Interesting studies thread

quasi · Jan 10, 2021

post studies you find interesting here.

make sure to include the title and abstract.

Cepa · Jan 10, 2021

No evidence for general intelligence in a fish

https://www.biorxiv.org/content/10.1101/2021.01.08.425841v1

Abstract
Differences in human general intelligence or reasoning ability can be quantified with the psychometric factor g, because individual performance across cognitive tasks is positively correlated. g also emerges in mammals and birds, is correlated with brain size and may similarly reflect general reasoning ability and behavioural flexibility in these species. To exclude the alternative that these positive cross-correlations may merely reflect the general biological quality of an organism or an inevitable by-product of having brains it is paramount to provide solid evidence for the absence of g in at least some species. Here, we show that wild-caught cleaner fish Labroides dimidiatus, a fish species otherwise known for its highly sophisticated social behaviour, completely lacks g when tested on ecologically non-relevant tasks. Moreover, performance in these experiments was not or negatively correlated with an ecologically relevant task, and in none of the tasks did fish caught from a high population density site outperform fish from a low-density site. g is thus unlikely a default result of how brains are designed, and not an automatic consequence of variation in social complexity. Rather, the results may reflect that g requires a minimal brain size, and thus explain the conundrum why the average mammal or bird has a roughly 10 times larger brain relative to body size than ectotherms. Ectotherm brains and cognition may therefore be organized in fundamentally different ways compared to endotherms.
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quasi · Jan 12, 2021 at 4:36 AM

The influence of immigrant generation on obesity among Asian Americans in California from 2013 to 2014

https://pubmed.ncbi.nlm.nih.gov/30794650/

Abstract
Objectives: We aimed to examine the association between immigrant generation and obesity among Californian adults and Asian Americans.

Methods: We pooled weighted data (n = 2,967) on Asian Americans from the 2013-2014 California Health Interview Survey. Overweight and obesity were defined using body mass indices (BMI) of 25 kg/m2 and 30 kg/m2, respectively, in non-Asians, compared with BMI of 23 kg/m2 (for being overweight) and 27.5 kg/m2 (for being obese) in Asians. First-generation or immigrant Asian Americans were defined as those born outside of the U.S. Second-generation Asian Americans were defined as those born in the U.S. with at least one foreign-born parent. All other Asian participants were classified as third-generation or higher. Multiple logistic regression analyses were used with adjustment for age, sex, family income, smoking status, marital status, education, physical activity, and fast food consumption.

Results: Overall, 23.3% of the Asian population was obese, and 40.0% was overweight. The percentage of 1st, 2nd, and 3rd generation were 72.7%, 22.6%, and 4.6%, respectively. Overall, 1st generation of Asians had lower odds of being obese compared to Whites (OR = 0.34, 95%CI = 0.26-0.45). Multiple logistic regression analyses showed that overall, 2nd generation (OR = 1.69, 95%CI = 1.10-2.60) and 3rd generation (OR = 2.33, 95%CI = 1.29-4.22) Asians had higher odds of being obese compared to 1st generation Asians. Among Chinese, compared to the 1st generation, the 3rd generation had increased likelihood of being obese (OR = 6.29, 95%CI = 2.38-16.6).

Conclusion: Compared to Whites, Hispanics, and Blacks, Asian immigrants are less likely to be obese. Among Asians, 2nd and 3rd generations were more likely to be obese compared to 1st generation. The obesity rate seems to increase the longer Asian immigrants remain in the U.S.
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quasi · Jan 14, 2021 at 6:36 AM

Facial recognition technology can expose political orientation from naturalistic facial images

https://www.nature.com/articles/s41598-020-79310-1

Abstract

Ubiquitous facial recognition technology can expose individuals’ political orientation, as faces of liberals and conservatives consistently differ. A facial recognition algorithm was applied to naturalistic images of 1,085,795 individuals to predict their political orientation by comparing their similarity to faces of liberal and conservative others. Political orientation was correctly classified in 72% of liberal–conservative face pairs, remarkably better than chance (50%), human accuracy (55%), or one afforded by a 100-item personality questionnaire (66%). Accuracy was similar across countries (the U.S., Canada, and the UK), environments (Facebook and dating websites), and when comparing faces across samples. Accuracy remained high (69%) even when controlling for age, gender, and ethnicity. Given the widespread use of facial recognition, our findings have critical implications for the protection of privacy and civil liberties.

Introduction
There is a growing concern that the widespread use of facial recognition will lead to the dramatic decline of privacy and civil liberties1. Ubiquitous CCTV cameras and giant databases of facial images, ranging from public social network profiles to national ID card registers, make it alarmingly easy to identify individuals, as well as track their location and social interactions. Moreover, unlike many other biometric systems, facial recognition can be used without subjects’ consent or knowledge.

Pervasive surveillance is not the only risk brought about by facial recognition. Apart from identifying individuals, the algorithms can identify individuals’ personal attributes, as some of them are linked with facial appearance. Like humans, facial recognition algorithms can accurately infer gender, age, ethnicity, or emotional state2,3. Unfortunately, the list of personal attributes that can be inferred from the face extends well beyond those few obvious examples.

A growing number of studies claim to demonstrate that people can make face-based judgments of honesty4, personality5, intelligence6, sexual orientation7, political orientation8,9,10,11,12, and violent tendencies13. There is an ongoing discussion about the extent to which face-based judgments are enabled by stable facial features (e.g., morphology); transient facial features (e.g., facial expression, makeup, facial hair, or head orientation); or targets’ demographic traits that can be easily inferred from their face (e.g., age, gender, and ethnicity)14. Moreover, the accuracy of the human judgment is relatively low. For example, when asked to distinguish between two faces—one conservative and one liberal—people are correct about 55% of the time (derived from Cohen’s d reported in Tskhay and Rule15), only slightly above chance (50%). Yet, as humans may be missing or misinterpreting some of the cues, their low accuracy does not necessarily represent the limit of what algorithms could achieve. Algorithms excel at recognizing patterns in huge datasets that no human could ever process16, and are increasingly outperforming us in visual tasks ranging from diagnosing skin cancer17 to facial recognition18 to face-based judgments of intimate attributes, such as sexual orientation (76% vs. 56%)7,19, personality (64% vs. 57%; derived from Pearson’s rs)20,21,22, and—as shown here—political orientation. (For ease of interpretation and comparisons across studies, across this text, accuracy is expressed as the area under the receiver operating characteristic curve (AUC), an equivalent of the Wilcoxon signed-rank test coefficient and the common language effect size.)

Methods
We used a sample of 1,085,795 participants from three countries (the U.S., the UK, and Canada; see Table 1) and their self-reported political orientation, age, and gender. Their facial images (one per person) were obtained from their profiles on Facebook or a popular dating website. These self-selected, naturalistic images combine many potential cues to political orientation, ranging from facial expression and self-presentation to facial morphology. The ethnic diversity of our sample (it included over 347,000 non-white participants), the relative universality of the conservative–liberal spectrum23, and the generic type of facial images used here increase the likelihood that our findings apply to other countries, cultures, and types of images.
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quasi · Jan 14, 2021 at 7:29 AM

A Siberian link with Na-Dene languages

http://hosting03.snu.ac.kr/~korean/old/data/genetic/Vajda(2008).pdf

sample of conclusion summarizing findings:

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