Positive and negative effects of gender expertise on episodic memory. Ainat Pansky et al. Memory & Cognition, https://link.springer.com/article/10.3758/s13421-018-0863-z
Abstract: In two experiments, we examined the role of differential levels of knowledge between the genders in different domains, which we term gender expertise, in accounting for differences in episodic memory performance. In Experiment 1, we validated the assumption of differential gender expertise among men and women and selected the categories for the subsequent experiments. In Experiment 2, participants from both genders studied exemplars from these female-oriented, male-oriented, and gender-neutral categories and were tested after 24 hours on studied items, critical lures, and unrelated lures. A gender-congruity effect was found in terms of the recognition rates of both studied items and critical lures: Participants from each gender recognized more studied items and more critical lures from gender-congruent categories than from gender-incongruent categories. A parallel pattern of results was found for subjective confidence, supporting the notion that gender congruity enhanced the phenomenological experience that an item was studied. Our findings highlight the unique role of gender expertise in accounting for gender-congruity effects in episodic memory performance, using a well-defined operationalization of gender expertise. These findings show that in addition to benefits in terms of enhancing true memory, gender expertise also has a “dark side” of increasing false memories.
Friday, September 28, 2018
Measuring human capital: a systematic analysis of 195 countries and territories, 1990-2016
Measuring human capital: a systematic analysis of 195 countries and territories, 1990-2016. Stephen S Lim, Rachel L Updike, Alexander S Kaldjian, Ryan M Barber, Krycia Cowling, Hunter York, Joseph Friedman, R Xu, Joanna L Whisnant, Heather J Taylor, Andrew T Leever, Yesenia Roman, Miranda F Bryant, Joseph Dieleman, Emmanuela Gakidou, Christopher J L Murray. The Lancet, http://dx.doi.org/10.1016/S0140-6736(18)31941-X
Summary
Background: Human capital is recognised as the level of education and health in a population and is considered an important determinant of economic growth. The World Bank has called for measurement and annual reporting of human capital to track and motivate investments in health and education and enhance productivity. We aim to provide a new comprehensive measure of human capital across countries globally.
Methods: We generated a period measure of expected human capital, defined for each birth cohort as the expected years lived from age 20 to 64 years and adjusted for educational attainment, learning or education quality, and functional health status using rates specific to each time period, age, and sex for 195 countries from 1990 to 2016. We estimated educational attainment using 2522 censuses and household surveys; we based learning estimates on 1894 tests among school-aged children; and we based functional health status on the prevalence of seven health conditions, which were taken from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016). Mortality rates specific to location, age, and sex were also taken from GBD 2016.
Findings: In 2016, Finland had the highest level of expected human capital of 28·4 health, education, and learning- adjusted expected years lived between age 20 and 64 years (95% uncertainty interval 27·5-29·2); Niger had the lowest expected human capital of less than 1·6 years (0·98-2·6). In 2016, 44 countries had already achieved more than 20 years of expected human capital; 68 countries had expected human capital of less than 10 years. Of 195 countries, the ten most populous countries in 2016 for expected human capital were ranked: China at 44, India at 158, USA at 27, Indonesia at 131, Brazil at 71, Pakistan at 164, Nigeria at 171, Bangladesh at 161, Russia at 49, and Mexico at 104. Assessment of change in expected human capital from 1990 to 2016 shows marked variation from less than 2 years of progress in 18 countries to more than 5 years of progress in 35 countries. Larger improvements in expected human capital appear to be associated with faster economic growth. The top quartile of countries in terms of absolute change in human capital from 1990 to 2016 had a median annualised growth in gross domestic product of 2·60% (IQR 1·85.3·69) compared with 1·45% (0·18.2·19) for countries in the bottom quartile.
Interpretation: Countries vary widely in the rate of human capital formation. Monitoring the production of human capital can facilitate a mechanism to hold governments and donors accountable for investments in health and education.
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Human capital refers to the attributes of a population that, along with physical capital such as buildings, equipment, and other tangible assets, contribute to economic productivity. Human capital is characterised as the aggregate levels of education, training, skills, and health in a population, affecting the rate at which technologies can be developed, adopted, and employed to increase productivity.
My comment: In the background, first sentence, "Human capital is recognised as the level of education and health in a population," but in the second sentence in the paper, body, "Human capital is characterised as the aggregate levels of education, training, skills, and health in a population." It is much more complex to measure "aggregate levels of education, training, skills, and health in a population" than to measure just "the level of education and health in a population."
How this came to be? If you say that HC is education, training, skills, and health, is Cuba above Russia in human capital? Really? And North Korea above Egypt? Palestine above Iran? And Brunei above the UK, New Zealand, Italy and Israel? And Malta above China and Russia? What the reviewers say about these strange results?
Summary
Background: Human capital is recognised as the level of education and health in a population and is considered an important determinant of economic growth. The World Bank has called for measurement and annual reporting of human capital to track and motivate investments in health and education and enhance productivity. We aim to provide a new comprehensive measure of human capital across countries globally.
Methods: We generated a period measure of expected human capital, defined for each birth cohort as the expected years lived from age 20 to 64 years and adjusted for educational attainment, learning or education quality, and functional health status using rates specific to each time period, age, and sex for 195 countries from 1990 to 2016. We estimated educational attainment using 2522 censuses and household surveys; we based learning estimates on 1894 tests among school-aged children; and we based functional health status on the prevalence of seven health conditions, which were taken from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016). Mortality rates specific to location, age, and sex were also taken from GBD 2016.
Findings: In 2016, Finland had the highest level of expected human capital of 28·4 health, education, and learning- adjusted expected years lived between age 20 and 64 years (95% uncertainty interval 27·5-29·2); Niger had the lowest expected human capital of less than 1·6 years (0·98-2·6). In 2016, 44 countries had already achieved more than 20 years of expected human capital; 68 countries had expected human capital of less than 10 years. Of 195 countries, the ten most populous countries in 2016 for expected human capital were ranked: China at 44, India at 158, USA at 27, Indonesia at 131, Brazil at 71, Pakistan at 164, Nigeria at 171, Bangladesh at 161, Russia at 49, and Mexico at 104. Assessment of change in expected human capital from 1990 to 2016 shows marked variation from less than 2 years of progress in 18 countries to more than 5 years of progress in 35 countries. Larger improvements in expected human capital appear to be associated with faster economic growth. The top quartile of countries in terms of absolute change in human capital from 1990 to 2016 had a median annualised growth in gross domestic product of 2·60% (IQR 1·85.3·69) compared with 1·45% (0·18.2·19) for countries in the bottom quartile.
Interpretation: Countries vary widely in the rate of human capital formation. Monitoring the production of human capital can facilitate a mechanism to hold governments and donors accountable for investments in health and education.
---
Human capital refers to the attributes of a population that, along with physical capital such as buildings, equipment, and other tangible assets, contribute to economic productivity. Human capital is characterised as the aggregate levels of education, training, skills, and health in a population, affecting the rate at which technologies can be developed, adopted, and employed to increase productivity.
My comment: In the background, first sentence, "Human capital is recognised as the level of education and health in a population," but in the second sentence in the paper, body, "Human capital is characterised as the aggregate levels of education, training, skills, and health in a population." It is much more complex to measure "aggregate levels of education, training, skills, and health in a population" than to measure just "the level of education and health in a population."
How this came to be? If you say that HC is education, training, skills, and health, is Cuba above Russia in human capital? Really? And North Korea above Egypt? Palestine above Iran? And Brunei above the UK, New Zealand, Italy and Israel? And Malta above China and Russia? What the reviewers say about these strange results?
Imagination inflation occurs when participants increase their certainty that they have experienced an event after they imagine the event occurring
Imagining Experiencing an Event in the Future Inflates Certainty That It Occurred in the Past. Dustin P. Calvillo et al. Imagination, Cognition and Personality, https://doi.org/10.1177/0276236618803308
Abstract: Imagination inflation occurs when participants increase their certainty that they have experienced an event after they imagine the event occurring. Two experiments (with a total of 291 participants) examined the effects of imagining events in the future on participants’ certainty they had experienced those events in the past. Participants rated their certainty in having experienced events and then imagined experiencing some of those events either in the future or in the past. One or two weeks later, participants completed certainty ratings a second time and completed some individual difference measures. In both imagination conditions (future and past), certainty ratings increased more for imagined events than for control events. Autobiographical memory specificity and self-concept clarity did not significantly predict this effect. These findings suggest that imagining events in the future makes people more certain that they have happened in the past.
Keywords: imagination inflation, episodic future thinking, autobiographical memory specificity, self-concept clarity
Abstract: Imagination inflation occurs when participants increase their certainty that they have experienced an event after they imagine the event occurring. Two experiments (with a total of 291 participants) examined the effects of imagining events in the future on participants’ certainty they had experienced those events in the past. Participants rated their certainty in having experienced events and then imagined experiencing some of those events either in the future or in the past. One or two weeks later, participants completed certainty ratings a second time and completed some individual difference measures. In both imagination conditions (future and past), certainty ratings increased more for imagined events than for control events. Autobiographical memory specificity and self-concept clarity did not significantly predict this effect. These findings suggest that imagining events in the future makes people more certain that they have happened in the past.
Keywords: imagination inflation, episodic future thinking, autobiographical memory specificity, self-concept clarity
Are People Attracted to Others Who Resemble Their Opposite-Sex Parents? An Examination of Mate Preferences and Parental Ethnicity Among Biracial Individuals
Are People Attracted to Others Who Resemble Their Opposite-Sex Parents? An Examination of Mate Preferences and Parental Ethnicity Among Biracial Individuals. Marie E. Heffernan, Jia Y. Chong, R. Chris Fraley. Social Psychological and Personality Science, https://doi.org/10.1177/1948550618794679
Abstract: It is generally believed that people tend to be attracted to and pair with others who resemble their opposite-sex parents. Studies 1A (n = 1,025) and 1B (n = 3,105) tested this assumption by examining whether biracial adults were more likely to be paired with partners who matched their opposite-sex parent’s ethnicity. Study 2 (n = 516) examined whether biracial adults were more likely to be attracted to targets whose ethnicity matched that of their opposite-sex parent. Although biracial adults were more likely to pair with and be attracted to others who resembled their parents compared to those who did not, the sex of the parent was largely inconsequential. These findings have implications for models of mate preferences, including the traditional perspectives (which assume that the opposite-sex parent has greater influence on adult mating preferences) and ethological models (which assume that the sex of the parent is irrelevant with regard to influence on mating preferences).
Keywords: romantic attraction, biracial individuals, mate preferences, close relationships
Abstract: It is generally believed that people tend to be attracted to and pair with others who resemble their opposite-sex parents. Studies 1A (n = 1,025) and 1B (n = 3,105) tested this assumption by examining whether biracial adults were more likely to be paired with partners who matched their opposite-sex parent’s ethnicity. Study 2 (n = 516) examined whether biracial adults were more likely to be attracted to targets whose ethnicity matched that of their opposite-sex parent. Although biracial adults were more likely to pair with and be attracted to others who resembled their parents compared to those who did not, the sex of the parent was largely inconsequential. These findings have implications for models of mate preferences, including the traditional perspectives (which assume that the opposite-sex parent has greater influence on adult mating preferences) and ethological models (which assume that the sex of the parent is irrelevant with regard to influence on mating preferences).
Keywords: romantic attraction, biracial individuals, mate preferences, close relationships
Indices of comparative cognition: assessing animal models of human brain function; learning algorithms can help to identify the most relevant species to model human brain function and dysfunction
Indices of comparative cognition: assessing animal models of human brain function. Sebastian D. McBride, A. Jennifer Morton. Experimental Brain Research, https://link.springer.com/article/10.1007/s00221-018-5370-8
Abstract: Understanding the cognitive capacities of animals is important, because (a) several animal models of human neurodegenerative disease are considered poor representatives of the human equivalent and (b) cognitive capacities may provide insight into alternative animal models. We used a three-stage process of cognitive and neuroanatomical comparison (using sheep as an example) to assess the appropriateness of a species to model human brain function. First, a cognitive task was defined via a reinforcement-learning algorithm where values/constants in the algorithm were taken as indirect measures of neurophysiological attributes. Second, cognitive data (values/constants) were generated for the example species (sheep) and compared to other species. Third, cognitive data were compared with neuroanatomical metrics for each species (endocranial volume, gyrification index, encephalisation quotient, and number of cortical neurons). Four breeds of sheep (n = 15/sheep) were tested using the two-choice discrimination-reversal task. The ‘reversal index’ was used as a measure of constants within the learning algorithm. Reversal index data ranked sheep as third in a table of species that included primates, dogs, and pigs. Across all species, number of cortical neurons correlated strongest against the reversal index (r2 = 0.66, p = 0.0075) followed by encephalization quotient (r2 = 0.42, p = 0.03), endocranial volume (r2 = 0.30, p = 0.08), and gyrification index (r2 = 0.16, p = 0.23). Sheep have a high predicted level of cognitive capacity and are thus a valid alternative model for neurodegenerative research. Using learning algorithms within cognitive tasks increases the resolution of methods of comparative cognition and can help to identify the most relevant species to model human brain function and dysfunction.
Keywords: Cognition Sheep Animal model Brain
Abstract: Understanding the cognitive capacities of animals is important, because (a) several animal models of human neurodegenerative disease are considered poor representatives of the human equivalent and (b) cognitive capacities may provide insight into alternative animal models. We used a three-stage process of cognitive and neuroanatomical comparison (using sheep as an example) to assess the appropriateness of a species to model human brain function. First, a cognitive task was defined via a reinforcement-learning algorithm where values/constants in the algorithm were taken as indirect measures of neurophysiological attributes. Second, cognitive data (values/constants) were generated for the example species (sheep) and compared to other species. Third, cognitive data were compared with neuroanatomical metrics for each species (endocranial volume, gyrification index, encephalisation quotient, and number of cortical neurons). Four breeds of sheep (n = 15/sheep) were tested using the two-choice discrimination-reversal task. The ‘reversal index’ was used as a measure of constants within the learning algorithm. Reversal index data ranked sheep as third in a table of species that included primates, dogs, and pigs. Across all species, number of cortical neurons correlated strongest against the reversal index (r2 = 0.66, p = 0.0075) followed by encephalization quotient (r2 = 0.42, p = 0.03), endocranial volume (r2 = 0.30, p = 0.08), and gyrification index (r2 = 0.16, p = 0.23). Sheep have a high predicted level of cognitive capacity and are thus a valid alternative model for neurodegenerative research. Using learning algorithms within cognitive tasks increases the resolution of methods of comparative cognition and can help to identify the most relevant species to model human brain function and dysfunction.
Keywords: Cognition Sheep Animal model Brain
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