Tuesday, December 20, 2022

Women’s legs in comic books are supernormal stimuli, being longer than those of actual women, and further extended by being drawn in heels or on tiptoe

Burch, R. L., & Widman, D. (2022). She's got legs: Longer legs in female comic book characters correspond to global preferences. Evolutionary Behavioral Sciences, Dec 2022. https://doi.org/10.1037/ebs0000318

Abstract: Previous studies have shown that comic book bodies are supernormal stimuli, exaggerated in dimensions that are attractive to primarily male comic book consumers. Following the same methodologies as previous experiments, this study examined height and leg length measurements of comic book characters in both Marvel and DC comics. In accordance with the literature on leg length and attractiveness, we predicted that comic book women would have longer legs than comic book men and would have longer than average legs, matching preferences shown in cross-cultural studies. We also hypothesized that comic book women would be depicted as wearing heels or walking on tiptoe more often, as this further elongates the legs. Results showed that female comic character leg length matched the most common preferred leg length in cross-cultural studies and 86%–88% of female characters were drawn as either wearing high heels or walking or standing on tiptoe.

Check also Comic book bodies are supernormal stimuli that cater to the unrealistic sexual imagination of a predominantly male audience

Burch, R. L., & Widman, D. R. (2021). Comic book bodies are supernormal stimuli: Comparison of DC, Marvel, and actual humans. Evolutionary Behavioral Sciences, Nov 2021. https://www.bipartisanalliance.com/2021/11/comic-book-bodies-are-supernormal.html

MicroRNAs are deeply linked to the emergence of the complex octopus brain

MicroRNAs are deeply linked to the emergence of the complex octopus brain. Grigoriy Zolotarov et al. Science Advances, Nov 25 2022, Vol 8, Issue 47. DOI: 10.1126/sciadv.add99

Abstract: Soft-bodied cephalopods such as octopuses are exceptionally intelligent invertebrates with a highly complex nervous system that evolved independently from vertebrates. Because of elevated RNA editing in their nervous tissues, we hypothesized that RNA regulation may play a major role in the cognitive success of this group. We thus profiled messenger RNAs and small RNAs in three cephalopod species including 18 tissues of the Octopus vulgaris. We show that the major RNA innovation of soft-bodied cephalopods is an expansion of the microRNA (miRNA) gene repertoire. These evolutionarily novel miRNAs were primarily expressed in adult neuronal tissues and during the development and had conserved and thus likely functional target sites. The only comparable miRNA expansions happened, notably, in vertebrates. Thus, we propose that miRNAs are intimately linked to the evolution of complex animal brains.


Coleoid cephalopods are unusual among invertebrates in having a nervous system comparable to the central nervous system of vertebrates, at least in terms of the neuronal number and anatomical specialization (3) and hence in terms of its complexity (3233). Nonetheless, the complexity of the coleoid nervous system belies the generality of its protein-encoding genomic content, in particular its set of transcription factors (table S2). Aside from independent expansions of the C2H2 zinc finger–encoding and protocadherin-encoding genes in the squid and octopus lineages, the octopus has a canonical repertoire of transcription factors similar to other lophotrochozoans (56). Coupling this generalized protein-encoding repertoire with the reported elevated rates of A-to-I editing in coleoid neural tissues (89) led us to hypothesize that RNA regulation in general might be involved in driving an apparent increase in the complexity of the coleoid nervous system. Our data and analyses argue that in terms of alternative splicing diversity and rates (including back-splicing that generates circRNAs), as well as mRNA cleavage and polyadenylation patterns, there is no major departure from other invertebrates. Further, we find no evidence for substantial editing in miRNA seed sequences nor in potential target sites either in the abrogation of a genetically encoded site or in the creation of a newly relevant site (figs. S7 and S8). Furthermore, a recent study in O. bimaculoides and squid Doryteuthis pealeii reports no enrichment of A-to-I editing in any particular protein domain genome-wide with the vast majority of editing events found outside of coding regions (34). Of course, A-to-I editing may still be functionally important in individual cases (35), but the main function of this process in coleoids remains elusive.
On the other hand, a clear distinction in RNA regulation between coleoid cephalopods and all other known invertebrates is reflected in the marked expansion of their miRNA repertoire. The conservation of more than 50 miRNA loci in both the squid and octopus lineages since they diverged from one another nearly 300 million years ago (20) coupled with the 3′UTR (Fig. 2B), miRNA expression (Figs. 3 and 4), and target site (Fig. 5) analyses discussed above all strongly suggest that these miRNAs are functionally important during the development of the coleoid nervous system. In stark contrast to Octopus that evolved 90 novel miRNA families since its last common ancestor with the oyster Crassosstrea, the genus Crassostrea evolved only five novel miRNA families over the same span of geological time (36) as assessed through comparable levels and samples of small RNA sequencing data. Like in virtually all other increases to a miRNA repertoire, both the source and evolutionary pressures for the rise of these novel miRNA loci are not known; whole-genome duplications can be ruled out (56), and scenarios may apply where novel miRNAs arise from the extensive genomic reorganizations found in coleoid taxa (537). Whatever their source, once under selection, miRNAs in general are believed to improve the robustness of the developmental processes (3842), increasing the heritability of the interaction (4345), which might then allow for the evolution of new cell types (46) and ultimately morphological and behavior complexity (3247). With respect to the development of the nervous system, we note that at least in vertebrates, miRNAs are known to have highly complex expression patterns with, for example, miRNA transcripts localized to the synapse and modulating their function (48). Although it remains to be seen whether these types of pathways operate in coleoids, the notable explosion of the miRNA gene repertoire in coleoid cephalopods may indicate that miRNAs and, perhaps, their specialized neuronal functions are deeply linked and possibly required for the emergence of complex brains in animals.

People are bad at evaluating their own olfactory abilities, overestimating and underestimating them

Why We Both Trust and Mistrust Our Sense of Smell. Ophelia Deroy. Chp 3 in Theoretical Perspectives on Smell, Andreas Keller and Benjamin D. Young, Eds. Routledge, 2023. DOI: 10.4324/9781003207801

3.2 Trusting Our Sense of Smell: One Problem or Many?

Let’s go back to the initial claim, exemplified by the medical quote above, that people are bad at evaluating their own olfactory abilities. Besides such anecdotal evidence, this claim has been tested in controlled and systematic ways: subjects are asked to assess their olfactory capacities, before their actual capacities are measured using standardised test.

When such tests have been conducted, they found no correlation between subjective and objective olfactory ratings,1 or only a poor one. In what is perhaps the largest scale study, 2 involving more than 6,000 patients who were coming to a smell clinic, self-ratings of “good or excellent” sense of smell could predict at 64% the fact that one had a normal sense of smell, while almost 30% (355 subjects) of anosmic patients judged their ability to smell as at least “average”. Repeatedly, reports show that people are often unaware that their olfactory sense is missing—a tendency which also increases with age (Nordin et al. 1995; Shu et al. 2009; Oleszkiewicz et al. 2020; Oleszkiewicz & Hummel 2019). In another study, White and Kurtz (2003) asked young and old individuals to determine whether their sense of smell was in the lower, middle/average tier, or upper tier compared to the rest of the population, and compared their subjective evaluations with their actual score on the Detection, Discrimination, Identification test.

Their results showed that young people tended to judge their sense of smell as less good than what it was, while older people judged their sense of smell to better than in reality. Most participants over-estimated their sense of smell, and very few would provide assessments in line with their performance on the test.

Though such statistics can be surprising, they ask a more philosophical question: Why even expect that people could tell how good their sense of smell is? Do we after all, expect people to know how good their memory or digestion are, or just have a rough idea when things go very wrong?

The literature on olfactory self-evaluation is here largely coloured by the existence of “olfactory anosognosia”: cases where olfactory loss gets unnoticed, and people who can’t smell still think they can. For people with normal olfactory capacities however, the problem is that of a bias in their evaluation of how good their sense of smell is: they can be either over-confident and think their sense of smell is better than it is or underconfident and think their sense of smell is worse that it is.

Countries with higher estimated IQs are *generally* more prosperous, better educated, more innovative, healthier, and more democratic

National Mean IQ Estimates: Validity, Data Quality, and Recommendations. Russell T. Warne. Evolutionary Psychological Science, Dec 19 2022. https://link.springer.com/article/10.1007/s40806-022-00351-y

Abstract: Estimates of mean IQ scores for different nations have engendered controversy since their first publication in 2002. While some researchers have used these mean scores to identify relationships between the scores and other national-level variables (e.g., economic and health variables) or test theories, others have argued that the scores are without merit and that any study using them is inherently and irredeemably flawed. The purpose of this article is to evaluate the quality of estimates of mean national IQs, discuss the validity of different interpretations and uses of the scores, point out shortcomings of the dataset, and suggest solutions that can compensate for the deficiencies in the data underpinning the estimated mean national IQ scores. My hope is that the scientific community can chart a middle course and reject the false dichotomy of either accepting the scores without reservation or rejecting the entire dataset out of hand.


  1. This Flynn effect adjustment is often misunderstood. It does not increase or decrease the score of the country to reflect the age of the test. Rather, it adjusts the international IQ standard (where 100 = the mean in the UK) to the year of the test administration in a country so that the country’s measured IQ is compared to the estimated standard for the same year.

  2. Only one sample had an overall quality rating of .18; it was collected in the United States. Four samples achieved an overall quality rating of .90. The data for these samples were collected in Tajikistan, the UK, the USA, and Yemen.

  3. The width of a confidence interval is equal to ± 1.96(σn), where σn is equal to the standard error of the mean, σ = 15 (the default SD of a population on the IQ metric), and n is the combined sample size of all samples that contribute to a country’s mean IQ estimate (Warne, 2021, pp. 199–201).

  4. These statistics are calculated using the absolute value of the differences between the QNW + SAS + GEO IQ in the Lynn and Becker (2019b) dataset and the IQ + GEO IQ for the previous version.

  5. Listed in descending order of the magnitude of IQ change: Nicaragua (− 23.78 IQ points); Haiti (21.60 IQ points); Honduras (− 18.84 IQ points); Nepal (− 18.00 IQ points); Guatemala (− 17.71 IQ points); Saint Helena, Ascension, and Tristan da Cunha (− 17.01 IQ points); Belize (− 16.25 IQ points); Cabo Verde (− 16.00 IQ points); Morocco (− 15.39 IQ points); Yemen (− 14.39 IQ points); Mauritania (− 14.00 IQ points); Chad (11.83 IQ points); Saint Lucia (11.71 IQ points); Barbados (11.69 IQ points); Senegal (− 10.50 IQ points); Republic of the Congo (− 10.03 IQ points); Côte d’Ivoire (− 10.02 IQ points); and Vanuatu (10.02 IQ points). Positive values in this list indicate that the new IQ estimates from Lynn and Becker (2019b) are higher than the earlier estimate. Negative values indicate the new value is lower.

  6. This finding also occurs in cross-national comparisons of educational achievement test scores. See, for example, Angrist et al. (2021), Gust et al. (2022), and Patel and Sandefur (2020).

  7. The PERCE 1997 and SERCE 2006 data are taken from official publications reporting country means for each grade level and subject (Oficina Regional de Educación para América Latina y el Caribe/UNESCO, 2001, p. 176; 2008, Tables A.3.1, A.3.5, A.4.1, A.4.5, and A.5.1). TERCE 2013 and ERCE 2019 data can be downloaded at https://raw.githubusercontent.com/llece/comparativo/main/datos_grafico_1-1.csv

  8. Cuba did not participate in TERCE 2013. Its ERCE 2019 data are much more similar to data from other countries in Latin America.

  9. The 1995 SACMEQ test only produced reading scores. The 2000 and 2007 SACMEQ tests produced a reading and mathematics score. The 2000 and 2007 scores were combined as an unweighted mean for each country when calculating correlations with the estimated national-level IQs.

  10. The correlations between PASEC scores and the GEO IQ scores from the Lynn and Becker (2019b) dataset—i.e., with Benin and Burkina Faso removed—are r = .142 (for PASEC grade 2 language), r = .153 (for PASEC grade 2 mathematics), r =  − .662 (for PASEC grade 6 language), and r = −.262 (for PASEC grade 6 mathematics). This does not change the conclusion that geographically imputed scores have a poor correspondence with data drawn from a country.

  11. The national PIRLS/TIMSS scores and the chart to convert LLECE and PASEC scores to PIRLS/TIMSS scores to one another are available at https://www.cgdev.org/sites/default/files/patel-sandefur-human-capital-final-results.xlsx

  12. I used the American mean in this calculation because the UK was not one of the countries in Patel and Sandefur’s (2020) study. The 2.5 IQ point adjustment is the standard adjustment that Lynn and Becker (2019b) used when examinees took a test normed in the USA instead of the UK.

  13. Two regions, England and Northern Ireland, were part of the same country. When calculating correlations with QNW + SAS IQs, the Northern Ireland data were dropped, and the data for England was compared to QNW + SAS IQs for the entire UK.

  14. Sear’s (2022) criticism of using IQ data from children to estimate IQs for an entire population shows that she does not understand that IQ scores are calculated by comparing examinees to their age peers. This functionally controls for age and allows scores from different age groups to have the same meaning. For an accessible explanation of how IQ scores are calculated, see Warne (2020), pp. 5–9.

  15. Readers may be aware of Lim et al.’s (2018) study that measures human capital in 195 countries. These scores are not included in the discussion in this article because the underlying data are not solely cognitive/educational scores. Lim et al. (2018) also used health data and longevity/life expectancy data in the calculation of their human capital scores. Therefore, the Lim et al. (2018) data cannot be interpreted as a cognitive measure, which makes it inadequate to use for convergent validity purposes when studying the Lynn and Becker (2019b) dataset.

  16. Available at https://datacatalog.worldbank.org/search/dataset/0038001.

  17. This statistical truism is why the Flynn effect (a purely environmental effect) can coexist with high heritability (a variance statistic measuring the strength of generic influence on a phenotype in a population) of IQ. The same secular mean increase occurred in height (a phenotype with high heritability) in many countries during the twentieth century. Changes in the mean do not automatically result in changes in the variance—and vice versa.

  18. It is important to recognize that mean QNW + SAS IQs below 70 are also found in some Central American nations (Belize, El Salvador, Guatemala, Honduras, Nicaragua), the Caribbean (Dominica and Saint Vincent and the Grenadines), and Morocco, Nepal, and Yemen.

  19. For the 2018 PISA, the SD for the UK data was 93 for math scores and 99 for science scores (Schleicher, 2019, pp. 7–8). In these calculations, I used the standard deviation of 99 to be more conservative. My choice of standard deviation will not affect any correlations, but it will change differences between these IQs and others and make outlier national mean IQs slightly less extreme.

  20. This is not an artifact of the extrapolation based on nearby countries’ data that Gust et al. (2022) used. The correlation between scores for the 12 countries that had imputed data in both datasets was r = .608; for the 13 countries that had geographically imputed scores in the Lynn and Becker (2019b) dataset and scores based on educational achievement testing data in the Gust et al. (2022) dataset, the correlation was r = .511. The average difference between the two sets of scores is also similar.

  21. Gust et al. (2022, p. A1) noted that Angrist et al.’s (2021) method overestimates academic achievement HLOs, compared to the Gust et al. (2022) method. The average scores in Table 2 are much more similar than would be expected because of the different means for the UK that were used to calculate z-scores and IQs. The HLO mean for the UK is 527.8 in the Angrist et al. (2021) data, compared to the Gust et al. (2022) mean of 503.2. The higher HLO mean for the UK provides a correction to the HLO scores, when converted to IQs, and makes the weighted mean IQs for both datasets in Table 2 much more similar.

  22. The QNW + SAS IQs for these countries are 69.45 (Botswana), 60.98 (Ghana), and 69.80 (South Africa). However, note that these are not independent of the PIRLS and TIMSS data because Lynn and Becker (2019b) used the educational achievement data to calculate SAS IQs, which contributed data to the QNW + SAS IQs.

  23. The largest discrepancies were for the Dominican Republic (+ 20.11 IQ points), Yemen (+ 19.34 IQ points), Tunisia (+ 12.39 IQ points), Argentina (+ 11.45 IQ points), Kuwait (+ 10.59 IQ points), and Honduras (− 10.47 IQ points). In this list, positive numbers indicate a higher QNW + SAS score in the Lynn and Becker (2019b) dataset, and negative numbers indicate a higher IQ derived from the Patel and Sandefur (2020) study.

  24. The four countries with geographically imputed IQs in Lynn and Becker’s (2019b) dataset that have discrepancies of at least 10 IQ points are Paraguay (+ 17.26 IQ points), Senegal (− 15.76 IQ points), Chad (+ 13.92 IQ points), and Niger (+ 10.10 IQ points). In this list, positive numbers indicate a higher QNW + SAS + GEO score in the Lynn and Becker (2019b) dataset, and negative numbers indicate a higher IQ derived from the Patel and Sandefur (2020) study.

  25. The largest discrepancies were for Cambodia (+ 26.4 IQ points), Venezuela (− 23.1 IQ points), Cuba (− 20.6 IQ points), Pakistan (+ 18.4 IQ points), Nicaragua (− 15.9 IQ points), Sri Lanka (+ 15.9 IQ points), Guatemala (− 15.4 IQ points), the Dominican Republic (+ 15.3 IQ points), the Philippines (+ 14.8 IQ points), Kyrgyzstan (+ 13.1 IQ points), Argentina (+ 12.4 IQ points), Haiti (+ 12.2 IQ points), Morocco (− 11.4 IQ points), Mongolia (+ 10.8 IQ points), and the United Arab Emirates (− 10.1 IQ points). In this list, positive numbers indicate a higher QNW + SAS score in the Lynn and Becker (2019b) dataset, and negative numbers indicate a higher IQ derived from the Gust et al. (2022) study. The inclusion of Cuba on this list is due to the use of SERCE 2006 data in the Gust et al. (2022) paper. As I stated earlier in this article, the Cuban data for this test are an outlier and likely fraudulent. This shows that when national IQ discrepancies arise in different datasets, it does not always indicate that Lynn and Becker’s (2019b) data are wrong.

  26. In descending order of the magnitude of the discrepancy, these countries were Honduras (22.62 IQ points lower), Botswana (18.52 IQ points lower), South Africa (13.80 IQ points lower), and Egypt (11.65 IQ points lower).

  27. Testing students one grade higher typical is standard practice for South Africa when administering PIRLS and TIMSS tests.

  28. The Burundi data are clearly an outlier. Patel and Sandefur (2020) reported that 43% of examinees in Burundi met or exceeded the TIMSS low international benchmark in reading, which is typical of PASEC countries (PASEC, 2015, p. 50). The discrepancy between Burundi’s math and reading performance originates in the PASEC data and is not an error in Patel and Sandefur’s conversion of PASEC scores to TIMSS scores.

  29. Pupil age is another factor to consider in making these comparisons. Repeating a grade is much more common in sub-Saharan Africa than it is in Western countries. However, these older pupils score worse on the PASEC than their classmates who have never repeated a grade (PASEC, 2015, pp. 78–81). Unlike testing students in a higher grade, the inclusion of these older students does not increase the countries’ percentages of students who meet the TIMSS low international benchmark.

  30. I only compared mathematics scores here because language differences (e.g., one language being easier to learn to read than another) make comparing reading scores and competency less straightforward than comparing proficiency in mathematics (Gust et al., 2022). Additionally, many children in African learn to read in a non-native language (i.e., Swahili, or a colonial language instead of their local African language), which would be a penalty when comparing reading scores to children in economically developed nations where most children are tested in their native language.

  31. There are three versions of the Raven’s: the Colored Progressive Matrices, Progressive Matrices, and Advanced Matrices (listed in ascending order of difficulty).

  32. Countries with a low NWQ + SAS IQ (≤ 75) based solely on matrix test data are Benin, the Republic of the Congo, Djibouti, Dominica, Eritrea, Ethiopia, The Gambia, Guatemala, Malawi, Mali, Morocco, Namibia, Nepal, Saint Vincent and the Grenadines, Sierra Leone, Somalia, South Sudan, Syria, Tanzania, Yemen, and Zimbabwe.

  33. This is why I have preferred to use the QNW + SAS IQs whenever possible in this article. QNW + SAS IQs are based on the most data and do not include countries with geographically imputed mean IQs.

  34. That is, unless one does not believe that educational performance, life outcomes, health and disease, economic prosperity, and strong civic institutions are important.