Saturday, March 20, 2021

Sex differences in early experience and the development of aggression in wild chimpanzees

Sex differences in early experience and the development of aggression in wild chimpanzees. Kris H. Sabbi et al. Proceedings of the National Academy of Sciences, March 23, 2021 118 (12) e2017144118;

Significance: Chimpanzees, human’s close evolutionary relatives, are a tractable model system for understanding how physical aggression can develop in the absence of gender socialization. Here we used 13 y of behavioral data and a targeted 3-y social development study to document clear sex differences in chimpanzees’ early aggressive experiences, supporting the possibility for social experience to shape sex-typed behavior in the absence of explicitly taught gender norms. However, as young males’ own aggressive behavior provoked aggressive responses from others, experiential differences were influenced by early-emerging behavioral differences that already resembled adult patterns. By demonstrating interactions between exposure to aggression and developing behavior, our results add an important perspective to long-standing debates over the origins of gender differences in human aggression.

Abstract: Sex differences in physical aggression occur across human cultures and are thought to be influenced by active sex role reinforcement. However, sex differences in aggression also exist in our close evolutionary relatives, chimpanzees, who do not engage in active teaching, but do exhibit long juvenile periods and complex social systems that allow differential experience to shape behavior. Here we ask whether early life exposure to aggression is sexually dimorphic in wild chimpanzees and, if so, whether other aspects of early sociality contribute to this difference. Using 13 y of all-occurrence aggression data collected from the Kanyawara community of chimpanzees (2005 to 2017), we determined that young male chimpanzees were victims of aggression more often than females by between 4 and 5 (i.e., early in juvenility). Combining long-term aggression data with data from a targeted study of social development (2015 to 2017), we found that two potential risk factors for aggression—time spent near adult males and time spent away from mothers—did not differ between young males and females. Instead, the major risk factor for receiving aggression was the amount of aggression that young chimpanzees displayed, which was higher for males than females throughout the juvenile period. In multivariate models, sex did not mediate this relationship, suggesting that other chimpanzees did not target young males specifically, but instead responded to individual behavior that differed by sex. Thus, social experience differed by sex even in the absence of explicit gender socialization, but experiential differences were shaped by early-emerging sex differences in behavior.

Keywords: aggressive developmentsocial developmentfission–fusionearly social experienceexposure to aggression

Development of aggression in wild chimpanzees: Social experience differed by sex even in the absence of explicit gender socialization, but experiential differences were shaped by early-emerging sex differences in behavior

Sympathy for the underdog: people are inclined to adopt the emotional perspective of powerless (versus powerful) others

Sympathy for the underdog: people are inclined to adopt the emotional perspective of powerless (versus powerful) others. Fran├žois Quesque, Alexandre Foncelle, Elodie Barat, Eric Chabanat, Yves Rossetti & Jean-Baptiste Van der Henst. Cognition and Emotion, Mar 16 2021.

Abstract: Upon learning of the story of Cinderella, most people spontaneously adopt the emotional perspective of this helpless young woman rather than of her older sisters who oppress her. The present research examines whether this pattern reveals a general human tendency to empathise more with the emotions of individuals with low (versus high) power. Six experiments (N = 878) examined how power influences the focus of people’s emotional attributions. Participants were presented with situations in which one character exercised power over another one and had to resolve a referential ambiguity by considering the perspective of one or the other character. Results show that participants largely privileged the emotional states of the low-power character over those of the high-power character. This effect was observed with different types of stimuli (comics and video clips), with high- and low-power roles attributed to pairs of different genders (Experiments 1–4) or same gender (Experiments 5–6). Finally, the tendency persisted – though it was reduced – when participants adopted a less passive role with respect to the characters (Experiment 3) and when power occurred in a less despotic way (Experiment 6). Results are discussed with respect to social attention and sensitivity to fairness.

KEYWORDS: Perspective-takingemotionpowermentalisingreferential ambiguity

Spending on clothing is linked to more happiness among males (less happiness among females); spending less on alcohol is linked to less happiness; less luxury has only a limited relationship with happiness

Happiness and Consumption: A Research Synthesis Using an Online Finding Archive. Ruut Veenhoven et al. SAGE Open, March 19, 2021.

Abstract: There is a considerable amount of research on the effect of income on happiness, but only a limited number of studies have considered how the spending of income works out on one’s happiness. In this article, we take stock of the scattered findings on the relation between consumption and happiness. We cover 379 research findings observed in 99 empirical studies. We use a new method of research synthesis, in which research findings are first described in a comparable format and then entered in an online “findings archive” (World Database of Happiness). This technique allows a condensed presentation of the many research findings, while providing readers access to the full results through hyperlinks from the text. Our systematic review reveals some unexpected findings, but does not provide a conclusive answer to the question of what patterns of consumption provide the most happiness for what type of people. Suggestions for further research are provided.

Keywords: life satisfaction, consumption, informed choice, research synthesis, findings archive

In this article, we explored a new strand of consumer research, using a novel presentation method of “link-facilitated research synthesis.” The aim was to see what patterns of consumption produce the most happiness for what kind of people. What have we learned?

What We Know Now

Consumption is related to happiness, at least some kinds of consumption are. Although most of the correlations are small and insignificant, we did see several substantive links. When interpreted as denoting a causal effect, some findings suggest that a “Calvinist” or conservative consumption style tends to foster happiness. This appears in the findings on spending on durables and education. The observed correlation between house ownership and happiness can also be seen as a fruit of solid spending. Yet, we also found links between experience consumption and happiness.

Contrary to claims by critics of consumerism, we did not find much evidence of consumption reducing happiness. Owning a car does not seem to lower happiness. Some counter-intuitive findings reported in section “Daily Expenses and Happiness” are (a) spending on clothing is associated with greater happiness among males, but with less happiness among females; (b) high expenditure on communication is associated with less happiness; (c) the expected negative correlation between health expenditure and happiness persists when self-rated health is controlled; (d) spending less on alcohol is associated with lower happiness levels; (e) less luxury has only a limited relationship with happiness. An unexpected finding reported is section “House Ownership and Happiness” was that home ownership is most related to happiness among people with poor mental health. A suggestive finding reported in section “Car ownership and happiness” was that driving an expensive car does not go with greater happiness. All this requires further research.

What We Do Not Know Yet

The available studies on consumption and happiness do not show to what extent the correlations between consumption and happiness stem from a causal effect of consumption on happiness; only in the case of house ownership, there is some causal evidence.

We are still largely in the dark about the relationship between happiness and the use of goods and services purchased, such as in the above-mentioned case of car ownership and happiness. This aspect of consumption is intertwined with wider lifestyle and time-use issues. We can learn more about consumption’s effects on happiness using the methods of multiple moment assessments, such as the experience sampling method (ESM) or day reconstruction method (DRM; see, for example, Burger et al., 2020 on the relationship between lottery play and happiness for a study that uses multiple moment assessment).

This review focuses on what the relationship between consumption and happiness is, which is what consumers need to know to make informed decisions. This information will be more convincing if they also understand why particular kinds of consumption add or detract from happiness. Such effects are likely to differ across products, people, and situations; some will involve different psychological processes, such as need gratification, social comparison, as well as identification. For that purpose, we need studies that focus on particular products and particular consumers, as is common practice in marketing research.

Why So Many Blank Spaces?

The number of research findings on consumption and happiness is small, in particular when compared with the large body of research literature on consumer satisfaction with products and services. Another striking observation is that the few available studies are not very sophisticated: Most of the findings are cross-sectional, the columns for longitudinal and experimental studies in the tables are largely empty and the tables with specifications also show many blank spaces. Why is this the case?

We follow Stanca and Veenhoven (2015) who note that one of the reasons seems to be theoretical short-sightedness. Many mainstream economists still equate consumption with happiness in general and consumer satisfaction in particular. These economists are unaware of the above noted difference between expected and experienced utility and do not see the difference between needs and wants, nor do they know that happiness depends more on meeting the former than the latter. (Veenhoven, 2009)

Another reason is in commercial self-interest. Producers are interested in selling their products in the first place. They spend a lot of money on marketing research to get a better picture of what consumers expect will make them happy and on advertisements to influence these expectations and link to their products. Whether these products actually add to a consumer’s happiness is not the producer’s prime concern. Although there is a considerable body of research on consumer’s experienced satisfaction with products and services, there is little research on the effect of using products on satisfaction with life, not even in sectors where wider life satisfaction is evidently at stake, such as in the case of life insurances or residential care. This lack of research is part of a wider market failure. As there is limited dependable information on the long-term consequences of big consumer purchases on happiness, this could explain why there is no market competition on happiness effects and hence no product development in this direction.

The market is unlikely to solve this problem; governments and consumer unions are in a better position to press for more research on the effects of consumption on happiness. Scientists can also make a difference by informing the public about what kinds of consumption are conducive to happiness.

Lines for Further Research

How can we further expand our current body of knowledge? We have learned that more cross-sectional studies will not provide much more information. Thus, the focus should be on longitudinal studies that allow a view on changes in happiness following changes in consumption. One way to obtain follow-up data is to insert questions on consumer choice in ongoing large-scale longitudinal studies in which happiness is measured, such as the Australian HILDA, the British “Understanding Society Survey” and the German Socio-Economic Panel (GSOEP).14 Another option is to add questions on happiness to ongoing follow-up studies on consumption. One can think here of longitudinal studies on broad consumer behavior or particular kinds of consumption, such as the U.S. National Consumer Panel15 or the Quebec Longitudinal Study on Nutrition and Aging.16

The most informative research will be experimental studies, in which consumption change is induced externally and subsequent effects on inner happiness are traced, such as in the above-mentioned examples of subsidized house ownership (Rohe & Stegman, 1994) and the natural experiment with compulsory health insurance (Keng & Wu, 2014).

Next to such descriptive studies on what effects consumption exerts on happiness; we need more research on how consumption affects happiness; in other words, we need to understand the causal mechanisms involved.

Adaptation of sperm whales to open-boat whalers: Captures dropped 58pct in a few years, it appears that whales swiftly learned effective defensive behaviour

Adaptation of sperm whales to open-boat whalers: rapid social learning on a large scale? Hal Whitehead, Tim D. Smith and Luke Rendell. The Royal Society Biology Letters, Volume 17, Issue 3, Mar 17 2021.

Abstract: Animals can mitigate human threats, but how do they do this, and how fast can they adapt? Hunting sperm whales was a major nineteenth century industry. Analysis of data from digitized logbooks of American whalers in the North Pacific found that the rate at which whalers succeeded in harpooning (‘striking’) sighted whales fell by about 58% over the first few years of exploitation in a region. This decline cannot be explained by the earliest whalers being more competent, as their strike rates outside the North Pacific, where whaling had a longer history, were not elevated. The initial killing of particularly vulnerable individuals would not have produced the observed rapid decline in strike rate. It appears that whales swiftly learned effective defensive behaviour. Sperm whales live in kin-based social units. Our models show that social learning, in which naive social units, when confronted by whalers, learned defensive measures from grouped social units with experience, could lead to the documented rapid decline in strike rate. This rapid, large-scale adoption of new behaviour enlarges our concept of the spatio-temporal dynamics of non-human culture.

4. Discussion

While a combination of H1–H3 might produce a steep decline in strike rate, social learning of defensive measures between social units (HX) is the best-supported explanation for the rapid decline in strike rate following the first sperm whale sighting within a region. The whalers themselves wrote of defensive methods that they believed the whales were adopting, including communicating danger within the social group, fleeing—especially upwind—or attacking the whalers [17,18] (figure 1). Deep dives would also have been effective. But, perhaps the most straightforward change would be for sperm whales to cease their characteristic defensive behaviour against their most serious previous predator, the killer whale, Orcinus orca. Gathering in slow-moving groups at the surface and fighting back with jaws or flukes often works against killer whales [19,20], but will have only assisted the relatively slow-moving, surface-limited, harpoon-bearing open-boat whalers.

There are other behavioural changes that the whales may have made in response to whaling, but their impact on strike rates is less clear. There is some evidence that sperm whales formed larger groups in response to whaling [15], but this would likely have increased rather than decreased strike rates. They may have learned to avoid the whalers before the whalers detected them, but this should generally have reduced the mean detection range of the whalers and so increased the strike rate. However, if whales fleeing at long ranges made themselves more visible by blowing hard and showing their bodies forcefully, so increasing the number of sightings with groups that were not easily struck, this might have additionally decreased the strike rate.

Thus, there are learned behavioural changes that the sperm whales could have made to reduce strike rates, and some anecdotal witness that they did so. However, learning as individuals or within social units is not supported as the sole cause of the initial decline in strike rate. To achieve the observed reduction in strike rate through behavioural change, some mechanism must have allowed naive whales without the experience of whalers to receive the benefits of experience.

We suggest that naive social units learned defensive measures from grouped experienced social units and adopted them. Encounters with whalers typically lasted hours, and sperm whales through their echolocation and communication systems can probably sense and coordinate behaviour over ranges of several kilometres. Other processes could have enhanced the social learning process. If groups were particularly likely to split between or within social units after an experience with whalers, and then to join other units, this will have increased the probability that a naive animal was grouped with an experienced individual during its first encounter with whalers.

Our analysis provides substantial support for rapid (less than 20% generation time, so much too fast for genetic evolution) social learning over large spatial scales. The ability of sperm whales, or potentially other species, to rapidly change behaviour in the face of a new anthropogenic threat by making use of social learning has implications for the population significance of new threats, and their assessment. Data from the earliest exposures may not generalize to later periods, and vice versa.

Increased subjective age (personal aging rate perception) increases mortality very much; biggest factors in SubjAge are physical health and expections of sex life in 10 years

PsychoAge and SubjAge: development of deep markers of psychological and subjective age using artificial intelligence. Alex Zhavoronkov et al. Aging, Volume 12, Issue 23 pp 23548—23577, December 8, 2020.

Rolf Degen's take: Higher subjective age doubles mortality risk, and one of the top concomitants of subjective age is how satisfying people expect their sex life to be in 10 years’ time


Aging clocks that accurately predict human age based on various biodata types are among the most important recent advances in biogerontology. Since 2016 multiple deep learning solutions have been created to interpret facial photos, omics data, and clinical blood parameters in the context of aging. Some of them have been patented to be used in commercial settings. However, psychological changes occurring throughout the human lifespan have been overlooked in the field of “deep aging clocks”.

In this paper, we present two deep learning predictors trained on social and behavioral data from Midlife in the United States (MIDUS) study: (a) PsychoAge, which predicts chronological age, and (b) SubjAge, which describes personal aging rate perception. Using 50 distinct features from the MIDUS dataset these models have achieved a mean absolute error of 6.7 years for chronological age and 7.3 years for subjective age. We also show that both PsychoAge and SubjAge are predictive of all-cause mortality risk, with SubjAge being a more significant risk factor.

Both clocks contain actionable features that can be modified using social and behavioral interventions, which enables a variety of aging-related psychology experiment designs. The features used in these clocks are interpretable by human experts and may prove to be useful in shifting personal perception of aging towards a mindset that promotes productive and healthy behaviors.


In this article, we present two novel aging clocks created within the deep learning paradigm — PsychoAge and SubjAge. Both these clocks use the same set of 50 psychosocial features to estimate human chronological age and subjective age, respectively. These clocks showed superior performance during CV in MIDUS 1 (MAEPsychoAge= 6.70 years; MAESubjAge= 7.32 years) and were verified in two other large data sets — MIDUS 2 and MIDUS Refresher (Table 1). In terms of epsilon accuracy, PsychoAge reached a score of 0.78 in MIDUS 1, and SubjAge — 0.74.

Having trained and verified the final models, we aimed to understand how PsychoAge and SubjAge see human aging and what features they pay the most attention to. With a tandem PFI-DFS approach we ranked all features according to their relative importance. Top-5 important features in both clocks were associated with health conditions (e.g. headache frequency) and relationship status (marital status, expectations from sex life in 10 years). Less significant features greatly differ in their relative importance for SubjAge and PsychoAge predictions. For example, top-20 PsychoAge features contain only one personality trait — neuroticism. Meanwhile, the only personality traits encountered among top-20 SubjAge features are — extraversion and openness.

These three personality traits, along with conscientiousness and agreeableness form “the big five traits”, which are commonly used in practice and scientific research to describe the human mental state landscape. High neuroticism is characteristic of emotional instability and common mental disorders, such as mood disorders, anxiety, and substance use disorders. Openness and extraversion, on the other hand, are considered more balanced traits, although their abnormally low scores are also related to social phobia and agoraphobia [27]. Positive orientation, seeking warmth, social interaction, and emotional stability may play an important role in psychological aging.

We hypothesized that the human mind evolves throughout the lifespan, which results in some traits, beliefs, or priorities shifting — not always in unison or at the same speed. At certain life stages, career-related priorities may rise, while at others they may fade and be replaced by different priorities. These lifelong progressions of the psyche eventually get recognized by the neural networks we constructed to let them build an image of psychological aging.

This idea of human mind progression is described in much more detail in the review of SST by Laura Carstensen. SST suggests that younger people are more goal-oriented, interested to obtain new knowledge and skills, while older people tend to value emotionally meaningful goals more.

To identify the psychosocial features that change while a person advances from one age group to another we trained separate DNNs on MIDUS 1 samples from three age groups (25-39, 40-64, 65-75 years). First, we defined the psychological aging core — variables that remain highly important (top-25) across all age groups (Table 2). The core contained not only strictly psychological features, however. To illustrate, marital status, hypertension medication, headaches, and body mass index were among the seven core features required for accurate chronological age prediction. Interestingly, neuroticism score also belonged to the same psychological aging core, as well as seeing the community as a source of comfort. Psychological traits within the subjective aging core contained aspirations scale, extraversion, openness, positive reappraisal prevalence, and two career-related variables — effort put into work now and work expectations in 10 years. In contrast to the first psychological core, which contained few psychological traits, the subjective core consisted almost exclusively of psychological features.

This highlights an important distinction between aging per se (as judged by PsychoAge) and our perception of it (as judged by SubjAge): subjective aging is mostly dependent on internal causes.

We also explored the uniquely important features for each age group — features that emerged only in one top-25 set. Since these features were recognized as important only in these groups, it may be assumed that they shift the most markedly during the corresponding life periods. To illustrate, young adults were not the only age group who responded affirmatively to the statement “Forceful describes you well”, but rather many of these people went through a transformation that affected their forcefulness. Detecting such a change was essential for a predictor to accurately predict whether a person was at the beginning or the end of this phase of life.

On their own, DNNs are unable to tell generational and age-related changes apart or tell the difference between pro-longevity and progeroid features. Thus, the results of the feature importance analysis should always be cautiously inspected and verified in more rigorous settings. Still, feature importance analysis is a powerful tool for hypothesis generation and the verification of overall biological relevance.

While neuroticism was identified as a part of the psychological aging core, it was also a uniquely important subjective aging feature in the elderly MIDUS 1 subsample. This may be interpreted as neuroticism progressing unnoticed by an individual until old age when it starts to affect the perception of age. Previous studies identified that neuroticism tends to cause low emotional differentiation, anxiety, and depression in old people [2829].

Other personality traits rendered important for subjective age estimation in the elderly were optimism, being outgoing, and content with life in general. These results indicate that these might be top-priority features to focus on while developing policies aimed to involve the elderly in social life. Several studies have shown the importance of a social and productive lifestyle during aging [3031]. psychologically important and active events may protect against aging diseases, such as dementia [32].

After establishing which variables are important in absolute terms, we aimed to measure the models’ response to changes in their values. Using mixed-effects linear models, we explored the monotonic trends between 50 variables, PsychoAge and SubjAge predictions (Supplementary Figure 1 and Supplementary Figure 2).

Once again, neuroticism showed unique behavior. Contrary to the other big five traits, neuroticism score was associated with higher SubjAge and lower PsychoAge. More specifically, people within the same PsychoAge group could have >5 years of SubjAge difference due to differences in neuroticism score alone. This verifies our previous conjecture that neuroticism is a key marker of subjective aging and may be used as a sensitive measure of emotional states and late-life depressive symptoms.

Other big five traits also had significantly large effects on both PsychoAge and SubjAge. For example, a person with the bottom openness score would feel 7.2 years older than their PsychoAge counterpart with the top score. In the meantime, a person with the bottom openness score would be 5.3 years younger, as measured by PsychoAge, than their SubjAge counterpart with the top score. Similar tendencies could be observed for most other personality traits, thus building a strong case for SST.

Interestingly, personal opinion on when middle age starts and ends was significantly associated with higher PsychoAge but does not affect SubjAge. We hypothesize that this is an indication of “time dilation” associated with aging. As people get older, they place “middle age” higher and higher, as if their lifetime dilates, while younger participants may have stereotypes about aging and place “middle age” lower. An excellent study on the topic of perception of age stereotypes and self-perception of aging has been written by Hummert [33].

Among the health-related features, the distinction between internal and external health locus of control is of utmost interest. Health locus of control is a set of personal beliefs and experiences that determine whether a person takes responsibility for their health (internal locus) or considers it to be outside of their power, fully dependent on external factors (external locus). Internal locus of control is associated with a problem-solving mindset, while external locus is tied to depression, anxiety, and suicidal thoughts, as well as maladaptive behaviors [34]. We demonstrated that external locus of health control is a rare feature that demonstrated a linearly positive effect on both PsychoAge and SubjAge. It was the only feature to offer no payoff in at least one aging dimension, except for “Taking prescription medications for blood pressure”. Internal control, per contra, did not display concordant linearly negative effect on. Instead, it decreased SubjAge and decreased PsychoAge, just as most other health-related variables.

While the external locus of control was a senopositive (higher values increase age predictions) feature in both aging dimensions, some features were identified as double senonegative (higher values decrease age predictions). Increasing values for the variables from the relationships category were associated with lower PsychoAge and SubjAge, thus favoring single people content with their sex life, who expected to remain sexually active in 10 years. In this case, it is difficult to conclude the cause-effect relation between psychological aging and sexuality. Is reduced libido a precondition to becoming subjectively old? Or does feeling old due to other factors make people less interested in the sexual aspect of life? Can more satisfying sex life prolong healthy longevity, or does PsychoAge simply see higher sex drive as a feature more frequently encounter in the youth? More thorough research is required to answer these questions as well as similar questions concerning other variables.

Although the effect of most variables on PsychoAge and SubjAge was shown to be discordant, the magnitude of their effects on these two measures of psychological aging is not equal. Since the target variable in the mixed-effects model is expressed in years, Table 3 can be used to approximate how a shift in a psycho-social parameter will affect PsychoAge or SubjAge, and which one of them will change more. For example, the variable “Rate health in 10 years” is a survey question that measures health expectation on a scale from 0 to 10, from worst to best. Each increment increases PsychoAge by 0.5 years but also decreases SubjAge by 1.0 years. This yields an “exchange rate” of 2 subjective years lost per 1 chronological age gained. Other features have their own exchange rates, which may be manipulated to accumulate “net profit” in both SubjAge and PsychoAge dimensions.

Other directional feature analysis methods may be more appropriate for navigating the psychological aging landscape since linear mixed effect models operate based on multiple assumptions and simplifications. More specifically, they treat all features independently and approximate the complex interrelations between PsychoAge and SubjAge that may be in place with a random intercept. Accumulated local effects or more sophisticated Shapley value analysis may handle the convoluted feature interrelations more efficiently.

To further validate PsychoAge and SubjAge we tested their prediction errors (delta) as all-cause mortality risk factors (Figure 6). SubjAge delta was proven to be a more powerful risk factor than PsychoAge. More specifically, the SubjAge delta beyond ±5 years was associated with roughly doubling or halving the mortality rate.

We also tested the 50 psychosocial markers of aging as risk factors. We identified significant mortality risks associated with certain factors among (i) health features (“Health compared to others your age”, “Rate current health”, “Shortness of breath while walking up a slight hill”), (ii) personality traits (“Conscientiousness personality trait”, “Agency personality trait”), (iii) psychological beliefs (“Live for today”, “Positive reappraisal”, “Lower aspirations”), (iv) well-being (“Satisfied with life at present”), and, (v) demographic factors (“Chronological age”) (Figure 7).

A problem frequently encountered even by psychologists is obtaining sufficiently detailed information about their patients while keeping the data collection process as short as possible to avoid survey fatigue. In this work, we propose a solution to this survey length-descriptiveness balance problem based on modern deep learning and biogerontological methods. The solution is a relatively short list of features that are both modifiable and provably important in the context of aging.

Some studies show that family history is a source of numerous highly important aging-related features [35]. For example, having long-lived parents and grandparents is strongly correlated with a longer lifespan. However, such factors are not easily modified, especially if the grandparents are no longer alive. Therefore, in this study, we have deliberately limited questions on non-modifiable historical factors to give our surveys more practical value. We demonstrated that variables related to health and closer personal relationships play a crucial role in chronological and subjective age prediction. Furthermore, images about life changes, for instance, when females or males enter middle age, demonstrate a strong predictive power. We suggest that modifying the behavior and the mindset via these variables may be a promising therapeutic concept.

The factors comprising the developed aging clocks can be used to create individual behavioral therapies that would make them feel and actually become biologically younger. For example, PsychoAge can be used to quantify the beneficial effects of daily vigorous-intensity activity on their rate of aging. SubjAge, in its turn, can be used to quantify the beneficial effects of physical activity on personal perception of age.

Focusing on such modifiable age-related features while being able to score lifestyle choices numerically offers interesting opportunities to both professional therapists and individuals seeking self-improvement. We believe that the described approach has a high potential to increase longevity conscience on a population level.

When the category of close relationships is considered, people with access to PsychoAge and SubjAge may choose to develop stronger bonds, get married, or stay married out of an egoistic incentive to prolong their healthspan. The beneficial effect of close relationships and intimacy on health was previously shown in multiple studies, and we believe that drawing people’s attention to such physical-mental health connections should not be neglected [36].

Extracting actionable items from human biological profiles, such as transcriptomic or proteomic profiles, is an actively researched subject. The profiles associated with human psychology can also be subjected to similar workflows to devise personal behavioral therapy plans. In this study, we have demonstrated how the combination of deep learning and aging clocks can be used to create psychological surveys that promote longevity consciousness and personal improvement. These tools and methods could be applied in a wide range of research areas, including psychiatry, longevity, psychology, and psychophysiology for the greater good of society.

Inducing cognitive structure led novice consumers to experience numbness (less intense emotion); however, shifting experts away from using their cognitive structure restored their experience of emotion

Emotionally Numb: Expertise Dulls Consumer Experience. Matthew D Rocklage, Derek D Rucker, Loran F Nordgren. Journal of Consumer Research, ucab015, March 15 2021.

Abstract: Expertise provides numerous benefits. Experts process information more efficiently, remember information better, and often make better decisions. Consumers pursue expertise in domains they love and chase experiences that make them feel something. Yet, might becoming an expert carry a cost for these very feelings? Across more than 700,000 consumers and 6 million observations, developing expertise in a hedonic domain predicts consumers becoming more emotionally numb – i.e., having less intense emotion in response to their experiences. This numbness occurs across a range of domains – movies, photography, wine, and beer – and across diverse measures of emotion and expertise. It occurs in cross-sectional real-world data with certified experts, and in longitudinal real-world data that follows consumers over time and traces their emotional trajectories as they accrue expertise. Further, this numbness can be explained by the cognitive structure experts develop and apply within a domain. Experimentally inducing cognitive structure led novice consumers to experience greater numbness. However, shifting experts away from using their cognitive structure restored their experience of emotion. Thus, although consumers actively pursue expertise in domains that bring them pleasure, the present work is the first to show that this pursuit can come with a hedonic cost.

Keywords: expertise, emotion, hedonic, consumer knowledge, language, attitudes