Learning Without Questioning –Why Asians do not win Nobel prizes
Kenya Kura
http://drjamesthompson.blogspot.co.at/2014/04/lci14-conference-proceedings-kenya-kura.html
A Non-Fiction Blog. Ein Sachblog. A collection of some bits of information extracted from the scientific and from the non-fiction literature. (Until June 2025 there were also some poems and aphorisms posted on this blog.) Sachthemen und Sachtexte. (Bis Ende Juni 2025 wurden hier auch regelmäßig Gedichte und Aphorismen zu beliebigen Themen veröffentlicht.)
Montag, 28. April 2014
Sonntag, 27. April 2014
Women remember more faces than men do
Women remember more faces than men do
Jenny Rehnman Agneta Herlitz
Acta Psychologica (March 2007)
Abstract
Jenny Rehnman Agneta Herlitz
Acta Psychologica (March 2007)
Abstract
Women have been found to outperform men on face recognition tasks, specifically in the recognition of female faces. Men do not seem to exhibit a corresponding own-sex bias. To examine the generality and possible reasons for these patterns, 107 men and 112 women viewed faces of both children and adults of either Swedish or Bangladeshi origin, for later recognition. As expected, women were especially good at remembering female faces, but also outperformed men on male faces. Men did not show an own-sex bias. Thus, regardless of age and ethnicity of the faces, women performed at a higher level than men on both female and male faces, possibly reflecting enhanced interest in faces, and in particular, female faces.
Freitag, 25. April 2014
The Relationship between Learning and Intelligence
>In reviewing the then entire literature on the relation of learning to IQ I (Jensen 1979) arrived at a number of empirical generalizations regarding the conditions that seem most clearly related to the magnitude of the learning-IQ correlations, or the degree to which various learning tasks are g loaded. To summarize: Learning is more highly g loaded when:
1. learning is intentional and calls forth conscious mental effort;
2. the learning or practice trials are paced in such a way as to allow the subject time to think;
3. the material to be learned is hierarchical in the sense that the learning of later elements depends on mastery of earlier elements;
4. the material to be learned is meaningful in the sense of being related to other knowledge or experience already possessed by the learner;
5. the learning task permits transfer-from somewhat different but related past learning;
6. the learning is insightful, that is, it involves “catching on” or “getting the idea”;
7. the material to be learned is of moderate difficulty and complexity, in the sense of the number of elements that must be integrated simultaneously for the learning to progress;
8. the amount of time for learning a given amount of material to a specified criterion of mastery is fixed for all students;
9. the learning material is positively age-related, that is, some kinds of material are more readily learned (hence the concept of “readiness”) by older than by younger children; and
10. performance gains are measured at an early stage of learning something “new” than at a late stage of practice on the same task.<
Source:
The Relationship between Learning and Intelligence
Arthur R. Jensen (1989)
Learning and Individual Differences
[See also: http://meinnaturwissenschaftsblog.blogspot.co.at/2013/06/learning-iq.html]
Investment and Intellect: A Review and Meta-Analysis
Investment and Intellect: A Review and Meta-Analysis
Sophie von Stumm, Phillip L. Ackerman (2012)
Abstract
Sophie von Stumm, Phillip L. Ackerman (2012)
Abstract
Cognitive or intellectual investment theories propose that the development of intelligence is partially influenced by personality traits, in particular by so-called investment traits that determine when, where, and how people invest their time and effort in their intellect. This investment, in turn, is thought to contribute to individual differences in cognitive growth and the accumulation of knowledge across the life span. We reviewed the psychological literature and identified 34 trait constructs and corresponding scales that refer to intellectual investment. The dispositional constructs were further classified into 8 related trait categories that span the construct space of intellectual investment. Subsequently, we sought to estimate the association between the identified investment traits and indicators of adult intellect, including measures of crystallized intelligence, academic performance (e.g., grade point average), college entry tests, and acquired knowledge. A meta-analysis of 112 studies with 236 coefficients and an overall sample of 60,097 participants indicated that investment traits were mostly positively associated with adult intellect markers. Meta-analytic coefficients ranged considerably, from 0 to .58, with an average estimate of .30. We concluded that investment traits are overall positively related to adult intellect; the strength of investment–intellect associations differs across trait scales and markers of intellect; and investment traits have a diverse, multifaceted nature. The meta-analysis also identified areas of inquiry that are currently lacking in empirical research. Limitations, implications, and future directions are discussed.
Delay discounting and intelligence: A meta-analysis
Delay discounting and intelligence: A meta-analysis
Noah A. Shamosh, Jeremy R. Gray
Intelligence (July - August 2008)
Abstract
Noah A. Shamosh, Jeremy R. Gray
Intelligence (July - August 2008)
Abstract
Delay discounting (DD), the tendency to prefer smaller, sooner rewards to larger, later ones, is an important indicator of self-control. Assessments of DD superficially require individuals to make choices based on motivational processes. However, several lines of evidence suggest that DD may be systematically related to cognitive ability. We sought to provide a definitive assessment of the relation between DD and intelligence via quantitative research synthesis. A comprehensive literature search in two electronic databases yielded 24 eligible studies with 26 effect sizes in total. Meta-analysis revealed that, across studies, higher intelligence was associated with lower DD (random effects model weighted mean r = −0.23). Studies using reward schemes in which payoffs were subject to chance (i.e., involving either a chance of receiving one choice or random selection of one choice) showed weaker associations between DD and intelligence than did studies in which payoffs were all hypothetical or all real. Other moderator analyses revealed no influence of DD measure, DD choice paradigm, or intelligence type. There was no evidence of publication bias. Given clear evidence for a negative relation between DD and intelligence, investigating the processes that support or moderate this relation would be worthwhile.
Dienstag, 22. April 2014
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