Are the roots of human economic systems shared with non-human primates? Elsa Addessi, Sacha Bourgeois-Gironde. Neuroscience & Biobehavioral Reviews, December 23 2019. https://doi.org/10.1016/j.neubiorev.2019.12.026
Highlights
• Review of extant approaches to non-human primate economic behaviour in terms of several economic paradigms.
• We spell our definitional criteria for what can count of economic behaviour – an issue which is generally side-lined through the application of economic models to animal behaviour.
• We focus on a plausible evolutionary continuity between non-human and human primates’ economic behaviour.
• We report similar cognitive abilities and behavioural performances through a series of individual tasks. As far as individual decision-making is concerned, Prospect Theory is a predictive and biologically unifying model.
• We report behavioural differences between human and non-human primates and between distinct species of non-human primates in cooperative and competitive dyadic tasks (experimental games).
• We report and analyse laboratory and field-experiments showing symbolic activities linked to the use of intrinsically valueless tokens in trading activities.
• Some of these symbolic activities show the capacity to barter and even proto-monetary organization.
• We argue in favour of evolutionary precursors of complex human economic abilities present among non-human primates.
• We, however, point to the relative inability among non-human primates, to exert a joint control over combined cognitive computational and symbolic abilities, which may explain their non-having developing economic sophistication as well as humans.
Abstract: We review and analyze evidence for an evolutionary rooting of human economic behaviors and organization in non-human primates. Rather than focusing on the direct application of economic models that a priori account for animal decision behavior, we adopt an inductive definition of economic behavior in terms of the contribution of individual cognitive capacities to the provision of resources within an exchange structure. We spell out to what extent non-human primates’ individual and strategic decision behaviors are shared with humans. We focus on the ability to trade, through barter or token-mediated exchanges, as a landmark of an economic system among members of the same species. It is an open question why only humans have reached a high level of economic sophistication. While primates have many of the necessary cognitive abilities (symbolic and computational) in isolation, one plausible issue we identify is the limits in exerting cognitive control to combine several sources of information. The difference between human and non-human primates’ economies might well then be in degree rather than kind.
Monday, December 23, 2019
Teen childbearing leads to lower educational attainment, lower income, & greater use of welfare for individuals who come from counties with better socioeconomic conditions
Heterogeneous Consequences of Teenage Childbearing. Devon Gorry. Demography, volume 56, pages 2147–2168 (2019). https://link.springer.com/article/10.1007/s13524-019-00830-1
Abstract: This study finds heterogeneous effects of teen childbearing on education and labor market outcomes across socioeconomic status and race. Using miscarriages to put bounds on the causal effects of teen childbearing, results show that teen childbearing leads to lower educational attainment, lower income, and greater use of welfare for individuals who come from counties with better socioeconomic conditions. However, there are no significant adverse effects for individuals who come from counties with worse socioeconomic conditions. Across race, teen childbearing leads to negative consequences for white teens but no significant negative effects for black or Hispanic and Latino teens.
Abstract: This study finds heterogeneous effects of teen childbearing on education and labor market outcomes across socioeconomic status and race. Using miscarriages to put bounds on the causal effects of teen childbearing, results show that teen childbearing leads to lower educational attainment, lower income, and greater use of welfare for individuals who come from counties with better socioeconomic conditions. However, there are no significant adverse effects for individuals who come from counties with worse socioeconomic conditions. Across race, teen childbearing leads to negative consequences for white teens but no significant negative effects for black or Hispanic and Latino teens.
V. Discussion
For teens from less educated and lower income counties and teens in minority groups, poor education and labor market outcomes are not a result of teen childbearing. Instead it is likely that teen childbearing is complimentary with poor labor market prospects and in these cases teen childbearing may encourage some young women in poor circumstances to get more education and attain better labor market outcomes than they otherwise would have.
It is important to understand this heterogeneity when targeting policy mechanisms directed at reducing teen childbearing. While previous work suggests that such policies may only have modest positive effects on teen outcomes, these results suggest that there could be large positive effects of reducing teen childbearing concentrated among teens who are relatively better off. However, teen pregnancy prevention policies will not help teens who come from poor socioeconomic backgrounds nor will they help black, Hispanic or Latino teens on average.
Thus, broad policies targeting all teen pregnancies may not help the populations that they intend to help most. Instead of focussing on reducing childbearing of poor and minority teens directly, results of this paper suggest that policymakers would be better off to first target the conditions that make teen childbearing an optimal choice.
Children: The genetic correlation between abuse & neglect was ρg = .73 (p = .02); common environmental variance increased as socioeconomic status (SES) decreased (p = .05)
Estimating the Heritability of Experiencing Child Maltreatment in an Extended Family Design. Katharina Pittner et al. Child Maltreatment, November 27, 2019. https://doi.org/10.1177/1077559519888587
Abstract: Child-driven genetic factors can contribute to negative parenting and may increase the risk of being maltreated. Experiencing childhood maltreatment may be partly heritable, but results of twin studies are mixed. In the current study, we used a cross-sectional extended family design to estimate genetic and environmental effects on experiencing child maltreatment. The sample consisted of 395 individuals (225 women; M age = 38.85 years, rangeage = 7–88 years) from 63 families with two or three participating generations. Participants were oversampled for experienced maltreatment. Self-reported experienced child maltreatment was measured using a questionnaire assessing physical and emotional abuse, and physical and emotional neglect. All maltreatment phenotypes were partly heritable with percentages for h 2 ranging from 30% (SE = 13%) for neglect to 62% (SE = 19%) for severe physical abuse. Common environmental effects (c 2) explained a statistically significant proportion of variance for all phenotypes except for the experience of severe physical abuse (c 2 = 9%, SE = 13%, p = .26). The genetic correlation between abuse and neglect was ρg = .73 (p = .02). Common environmental variance increased as socioeconomic status (SES) decreased (p = .05), but additive genetic and unique environmental variances were constant across different levels of SES.
Keywords: child maltreatment, genetics, etiology, families, risk factors, self-report
Abstract: Child-driven genetic factors can contribute to negative parenting and may increase the risk of being maltreated. Experiencing childhood maltreatment may be partly heritable, but results of twin studies are mixed. In the current study, we used a cross-sectional extended family design to estimate genetic and environmental effects on experiencing child maltreatment. The sample consisted of 395 individuals (225 women; M age = 38.85 years, rangeage = 7–88 years) from 63 families with two or three participating generations. Participants were oversampled for experienced maltreatment. Self-reported experienced child maltreatment was measured using a questionnaire assessing physical and emotional abuse, and physical and emotional neglect. All maltreatment phenotypes were partly heritable with percentages for h 2 ranging from 30% (SE = 13%) for neglect to 62% (SE = 19%) for severe physical abuse. Common environmental effects (c 2) explained a statistically significant proportion of variance for all phenotypes except for the experience of severe physical abuse (c 2 = 9%, SE = 13%, p = .26). The genetic correlation between abuse and neglect was ρg = .73 (p = .02). Common environmental variance increased as socioeconomic status (SES) decreased (p = .05), but additive genetic and unique environmental variances were constant across different levels of SES.
Keywords: child maltreatment, genetics, etiology, families, risk factors, self-report
This extended family study demonstrates that experiencing maltreatment during childhood is partly heritable. Heritability was not restricted to a specific type of maltreatment, and shared genetic factors contributed to abuse and neglect. Common and unique environmental factors explained a considerable proportion of phenotypic variance, and common environment had a greater effect on maltreatment in low-SES families.
Heritability estimates ranged from 30% for neglect to 62% for severe physical abuse. These findings suggest that child maltreatment is in part genetically mediated by child effects.
The finding that child factors contribute to maltreatment does not imply, however, that the responsibility for maltreatment perpetrated by parents lies with the child. It is the role of parents to respond appropriately to challenging child behavior, and they might need support to fulfill this role adequately in case of challenging child behaviors. Interventions may benefit from incorporating parent training that supports more effective strategies of responding to potentially challenging behavior. Results from earlier genetically informed studies (i.e., adoption and twin designs) provide support for an evocative role of externalizing problems in negative parenting and maltreatment (Marceau et al., 2013; O’Connor, Deater-Deckard, Fulker, Rutter, & Plomin, 1998; Schulz-Heik et al., 2010). The association between externalizing behavior and maltreatment may be bidirectional as maltreatment increases antisocial behavior over time, even when taking into account genetic effects (Jaffee, Caspi, Moffitt, & Taylor, 2004).
In the present study, we used a continuous variable of maltreatment ranging from “no maltreatment,” to “harsh parenting,” and to “maltreatment.” Most participants reported experiences of maltreatment at the lower end of the spectrum. Consequently, our findings may be restricted to more typical harsh parenting rather than maltreatment. However, we found that severe physical abuse seemed highly heritable, in contrast with an earlier twin study showing that harsh parenting, but not maltreatment, was heritable (Jaffee, Caspi, Moffitt, Polo-Tomas et al., 2004). Whereas Jaffee, Caspi, Moffitt, Polo-Tomas et al. (2004) assessed maltreatment up to the age of 5 years, the present study covered maltreatment to the end of adolescence, when individuals gain agency to shape their environment (Bergen, Gardner, & Kendler, 2007; Elkins, McGue, & Iacono, 1997), which may increase child-based genetic influences. Another explanation for the discrepancy might be that the present study used self-report, while in their twin study Jaffee, Caspi, Moffitt, Polo-Tomas et al. (2004) used mother report about the twins’ maltreatment histories, which may have led to an overestimation of shared environment because parents tend to perceive the environment of their children as more similar than the children themselves do (Wade & Kendler, 2000). Other studies using self-report measures also found experienced maltreatment to be partly heritable (Fisher et al., 2015; Schulz-Heik et al., 2009; South et al., 2015). Arguably, children may have a tendency to emphasize the difference between the way they themselves were treated and how their siblings were treated. However, since siblings completed the questionnaires independent of each other, it is unlikely that in our study, using self-report has strongly increased the similarity between siblings. Moreover, using a multi-informant approach that included parent reports when available, we see the same pattern of results with slightly higher estimates for heritability and common environment. This likely stems from a reduction of measurement error evident in the lower unique environment estimates.
Common and Unique Environment
The present findings suggest that similarity between siblings in terms of maltreatment experiences should not be attributed to genetic effects only but also to common environment. This points to the role of the family environment and is in line with studies showing that parental psychopathology, parenting stress, lack of social support, and larger family size are important risk factors for maltreatment. Low SES has repeatedly been shown to be associated with maltreatment (Euser et al., 2013; Sedlak et al., 2010; Slack, Holl, Mcdaniel, Yoo, & Bolger, 2004; Stith et al., 2009). In our study, SES was related to neglect. While some of these factors, such as family size, are difficult to change, addressing factors such as parenting stress and social support may have a particularly high payoff as they would benefit all children in the family.
For the etiology of maltreatment, it is important to not only understand what makes children growing up in the same family similar but also what makes them different in the experience of maltreatment—the unique environment (Plomin, 2011). We found an estimated influence of unique environment (including measurement error) of 29–42%, which concurs with previous studies (Fisher et al., 2015; Schulz-Heik et al., 2009). The importance of the unique environment points to the need for an individual child approach in addition to a family-centered approach when estimating risk. That is, it is important to improve the specific parent–child relationship. Nonetheless, specific unique environmental risk factors have remained elusive after taking measurement error into account (Deater-Deckard et al., 2001; Mullineauxa, Deater-Deckard, Petrillb, & Thompson, 2009). One suggested factor is that parents might perceive siblings as differently attractive or difficult, and thus trigger differential parenting (Burt, McGue, Iacono, & Krueger, 2006; Deater-Deckard, Smith, Ivy, & Petril, 2005; Feinberg & Hetherington, 2001; Reiss et al., 1995), which constitutes a potential target of intervention.
Genetic Correlation Between Abuse and Neglect
Our bivariate analysis indicates that the same common environmental factors are related to abuse and neglect. Approximately 50% of the genetic factors were overlapping, and the other 50% were uniquely related to abuse or neglect. This may suggest that some child factors put a child at risk of experiencing abuse but not of neglect and vice versa. Our findings illustrate why abuse and neglect often co-occur (Euser et al., 2013; Vachon, Krueger, Rogosch, & Cicchetti, 2015) notwithstanding etiological differences. Conversely, interventions may need to address abuse and neglect individually, even when they co-occur, since the heritable and unique environmental risk factors do not (fully) overlap.
Genotype × SES
Moreover, a genotype × SES interaction analysis demonstrated that in low-SES families, common environment explained more variance in experienced maltreatment than in high-SES families. Overall, low-SES families showed greater variance in experienced maltreatment, and our findings suggest that this can be attributed to common environment. Lower SES may add a range of common environmental factors negatively affecting child development. For instance, children from low-SES families experience more instability, more crowding at home, more pollution, and more danger in the neighborhood (Chen & Miller, 2013; Evans, 2004; Miller et al., 2009). Together, these factors may increase the risk of developing externalizing problems. On a population level, this suggests that fighting child poverty may have far-reaching preventive consequences.
Additive genetic variance, in absolute terms, remained stable across different levels of SES. Since overall variance decreased as SES increased, relative contribution of genetic variance component increased. Given the substantial effects of heritability this and previous studies have indicated, a more comprehensive exploration of environmental effects on heritability may uncover new intervention targets. A better understanding of the child traits mediating the heritable risk might offer insight into which environmental manipulations would be most effective in lowering heritable risk.
In order to interpret any variance component across a changing environment, it is important to consider changes in the other variance components. In a genotype-by-sex interaction study of physical activity behavior by Diego et al. (2015), the issue of the indeterminacy of environment-specific heritability was broached. The authors found that the heritability could be constant across an environmental contrast if the constituent variance components changed in the same direction and at the same rate. They also noted that it was theoretically possible that a nonconstant heritability across an environmental contrast could arise from a changing residual environment component in the face of a constant additive genetic variance. This concept is relevant to properly contextualizing our results with existing reports on the heritability of maltreatment. In particular, Schulz-Heik et al. (2009) and South, Schafer, and Ferraro (2015), respectively, reported a higher and lower proportion of the total phenotypic variance attributed to the shared environment relative to the heritability. Regarding our study, we can actually claim both scenarios because the shared environment variance component declined relative to a constant additive genetic variance from the low end of the SES spectrum to the high end.
Extended Family Design
For the current study, we decided to use an extended family design to add to the existing twin research. Extended family designs have more variability in genetic relatedness and common environment than twin designs. In addition, twins create a unique family constellation and parenting demands may be atypical when caring for two same-aged children (Olivennes, Golombok, Ramogida, & Rust, 2005). Consequently, results from twin studies may not be generalizable to typical family constellations.
Moreover, the extended family design decreases the confounding between genetic relatedness and shared environment compared to nuclear families (Almasy & Blangero, 2010; Diego, Kent, & Blangero, 2015). By including horizontal relationships (e.g., cousins, half-siblings), in addition to vertical relationships (e.g., grandparent–grandchild), a systematic correlation between genetic distance and age difference is eliminated. For instance, half-siblings and grandparent–grandchild pairs have the same genetic distance, but half-siblings tend to be similar in age whereas grandparent–grandchild pairs are not.
Limitations
A limitation of this study is the retrospective assessment of maltreatment; no conclusions about causality can therefore be drawn. For ethical reasons, research on maltreatment is generally incompatible with experimental designs except for intervention studies that combined with a prospective design can be highly informative. The present study assessed maltreatment retrospectively, and time between potential maltreatment and assessment varied. Moreover, estimates of unique environment should be interpreted with caution as it is impossible to disentangle unique environmental effects from measurement. It is interesting to note, however, that estimates of unique environment decreased when including parent reports for a multi-informant approach. This could point to a reduction in measurement error. Future research should strive to replicate these findings in a larger, representative sample and in other populations (e.g., non-Western). Estimates from quantitative genetic research are population-specific. Even if genetic variation is stable across populations (which we do not know), environmental variability will affect estimates for both heritability and environment because these estimates represent relative contributions (Plomin, 2018; Velden, 1997). Lastly, the present sample may have been too small to estimate moderator effects reliably (Glahn et al., 2010)—especially because the effect of SES on common environment was small and the moderation analysis was exploratory. Future studies should replicate the genotype × SES effect in larger samples as these findings suggest that environmental interventions can be particularly useful.
Implications
Ideally, interventions are based on empirically supported, theoretical frameworks of etiology. The current study suggests that such frameworks should incorporate the heritability of experiencing maltreatment and that interventions should address both heritable and environmental risk factors. More research is needed to determine how to best reduce those risk factors. Moreover, it would be useful to explore other environmental factors than SES and how they moderate heritability—preferably factors that can be the focus of interventions.
The associations between parenting and child personality were comparable in magnitude to those between factors such as socio-economic level, birth order, and child personality—that is, small
Longitudinal Relations Betwwen Parenting and Child Big Five Personality Traits. Mona Ayou. PhD Psychology Thesis, University of Illinois at Urbana-Champaign, 2019. https://www.ideals.illinois.edu/bitstream/handle/2142/105686/AYOUB-DISSERTATION-2019.pdf?sequence=1&isAllowed=y
The goal of this research was to examine the relationships between parenting practices and child personality development. There is some lack of consensus on whether and to what extent parenting practices do affect child personality development. For example, social learning and attachment theories assume that parenting practices influence child personality development. Also, a third theory, the psychological resources principle, holds similar assumptions and provides specific predictions about relations between parenting and personality traits. In contrast, some perspectives derived from research in behavior genetics minimize the role of parenting practices on children’s personality development. In order to shed some empirical light on these issues, I examined the long-term relations between parenting and child Big Five personality traits through fitting cross-lagged panel models and bivariate latent growth models in two datasets. Unlike previous studies, I used large samples (N= 3850; N=674), examined multiple parenting measures, and used data from multiple raters. Results from cross lagged models showed a preponderance of insignificant relations between parenting and child personality. A different approach to interpreting the results is to focus on the magnitudes of the associations rather than their statistical significance. In this light, I found that the average regression coefficient between parenting and child personality was .04 in both studies. The average regression coefficient between child personality and parenting was .04 in Study 1 and .06 in Study 2. Results from growth models showed decreasing trends in parenting and child personality across time. The growth models also revealed a preponderance of null relations between parenting and child personality, and especially between changes in parenting and changes in child personality. Focusing on the magnitudes of the associations, we found that the average correlation between the initial levels of parenting and child personality was .08 in Study 1 and .10 in Study 2. The average correlation between initial levels of parenting and changes in child personality was .04 in Study 1 and .10 in Study 2. The average correlation between changes in child personality and initial levels of parenting was .04 in both studies. The average correlation between changes in parenting and changes in child personality was .08 in Study 1 and .13 in Study 2. In general, the obtained associations between parenting and child personality were comparable in magnitude to those between factors such as SES, birth order, and child personality—that is, small. The small associations between environmental factors and personality suggest that personality developmental in childhood and adolescence is driven by multiple factors, each of which makes a small contribution.
The goal of this research was to examine the relationships between parenting practices and child personality development. There is some lack of consensus on whether and to what extent parenting practices do affect child personality development. For example, social learning and attachment theories assume that parenting practices influence child personality development. Also, a third theory, the psychological resources principle, holds similar assumptions and provides specific predictions about relations between parenting and personality traits. In contrast, some perspectives derived from research in behavior genetics minimize the role of parenting practices on children’s personality development. In order to shed some empirical light on these issues, I examined the long-term relations between parenting and child Big Five personality traits through fitting cross-lagged panel models and bivariate latent growth models in two datasets. Unlike previous studies, I used large samples (N= 3850; N=674), examined multiple parenting measures, and used data from multiple raters. Results from cross lagged models showed a preponderance of insignificant relations between parenting and child personality. A different approach to interpreting the results is to focus on the magnitudes of the associations rather than their statistical significance. In this light, I found that the average regression coefficient between parenting and child personality was .04 in both studies. The average regression coefficient between child personality and parenting was .04 in Study 1 and .06 in Study 2. Results from growth models showed decreasing trends in parenting and child personality across time. The growth models also revealed a preponderance of null relations between parenting and child personality, and especially between changes in parenting and changes in child personality. Focusing on the magnitudes of the associations, we found that the average correlation between the initial levels of parenting and child personality was .08 in Study 1 and .10 in Study 2. The average correlation between initial levels of parenting and changes in child personality was .04 in Study 1 and .10 in Study 2. The average correlation between changes in child personality and initial levels of parenting was .04 in both studies. The average correlation between changes in parenting and changes in child personality was .08 in Study 1 and .13 in Study 2. In general, the obtained associations between parenting and child personality were comparable in magnitude to those between factors such as SES, birth order, and child personality—that is, small. The small associations between environmental factors and personality suggest that personality developmental in childhood and adolescence is driven by multiple factors, each of which makes a small contribution.
Items of Instruments in TRAIN Dataset
Parental Involvement
I have enough time and energy to
1) talk intensively about school day
2) take care that child is doing his/her homework
3) go through schoolwork with child
4) get involved in child school
5) go to parents’ evenings
6) study classwork with child
Parental Structure
I make sure that
1) my child goes to bed early on school days
2) my child does his homework at fixed times everyday
3) my child has breakfast in the morning
4) we get up together and have breakfast at the weekend
5) my child brushes his/her teeth in the morning and in the evening
6) my child packs the school bag for the next day in the evening
7) family eats together at least once a day
8) my child gets up on time in the morning on school days
Parental Cultural Stimulation
How often does it happen that you
1) go to the theater together with your child
2) go to the museum together with your child
3) go to classical concerts together with your child
4) go to an opera / ballet performance together
5) go to a book reading with your child
Parental Goals
In your opinion, how important that family teaches child
1) personal independence
2) performance and effort
3) order and discipline
4) versatile knowledge
5) political judgement
6) sound knowledge in main subjects
7) social responsibility
8) appropriate social manners
9) respect/respect for parents
10) mastery of cultural skills
11) willingness to learn
12) righteous and helpful behavior
13) knowledge for profession
14) moral judgment
15) Life
---
Items of Instruments in CFP Dataset
Mother/Father Involvement
In the past year, you
1) helped child with homework or school project
2) encouraged child to study
3) helped child study for a test
4) checked to see that child had done his homework
Mother/Father Monitoring
Over the past three months,
1) knew what child was doing after school
2) knew how child spent his money
3) knew the parents of the child’s friends
4) knew who child’s friends are
5) if the child was going to get home late, he was expected to call
6) child told you who he/she was going to be with before he/she went out
7) when child went out at night, you knew where he/she was going to be
8) knew about the plans child had with friends
9) when child went out, you asked him/her where he/she was going
10) knew how child was doing in his/her schoolwork
11) knew where child was and what he/she was doing
12) talked with child about what was going on in his/her life
13) knew if child did something wrong
14) knew when child did something really well at school or other place
Mother/Father Family Routines
How often
1) talk to child about his/her homework
2) help child with his/her homework
3) child does his/her homework at the same time each day or night during the week
4) child takes part in regular activities after school
5) is there an adult at home when child comes back from school
6) child go to bed at the same time each night
7) your family eat a meal together
8) child does regular household chores
Mother/Father Parental Goals
How important is it that
1) child does well in school
2) child is popular
3) sets goals and accomplishes them
4) is good at sports
5) child does chores at home
6) attends church every week
7) respects and pays attention to his/her teachers
8) is courteous toward other people
9) plans for the future
10) develops his/her talents and abilities
11) respects and pays attention to you
Mother/Father Warmth
During the past year, how often did your mother/father
1) Ask you for your opinion about an important matter
2) Listen carefully to your point of view
3) Let you know she really cares about you
4) Act loving and affectionate toward you
5) Let you know that she appreciates you, your ideas, or things you do
6) Help you do something that was important to you
7) Have a good laugh with you about something that was funny
8) Act supportive and understanding toward you
9) Tells you she loves you
10) Talks about things that bother you
11) Ask you what you think before deciding on family matters that involve you
12) Gives you reasons for his/her decisions
13) Asks you what you think before making a decision about you
14) Lets you know he/she is pleased
15)Rewards with money or good things when you get good grades
16) Go to special events that involve you, like a play or sports
17) Understands why your parents make a rule
18) Discipline by reason, explaining, or talking to you
Mother/Father Hostility
During the past year, how often did your mother/father
1) Shouts or yells at you because he/she was mad at you
2) Ignores you when you tried to talk to him/her
3) Gives a lecture about how you should behave
4) Boss around a lot
5) Hit, push, grab or shove you
6) Did not listen, but does all talking himself/herself
7) Argue with you whenever they disagree about something
8) Insults or swears at you
9) Tells you he/she is right, and you are wrong about things
10)Calls you bad names
11) Threatens to hurt you by hitting you with his/her fist, an object, or something else
12) Get angry at you
13)Criticize you or your ideas
Parental Involvement
I have enough time and energy to
1) talk intensively about school day
2) take care that child is doing his/her homework
3) go through schoolwork with child
4) get involved in child school
5) go to parents’ evenings
6) study classwork with child
Parental Structure
I make sure that
1) my child goes to bed early on school days
2) my child does his homework at fixed times everyday
3) my child has breakfast in the morning
4) we get up together and have breakfast at the weekend
5) my child brushes his/her teeth in the morning and in the evening
6) my child packs the school bag for the next day in the evening
7) family eats together at least once a day
8) my child gets up on time in the morning on school days
Parental Cultural Stimulation
How often does it happen that you
1) go to the theater together with your child
2) go to the museum together with your child
3) go to classical concerts together with your child
4) go to an opera / ballet performance together
5) go to a book reading with your child
Parental Goals
In your opinion, how important that family teaches child
1) personal independence
2) performance and effort
3) order and discipline
4) versatile knowledge
5) political judgement
6) sound knowledge in main subjects
7) social responsibility
8) appropriate social manners
9) respect/respect for parents
10) mastery of cultural skills
11) willingness to learn
12) righteous and helpful behavior
13) knowledge for profession
14) moral judgment
15) Life
---
Items of Instruments in CFP Dataset
Mother/Father Involvement
In the past year, you
1) helped child with homework or school project
2) encouraged child to study
3) helped child study for a test
4) checked to see that child had done his homework
Mother/Father Monitoring
Over the past three months,
1) knew what child was doing after school
2) knew how child spent his money
3) knew the parents of the child’s friends
4) knew who child’s friends are
5) if the child was going to get home late, he was expected to call
6) child told you who he/she was going to be with before he/she went out
7) when child went out at night, you knew where he/she was going to be
8) knew about the plans child had with friends
9) when child went out, you asked him/her where he/she was going
10) knew how child was doing in his/her schoolwork
11) knew where child was and what he/she was doing
12) talked with child about what was going on in his/her life
13) knew if child did something wrong
14) knew when child did something really well at school or other place
Mother/Father Family Routines
How often
1) talk to child about his/her homework
2) help child with his/her homework
3) child does his/her homework at the same time each day or night during the week
4) child takes part in regular activities after school
5) is there an adult at home when child comes back from school
6) child go to bed at the same time each night
7) your family eat a meal together
8) child does regular household chores
Mother/Father Parental Goals
How important is it that
1) child does well in school
2) child is popular
3) sets goals and accomplishes them
4) is good at sports
5) child does chores at home
6) attends church every week
7) respects and pays attention to his/her teachers
8) is courteous toward other people
9) plans for the future
10) develops his/her talents and abilities
11) respects and pays attention to you
Mother/Father Warmth
During the past year, how often did your mother/father
1) Ask you for your opinion about an important matter
2) Listen carefully to your point of view
3) Let you know she really cares about you
4) Act loving and affectionate toward you
5) Let you know that she appreciates you, your ideas, or things you do
6) Help you do something that was important to you
7) Have a good laugh with you about something that was funny
8) Act supportive and understanding toward you
9) Tells you she loves you
10) Talks about things that bother you
11) Ask you what you think before deciding on family matters that involve you
12) Gives you reasons for his/her decisions
13) Asks you what you think before making a decision about you
14) Lets you know he/she is pleased
15)Rewards with money or good things when you get good grades
16) Go to special events that involve you, like a play or sports
17) Understands why your parents make a rule
18) Discipline by reason, explaining, or talking to you
Mother/Father Hostility
During the past year, how often did your mother/father
1) Shouts or yells at you because he/she was mad at you
2) Ignores you when you tried to talk to him/her
3) Gives a lecture about how you should behave
4) Boss around a lot
5) Hit, push, grab or shove you
6) Did not listen, but does all talking himself/herself
7) Argue with you whenever they disagree about something
8) Insults or swears at you
9) Tells you he/she is right, and you are wrong about things
10)Calls you bad names
11) Threatens to hurt you by hitting you with his/her fist, an object, or something else
12) Get angry at you
13)Criticize you or your ideas
We have perceptual biases to see man-made objects; maybe extended exposure to manufactured environments in our cities has changed the way we see the world
A perceptual bias for man-made objects in humans. Ahamed Miflah Hussain Ismail, Joshua A. Solomon, Miles Hansard and Isabelle Mareschal. Proceedings of the Royal Society B, Volume 286, Issue 1914, November 6 2019. https://doi.org/10.1098/rspb.2019.1492
Abstract: Ambiguous images are widely recognized as a valuable tool for probing human perception. Perceptual biases that arise when people make judgements about ambiguous images reveal their expectations about the environment. While perceptual biases in early visual processing have been well established, their existence in higher-level vision has been explored only for faces, which may be processed differently from other objects. Here we developed a new, highly versatile method of creating ambiguous hybrid images comprising two component objects belonging to distinct categories. We used these hybrids to measure perceptual biases in object classification and found that images of man-made (manufactured) objects dominated those of naturally occurring (non-man-made) ones in hybrids. This dominance generalized to a broad range of object categories, persisted when the horizontal and vertical elements that dominate man-made objects were removed and increased with the real-world size of the manufactured object. Our findings show for the first time that people have perceptual biases to see man-made objects and suggest that extended exposure to manufactured environments in our urban-living participants has changed the way that they see the world.
Abstract: Ambiguous images are widely recognized as a valuable tool for probing human perception. Perceptual biases that arise when people make judgements about ambiguous images reveal their expectations about the environment. While perceptual biases in early visual processing have been well established, their existence in higher-level vision has been explored only for faces, which may be processed differently from other objects. Here we developed a new, highly versatile method of creating ambiguous hybrid images comprising two component objects belonging to distinct categories. We used these hybrids to measure perceptual biases in object classification and found that images of man-made (manufactured) objects dominated those of naturally occurring (non-man-made) ones in hybrids. This dominance generalized to a broad range of object categories, persisted when the horizontal and vertical elements that dominate man-made objects were removed and increased with the real-world size of the manufactured object. Our findings show for the first time that people have perceptual biases to see man-made objects and suggest that extended exposure to manufactured environments in our urban-living participants has changed the way that they see the world.
3. Discussion
We examined biases in people's classification of different types of natural images. In experiment 1, we found that when an ambiguous hybrid image was formed of structures from two different image categories, classification was biased towards the man-made categories (houses and vehicles) rather than towards the non-man-made categories (animals and flowers). This ‘man-made bias’ is not a bias towards any specific spatial frequency content. Additional experiments (see electronic supplementary material, §S5) revealed that the bias is (1) common across urban-living participants in different countries, and (2) not simply a response bias. The results of experiment 2 replicated and extended the results of experiment 1 to demonstrate that the bias was affected by the real-world size of man-made objects (but not animal size), with a stronger bias for larger man-made objects. Reduced biases for small man-made objects may be explained by shared feature statistics (e.g. curvature) between small (but not large) man-made objects and both small and large animals [22]. However, we highlight that the bias is not only for larger man-made objects, because we still obtained man-made biases even when small man-made objects were paired with animals. We propose that this man-made bias is the result of expectations about the world that favour the rapid interpretation of complex images as man-made. Given that the visual diet of our urban participants is rich in man-made objects, our results are consistent with a Bayesian formulation of perceptual biases whereby ambiguous stimuli result in biases towards frequently occurring attributes [5].
We stress that the man-made bias is not merely a manifestation of the relative insensitivity to tilted (i.e. neither vertical nor horizontal) contours, commonly known as the ‘oblique effect’ [23,24]. Our participants exhibited biases in favour of man-made objects even when cardinal orientations had been filtered out of them. This occurred despite the fact that the power spectra of houses and vehicles were largely dominated by cardinal orientations, whereas those of animals and flowers were largely isotropic (electronic supplementary material, §S6 and figure S6). Whereas the oblique effect was established using narrow-band luminance gratings on otherwise uniform backgrounds, it cannot be expected to influence the perception of broad-band, natural images, such as those used in our experiments. Indeed, if anything, detection thresholds for cardinally oriented structure tend to be higher than those for tilted structure, when those structures are superimposed against broad-band masking stimuli [25].
We note however that we do not claim that intercardinal filtering removes all easily detectable structures from the images in man-made categories. Indeed, houses and vehicles almost certainly contain longer, straighter and/or more rectilinear contours than flowers and animals. Therefore, we also performed a detection experiment to examine if increased sensitivity to structural features that might dominate man-made categories could account for the man-made biases by measuring detection thresholds (see electronic supplementary material, §S7). It revealed that houses and vehicles did not have lower detection thresholds (i.e. the minimum root mean square contrast required to reliably detect images from each category) than images from the non-man-made categories. This finding provides strong ammunition against any sensitivity-based model of the man-made bias. Whatever structure is contained in the unfiltered images of houses and vehicles, that structure proved to be, on average, no easier to detect than the structure contained in unfiltered images of animals and flowers.
The lack of a bias for animals and a difference in sensitivity between image categories appears to contradict past findings from Crouzet et al. [15], who report that the detection of animals precedes that of vehicles using a saccadic choice task. However, comparing contrast sensitivity (detection) to saccadic reaction (decision) is problematic, especially with high contrast stimuli [26]. Secondly, the difference could be attributed to the background of images that must be classified. While Crouzet et al. [15] controlled contextual masking effects on image category by presenting images occurring in both man-made and natural contexts, our images in the detection experiment were embedded in white noise with the same amplitude spectrum as the image (electronic supplementary material, figure S7). As Hansen & Loschky [27] report, the type of mask used (e.g. using a mask sharing only the amplitude spectrum with the image versus one sharing both amplitude and phase information with the image) affects masking strength. It is still unclear which type of masks work best across different image categories [27].
Although we carefully controlled the spatial frequency content of our stimuli in experiments 1 and 2, it is conceivable that the bias towards man-made objects arises at a level intermediate between the visual system's extraction of these low-level features and its classification of stimuli into semantic categories. To investigate whether any known ‘mid-level’ features might be responsible for the bias towards man-made objects, we repeated experiments 1 and 2 with HMAX, a computer-based image classifier developed on the basis of the neural computations mediating object recognition in the ventral stream of the visual cortex [28,29], allowing it to exploit mid-level visual features in its decision processes (see electronic supplementary material, §§S4 and S10). We also classified hybrids from experiment 2 with the AlexNet Deep Convolutional Neural Network (DNN), which could potentially capture more mid-level features [30] (see electronic supplementary material, §S9). Results indicate that human observers' bias for man-made images seems not to be a simple function of the lower and mid-level features exploited by conventional image-classification techniques.
However, we must concede that HMAX and AlexNet do not account for all possible intermediate feature differences between object categories, for instance 3D viewpoint [31]. If we are frequently exposed to different viewpoints of man-made but not non-man-made objects, this might lead to a man-made bias too. Therefore, more experiments where categorical biases can be measured after equating object categories for intermediate features are needed to pinpoint the level at which the man-made bias occurs. Indeed, the bias for man-made objects might have nothing to do with visual features at all. It may stem from (non-visual) expectations that exploit regularities of the visual environment [6]. To be clear: we are speculating that the preponderance of man-made objects in the environment of urban participants could bias their perception such that it becomes efficient at processing these types of stimuli.
When might such a bias develop? Categorical concepts and dedicated neural mechanisms for specific object categories seem to develop after birth, with exposure [32–34]. This suggests that expectations for object categories are likely to develop with exposure too. However, if expectations occur at the level of higher-level features associated with object categories, we cannot discount the possibility that expectations may be innate. For instance, prior expectations for low-level orientation has been attributed to a hardwired non-uniformity in orientation preference of V1 neurons [6]. Similarly, we may have inhomogeneous neural mechanisms for higher-level features too. Recently identified neural mechanisms selectively encoding higher-level features of objects (e.g. uprightness [35]) add to this speculation. It remains to be determined when and how man-made biases arise and whether they are adaptable to changes in the environment. Further, the perceptual bias that we demonstrate may be altered by testing conditions, which limit its generalizability. For instance, low spatial frequency precedence in image classification is altered by the type of classification that must be performed (e.g. classifying face hybrids for its gender versus expression) [36].
The Chinese Economic Miracle—Half of China’s river water and 90% of its groundwater is unfit to drink; Beijing has roughly the same amount of water per person (145 cubic meters) as Saudi Arabia
Chapter 6. The Chinese Economic Miracle: How Much Is Real… How Much Is a Mirage? Michael Beckley. Dec 2019, adapted from his book Unrivaled: Why American Will Remain the World’s Sole Superpower (Cornell University Press, 2018). https://www.aei.org/wp-content/uploads/2019/12/Chapter-6-The-Chinese-Economic-Miracle-How-Much-Is-Real%E2%80%A6-How-Much-Is-a-Mirage.pdf
Abstract: China’s economic growth over the past three decades has been spectacular, but the veneer of doubledigit growth rates has masked gaping liabilities that constrain China’s ability to close the wealth gap with the United States. China has achieved high growth at high costs, and now the costs are rising while growth is slowing. New data that accounts for these costs reveals that the United States is several times wealthier than China, and the gap may be growing by trillions of dollars every year.
Introduction
This conclusion may surprise many people, given that China has a bigger GDP, a higher investment rate, larger trade flows, and a faster economic growth rate than the United States. How can China outproduce, outinvest, and outtrade the United States—and own nearly $1.2 trillion in US debt—yet still have substantially less wealth?
The reason is that China’s economy is big but inefficient. It produces vast output but at an enormous expense. Chinese businesses suffer from chronically high production costs, and China’s 1.4 billion people impose substantial welfare and security burdens. The United States, by contrast, is big and efficient. American businesses are among the most productive in the world, and with four times fewer people than China, the United States has much lower welfare and security costs.
GDP and other standard measures of economic heft ignore these costs and create the false impression that China is overtaking the United States economically. In reality, China’s economy is barely keeping pace as the burden of propping up loss-making companies and feeding, policing, protecting, and cleaning up after one-fifth of humanity erodes China’s stocks of wealth.
The Real Wealth of Nations
For decades, analysts have measured national wealth in gross rather than net terms, relying primarily on GDP and its components, such as trade and financial flows and investment spending. These gross indicators, however, overstate the wealth of populous countries because they count the benefits of having a large workforce but not the costs of having many people to feed, police, protect, and serve. These costs add up. In fact, they consume most of the resources in every nation. Analysts, therefore, must deduct them to accurately assess the wealth of nations.
...
Natural Capital
The main elements of natural capital are water, energy resources, and arable land, all of which are
necessary to sustain life and power agriculture and industry. The United States has 10% more
renewable freshwater than China, and the actual gap is much larger, because half of China’s river
water and 90% of its groundwater is unfit to drink, and 25% of China’s river water and 60% of its
groundwater is so polluted that the Chinese government has deemed it “unfit for human contact” and
unusable even for agriculture or industry.
China’s per capita availability of water is less than one-quarter of the United States’ and less than
one-third the world’s average, and roughly one-third of China’s provinces and two-thirds of its major
cities suffer from extreme water scarcity. Beijing, for example, has roughly the same amount of water per person (145 cubic meters) as Saudi Arabia. Dealing with water scarcity costs China roughly $140 billion per year in government expenditures and reduced productivity versus $12 billion for the United States.
The United States has three times as much oil and natural gas as China and twice as much coal. China heavily subsidizes its renewable energy and nuclear power industries, but both combined still
account for less than 5% of China’s energy use compared to 12% of the United States’.
China has large reserves of shale oil and natural gas, but it has not been able to tap them and may
never do so. One reason is that China’s shale deposits were left behind by prehistoric lakes and,
consequently, have rock layers that are more ductile and less amenable to hydraulic fracturing than
the brittle marine shales in North America. Another reason is that China lacks the water necessary
for fracking. Each shale-gas well requires fifteen thousand tons of water a year to run, and China
would need to drill thousands of wells a year to launch a successful industry. China has nowhere near that amount of water located close to its major shale basins, which are concentrated in Jilin and Liaoning, two of China’s driest provinces.
China currently depletes $400 billion of its energy resources per year and pays foreign countries
another $500 billion in energy imports, whereas US annual depletion and net import costs are
currently $140 billion and $120 billion respectively. This divergence in energy fortunes is likely to
expand in the decades ahead, because the United States will become a net energy exporter around
2025, whereas China, already the world’s largest net energy importer, will import 80% of its oil and
45% of its natural gas.
Finally, the United States has 45% more arable land than China, and again the true size of the gap is probably much larger because large chunks of China’s farmland are too polluted, desiccated, or both to support agriculture. According to a recent Chinese government study, water pollution has destroyed nearly 20% of China’s arable land, an area the size of Belgium. An additional 1 million square miles of China’s farmland has become desert, forcing the resettlement of 24,000 villages and pushing the edge of the Gobi Desert to within 150 miles of Beijing. In 2008, China became a net importer of grain, breaking its traditional policy of self-sufficiency, and in 2011 China became the world’s largest importer of agricultural products. The United States, by contrast, is the world’s top food exporter and China’s top supplier.
Abstract: China’s economic growth over the past three decades has been spectacular, but the veneer of doubledigit growth rates has masked gaping liabilities that constrain China’s ability to close the wealth gap with the United States. China has achieved high growth at high costs, and now the costs are rising while growth is slowing. New data that accounts for these costs reveals that the United States is several times wealthier than China, and the gap may be growing by trillions of dollars every year.
Introduction
This conclusion may surprise many people, given that China has a bigger GDP, a higher investment rate, larger trade flows, and a faster economic growth rate than the United States. How can China outproduce, outinvest, and outtrade the United States—and own nearly $1.2 trillion in US debt—yet still have substantially less wealth?
The reason is that China’s economy is big but inefficient. It produces vast output but at an enormous expense. Chinese businesses suffer from chronically high production costs, and China’s 1.4 billion people impose substantial welfare and security burdens. The United States, by contrast, is big and efficient. American businesses are among the most productive in the world, and with four times fewer people than China, the United States has much lower welfare and security costs.
GDP and other standard measures of economic heft ignore these costs and create the false impression that China is overtaking the United States economically. In reality, China’s economy is barely keeping pace as the burden of propping up loss-making companies and feeding, policing, protecting, and cleaning up after one-fifth of humanity erodes China’s stocks of wealth.
The Real Wealth of Nations
For decades, analysts have measured national wealth in gross rather than net terms, relying primarily on GDP and its components, such as trade and financial flows and investment spending. These gross indicators, however, overstate the wealth of populous countries because they count the benefits of having a large workforce but not the costs of having many people to feed, police, protect, and serve. These costs add up. In fact, they consume most of the resources in every nation. Analysts, therefore, must deduct them to accurately assess the wealth of nations.
...
Natural Capital
The main elements of natural capital are water, energy resources, and arable land, all of which are
necessary to sustain life and power agriculture and industry. The United States has 10% more
renewable freshwater than China, and the actual gap is much larger, because half of China’s river
water and 90% of its groundwater is unfit to drink, and 25% of China’s river water and 60% of its
groundwater is so polluted that the Chinese government has deemed it “unfit for human contact” and
unusable even for agriculture or industry.
China’s per capita availability of water is less than one-quarter of the United States’ and less than
one-third the world’s average, and roughly one-third of China’s provinces and two-thirds of its major
cities suffer from extreme water scarcity. Beijing, for example, has roughly the same amount of water per person (145 cubic meters) as Saudi Arabia. Dealing with water scarcity costs China roughly $140 billion per year in government expenditures and reduced productivity versus $12 billion for the United States.
The United States has three times as much oil and natural gas as China and twice as much coal. China heavily subsidizes its renewable energy and nuclear power industries, but both combined still
account for less than 5% of China’s energy use compared to 12% of the United States’.
China has large reserves of shale oil and natural gas, but it has not been able to tap them and may
never do so. One reason is that China’s shale deposits were left behind by prehistoric lakes and,
consequently, have rock layers that are more ductile and less amenable to hydraulic fracturing than
the brittle marine shales in North America. Another reason is that China lacks the water necessary
for fracking. Each shale-gas well requires fifteen thousand tons of water a year to run, and China
would need to drill thousands of wells a year to launch a successful industry. China has nowhere near that amount of water located close to its major shale basins, which are concentrated in Jilin and Liaoning, two of China’s driest provinces.
China currently depletes $400 billion of its energy resources per year and pays foreign countries
another $500 billion in energy imports, whereas US annual depletion and net import costs are
currently $140 billion and $120 billion respectively. This divergence in energy fortunes is likely to
expand in the decades ahead, because the United States will become a net energy exporter around
2025, whereas China, already the world’s largest net energy importer, will import 80% of its oil and
45% of its natural gas.
Finally, the United States has 45% more arable land than China, and again the true size of the gap is probably much larger because large chunks of China’s farmland are too polluted, desiccated, or both to support agriculture. According to a recent Chinese government study, water pollution has destroyed nearly 20% of China’s arable land, an area the size of Belgium. An additional 1 million square miles of China’s farmland has become desert, forcing the resettlement of 24,000 villages and pushing the edge of the Gobi Desert to within 150 miles of Beijing. In 2008, China became a net importer of grain, breaking its traditional policy of self-sufficiency, and in 2011 China became the world’s largest importer of agricultural products. The United States, by contrast, is the world’s top food exporter and China’s top supplier.
Sunday, December 22, 2019
Cerebral blood flow rates in recent great apes are greater than in Australopithecus species that had equal or larger brains
Cerebral blood flow rates in recent great apes are greater than in Australopithecus species that had equal or larger brains. Roger S. Seymour, Vanya Bosiocic, Edward P. Snelling, Prince C. Chikezie, Qiaohui Hu, Thomas J. Nelson, Bernhard Zipfel and Case V. Miller. Volume 286, Issue 1915, November 13 2019. https://doi.org/10.1098/rspb.2019.2208
Abstract: Brain metabolic rate (MR) is linked mainly to the cost of synaptic activity, so may be a better correlate of cognitive ability than brain size alone. Among primates, the sizes of arterial foramina in recent and fossil skulls can be used to evaluate brain blood flow rate, which is proportional to brain MR. We use this approach to calculate flow rate in the internal carotid arteries (Q˙ICA), which supply most of the primate cerebrum. Q˙ICA is up to two times higher in recent gorillas, chimpanzees and orangutans compared with 3-million-year-old australopithecine human relatives, which had equal or larger brains. The scaling relationships between Q˙ICA and brain volume (Vbr) show exponents of 1.03 across 44 species of living haplorhine primates and 1.41 across 12 species of fossil hominins. Thus, the evolutionary trajectory for brain perfusion is much steeper among ancestral hominins than would be predicted from living primates. Between 4.4-million-year-old Ardipithecus and Homo sapiens, Vbr increased 4.7-fold, but Q˙ICA increased 9.3-fold, indicating an approximate doubling of metabolic intensity of brain tissue. By contrast, Q˙ICA is proportional to Vbr among haplorhine primates, suggesting a constant volume-specific brain MR.
[Q with a dot is first derivative of Q (rate of change with time, in this case)]
Abstract: Brain metabolic rate (MR) is linked mainly to the cost of synaptic activity, so may be a better correlate of cognitive ability than brain size alone. Among primates, the sizes of arterial foramina in recent and fossil skulls can be used to evaluate brain blood flow rate, which is proportional to brain MR. We use this approach to calculate flow rate in the internal carotid arteries (Q˙ICA), which supply most of the primate cerebrum. Q˙ICA is up to two times higher in recent gorillas, chimpanzees and orangutans compared with 3-million-year-old australopithecine human relatives, which had equal or larger brains. The scaling relationships between Q˙ICA and brain volume (Vbr) show exponents of 1.03 across 44 species of living haplorhine primates and 1.41 across 12 species of fossil hominins. Thus, the evolutionary trajectory for brain perfusion is much steeper among ancestral hominins than would be predicted from living primates. Between 4.4-million-year-old Ardipithecus and Homo sapiens, Vbr increased 4.7-fold, but Q˙ICA increased 9.3-fold, indicating an approximate doubling of metabolic intensity of brain tissue. By contrast, Q˙ICA is proportional to Vbr among haplorhine primates, suggesting a constant volume-specific brain MR.
[Q with a dot is first derivative of Q (rate of change with time, in this case)]
1. Introduction
Brain size is the usual measure in discussions of the evolution of cognitive ability among primates, despite recognized shortcomings [1]. Although absolute brain size appears to correlate better with cognitive ability than encephalization quotient, progression index or neocortex ratio [2,3], an even better correlate might be brain metabolic rate (MR), because it represents the energy cost of neurological function. However, brain MR is difficult to measure directly in living primates and impossible in extinct ones.
One solution to the problem has been to measure oxygen consumption rates and glucose uptake rates on living mammals in relation to brain size and then apply the results to brain sizes of living and extinct primates. Because physiological rates rarely relate linearly to volumes or masses of tissues, any comparison requires allometric analysis. For example, brain MR can be analysed in relation to endocranial volume (≈ brain volume, Vbr) with an allometric equation of the form, MR = aVbrb, where a is the elevation (or scaling factor, indicating the height of the curve) and b is the scaling exponent (indicating the shape of the curve on arithmetic axes). If b = 1.0, then MR is directly proportional to brain size. If b is less than 1, then MR increases with brain size, but the metabolic intensity per unit volume of neural tissue decreases. If b is greater than 1, the metabolic intensity of neural tissue increases. The exponent for brain MR measured as oxygen consumption and glucose use across several mammalian species is approximately 0.86, and the exponent for cortical brain blood flow rate in mammals is between 0.81 and 0.87 [4,5]. The similarity of the exponents indicates that blood flow rate is a good proxy for brain MR in mammals in general. The exponents are less than 1.0, which shows that brain MR and blood flow rate increase with brain size but with decreasing metabolic and perfusion intensities of the neural tissue.
Recent studies show that blood flow rate in the internal carotid artery can be calculated from the size of the carotid foramen through which it passes to the brain [6]. The artery occupies the foramen lumen almost entirely [7–9], therefore defining the outer radius of the artery (ro), from which inner lumen radius (ri) can be estimated, assuming that arterial wall thickness (ro – ri) is a constant ratio (w) with lumen radius (w = (ro – ri)/ri), according to the law of Laplace. The haemodynamic equation used to calculate is referred to as the ‘shear stress equation’, and attributed to Poiseuille: , where is the blood flow rate (cm3 s−1), τ is the wall shear stress (dyn cm−2), ri is the arterial lumen radius (cm) and η is the blood viscosity (dyn s cm−2) [10]. The technique was validated in mice, rats and humans, but was initially criticized [11], defended [12] and subsequently accepted [13]. However, the calculations involved three questionable assumptions: flow in the cephalic arteries conforms to Poiseuille flow theory, arterial wall shear stress can be calculated accurately from body mass (although there is no clear functional relationship between them) and the arterial wall thickness-to-lumen radius ratio (w) was a certain constant derived from only two values in the literature.
We have now made significant advancements to the initial methodology by replacing the shear stress equation, and its assumptions, with a new equation derived empirically from a meta-analysis of versus ri in 30 studies of seven cephalic arteries of six mammalian genera, arriving at an allometric, so-called ‘empirical equation’, = 155 ri2.49 (R2 = 0.94) [14]. The equation is based on stable cephalic flow rates, which vary little between rest, intense physical activity, mental exercise or sleep [14]. The equation also eliminates reliance on the somewhat tenuous estimation of arterial wall shear stress from body mass. We have also improved the calculation with a more extensive re-evaluation of carotid arterial wall thickness ratio (w = 0.30) from 14 imaging studies on humans (electronic supplementary material, text and table S1 for data and references). The present investigation implements these recent methodological advancements and re-evaluates the scaling of as a function of Vbr in extant haplorhine primates and in fossil hominins. The point of our study is to clarify these relationships between Homo sapiens, Australopithecus and modern great apes (Pongo, Pan, Gorilla) to resolve an apparent allometric conundrum within our previous studies: one analysis based on 34 species of extant Haplorhini, including H. sapiens, resulted in the equation Vbr0.95 [6], while another analysis of 11 species of fossil hominin, also including H. sapiens, produced the equation Vbr1.45 [15]. Humans are on both analyses with the largest brains, but the exponents of these equations are markedly different, and the lines converge. The present study confirms that hominin ancestors had lower than predicted from Vbr with the haplorhine equation. in modern great apes is about twice that in Australopithecus species, despite similar or smaller Vbr.
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