The Higher Power of Religiosity Over Personality on Political Ideology. Aleksander Ksiazkiewicz, Amanda Friesen. Political Behavior, August 29 2019. https://link.springer.com/article/10.1007/s11109-019-09566-5
Abstract: Two streams of research, culture war and system justification, have proposed that religious orientations and personality, respectively, play critical roles in political orientations. There has been only limited work integrating these two streams. This integration is now of increased importance given the introduction of behavior-genetic frameworks into our understanding of why people differ politically. Extant research has largely considered the influence of personality as heritable and religiosity as social, but this view needs reconsideration as religiosity is also genetically influenced. Here we integrate these domains and conduct multivariate analyses on twin samples in the U.S. and Australia to identify the relative importance of genetic, environmental, and cultural influences. First, we find that religiosity’s role on political attitudes is more heritable than social. Second, religiosity accounts for more genetic influence on political attitudes than personality. When including religiosity, personality’s influence is greatly reduced. Our results suggest religion scholars and political psychologists are partially correct in their assessment of the “culture wars”—religiosity and ideology are closely linked, but their connection is grounded in genetic predispositions.
Keywords: Religion Religiosity Personality Ideology Attitudes Genetics
Thursday, August 29, 2019
Why attitude to good people is not always positive?
Why attitude to good people is not always positive: explanation based on
decision theory. Part 3 (Why Should We Play Down Emotions: A
Theoretical Explanation) of "How to Make Decisions: Consider Multiple
Scenarios, Consult Experts, Play Down Emotions– Quantitative Explanation
of Common sense Ideas", by Julio Urenda et al. Technical Report
UTEP-CS-19-94, August 2019, to appear in Journal of Uncertain Systems,
2020, Vol. 14. Check http://www.cs.utep.edu/vladik/2019/tr19-94.pdf for a mathematical derivation of the conclusions.
Formulation of the problem. There are very good people in this world, people who empathize with others, people who actively help others. Based onall the nice and helpful things that these good people do, one would expect that other people would appreciate them, cherish them, and that, in general, their attitude towards these good people would be positive. However, in real life,the attitude is often neutral or even negative. The resulting emotions hurt our ability to listen to their advice and thus, improve our decisions. Why? Is there a rational explanation for these emotions?
...
Common sense explanation. From the common sense viewpoint, the above mathematics makes perfect sense: A very good person is unhappy if other people are unhappy. If we empathize with this person, we become unhappy too, and since people do not want to be unhappy, they prefer (at best) to ignore others’ unhappiness – or even blame them for their own unhappiness.
Formulation of the problem. There are very good people in this world, people who empathize with others, people who actively help others. Based onall the nice and helpful things that these good people do, one would expect that other people would appreciate them, cherish them, and that, in general, their attitude towards these good people would be positive. However, in real life,the attitude is often neutral or even negative. The resulting emotions hurt our ability to listen to their advice and thus, improve our decisions. Why? Is there a rational explanation for these emotions?
...
Common sense explanation. From the common sense viewpoint, the above mathematics makes perfect sense: A very good person is unhappy if other people are unhappy. If we empathize with this person, we become unhappy too, and since people do not want to be unhappy, they prefer (at best) to ignore others’ unhappiness – or even blame them for their own unhappiness.
Why IQ Test Scores Are Slightly Decreasing: Possible System-Based Explanation for the Reversed Flynn Effect
Why IQ Test Scores Are Slightly Decreasing: Possible System-Based Explanation for the Reversed Flynn Effect. Griselda Acosta, Eric Smith, and Vladik Kreinovich. Technical Report UTEP-CS-19-61, July 2019. http://www.cs.utep.edu/vladik/2019/tr19-61.pdf
Abstract: Researchers who monitor the average intelligence of human population have reasonably recently made an unexpected observation: that aftermany decades in which this level was constantly growing (this is knownas the Flynn effect), at present, this level has started decreasing again. Inthis paper, we show that this reversed Flynn effect can be, in principle, explained in general system-based terms: namely, it is similar to the fact that a control system usually overshoots before stabilizing at the desiredlevel. A similar idea may explain another unexpected observation - that the Universe's expansion rate, which was supposed to be decreasing, isactually increasing.1 Formulation of the Problem IQ tests: a brief reminder.
For many decades, researchers have been using standardized test to measure Intelligent Quotient (IQ, for short), a numerical values that describes how smarter is a person that an average population:
.the IQ value of 100 means that this person has average intelligence,
.values above 100 means that this person's intelligence is above average,and
.values below 100 means that this person's intelligence is below average.1
Of course, this is a rough estimation. Researchers have known that there are different types of intelligence, and that it is therefore not possible to adequately characterize one person's intelligence by using a single number. However, the IQ test score remains a reasonable overall (approximate) measure both of the individual intelligence and of the relative intelligence of different population groups. For example, a recent study showed that non-violent criminals are,on average, smarter than violent ones; this makes sense, since it takes someintelligence (ill-used but still intelligence) to steal without using violence.Average IQ scores grow: Flynn's effect.
Since the IQ scores describethe relation of a tested person's intelligence to an average intelligence at thegiven moment of time, researchers periodically estimate this average level of intelligence.
Somewhat unexpectedly, it turned out that for almost 100 years, the averagelevel of intelligence has been growing; see, e.g., [2, 4, 5, 8, 9, 11, 15, 18, 21]. Specifically:
.if we give average current folks the test from the 1930s, they will, onaverage, score way above 100, and
.vice versa, if we measure the intelligence of the 1930s folks in a currentscale, their average intelligence will be way below 100, at about the 80-90level.
This steady increase in intelligence is known as the Flynn effect, after a scientist who actively promoted this idea.
Why IQ scores grow: possible explanation.
There are many explanations for the growth in intelligence. One of the natural ones is that, in contrastthe old days, when in many professions, physical force was all that is neededto earn a living, nowadays intelligence is very important . non-intelligent jobshave been mostly taken up by machines. No one needs a galley slave to rowa boat, no one needs a strong man to lift heavy things, etc. It is thereforereasonable that modern life requires more intelligent activities, and this increasein solving intelligent problems naturally leads to an increased intelligence . justlike exercising the muscles leads to an improved physique.
Reverse Flynn effect.
While the intelligence scores have been steadily risingfor several decades, lately, a reverse phenomenon has been observed, when theaverage scores no longer grow; instead, they decline. This decline is not as bigas to wipe out the results of the previous decades of growth, but it is big enoughto be statistically significant; see, e.g., [1, 6, 7, 10, 13, 14, 16, 17, 19, 20].
How can we explain the reverse Flynn effect?
There are many differentexplanations for the reverse Flynn effect: that it has been caused by pollution,that it has been caused by declining education standards, etc.In this paper, we analyze this phenomenon from the general systems view-point, and conclude that, from the system.s viewpoint, a current small decline isnatural - and that we therefore do not need to be unnecessarily alarmed by this2 decline. In other words, in spite of this decline, it is still reasonable to remain optimistic.
Abstract: Researchers who monitor the average intelligence of human population have reasonably recently made an unexpected observation: that aftermany decades in which this level was constantly growing (this is knownas the Flynn effect), at present, this level has started decreasing again. Inthis paper, we show that this reversed Flynn effect can be, in principle, explained in general system-based terms: namely, it is similar to the fact that a control system usually overshoots before stabilizing at the desiredlevel. A similar idea may explain another unexpected observation - that the Universe's expansion rate, which was supposed to be decreasing, isactually increasing.1 Formulation of the Problem IQ tests: a brief reminder.
For many decades, researchers have been using standardized test to measure Intelligent Quotient (IQ, for short), a numerical values that describes how smarter is a person that an average population:
.the IQ value of 100 means that this person has average intelligence,
.values above 100 means that this person's intelligence is above average,and
.values below 100 means that this person's intelligence is below average.1
Of course, this is a rough estimation. Researchers have known that there are different types of intelligence, and that it is therefore not possible to adequately characterize one person's intelligence by using a single number. However, the IQ test score remains a reasonable overall (approximate) measure both of the individual intelligence and of the relative intelligence of different population groups. For example, a recent study showed that non-violent criminals are,on average, smarter than violent ones; this makes sense, since it takes someintelligence (ill-used but still intelligence) to steal without using violence.Average IQ scores grow: Flynn's effect.
Since the IQ scores describethe relation of a tested person's intelligence to an average intelligence at thegiven moment of time, researchers periodically estimate this average level of intelligence.
Somewhat unexpectedly, it turned out that for almost 100 years, the averagelevel of intelligence has been growing; see, e.g., [2, 4, 5, 8, 9, 11, 15, 18, 21]. Specifically:
.if we give average current folks the test from the 1930s, they will, onaverage, score way above 100, and
.vice versa, if we measure the intelligence of the 1930s folks in a currentscale, their average intelligence will be way below 100, at about the 80-90level.
This steady increase in intelligence is known as the Flynn effect, after a scientist who actively promoted this idea.
Why IQ scores grow: possible explanation.
There are many explanations for the growth in intelligence. One of the natural ones is that, in contrastthe old days, when in many professions, physical force was all that is neededto earn a living, nowadays intelligence is very important . non-intelligent jobshave been mostly taken up by machines. No one needs a galley slave to rowa boat, no one needs a strong man to lift heavy things, etc. It is thereforereasonable that modern life requires more intelligent activities, and this increasein solving intelligent problems naturally leads to an increased intelligence . justlike exercising the muscles leads to an improved physique.
Reverse Flynn effect.
While the intelligence scores have been steadily risingfor several decades, lately, a reverse phenomenon has been observed, when theaverage scores no longer grow; instead, they decline. This decline is not as bigas to wipe out the results of the previous decades of growth, but it is big enoughto be statistically significant; see, e.g., [1, 6, 7, 10, 13, 14, 16, 17, 19, 20].
How can we explain the reverse Flynn effect?
There are many differentexplanations for the reverse Flynn effect: that it has been caused by pollution,that it has been caused by declining education standards, etc.In this paper, we analyze this phenomenon from the general systems view-point, and conclude that, from the system.s viewpoint, a current small decline isnatural - and that we therefore do not need to be unnecessarily alarmed by this2 decline. In other words, in spite of this decline, it is still reasonable to remain optimistic.
When their own payoff is unknown, all subjects prefer a more unequal distribution provided it makes everyone weakly better off; 25% burn money to make things less unequal, even when it does not make anyone better off
Preferences over income distribution: Evidence from a choice experiment. Sophie Cetre et al. Journal of Economic Psychology, August 28 2019, 102202. https://doi.org/10.1016/j.joep.2019.102202
Highlights
• We elicit subjects preferences over pairs of payoff distributions within small groups, in a firm-like setting.
• When they do not know what their own payoff will be, all subjects prefer the more unequal distribution provided it makes everyone weakly better off. This is true, no matter whether income positions will be based on merit or luck.
• People change their choice when they learn about their own payoff rank.
• Even then, 75% of subjects prefer Pareto-dominant distributions.
• However, 25% engage into money burning at the top in order to reduce inequality, even when it does not make anyone better off.
• Even when their own money is at stake, 20% of subjects are willing to pay just to reduce the degree of inequality.
Abstract: Using a choice experiment in the lab, we assess the relative importance of different attitudes to income inequality. We elicit subjects’ preferences regarding pairs of payoff distributions within small groups, in a firm-like setting. We find that distributions that satisfy the Pareto-dominance criterion attract unanimous suffrage: all subjects prefer larger inequality provided it makes everyone weakly better off. This is true no matter whether payoffs are based on merit or luck. Unanimity only breaks once subjects’ positions within the income distribution are fixed and known ex-ante. Even then, 75% of subjects prefer Pareto-dominant distributions, but 25% of subjects engage in money burning at the top in order to reduce inequality, even when it does not make anyone better off. A majority of subjects embrace a more equal distribution if their own income or overall efficiency is not at stake. When their own income is at stake and the sum of payoffs remains unaffected, 20% of subjects are willing to pay for a lower degree of inequality.
Highlights
• We elicit subjects preferences over pairs of payoff distributions within small groups, in a firm-like setting.
• When they do not know what their own payoff will be, all subjects prefer the more unequal distribution provided it makes everyone weakly better off. This is true, no matter whether income positions will be based on merit or luck.
• People change their choice when they learn about their own payoff rank.
• Even then, 75% of subjects prefer Pareto-dominant distributions.
• However, 25% engage into money burning at the top in order to reduce inequality, even when it does not make anyone better off.
• Even when their own money is at stake, 20% of subjects are willing to pay just to reduce the degree of inequality.
Abstract: Using a choice experiment in the lab, we assess the relative importance of different attitudes to income inequality. We elicit subjects’ preferences regarding pairs of payoff distributions within small groups, in a firm-like setting. We find that distributions that satisfy the Pareto-dominance criterion attract unanimous suffrage: all subjects prefer larger inequality provided it makes everyone weakly better off. This is true no matter whether payoffs are based on merit or luck. Unanimity only breaks once subjects’ positions within the income distribution are fixed and known ex-ante. Even then, 75% of subjects prefer Pareto-dominant distributions, but 25% of subjects engage in money burning at the top in order to reduce inequality, even when it does not make anyone better off. A majority of subjects embrace a more equal distribution if their own income or overall efficiency is not at stake. When their own income is at stake and the sum of payoffs remains unaffected, 20% of subjects are willing to pay for a lower degree of inequality.
Democrats tend to score higher than Republicans on open-minded cognitive style variables; but these partisan differences have very little relationships with how they assess the strength of arguments they disagree with
Differences that don’t make much difference: Party asymmetry in open-minded cognitive styles has little relationship to information processing behavior. April Eichmeier, Neil Stenhouse. Research & Politics, August 28, 2019. https://doi.org/10.1177/2053168019872045
Abstract: We investigated the link between party identification and several cognitive styles that are associated with open-minded thinking. We used a web-based survey which involved participants rating the strength of an argument they initially disagreed with. Results showed that Democrats tend to score higher and Republicans tend to score lower on open-minded cognitive style variables. However, mediation analyses showed that these partisan differences in cognitive style generally have negligible relationships with how individuals assess the strength of arguments they disagree with. In other words, partisan differences in cognitive style may often make little meaningful difference to information processing.
Keywords: Partisan bias, motivated social cognition, motivated reasoning, political psychology, partisan asymmetry, cognitive style
Check also Does Media Literacy Help Identification of Fake News? Information Literacy Helps, but Other Literacies Don’t. S. Mo Jones-Jang, Tara Mortensen, Jingjing Liu. American Behavioral Scientist, August 28, 2019. https://www.bipartisanalliance.com/2019/08/does-media-literacy-help-identification.html
Abstract: We investigated the link between party identification and several cognitive styles that are associated with open-minded thinking. We used a web-based survey which involved participants rating the strength of an argument they initially disagreed with. Results showed that Democrats tend to score higher and Republicans tend to score lower on open-minded cognitive style variables. However, mediation analyses showed that these partisan differences in cognitive style generally have negligible relationships with how individuals assess the strength of arguments they disagree with. In other words, partisan differences in cognitive style may often make little meaningful difference to information processing.
Keywords: Partisan bias, motivated social cognition, motivated reasoning, political psychology, partisan asymmetry, cognitive style
Check also Does Media Literacy Help Identification of Fake News? Information Literacy Helps, but Other Literacies Don’t. S. Mo Jones-Jang, Tara Mortensen, Jingjing Liu. American Behavioral Scientist, August 28, 2019. https://www.bipartisanalliance.com/2019/08/does-media-literacy-help-identification.html
We sought to test whether stereotypes that women have better language skills than men would make men to underperform in language tests; found little evidence for stereotype threat effects on men in language tasks
Chaffee, Kathryn E., Nigel M. Lou, and Kimberly A. Noels. 2019. “Does Stereotype Threat Affect Men in Language Domains?.” PsyArXiv. August 28. doi:10.31234/osf.io/jzhuk
Abstract: Currently, boys and men tend to underperform in language domains in school and on standardized tests, and they are also underrepresented in language-related fields of study. Stereotype threat is one way in which psychologists have found that stereotypes can affect students’ performance and sense of belonging in academic subjects and test settings Researchers have traditionally found that when girls and women are reminded of stereotypes that math is for boys, girls and women tend to underperform on math tests. Stereotype threat has also been found to affect women’s sense of belonging in math settings. We sought to test whether stereotypes that women have better language skills than men would affect men in the same way. We conducted a series of four experiments (N=542) testing the effect of explicit stereotype threats on men’s performance in language-related tasks, and their sense of belonging to language-related domains. We found little evidence for stereotype threat effects on men in language tasks. Mini-meta analyses revealed aggregate effect sizes indistinguishable from zero across our studies, and Bayesian analysis suggested that the null hypothesis was consistently more likely than the alternative. Future research should explore other explanations for gender gaps in language.
Abstract: Currently, boys and men tend to underperform in language domains in school and on standardized tests, and they are also underrepresented in language-related fields of study. Stereotype threat is one way in which psychologists have found that stereotypes can affect students’ performance and sense of belonging in academic subjects and test settings Researchers have traditionally found that when girls and women are reminded of stereotypes that math is for boys, girls and women tend to underperform on math tests. Stereotype threat has also been found to affect women’s sense of belonging in math settings. We sought to test whether stereotypes that women have better language skills than men would affect men in the same way. We conducted a series of four experiments (N=542) testing the effect of explicit stereotype threats on men’s performance in language-related tasks, and their sense of belonging to language-related domains. We found little evidence for stereotype threat effects on men in language tasks. Mini-meta analyses revealed aggregate effect sizes indistinguishable from zero across our studies, and Bayesian analysis suggested that the null hypothesis was consistently more likely than the alternative. Future research should explore other explanations for gender gaps in language.
Are Humans Prepared to Detect, Fear, and Avoid Snakes? The Mismatch between Laboratory and Ecological Evidence
Are Humans Prepared to Detect, Fear, and Avoid Snakes? The Mismatch between Laboratory and Ecological Evidence. Carlos M. Coelho et al. Front. Psychol. Aug 28 2019, doi: 10.3389/fpsyg.2019.02094
Abstract: Since Seligman's 1971 statement that the vast majority of phobias are about objects essential to the survival of a species, a multitude of laboratory studies followed, supporting the finding that humans learn to fear and detect snakes (and other animals) faster than other stimuli. Most of these studies used schematic drawings, images, or pictures of snakes, and only a small amount of fieldwork in naturalistic environments was done. We address fear preparedness theories, and automatic fast detection data from mainstream laboratory data and compares it with ethobehavioural information relative to snakes, predator-prey interaction, and snakes’ defensive kinematics strikes in order to analyse their potential matching. From this analysis four main findings arose, namely that: 1) Snakebites occur when people are very close to the snake and are unaware or unable to escape the bite; 2) Human visual detection and escape response is slow compared to the speed of snake strikes; 3) In natural environments, snake experts are often unable to see snakes existing nearby; 4) animate objects in general capture more attention over other stimuli and dangerous but recent objects in evolutionary terms are also able to be detected fast. The issues mentioned above pose several challenges to evolutionary psychology-based theories expecting to find special-purpose neural modules. The older selective habituation hypothesis (Schleidt, 1961), that prey animals start with a rather general predator image from which specific harmless cues are removed by habituation might deserve reconsideration.
Keywords: General feature detection, modular theory, snake bite kinematics, selective habituation hypothesis, evolutionary psychology
Abstract: Since Seligman's 1971 statement that the vast majority of phobias are about objects essential to the survival of a species, a multitude of laboratory studies followed, supporting the finding that humans learn to fear and detect snakes (and other animals) faster than other stimuli. Most of these studies used schematic drawings, images, or pictures of snakes, and only a small amount of fieldwork in naturalistic environments was done. We address fear preparedness theories, and automatic fast detection data from mainstream laboratory data and compares it with ethobehavioural information relative to snakes, predator-prey interaction, and snakes’ defensive kinematics strikes in order to analyse their potential matching. From this analysis four main findings arose, namely that: 1) Snakebites occur when people are very close to the snake and are unaware or unable to escape the bite; 2) Human visual detection and escape response is slow compared to the speed of snake strikes; 3) In natural environments, snake experts are often unable to see snakes existing nearby; 4) animate objects in general capture more attention over other stimuli and dangerous but recent objects in evolutionary terms are also able to be detected fast. The issues mentioned above pose several challenges to evolutionary psychology-based theories expecting to find special-purpose neural modules. The older selective habituation hypothesis (Schleidt, 1961), that prey animals start with a rather general predator image from which specific harmless cues are removed by habituation might deserve reconsideration.
Keywords: General feature detection, modular theory, snake bite kinematics, selective habituation hypothesis, evolutionary psychology
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