than the 49% for educational attainment)

4. Ancient DNA Integration

Allen Ancient DNA Resource (AADR) v62.0
3,458 ancient European individuals
A time span of 12,000 years

Policy and Practical Implications

1. Public Health

Sexless individuals, especially men, may face higher mental health risks
Need targeted mental health support

2. Education

Understand the complex interactions of genetic and environmental factors
Avoid the misunderstanding of genetic determinism

3. Research Direction

Need more detailed information on the causes of sexlessness
Distinguish between “inability to attract a partner” vs. “lack of sexual desire”
Triangular validation of different methodologies (e.g. Mendelian randomization)

Personal reflections and comments

Research Advantages

Large sample size and strong statistical testing power
Multi-level analysis: phenotype, GWAS, PGS, genetic correlation, ancient DNA
High transparency: clearly state limitations and precautions (Box 1)
Cross-sample validation: Increase the reliability of findings

Research Limitations

Limited Causal Inference: Only correlation can be described, and it is difficult to establish the direction of causation.
Culture Specificity: Findings may not generalize to non-Western societies
Self-reporting bias: Sexlessness is a sensitive issue and there may be reporting bias.
Heterosexuality: sexlessness may include many different causes (asexuality, social difficulties, religion, etc.)

Future research directions

Mendelian Randomization: Unraveling Causal Complexity in Larger Samples
Longitudinal Study: Tracking the Development of Sexlessness
Cross-Cultural Research: Test whether the findings are generalizable
Mechanism Research: In-depth exploration of the biological and social mechanisms related to intelligence-sexlessness

Key data summary table

GWAS samples and heritability

Population	Cases	Controls	Total	Number of valid samples	SNP-h²	SE	P-value
Overall	3,897	400,573	404,470	15,438	0.12	0.02	1.9×10⁻⁹
Men	1,844	184,191	186,035	7,303	0.17	0.04	2.3×10⁻⁵
Women	2,053	216,382	218,435	8,135	0.14	0.03	3.1×10⁻⁶

Environmental factor correlation

Factor	Population	Correlation Coefficient	P-Value
Regional sex ratio	Male	-0.07	0.0004
Income inequality	Men	0.09	2.8×10⁻¹⁵
Income inequality	Women	0.14	3.6×10⁻⁶
Income inequality	Overall	0.15	9.5×10⁻¹³

My understanding

After reading this paper, I think the most interesting thing is that it uses large-scale genetic data to explore a phenomenon that is rarely studied in science-lifelong sexual inexperience. This may have been dismissed as purely a matter of personal choice or luck in the past, but this paper tells us that there are actually more complex biological and social factors at work.

First, genetics does play a role. Although the effects of individual genes are small, combined, approximately 12-17% of the variation can be explained by heritability. This does not mean that “having a certain gene will make you single all your life.” It means that some genetic tendencies may affect your social skills and personality traits, which in turn indirectly affects your chances of establishing an intimate relationship.

What impressed me most was the discovery of the “nerd” characteristic group - smart, introverted, wearing glasses when he was young, relatively weak, not very likely to drink or smoke, and prone to nervousness and loneliness. These characteristics are statistically related to a lifetime of sexual inexperience. This is really similar to the stereotypes we see in pop culture, but I didn’t expect it to be supported by scientific evidence.

Even more counterintuitive is the matter of intelligence. It stands to reason that smart people should be more attractive and resourceful, but this study found that at the genetic level, genetic variations related to sexlessness are actually related to higher IQ and higher education. This is really contradictory! One explanation proposed by the researchers is that perhaps smart young people focus more on career development and postpone their love life. However, once this pattern is formed, it may continue for the rest of life.

Another discovery that I found really cool was the analysis of ancient DNA. They found that the frequency of genetic variants associated with sexlessness has been declining over the past 10,000 years. This fits with the predictions of evolutionary theory - if a gene makes it harder for you to reproduce, it should be eliminated. It’s really amazing to be able to verify this trend in ancient human bones.

Of course, this study also has many limitations. It can only tell us “correlation” and cannot prove causation. Moreover, all data come from the United Kingdom, and the cultural background is very specific, and may not be applicable to other societies. In addition, the phenomenon of “lifelong sexual inexperience” is complex in itself - it may be asexuality, it may be social difficulties, it may be religious choice, it may even be just bad luck. Mixing all these different causes into one analysis may obscure some important details.

Overall, I think the greatest value of this study is that it opens a new research perspective. It tells us that human intimacy and sexual behavior are not only the product of culture or personal choice, but also the interaction of multiple factors such as genetics, environment, and socioeconomic factors. This complexity is the most fascinating part of human behavior.

Paper reading: No gender differences in attraction to young partners: A study of 4,500 blind dates

Wed, 29 Oct 2025 00:00:00 +0000

#paper information

Title: No gender differences in attraction to young partners: A study of 4,500 blind dates Authors: Paul W. Eastwick, Eli J. Finkel, Eva M. Meza, Kellie Ammerman Journal: PNAS 2025 Vol. 122 No. 5 DOI:

Summary of thesis content

1. Research background: Challenges to traditional concepts

In cross-cultural studies of mate choice, one of the most consistent findings is often this: Men prefer younger women, while women prefer older men. This is usually explained by evolutionary psychology as: men seek fertility (younger), women seek resources and status (older). However, most of these studies are based on “questionnaire surveys” (What kind of people do you like?). This paper asks: Do these preferences matter when people actually date face to face?

Researchers collaborated with Tawkify, a US-based matchmaking company, to collect data from 6,262 participants.

Sample Characteristics: Average age 46.8 years (range 22-85 years), mainly middle-aged people seeking long-term partners.
Data volume: 4,542 real blind dates, resulting in 9,084 date reports.
Measurement: “Romantic Attraction” after the date, “Overall Rating” and “Willingness to go on a second date”.

3. Core findings

A. Both men and women love young people (No Gender Difference)

This is the most amazing discovery. The data showed that participants generally rated younger subjects more highly. What’s more, this preference for youth does not differ significantly between men and women.

Men like younger women (no surprise).
Women are equally attracted to younger men (contrary to what they said on the questionnaire).

B. The “upper age limit” is in vain

Participants will set an “Upper Age Limit” when joining the service.

Women usually set a higher upper limit than men (saying they can accept older people).
But in actual dating, regardless of whether the object exceeds this upper limit, it does not affect the attractiveness rating trend.
In other words, even if a man exceeds the upper age limit set by women, as long as he is relatively young (or has youthful qualities), women will still be attracted to him. Personal principles are insignificant in the face of the chemical reaction after meeting.

4. Discussion: Why is there such a contrast?

The authors propose several possible explanations:

Say no with your mouth, be honest with your body (Stated vs. Revealed Preferences): On an abstract level, women may think that they like mature and stable older men, but in face-to-face instinctive reactions, the vitality and physical attractiveness brought by youth may be more advantageous.
Changes in social roles: Most of the women in this sample are financially independent (average annual income of $150,000). When women no longer need to rely on men for resources, they may return to their more primitive mate selection criteria of appearance and vitality, making their preferences more similar to men.
Structural issues in the dating pool: In reality, “older men and younger women” pairings are common. This may not be because women like older men, but because men like younger women, which leads to women being “forced” to accept older partners. When given the choice (as in the case of blind dates in this study), women actually want to pick younger ones.

#My understanding and experience

When reading this paper, I couldn’t help but think of the old saying: “Men are very specific and always like 20-year-olds.” Now it seems that this sentence may have to be changed - **Human beings are very specific and always like young people. **

1. A revision of evolutionary psychology?

In the past, we often thought that women’s age preference was for “resources.” But this study implies that when women themselves have resources (the women in the sample have high incomes), their demand for “resource providers (usually older)” decreases and they instead pursue “genetically superior people (usually younger).” This is actually in line with evolutionary logic, but the environmental parameters have changed (female resources have increased).

2. Gender equality of “age anxiety”

This may be a warning sign to men, but it may also be liberating. In the past, society always believed that men are “more valuable as they get older,” but this study ruthlessly points out: Older men have no advantage when it comes to first-sight attraction. Middle-aged uncles may no longer be able to rely on “maturity and stability” and ignore appearance management, because your date may be secretly taking a look at the little girl next to you.

3. Data integrity

This once again proves that behavioral data (Revealed Preference) is often more honest than questionnaire data (Stated Preference). We think we have principles (e.g., “I won’t date anyone younger than me”), but when an energetic young man sits across from us, chatting and laughing, those principles fall apart in an instant. This paper uses 4,500 dates to tell us: **Instinct is always more powerful than reason. **

Reading date: 2025-10-29 Notes organized: Dylan Chiang

Paper reading: Single-cell transcriptomic atlas of the human testis across the reproductive lifespan

Fri, 24 Oct 2025 00:00:00 +0000

#paper information

Title: Single-cell transcriptomic atlas of the human testis across the reproductive lifespan Journal: Nature Aging Year: 2025 DOI:

Research background

Decline in male reproductive health with age is a common phenomenon, but the molecular mechanisms behind it have been unclear. In the past we knew:

Fertility decreases significantly in men over 40 years old
Offspring of older fathers are at higher risk of genetic diseases
Degeneration of testicular function affects hormonal balance and overall health

But the question is: **Which cell types age first? Which molecular pathways are affected? Is this process gradual or staged? **

This study uses single-cell RNA sequencing (scRNA-seq) technology to provide us with unprecedented detail: 214,369 single-cell transcriptome data from 35 donors ranging in age from 21-69 years old.

Research methods

1. Sample collection

Sample source:

35 male cadaveric organ donors
Age range: 21-69 years (covering the entire reproductive life span)
Key Screening Criteria: All donors have normal sperm production or have offspring (excluding disease states)

Sample Grouping:

In their 20s: 3 people
In their 30s: 4 people
Over 40 years old: 10 people
Over 50 years old: 8 people
Over 60 years old: 10 people

2. Single cell sequencing technology

Platform: 10x Genomics Chromium Data size:

214,369 cells
Average of 2,318 genes per cell
Average of 6,265 unique molecular identifiers (UMIs)

Cell type identification: 14 testicular cell types

Germ cells: undifferentiated spermatogonia, differentiated spermatogonia, primary spermatocytes, round spermatids, elongated spermatids
Somatic cells: Sertoli cells, testicular peritesticular cells (TPCs), Leydig cells, smooth muscle cells, endothelial cells, macrophages, lymphocytes, B cells

3. Machine learning analysis

Using a machine learning model (aging clock) to predict the age characteristics of cells, we found:

Somatic cells respond more strongly to aging than germ cells
Different cell types have different “aging clocks”

Main findings

1. Two waves of aging: 30 and 50 are key turning points

The most important finding of this study is: Testicle aging does not proceed at a uniform speed, but has two obvious “turning points”.

First wave (30s): Testicular peritesticular cells (TPCs) start firing

Significant increase in basement membrane thickness
Changes in extracellular matrix (ECM)-related gene expression
Collagen I related pathway activation
Decrease in oxidative stress-related genes (HSPA1A, HSPA1B)

💡 **What does this mean? ** In your 30s, the “package” of the testicles begins to thicken and harden, which may be the “priming state” of aging.

Wave 2 (50s): Functional deterioration in full swing

Leidich Cells: Decreased testosterone synthesis and metabolism
Sertoli cells: Impaired nutrient supply capacity (changes in IGF1R, INSR gene expression)
Macrophages: Enhanced immune response
All Somatic Cells: Significantly increased inflammatory response

💡 **What does this mean? ** In your 50s, the “function” of your testicles begins to break down - hormonal imbalances, malnutrition, and inflammation break out.

2. Somatic cells age faster than reproductive cells

Machine learning analysis shows:

The “aging clock” of somatic cells (such as Leydig cells, TPCs) runs faster
Germ cells (spermatogonia, sperm cells) are relatively stable

This explains why:

Older men can still produce sperm (germ cells are still alive)
But fertility decreases (the supportive environment provided by somatic cells becomes worse)

3. Aging pattern of germ cells: reduction in number + abnormal function

Fate of spermatogonia:

In your 30s: quantity increases first (compensatory mechanism?)
After 50s: Significant decline in numbers
Change in the ratio of G2/M phase of the cell cycle (decreased proliferation ability)

Changes in Haploid Germ Cells (round and elongated sperm cells):

Significant changes in expression of flagellum-related genes
Abnormal expression of genes related to sperm morphology
Reorganization of transcriptional regulatory network 💡 **What does this mean? ** Even if older men produce sperm, the “quality” of these sperm may not be as good as when they were younger.

4. The impact of BMI amplifies with age

Core findings:

When you are young (20-30 years old), high BMI has little impact
After age 45, high BMI (≥30) significantly reduces sperm cell count

Data support:

Donors with BMI ≥ 30: Extended sperm cell frequency < 20%
But age still has a greater impact on transcriptome changes than BMI

Pathway Analysis:

High BMI: ECM dysregulation is more severe in TPCs
High BMI: Testosterone metabolism of Leydig cells is more disordered
High BMI: Decreased muscle contraction function of smooth muscle cells

💡 **What does this mean? ** If you were overweight when you were young, the effects may not be noticeable. But once you pass the age of 45, obesity accelerates the deterioration of reproductive function.

5. Continuous enhancement of immune response

Observed in all age groups:

Upregulation of immune response-related genes
Increased inflammatory markers
Enhanced macrophage activity

This is a common feature of aging (inflammaging), which also exists in the testicles.

Research significance and inspiration

Theoretical contribution

Reveals the staged characteristics of aging
- It is not a uniform degradation, but there is a “critical point”
- 30 and 50 are critical transitions
Identified the aging “starting cells”
- TPCs are the first to respond to aging (30s)
- Other cells follow (50s)
Provides high-resolution molecular maps
- Gene expression profiles of 214,369 cells
- Provide a reference database for future research

Practical inspiration

For individuals:

The best time to have children: Having children before the age of 30 may avoid the first wave of aging
Weight Management: Controlling BMI becomes more important after age 45
Early Detection: Basement membrane thickness can be measured as an early warning in your 30s

Clinically:

Diagnostic markers: ECM-related genes, testosterone metabolism enzymes
Treatment Target:
- Drugs targeting ECM remodeling (30-40 years old)
- Therapy for hormone replacement (age 50+)
Reproductive Preservation: It is recommended to freeze sperm before the age of 30

Research:

Mechanism Exploration: Why do TPCs age first? How do ECM changes trigger a chain reaction?
Intervention Trial: Can aging be delayed by regulating the ECM?
Ethnic Comparison: What are the differences between different ethnic groups and lifestyles?

My understanding

After reading this paper, I was most shocked: it turns out that male reproductive aging is not a slow and steady process, but like a “staircase” with obvious “steps”. The age of 30 is the first step, and the age of 50 is the second step. This overturns my impression of aging as a “uniform process”.

1. Age 30: An underrated turning point

Society generally believes that men only start to “age” after the age of 40, but this study says: No, at the age of 30, some cells in the testicles have already begun to “prepare” to age.

The basement membrane of TPCs (the cells around the testicles) starts to thicken in your 30s, which is like the walls of a house starting to thicken—it looks “sturdier,” but it actually becomes more “stiff,” making it harder for nutrients and signaling molecules to pass through.

This made me think: perhaps the physiological basis of many “midlife crises” is actually laid in the early 30s. That vague feeling of “not as good as before” may not be an illusion, but a real molecular change.

2. Why do TPCs age first? that’s a good question

The paper found that TPCs are the “pioneers” of aging, but did not fully explain why. I have a guess:

TPCs are located in the outer layer of the testicles and are the first to come into contact with various stress factors (oxidative stress, inflammatory factors, metabolic waste products) in the blood. They may be like “sentinels”, the first to feel the deterioration of the environment.

And their response—thickening the basement membrane and remodeling the ECM—may begin as a “defense mechanism,” trying to protect the germ cells inside. But in the long term, this defense becomes a barrier, blocking nutrient supply and signaling.

This reminds me of the problem of “chronic inflammation” in the immune system. Many chronic inflammations begin as a defensive response, but end up harming the body. This may be a more common aging pattern: **Excessive “protection” eventually becomes “harm”. **

3. Somatic cells age quickly, while reproductive cells age slowly: the wisdom of evolution?

Machine learning analysis shows that the aging clock of somatic cells ticks much faster than that of germ cells. This makes sense from an evolutionary perspective: Germ cells carry genetic information and are the key to “inheritance.” If they age too quickly, their genetic quality will decline rapidly, which is not conducive to the continuation of the species. So evolution may “preferentially protect” germ cells, allowing them to age more slowly.

What about somatic cells? They are just “support systems” responsible for providing the environment. As they age, the worst they can do is find a younger partner and continue to reproduce (from a purely evolutionary perspective). Therefore, evolution does not “care” about the aging of somatic cells.

Of course, we cannot be so cruel in modern society. We want every individual to age healthily, not just pass on their genes. So this research is valuable for improving individual reproductive health.

4. The interaction of BMI and age: a cautionary tale

The study found that the impact of BMI was significantly amplified after age 45. This shows: **When you are young, your body can still “carry” it, but after the age of 45, the redundancy is exhausted, and the problem is exposed. **

This brings me to the concept of “health debt.” Smoking, drinking alcohol, staying up late, and being obese when you are young may not cause immediate problems because the body has strong repair capabilities and redundancy. But these unhealthy behaviors are actually “overdrafting” health. When the redundancy is exhausted in middle age, debts will explode.

From this perspective, age 45 is not just a turning point for reproductive health, but perhaps for overall health. Many chronic diseases (diabetes, cardiovascular disease) also occur intensively in this age group.

5. The revolution of single-cell technology: from “black box” to “transparent”

What impressed me most about this paper was the technological progress. Gene expression profiles of more than 200,000 single cells! This would have been impossible 10 years ago.

Traditional tissue sequencing can only tell you the “overall average level,” which is like asking “what is the average income in this city.” But single-cell sequencing can tell you “everyone’s income”, thereby discovering details such as the gap between the rich and the poor and the income distribution of different occupations.

In this study, if you just look at the overall picture, you might conclude that “testicles age at a uniform rate with age.” But the single-cell data tells you: No, it’s the TPCs that age first, then the Leydig cells, then the Sertoli cells… and each cell type has its own aging schedule.

This kind of detail is important for intervention. If you want to delay testicular aging, you should first target the TPCs at age 30 (for example, to prevent excessive ECM remodeling), rather than waiting until all cells have collapsed at age 50 and then “treat the head when it hurts and the feet when it hurts.”

6. Two waves of aging “staircase models” challenge traditional concepts

Traditional aging theories often assume that aging is a “continuous” and “uniform” process. But this study clearly demonstrates “stepwise” aging:

20-30 years old: plateau period
Early 30s: first steps (initiation of TPCs)
30-50 years: relatively stable (although cumulative changes)
Early 50s: Second step (functional breakdown)

This reminds us that there may be a “critical point” in ** aging. When the critical point is crossed, the system will undergo qualitative changes. **

This has implications for policy and personal health management: if aging is uniform, then starting intervention at any time is almost the same. But if there is a critical point in aging, then intervening before the critical point may achieve twice the result with half the effort, and intervening after crossing the critical point may achieve twice the result with half the effort.

For testicles, the critical intervention window is between the ages of 30 and 50. If you miss it, it may be too late.

7. Limitations: Dead man’s testicles vs. Living man’s testicles

The study had an inherent limitation: the samples came from body donors. Although the researchers screened “normal” samples (having normal sperm production or having offspring), after all, these people are dead, and we do not know their complete health status, lifestyle, medication history, etc. during their lifetime.

Biopsies (such as those obtained from infertile patients) can provide more detailed clinical information, but it is ethically impossible to perform testicular biopsies in healthy individuals. So it’s a trade-off: either more samples but less information (cadavers), or more information but less samples (patients).

The ideal solution for the future might be to combine large-scale data from cadaver samples + fine clinical information from biopsy samples + longitudinal follow-up studies (following the same people from young to old).

Extended thinking

1. What if we could reverse changes in the ECM?

Since the ECM changes in TPCs at the age of 30 are the “starting signal” of aging, can this change be reversed through drugs or gene therapy?

Some studies are already exploring inhibitors of ECM remodeling (such as MMP inhibitors). If intervention could be started around the age of 30 to prevent excessive thickening of the basement membrane, would it delay the entire aging process?

Of course, this requires great caution. ECM remodeling also has protective effects (such as preventing tissue damage). Excessive suppression may have side effects. But this is a direction worth exploring.

2. Will there be similar “two waves of aging” in the female reproductive system?

Female reproductive aging is often considered a “cliff” - egg production completely stops after menopause. But before menopause, is there a “step” similar to the age of 30 and 40?

Similar single-cell studies have been lacking in the past (because ovarian biopsies are more difficult). But as technology advances, we may be able to see similar aging maps of the female reproductive system in the future. Comparison of reproductive aging between men and women may reveal sex-specific aging mechanisms and may also find common aging pathways.

3. Can these findings be generalized to other organs?

Could the “two-wave aging” model of the testicle also exist in other organs? For example:

Brain: Neurons vs. Glia?
Liver: Hepatocytes vs. stellate cells?
Heart: Cardiomyocytes vs. Fibroblasts?

If different organs have similar “pioneer cells” (like TPCs) that first initiate aging and then trigger a chain reaction, then anti-aging strategies may require “precision strikes” - designing intervention plans for different pioneer cells in different organs.

4. How do long-lived species maintain reproductive health?

Some species (such as some sharks and turtles) can live for tens or even hundreds of years and maintain their reproductive capacity well. Do their testicles not have this kind of “two waves of aging”? Is it still there but the time scale has been lengthened?

Comparing the reproductive aging patterns of different species may reveal evolutionary “anti-aging” strategies and provide inspiration for human intervention.

(View raw data)

Reading date: 2025-10-24 Notes organized: Dylan Chiang

Paper reading: Benefit or burden? Attraction in cross-sex friendship

Wed, 09 Jul 2025 00:00:00 +0000

#paper information

Title: Benefit or burden? Attraction in cross-sex friendship Authors: April Bleske-Rechek, Erin Somers, Crieve Micke, Leah Erickson, Lindsay Matteson, Corey Stocco, Belle Schumacher, Laura Ritchie Journal: Journal of Social and Personal Relationships (2012) Link:

Summary of thesis content

1. Theoretical background and research motivation

The historical novelty of opposite-sex friendships

Traditional psychology defines friendship as a voluntary, cooperative relationship that typically lacks a romantic component. However, evolutionary psychology states that for 99% of human evolutionary history (the hunter-gatherer period), frequent interactions with non-related members of the opposite sex were often associated with reproduction. This kind of “heterosexual friendship” that is not related to reproduction in modern society is a historical novelty.

The Byproduct Hypothesis

The author puts forward the core hypothesis: Sexual attraction in heterosexual friendship is not a functional feature of friendship, but a by-product of human evolved mating strategies (Evolved Mating Strategies) activated in the new modern environment.

The human brain has an evolved mate selection mechanism (for example: men seek short-term sexual opportunities, women seek long-term resources and protection).
When men and women interact as friends in modern settings (work, school), these ancient mate selection mechanisms still operate subconsciously, resulting in attraction.

Evolutionary Basis of Sex Differences

According to parental investment theory (Trivers, 1972), there are significant differences in mate selection strategies between men and women:

Male: Evolutionarily inclined to seize short-term sexual opportunities, so there will be a lower sexual interest threshold, and it is easy to overestimate women’s sexual interest (because the cost of missing an opportunity is higher than the cost of showing wrong affection).
Females: bear higher reproductive costs (pregnancy, breastfeeding), so they are more prone to long-term mate selection and are more picky about sexual partners.

2. Study 1: Opposite-sex friend pairing study (Friendship Pairs)

Research methods

The researchers recruited 88 pairs of real college students with opposite-sex friends (176 people in total). In order to ensure honest responses, both parties were separated in different rooms to fill out the questionnaires, and anonymity and confidentiality were guaranteed. The questionnaire measured their attraction to their friends, their desire to date their friends, and their “guess” about their friends’ attraction to them.

Main findings

Significant gender differences:
- Men’s ratings of attractiveness to their female friends were significantly higher than women’s ratings of their male friends (d = 0.66).
- Men also expressed a stronger desire to date their female friends.
Misperception:
- Male Overestimation: Men tend to think that their female friends are interested in them, even if they don’t actually like him that much.
- Female Underestimation: Women tend to think that their male friends are not interested in them, even if they actually have feelings for her.
Influence of emotional status:
- Male: Regardless of whether he is single or not, his attraction to female friends remains at a high level (in line with the evolutionary short-term mate selection strategy, keeping opportunities open at any time).
- Female: If you are in a stable relationship, your attraction to male friends will decrease significantly; only when you are single, your attraction will be higher (in line with the evolutionary long-term mate selection strategy, focusing on existing partners).

3. Study 2: Cost-benefit analysis across age groups

Research methods

The study was extended to two age groups: Emerging Adults (18-23 years old) and Young and Middle-aged Adults (27-55 years old). Using the “Act Nomination” method, ask subjects to list the advantages (Benefits) and disadvantages (Costs) of opposite-sex friendship and see if they will take the initiative to mention “attraction.”

Main findings

Attractiveness is where the burdens outweigh the benefits:

Regardless of age and gender, subjects ranked attraction/romantic desire as a Cost of friendship 5 times more often than as a Benefit.
- Common cost descriptions include: causing jealousy, complicating relationships, making partners unhappy, and breaking up friendships.

Gender Perspective:
- Although generally viewed as a burden, men are more likely than women to view attraction as a “benefit” (e.g., the possibility of developing a sexual relationship or romance).
- Women are more likely to see it as a nuisance.
Age and Single Effect:
- Overall, middle-aged people are less attracted to friends of the opposite sex than young people.
- Key Exception: Single middle-aged men are just as attracted to friends of the opposite sex as younger men; the same is true for single middle-aged women. This confirms that attraction is highly related to “mate selection status” - when you need to find a partner, friends of the opposite sex are potential radar targets.
Impact on existing relationships:
- People who feel strong attraction to friends of the opposite sex are generally less satisfied with their current romantic partners. This is especially obvious among middle-aged people.

4. Comprehensive discussion

The paper concludes that attraction in opposite-sex friendships is a common phenomenon, not an exception. This supports the view put forward by O’Meara (1989) that opposite-sex friends must face “sexual” challenges.

Support from an evolutionary perspective: The research results are highly consistent with the predictions of evolutionary psychology. Men’s high attractiveness, tendency to overestimate, and their inability to be affected by their relationship status all reflect the short-term mate selection strategies that men have evolved.
Realistic Cost: While attraction may spice up a friendship, data shows it is more often seen as a cost. It can threaten an existing partnership, or it can lead to the breakdown of a friendship because of different intentions (for example, a man wants to develop, a woman just wants to be friends).

#My understanding and experience

After reading this paper, I had several deep feelings, which really overturned some of our established impressions of “depending on dad” - it turns out that many people are actually “depending on dad”, and this kind of dependence is more invisible and stubborn than we imagined. (Note: It seems that the experience of the previous article was mistakenly planted here. The following is a re-writing of this paper)

The process of reading this paper often makes me smile, because it uses very rigorous data to confirm many “stereotypes” we observe in our daily lives.

First of all, it explains why in heterosexual friendships, boys are often considered to be the party with “impure motives.” Data shows that men’s physiological mechanisms seem to make it really difficult for them to completely “turn off” their radar for friends of the opposite sex. This is not to say that boys are all bad, but evolution has implanted a scanning program in their brains that is always on call. This also explains why many girls feel, “We are obviously just good buddies, why did he suddenly confess to us?” Because according to research, it is really easy for girls to underestimate a boy friend’s interest in them.

Secondly, I find the idea of the “by-product hypothesis” very interesting. The “pure friendship between men and women” that we are accustomed to now is actually going against millions of years of evolutionary instinct. It’s no wonder that maintaining weird friendships is tiring, because we have to constantly use reason (prefrontal lobe) to suppress primitive impulses (limbic system).

Finally, it is especially true that attraction is a burden. Although romantic scenes of “friends turning into lovers” are often played in movies, in real life, once one-way or two-way attraction appears in a friendship, it usually brings trouble - embarrassment, suspicion, or jealousy of the other half. This paper tells us that if you find it difficult to find a balance between opposite-sex friendships, don’t worry, this is not your personal problem, this is an evolutionary problem faced by all human beings.

In summary, pure heterosexual friendship exists, but it may require more awareness and boundary management than we think, especially when both parties’ “interpretations” of the relationship may be inherently out of sync.

Reading date: 2025-07-09 Notes organized: Dylan Chiang

Paper reading: Multigenerational persistence: Evidence from 146 years of administrative data

Sun, 08 Jun 2025 00:00:00 +0000

#paper information

Title: Multigenerational persistence: Evidence from 146 years of administrative data Author: Jørgen Modalsli (Statistics Norway) Year: 2017 (Journal of Human Resources) Link:

Research background

This paper explores a core question: Does class reproduction (persistence) not only occur between parents and children, but spans more generations? **

In other words, is your economic status today not only affected by your father, but also by your grandfather, or even your great-grandfather? Past studies have often been limited by data, looking at only two generations (father-son). The few studies that look at multiple generations typically have small samples or are limited to a specific city.

This study draws on Norway’s very strong administrative data, concatenating census data from 1865 to 2011. This allowed the authors to observe 167,411 family lineages, covering the occupation and income information of three generations of grandfather, father and son.

Research methods

1. Data Samples (Four Samples)

The study constructed samples from four different periods to observe changes in different historical stages:

Sample A: Grandfather (1865) → Father (1900) → Son (1910)
Sample B: Grandfather (1865) → Father (1910) → Son (1960)
Sample C: Grandfather (1910) → Father (1960) → Son (1980)
Sample D: Grandfather (1960) → Father (1980) → Son (2011)

2. Occupational classification

Occupations are divided into four broad categories:

White-collar
Farmer
Manual skilled
Manual unskilled

3. Metrics

Odds Ratios: Measures the impact of the grandfather’s occupation on the chance of his grandson entering a specific occupation while controlling for the father’s occupation.
Altham Statistic: A comprehensive liquidity indicator.
Income Rank: Use income ranking to validate findings from occupational data in later samples for which income data are available.

Main findings

1. Grandfather’s influence is significant and lasting

The study found that grandfather’s occupation was significantly predictive of grandchildren in all four periods, even after controlling for father’s occupation.

The coefficient size is approximately 1/3 of the father’s influence.
This means that if two people’s fathers are workers, but one person’s grandfather is a white-collar worker, then the probability of this person becoming a white-collar worker will be significantly higher.

2. Specific professions are more replicable

White-collar workers and Farmers show the strongest multi-generational replicability.
Replication among the blue-collar class (technical and non-technical) is relatively weak and sometimes not significant.
The high replicability of farmers is not surprising (inherited land), but the high replicability of white-collar workers hints at some kind of “elite replication” phenomenon.

3. Institutional changes fail to eliminate multi-generational impacts

Norway has experienced tremendous social change during these 150 years, transforming from a poor agricultural society into a modern country with a comprehensive welfare system and free education.

While the degree of replication among white-collar workers has slightly declined over time (showing increased mobility), multi-generational influence remains.
This shows that the welfare system and the spread of education alone cannot completely cut off the long-term transmission of family advantages.

4. Income data confirms career analysis

In the sample with income data (Sample C & D), the grandfather’s income ranking also has a significant positive impact on the grandson’s income ranking. This confirms that the findings in the occupational data are not simply errors in occupational classification.

Mechanism discussion: Is it “love after meeting” or “family genes”?

In order to explore how this generation-skipping effect occurs, the author conducted two interesting tests:

1. Geographical Distance

If the grandfather’s influence on his grandson comes from “direct teaching” or “introduction through personal connections”, then living far away should have less influence.

Result: Grandsons who move away from their grandfather are indeed slightly less influenced by their grandfather, but the difference is very small. Even after moving far away, my grandfather’s influence was still evident.

2. Time of Death

If the grandfather dies before the grandson is born, or dies during the grandson’s childhood, direct interaction is theoretically impossible.

Result: There is little correlation** between the grandfather’s survival and his influence on his grandson.

Conclusion: This implies that the main mechanism of multi-generational class replication may not come from direct interpersonal interaction (Personal Contact), but from deeper and potential Latent Family Characteristics, which may include genetic talents, family culture, values, or a broader family social network.

My understanding

First of all, what surprised me the most was the “1/3 law”. Even taking your father’s career into account, your grandfather’s influence is still 1/3 as great as your father’s! This means that social mobility is much slower than when we look at pure father-son data. Imagine that a family has to climb from the bottom to the top, or fall from the top to the bottom. The speed of “return to the mean” is actually very slow. It’s like a flywheel with inertia. Once it starts spinning (whether for good or bad), it takes generations to stop.

Secondly, this study severely defeats the optimistic idea that the welfare state can solve everything. Norway is already recognized as one of the most equal countries in the world, with free education and good social welfare. But even in this environment, who your grandfather is is still important. This tells us that what families can give their children is far more than just money or schooling, but also a more abstract “human capital”—perhaps a certain way of thinking, the ability to plan for the future, or the self-confidence of “I know I belong here.” It is difficult for the government to redistribute these things through policies.

Finally, what I find most interesting is the discovery about the “mechanism”. It turns out that you don’t need to have met your grandfather, or even live near him, for his influence to still be passed on to you. This sounds a bit mysterious, but it makes sense if you think about it carefully. This shows that the transfer of family advantages is a “systematic” project. Maybe your grandfather taught your father certain values, and your father educated you in this way; or it may be some kind of hidden genetic advantage. This made me think that each of us is actually carrying the history of our family as we move forward. Part of our achievements are really not the result of our own efforts, but the result of the accumulation of many generations.

In summary, this paper makes me feel more pessimistic about “class replication”, but also more awe-inspiring. It reminds us to be more empathetic when looking at the success or failure of others, because what we see is often just the tip of the iceberg, and beneath the water is a century-long family accumulation.

Reading date: 2025-06-08 Notes organized: Dylan Chiang

Paper reading: Information sharing in financial markets

Sat, 05 Apr 2025 00:00:00 +0000

#paper information

Title: Information sharing in financial markets Author: Itay Goldstein, Yan Xiong, Liyan Yang Journal: Journal of Financial Economics (2024) Link:

Summary of thesis content

1. Research puzzle: Why should secrets be leaked?

In financial markets, “information” is money. Logically speaking, if you know that a certain stock will rise, you should buy it secretly rather than publicizing it everywhere. However, the real world is full of “information sharing”: from early investment clubs to now Reddit (r/wallstreetbets), Twitter, and Seeking Alpha, investors are keen to exchange opinions.

This paper attempts to answer two core questions:

Why are investors willing to share expensive private information?
Who is sharing? Should experts share with novices, or should novices share with experts?

2. Core model and discovery

The author builds an extended model based on Kyle (1985) that includes two types of investors:

H (High-informed): A “veteran” with high information accuracy and even perfect information.
L (Low-informed): A “rookie” with low information accuracy and noisy signals.

Shocking conclusion:

The model equilibrium shows that the direction of information flow is from L to H. In other words, ** “people with less information” will take the initiative to tell information to “people with more information”, while “people with more information” will choose to shut up. **

3. Key mechanism: Trading-Against-Error

Why is L so stupid to tell information to H who is more powerful than him? There is a subtle strategic consideration behind this:

L’s Dilemma: The information in L’s hand contains “real fundamentals” and “noise (error)”. L himself cannot tell the difference, so when he trades based on signals, the noise often pushes the price in the wrong direction (generating a price shock).
H’s ability: H has more accurate information. He can distinguish which part of L’s signal is real and which part is noise.
Benefits of Sharing:
- When L tells H information, H will find an error in L’s signal.
- H will perform Trade against error against this error.
- Result: H’s reverse trade offsets L’s erroneous order flow due to noise. This actually reduces L’s price impact, allowing L to trade at a better price.

To put it simply, L shared information in order to invite H to be his opponent and help him “correct” the market price, thus making L’s own transactions smoother.

4. The lament of the veteran (H): Did you lose money after listening to it?

The paper’s most ironic finding concerns H’s situation:

Ex-post: Once L shares the information, H will of course listen because it can help him earn profits on “wrong trades”.
Ex-ante: However, if H had a choice, he would actually prefer L not to share information.

The reason is that once the information becomes public:

If L knows that H is helping to “correct” the price, L will become Trade more aggressively.
The Market Maker discovers that the market is filled with informed traders and will reduce market liquidity (increase Kyle’s Lambda).
The harm caused by these two indirect effects to H exceeds the direct benefit he obtains from the information.

This is a Commitment Problem: H cannot promise “I won’t listen”, so he is forced into a lower-profit equilibrium.

#My understanding and experience

While reading this paper, I kept thinking of Reddit or stock market forums.

1. Why does “leek” like to post the most?

We often wonder why retail investors (L) especially like to analyze stocks at length on the Internet, while the real big institutional investors (H) usually make a fortune in silence? This paper gives a cool explanation: when retail investors share information, they are actually subconsciously seeking “verification” or “liquidity” in the market. Although retail investors may not think that deeply (what to reduce the price impact), from the perspective of evolutionary equilibrium, retail investors shouting loudly and attracting institutional investors (whether they are copying or counter-trading) may actually make it easier for retail investors’ orders to be completed.

2. The Curse of the Smart Man

I found the part about H particularly philosophical. H obviously has more information and stronger capabilities, but because of L’s “big mouth”, H is forced to get involved in a market with less liquidity and more fierce competition. This is very much like the current financial market, where information is awash. For a top fund manager, he is forced to receive countless tweets, news, and gossip every day. Although every piece of news seems to be profitable (can be used for arbitrage), the overall result is that everyone is fighting in the Red Sea, and the profits are not as good as those in the era of opaque information.

The paper ends by mentioning that this can be used to explain social media. The information on the community is usually of low quality (from L), but can attract the attention of high-frequency traders or hedge funds (H). H makes money by analyzing this “noise” (such as Sentiment Analysis), while L gains a certain degree of market influence through this collective behavior.

In summary, this paper tells us: In the market, silence is golden (for H), but noise may be a survival strategy (for L). **

Reading date: 2025-04-05 Notes organized: Dylan Chiang

Paper reading: Money can buy me love: Gifts are a more effective form of acute social support than conversations

Tue, 07 Jan 2025 00:00:00 +0000

#paper information

Title: Money can buy me love: Gifts are a more effective form of acute social support than conversations Authors: Holly S. Howe, Hillary J. D. Wiener, Tanya L. Chartrand Journal: Journal of Consumer Psychology (2024) Link:

Summary of thesis content

1. Research question: When a friend is sad, should you talk or give a gift?

Everyone goes through low points (a breakup, a bad job, a simple bad mood). When we seek support from others, we usually receive two forms of emotional support:

Conversational Support: Listen to the other party’s complaints and provide comfort. This is the most common form.
Gifts: Give a small material gift (such as snacks, flowers, drinks), aiming to make the other person happy rather than solving actual problems (such as not helping the other person pay the rent).

Past research has mostly focused on conversation, or the motivations of gift givers. This paper starts from the perspective of the Receiver: Which way can make me feel better? **

2. Core Discovery: Gift > Dialogue

Across seven studies (including real interactions and recollections), the authors consistently found: **Receiving small gifts (such as candy bars, bouquets) can significantly improve the recipient’s emotional recovery (Emotional Recovery) more than having a supportive conversation of the same quality and cost. **

This is a rather counter-intuitive finding, as we often think of “companionship” and “listening” as priceless, and even think that gift-giving may be too materialistic or perfunctory.

3. Why? Mechanism discussion

Key Mechanism: Perceived Sacrifice

Gifts are more effective because the recipient perceives gift giving as a greater sacrifice. But it’s not just about the money. The study found that even if the time and money costs are controlled (for example, comparing “spending 15 minutes chatting” vs “spending 15 minutes standing in line to receive a free gift”), gifts still win.

Receiver-Focused

A gift is considered a greater sacrifice because it is considered to be Receiver-focused.

Gift: One-way resource transfer. I bought this for you purely to make you happy.
Dialogue: Two-way interaction. Conversations are often co-created, and supporters may gain emotional connections or demonstrate their own value during the conversations. Therefore, the recipient may feel that the conversation is also a benefit to the supporter, rather than purely self-sacrifice.

4. Key experimental design and results

Study 1A: Real Behavior

Situation: Real-life friend pairing in the lab. The recipient writes down a worry.
Operation: Supporters are randomly assigned to receive either “a 5-minute comfort conversation” or a “snack gift worth $3.”
Results: People who received snacks had a significantly greater decrease in negative emotions than those who had a conversation.

Study 2: Same behavior, different framing (Framing)

Situation: A friend buys you breakfast and leaves a note.
Gift Frame: “I bought a snack for you!” (emphasis on buying it specially for you)
Conversation Frame: “I’ll go over and talk to you later!” (Emphasize that this is a preparation for subsequent chats, and buying breakfast is a rule of turns)
Result: Even though the content of the behavior is almost the same (both have breakfast), the comfort effect is better when framed as a “gift”.

Study 3A: Time vs. Money

Compare three groups:
1. Money Gift: Spend $8 on flowers.
2. Gift of time: Spend 15 minutes waiting in line to receive free flowers.
3. Conversation: Spend 15 minutes chatting.
Result: Whether it is money or time, as long as it is in the form of a “gift”, the effect is better than conversation. This proves that it is not the “money” itself that plays a role, but the act of “gifting” itself that is seen as a purer signal of care.

#My understanding and experience After reading this paper, I really felt a “brain-opening” feeling. It challenges the sacred status of “heart-to-heart talk” in our culture.

1. “Talk” is actually very tiring

The author mentions that conversations are “co-created,” which made me think about how sometimes I don’t want to talk when I’m feeling extremely low. Receiving “conversation support” actually requires the recipient to expend energy - I have to organize the language, I have to explain what happened, and I even have to take care of the listener’s emotions (can’t make the atmosphere too tense). In comparison, gifts are zero-burden. A friend hands me a glass of milk. I just put a straw in and drink it, and dopamine hits. I don’t need to explain why I’m sad, but I do get the “I care about you” signal. This is a purer, lower-resistance love.

2. The “ritual sense” and “sacrifice” of gifts

The discussion about “sacrifice” in the paper is interesting. We often say “Talk is cheap” (empty talk is cheap), which seems to be verified in psychology. The conversation may happen casually (talking over a meal), but going out of your way to buy something (even if it’s just supermarket chocolate) involves a “special” action. This “special” is a kind of sacrifice, a signal that “I interrupted my original schedule for you.”

3. Practical advice: Don’t ask, just buy it

This research gave me a great networking strategy. In the past, when a friend encountered something bad, my first reaction was usually to send a message and ask: “Are you okay? Do you want to chat?” Now it seems that a more effective approach may be: Buy his favorite drinks or snacks directly in front of him (or order takeout to deliver to him). This not only avoids the pressure of the other party to reply to the message, but also directly improves the physiological mood through the sensory pleasure (sugar, aroma) brought by the material.

Of course, this is not to say that dialogue is unimportant. In the long run, solving problems may still require in-depth communication. But in the moment of “Acute Stress”, that is, the moment when you have just lost love and been scolded, money (bought gifts) can really buy a little happiness.

Reading date: 2025-01-07 Notes organized: Dylan Chiang

Paper Reading | Dylan Chiang

Paper reading: Capsaicin diet drives gut inflammation and exosomal miR-17-3p elevation in idiopathic short stature

Research background

Research methods

Main findings

1. Capsaicin intake causes growth retardation and intestinal inflammation

2. Exosomal miR-17-3p is a key messenger

3. Molecular mechanism: inhibiting chondrocyte proliferation

Research significance

1. Revealed the new mechanism of the “gut-bone axis”

2. Provides a new etiological explanation for ISS

3. Potential therapeutic targets

My understanding

1. Molecular-level interpretation of “You are what you eat”

2. Growth hormone is not a panacea

3. The collision of traditional wisdom and modern science

Related resources

Paper reading: Exercise is good for the brain but exercising outside is potentially better

Research background

Research methods

Main findings

1. Outdoor sports significantly increase the P300 amplitude

2. The effect of indoor exercise is not as good as expected

3. No significant difference in reaction time

Research significance and inspiration

My understanding

1. Environment is an extension of cognition

2. Redefinition of “efficiency”

3. Simple and easy

Related resources

Paper reading: How people think about being alone shapes their experience of loneliness

Research background

Research methods

Main findings

1. The media’s “high stigma” against solitude

2. The causal impact of beliefs on loneliness

3. The “buffering and catalyzing” effect in daily life

Research significance and inspiration

1. The “echo effect” of public health propaganda

2. New approaches to cognitive intervention

My understanding

1. The self-fulfilling prophecy of cognitive frameworks

2. Media Ecology and Mental Toughness

3. A “moderate turn” in policy

4. Conclusion: Solitude is a kind of self-healing ability

Related resources

Paper reading: Topological turning points across the human lifespan

Research background

Research methods

1. Data analysis scale

2. Manifold Learning

Key findings: Five life stages and four turning points

Detailed description of key findings

Research significance and inspiration

1. Redefine “adolescence”

2. The guiding value of non-linear development

My understanding

1. Trade-off between structure and function

2. Synergy of biological markers

3. Reconstruction of the definition of “adolescence”

4. Precursors and nonlinear characteristics of aging

Related resources

Paper reading: Video game play is positively correlated with well-being

Research background

Research methods

Main findings

1. Game time is positively correlated with happiness

2. “Quality of experience” is more important than “length of time”

3. Self-report bias

Research significance and inspiration

1. Challenging the foundations of policymaking

2. A new paradigm for industry cooperation

My understanding

1. The power of “data honesty”

2. Correlation\Causality

3. Games as the “Third Space” in the Digital Age

Related resources

Paper reading: The Savanna Theory of Happiness

Research background

Research methods