Dienstag, 11. Juli 2017

Brain volume and intelligence: The moderating role of intelligence measurement quality

Brain volume and intelligence: The moderating role of intelligence measurement quality
Gilles E. Gignac, Timothy C. Bates (2017)


Correlation between brain volume and IQ in healthy adults is r ≈ .40.
The importance of correcting correlations for range restriction is demonstrated.
Intelligence measurement quality was a moderator of the brain volume/IQ effect.
Fair, good, and excellent measures of IQ yielded correlations of .23, .32, and .39.
p-Curve analysis indicated the significant results in the area likely not due to p-hacking.


A substantial amount of empirical research has estimated the association between brain volume and intelligence. The most recent meta-analysis (Pietschnig, Penke, Wicherts, Zeiler, & Voracek, 2015) reported a correlation of .24 between brain volume and intelligence – notably lower than previous meta-analytic estimates. This headline meta-analytic result was based on a mixture of samples (healthy and clinical) and sample correlations not corrected for range restriction. Additionally, the role of IQ assessment quality was not considered. Finally, evidential value of the literature was not formally evaluated. Based on the results of our meta-analysis of the Pietschnig et al.'s sample data, the corrected correlation between brain volume and intelligence in healthy adult samples was r = .31 (k = 32; N = 1758). Furthermore, the quality of intelligence measurement was found to moderate the effect between brain volume and intelligence (b = .08, p = .028). Investigations that used ‘fair’, ‘good’, and ‘excellent’ measures of intelligence yielded corrected brain volume and intelligence correlations of .23 (k = 9; N = 547), .32 (k = 10; N = 646), and .39 (k = 13; N = 565), respectively. The Henmi/Copas adjusted confidence intervals, the p-uniform results, and the p-curve results failed to suggest evidence of publication bias and/or p-hacking. The results were interpreted to suggest that the association between in vivo brain volume and intelligence is arguably best characterised as r ≈ .40. Researchers are encouraged to consider intelligence measurement quality in future meta-analyses, based on the guidelines provided in this investigation.

Sonntag, 9. Juli 2017

>In English, the words "explore" and "exploit" come loaded with completely opposite connotations. But to a computer scientist, these words have much more specific and neutral meanings. Simply put, exploration is gathering information, and exploitation is using the information you have to get a known good result.<

B. Christian & T. Griffiths

[Also see ...]

Creativity and Genius:

"Creativity and genius are unrelated to g except that a person’s level of g acts as a threshold variable below which socially significant forms of creativity are highly improbable. This g threshold is probably at least one standard deviation above the mean level of g in the general population. Besides the traits that Galton thought necessary for “ eminence” (viz., high ability, zeal, and persist­ence), genius implies outstanding creativity as well. Though such exceptional creativity is conspicuously lacking in the vast majority of people who have a high IQ, it is probably impossible to find any creative geniuses with low IQs. In other words, high ability is a necessary but not sufficient condition for the emergence of socially significant creativity. Genius itself should not be confused with merely high IQ, which is what we generally mean by the term “ gifted” (which also applies to special talents, such as music and art). True creativity involves more than just high ability. It is still uncertain what this is ..."

The g factor (1998)
Arthur R. Jensen 

Why are there so many explanations for primate brain evolution?

Why are there so many explanations for primate brain evolution?
R. I. M. Dunbar, Susanne Shultz (2017)


The question as to why primates have evolved unusually large brains has received much attention, with many alternative proposals all supported by evidence. We review the main hypotheses, the assumptions they make and the evidence for and against them. Taking as our starting point the fact that every hypothesis has sound empirical evidence to support it, we argue that the hypotheses are best interpreted in terms of a framework of evolutionary causes (selection factors), consequences (evolutionary windows of opportunity) and constraints (usually physiological limitations requiring resolution if large brains are to evolve). Explanations for brain evolution in birds and mammals generally, and primates in particular, have to be seen against the backdrop of the challenges involved with the evolution of coordinated, cohesive, bonded social groups that require novel social behaviours for their resolution, together with the specialized cognition and neural substrates that underpin this. A crucial, but frequently overlooked, issue is that fact that the evolution of large brains required energetic, physiological and time budget constraints to be overcome. In some cases, this was reflected in the evolution of ‘smart foraging’ and technical intelligence, but in many cases required the evolution of behavioural competences (such as coalition formation) that required novel cognitive skills. These may all have been supported by a domain-general form of cognition that can be used in many different contexts.
"If you memorize a thousand jokes, that doesn't make you a person with a sense of humor. Sense of humor is more subtle. A good sense of humor is about timing, the ability to say the funny thing at the right time and to the right people."

"While most humor research concerns jokes (with distinct “set-up lines” and “punch lines”), only about 10% to 15% of laughter in natural social contexts occurs in response to classically-structured jokes that would seem funny when repeated out of context (Provine, 2000). Rather, most laughter occurs in response to short utterances or nonverbal micro-performances during informal conversation. These might seem funny in the immediate social context, but would often seem fairly mundane or stupid if repeated later."


Samstag, 8. Juli 2017

"Thinking about Death and Pain Makes People Funnier"

"is it possible that the greatest comedians of all time are the ones that have the most painful thoughts [?]"

"Perhaps humor is rooted in tragedy, pain and struggle in ways we cannot imagine or fully understand yet."

Gil Greengross

Arthur Jensen on Mental Productivity:


A startling corollary of the multiplicative model of exceptional achievement is best stated in the form of a general law. This is Price’s Law, which says that if K persons have made a total of N countable contributions in a particular field, then N/2 of the contributions will be attributable to sqrt(K) (Price, 1963). Hence, as the total number of workers (K) in a discipline increases, the ratio sqrt(K) / K shrinks, increasing the elitism of the major contributors. This law, like any other, only holds true within certain limits. But within fairly homogeneous disciplines, Price’s Law seems to hold up quite well for indices of productivity — for example, in math, the empirical sciences, musical composition, and the frequency of performance of musical works. Moreover, there is a high rankorder relationship between sheer productivity and various indices of the importance of a contributor’s work, such as the frequency and half-life of scientific citations, and the frequency of performance and staying power of musical compositions in the concert repertoire. (Consider such contrasting famous contemporaries as Mozart and Salieri; Beethoven and Hummel; and Wagner and Meyerbeer.) 

If productivity and importance could be suitably scaled, however, I would imagine that the correlation between them would show a scatter-diagram of the “twisted pear” variety (Fisher, 1959). That is, high productivity and triviality are more frequently associated than low productivity and high importance. As a rule, the greatest creative geniuses in every field are astoundingly prolific, although, without exception, they have also produced their share of trivia. (Consider Beethoven’s King Stephen Overture and Wagner’s “United States Centennial March,” to say nothing of his ten published volumes of largely trivial prose writings — all incredible contrasts to these composers’ greatest works.) But such seemingly unnecessary trivia from such geniuses is probably the inevitable effluvia of the mental energy without which their greatest works would not have come into being. On the other hand, high productivity is probably much more common than great importance, and high productivity per se is no guarantee of the importance of what is produced. The “twisted pear” relationship suggests that high productivity is a necessary but not sufficient condition for making contributions of importance in any field. The importance factor, however, depends on creativity—certainly an elusive attribute. 

What might be the basis of individual differences in productivity? The word motivation immediately comes to mind, but it explains little and also seems too intentional and self-willed to fill the bill. When one reads about famous creative geniuses one finds that, although they may occasionally have to force themselves to work, they cannot will themselves to be obsessed by the subject of their work. Their obsessive-compulsive mental activity in a particular sphere is virtually beyond conscious control. I can recall three amusing examples of this, and they all involve dinner parties. Isaac Newton went down to the cellar to fetch some wine for his guests and, while filling a flagon, wrote a mathematical equation with his finger on the dust of the wine keg. After quite a long time had passed, his guests began to worry that he might have had an accident, and they went down to the cellar. There was Newton, engrossed in his mathematical formulas, having completely forgotten that he was hosting a dinner party. 

My second example involves Richard Wagner. Wagner, while his guests assembled for dinner, suddenly took leave of them and dashed upstairs. Alarmed that something was wrong, his wife rushed to his room. Wagner exclaimed, “I’m doing it!”—their agreed signal that she was not to disturb him under any circumstances because some new musical idea was flooding his brain and would have to work itself out before he could be sociable again. He had a phenomenal memory for musical ideas that spontaneously surfaced, and could postpone writing them down until it was convenient, a tedious task he referred to not as composing but as merely “copying” the music in his mind’s ear. 

Then there is the story of Arturo Toscanini hosting a dinner party at which he was inexplicably morose and taciturn, just as he had been all that day and the day before. Suddenly he got up from the dinner table and hurried to his study; he returned after several minutes beaming joyfully and holding up the score of Brahms’s First Symphony (which he was rehearsing that week for the NBC Symphony broadcast the following Sunday). Pointing to a passage in the first movement that had never pleased him in past performances, he exclaimed that it had suddenly dawned on him precisely what Brahms had intended at this troublesome point. In this passage, which never sounds “clean” when played exactly as written, Toscanini slightly altered the score to clarify the orchestral texture. He always insisted that his alterations were only the composer’s true intention. But few would complain about his “delusions”; as Puccini once remarked, “Toscanini doesn’t play my music as I wrote it, but as I dreamed it.”

Mental Energy

Productivity implies actual production or objective achievement. For the psychological basis of intellectual productivity in the broadest sense, we need a construct that could be labeled mental energy. This term should not be confused with Spearman’s g (for general intelligence). Spearman’s theory of psychometric g as “mental energy” is a failed hypothesis and has been supplanted by better explanations of g based on the concept of neural efficiency (Jensen, 1993). The energy construct I have in mind refers to something quite different from cognitive ability. It is more akin to cortical arousal or activation, as if by a stimulant drug, but in this case an endogenous stimulant. Precisely what it consists of is unknown, but it might well involve brain and body chemistry. 

One clue was suggested by Havelock Ellis (1904) in A Study of British Genius. Ellis noted a much higher than average rate of gout in the eminent subjects of his study; gout is associated with high levels of uric acid in the blood. So later investigators began looking for behavioral correlates of serum urate level (SUL), and there are now dozens of studies on this topic (reviewed in Jensen & Sinha, 1993). They show that SUL is only slightly correlated with IQ, but is more highly correlated with achievement and productivity. For instance, among high school students there is a relation between scholastic achievement and SUL, even controlling for IQ (Kasl, Brooks, & Rodgers, 1970). The “overachievers” had higher SUL ratings, on average. Another study found a correlation o f +.37 between SUL ratings and the publication rates of university professors (Mueller & French, 1974). Why should there be such a relationship? The most plausible explanation seems to be that the molecular structure of uric acid is nearly the same as that of caffeine, and therefore it acts as a brain stimulant. Its more or less constant presence in the brain, although affecting measured ability only slightly, considerably heightens cortical arousal and increases mental activity. There are probably a number of other endogenous stimulants and reinforcers of productive behavior (such as the endorphins) whose synergistic effects are the basis of what is here called mental energy. I suggest that this energy, combined with very high g or an exceptional talent, results in high intellectual or artistic productivity. Include trait psychoticism with its creative component in this synergistic mixture and you have the essential makings o f genius. To summarize: Genius = High Ability X High Productivity X High Creativity.

Humor: Differences Between Interest Indicator and Sexual Selection Models

1. Function: According to the sexual selection perspective, humor primarily serves a showing-off function; according to the interest indicator model, humor is used to communicate relationship interest. Thus, whereas sexual selection suggests that humor causes attraction to occur, interest indication predicts that humor initiation and perceptions of humor are driven by attraction. Consistent with the interest indicator model, the same exact joke can be perceived as highly funny or unamusing depending on who tells the joke. 

2. Differentiation from general conversation: Because a good-genes model emphasizes the conveying of intelligence, it does not necessarily differentiate between humor and general, intelligent conversation (i.e., both should be able to highlight cognitive skills). In contrast, the interest indicator model points to the specific function of humor to communicate interest. That is, although saying something creative or intelligent might be a way of showing off to a potential mate, saying something humorous should specifically convey relationship interest.

3. Direction of discourse: Research adopting a sexual selection perspective has emphasized the importance of men initiating humor and women responding (e.g., Bressler et al., 2006). In contrast, an interest indicator model emphasizes that any individual who is interested in a relationship should be more likely to initiate and respond positively to humor. 

4. Scope: Whereas sexual selection theory states that humor evolved in the courtship domain and thus emphasizes humor’s function in mate choice, the interest indicator model applies equally to humor’s function across all social domains. That is, just as people use and desire humor not only in courtship, but across all types of social relationships and across the different stages of those relationships, the interest indicator account provides an underlying framework for how humor functions across diverse social relationships.

Sexual Selection & Humor:


Freitag, 7. Juli 2017

Imperfect Information and Divorce

A Treatise on the Family
Gary S. Becker (1981)

"If participants in marriage markets have complete information about all prospects, divorce would be a fully anticipated response to a demand for variety in mates or to life-cycle changes in traits. Most divorces would then occur after many years of marriage, because traits change gradually. The facts, however, suggest the opposite: about 40 percent of all divorces (and annulments) occur prior to the fifth year of marriage, and separation usually precedes divorce by a year or more (U.S. Department of Health, Education, and Welfare, 1979).

If, however, participants had highly imperfect information, most divorces would occur early in marriage by virtue of the fact that information about traits increases rapidly after marriage. Several years of marriage is usually a far more effective source of information on love and many other traits than all the proxies available prior to marriage. I suggest that marriages fail early primarily because of imperfect information in marriage markets and the accumulation of better information during marriage. This suggestion is supported by the fact that unexpected changes in earnings and health do raise the probability of divorce (BLM,5 1977).

Women who divorced early in their marriage report that "difficult" spouses and value conflicts were major sources of their discontent, presumably because these traits are much better assessed after a few years of marriage. Personality conflict, sexual incompatibility, and similar traits should be less important sources of later than of earlier divorces; little additional information about these traits is acquired after a few years of marriage. On the other hand, some information, including information about other women and about earnings potential, is acquired more slowly and should be more important in later divorces. Indeed, another woman and/or financial conflict are frequently cited by women divorcing after ten years of marriage (Goode, 1956, pp. 128-129).

The major sources of discontent and divorce are not necessarily the major determinants of marital well-being. Education, age, physical appearance, and other easily assessed traits are not major sources of discontent because not much more is learned about them after marriage. Just as the emphasis on easily assessed traits in marriage markets does not imply that these traits contribute more to marital well-being than other traits, neither does the opposite emphasis on difficult-to-assess traits in "divorce markets" imply that those contribute more.

The more rapid accumulation of information during the first few years of marriage implies that divorce is more likely early in marriage than later. Divorce rates are highest during the first few years of marriage and decline steeply after four or five years, although the explanation is partly that those most prone to divorce tend to drop out early from the cohort of married persons (see Heckman, 1981, on the effects of heterogeneity).

Divorce is less likely later in the marriage for the additional reason that capital accumulates and becomes more valuable if a marriage stays intact ("marital-specific" capital). Children are the prime example, especially young children, although learning about the idiosyncrasies of one's spouse is also important (Heimer and Stinchcombe, 1979). Divorce is much less likely when there are children, especially young children-not only in the United States and other rich countries (Goode, 1963, pp. 85, 364; BLM, 1977), but also in primitive societies (Saunders and Thomson, 1979).

The accumulation of marital-specific capital is, in turn, discouraged by the prospect of divorce because, by definition, such capital is less valuable after a divorce. Presumably, trial or consensual marriages produce fewer children than legal marriages at least partly because the former are less durable (see the evidence in Kogut, 1972, on consensual and legal marriages in Brazil). Persons who marry outside their race or religion are far more likely to divorce than are others with similar measurable characteristics. Therefore, we can readily understand why marriages between persons of different races or religions have significantly fewer children even when intact marriages are compared (see the evidence for the United States in BLM, 1977), and why marriages between Indians of different castes have fewer children than marriages within a caste (Das, 1978).

Expectations about divorce are partly self-fulfilling because a higher expected probability of divorce reduces investments in specific capital and thereby raises the actual probability. For example, consensual and trial marriages are less stable than legal marriages, and marriages between persons of different religions or races are less stable than those within a religion or race, partly because mixed marriages have fewer children. At the same time, as indicated, mixed marriages have fewer children partly because they are expected to be less stable.

Specific investment and imperfect information can explain why homosexual unions are much less stable than heterosexual marriages (Saghir and Robins, 1973, pp. 56-58,226-227). Homosexual unions do not result in children, and generally they have a less extensive division of'labor and less marital-specific capital than heterosexual marriages. Moreover, the opprobrium attached to homosexuality has raised the cost of search to homosexuals and thereby has reduced the information available to them. Furthermore, homosexual unions, like trial marriages, can dissolve without legal adversary proceedings, alimony, or child support payments.

Women have usually married earlier than men partly because the maturation and independence of men has been delayed by greater investments in their human capital. Since investments in men and women have become more equal over time as the demand for children has decreased (see Chapter 3), men and women now marry for the first time at rather similar ages. For example, the difference in the United States between the median age at first marriage of men and women declined from four years in 1900 to about two and a half years in 1970 (U.S. Bureau of the Census, 1971c).

Yet divorced women have remarried more slowly than divorced men even when divorced at young ages. They almost always receive custody of children, a factor that discourages remarriage. For the same reason, women with illegitimate children marry for the first time more slowly than women without children (Berkov and Sklar, 1976).

Young children raise the cost of searching for another mate and significantly reduce the net resources of divorced women (Weitzman and Dixon, 1979). Possibly for these reasons they raise the probability that remarriage will fail, even though children born during the remarriage lower this probability (BLM, 1977). It is noteworthy that illegitimate children and other pregnancies prior to first marriage also raise the probability of marital failure (Christensen and Meissner, 1953; Berkov and Sklar, 1976) Divorced women might well remarry earlier than divorced men, just as single women without children marry earlier than single men, if divorced women did not receive custody of children. Indeed, perhaps 45 percent of divorced women would have remarried within the first two years of their divorce if they did not have custody, which is double their actual percentage (22) and considerably higher than the percentage for men (31). This estimate assumes that women without custody marry as rapidly as women without children. It is based on a regression equation that relates whether a woman remarries within a specified period of time to several variables, including number of children (BLM, 1977)."
"It turns out to be a lot easier to build a perfect chess player than a poker whiz. Chess is a perfect information game: if you look at a chessboard, you know the exact state of the game from both players’ perspectives. And the rules of the game are not affected by chance, like the drawing of a card.

But in poker, an imperfect information game, there are many unknown variables ..."

R for Data Science

Garrett Grolemund & Hadley Wickham

Samstag, 1. Juli 2017

The exploration/exploitation trade-off:

"It is fairly intuitive that never exploring is no way of live. But it's also worth mentioning that never exploiting can be every bit as bad."

The exploration-exploitation trade-off:

"The exploration-exploitation trade-off is a fundamental dilemma whenever you learn about the world by trying things out. The dilemma is between choosing what you know and getting something close to what you expect (‘exploitation’) and choosing something you aren’t sure about and possibly learning more (‘exploration’). ..."