Why g matters: The complexity of everyday life
TL;DR: In this paper, the authors provide evidence that intelligence has pervasive utility in work settings because it is essentially the ability to deal with cognitive complexity, in particular, with complex information processing, and the more complex a work task, the greater the advantages that higher g confers in performing it well.
About: This article is published in Intelligence.The article was published on 1997-01-01 and is currently open access. It has received 1300 citations till now. The article focuses on the topics: Everyday life & Cognitive complexity.
Summary (1 min read)
Jump to: [Why g Matters: The Complexity of Everyday Life] – [WHAT DOES “IMPORTANT” MEAN?] – [INFLUENCE OF INTELLIGENCE ON OVERALL LIFE OUTCOMES] and [THE FUTURE]
Why g Matters: The Complexity of Everyday Life
- This article provides evidence that g has pervasive utility in work settings because it is essentially the ability to deal with cognitive complexity, in particular, with complex information processing.
- Few claims in the social sciences are backed by such massive evidence but remain so hotly contested in public discourse.
- Besides demonstrating that g is important in practical affairs, I seek to demonstrate why intelligence has such surprisingly pervasive importance in the lives of individuals.
- I then use both the employment and literacy data to sketch a portrait of life’s challenges and opportunities at different levels of intelligence.
WHAT DOES “IMPORTANT” MEAN?
- The nature of the job and its context seem to determine whether g has any direct effect on task proficiency, net of job knowlege.
- As is well known in psychometrics (see also Gordon, 1997), the fact that an individual passes or fails any single test item says little about that person’s general intelligence level.
INFLUENCE OF INTELLIGENCE ON OVERALL LIFE OUTCOMES
- The effects of intelligence-like other psychological traits-are probabilistic, not deterministic.
- White adults in this range marry, work, and have children (Hermstein & Murray, 1994), but, as Table 10 shows, they are nonetheless at great risk of living in poverty (30%), bearing children out of wedlock (32%), and becoming chronic welfare dependents (31%).
- At this IQ level, fewer than half the high school graduates and none of the dropouts meet the military’s minimum AFQT enlistment standards.
- Most occupations are within reach cognitively, because these individuals learn complex material fairly easily and independently.
- Such as divorce, illness, and occasional unemployment, they rarely become trapped in poverty or social pathology.
THE FUTURE
- Complexity enriches social and cultural life, but it also risks leaving some individuals behind.
- Society has become more complex-and g loaded-as the authors have entered the information age and postindustrial economy.
- Accordingly, organizations are “flatter” (have fewer hierarchical levels), and increasing numbers of jobs require high-level cognitive and interpersonal skills (Camevale, 1991; Cascio, 1995; Hunt, 1995; Secretary’s Commission on Achieving Necessary Skills, 1991).
- There is evidence that increasing proportions of individuals with below-average IQs are having trouble adapting to their increasingly complex modern life (Granat & Granat, 1978) and that social inequality along IQ lines is increasing (Herrnstein & Murray, 1994).
- As the military experience also illustrates, however, what is good pedagogy for the low-aptitude learner may be inappropriate for the high-aptitude person.
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TL;DR: In this paper, a Computational Thinking Test (CTt) is administered on a sample of 1,251 Spanish students from 5th to 10th grade, so its descriptive statistics and reliability are reported in this paper.
447 citations
Cites background from "Why g matters: The complexity of ev..."
...While CT involves thinking skills to solve problems algorithmically (e.g., Brennan & Resnick, 2012; Grover & Pea, 2013), intelligence (i.e., general mental ability or general cognitive ability) involves primarily the ability to reason, plan and solve problems (Gottfredson, 1997)....
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..., general mental ability or general cognitive ability) involves primarily the ability to reason, plan and solve problems (Gottfredson, 1997)....
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TL;DR: Cognitive, psychometric, genetic and neuroimaging studies are converging, and the emergence of mechanistic models of intelligence is inevitable, and these exciting scientific advances encourage renewed responsiveness to the social and ethical implications of conducting such research.
Abstract: Human mental abilities, such as intelligence, are complex and profoundly important, both in a practical sense and for what they imply about the human condition. Understanding these abilities in mechanistic terms has the potential to facilitate their enhancement. There is strong evidence that the lateral prefrontal cortex, and possibly other areas, support intelligent behaviour. Variations in intelligence and brain structure are heritable, but are also influenced by factors such as education, family environment and environmental hazards. Cognitive, psychometric, genetic and neuroimaging studies are converging, and the emergence of mechanistic models of intelligence is inevitable. These exciting scientific advances encourage renewed responsiveness to the social and ethical implications of conducting such research.
440 citations
Cites background from "Why g matters: The complexity of ev..."
...The field is at an exciting juncture because nuanced conceptual and empirical approaches are now available, and intelligence is important for both practica...
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TL;DR: A number of well known informal definitions of human intelligence are taken, and mathematically formalised to produce a general measure of intelligence for arbitrary machines that formally captures the concept of machine intelligence in the broadest reasonable sense.
Abstract: A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.
438 citations
TL;DR: It is concluded that ongoing development of neurobiologically grounded measures of fluid cognitive skills appropriate for young children will play a key role in understanding early mental development and the adaptive success to which it is related, particularly for youngChildren facing social and economic disadvantage.
Abstract: This target article considers the relation of fluid cognitive functioning to general intelligence. A neurobiological model differentiating working memory/executive function cognitive processes of the prefrontal cortex from aspects of psychometrically defined general intelligence is presented. Work examining the rise in mean intelligence-test performance between normative cohorts, the neuropsychology and neuroscience of cognitive function in typically and atypically developing human populations, and stress, brain development, and corticolimbic connectivity in human and nonhuman animal models is reviewed and found to provide evidence of mechanisms through which early experience affects the development of an aspect of cognition closely related to, but distinct from, general intelligence. Particular emphasis is placed on the role of emotion in fluid cognition and on research indicating fluid cognitive deficits associated with early hippocampal pathology and with dysregulation of the hypothalamic-pituitary-adrenal axis stress-response system. Findings are seen to be consistent with the idea of an independent fluid cognitive construct and to assist with the interpretation of findings from the study of early compensatory education for children facing psychosocial adversity and from behavior genetic research on intelligence. It is concluded that ongoing development of neurobiologically grounded measures of fluid cognitive skills appropriate for young children will play a key role in understanding early mental development and the adaptive success to which it is related, particularly for young children facing social and economic disadvantage. Specifically, in the evaluation of the efficacy of compensatory education efforts such as Head Start and the readiness for school of children from diverse backgrounds, it is important to distinguish fluid cognition from psychometrically defined general intelligence.
428 citations
Cites background or methods from "Why g matters: The complexity of ev..."
...This is also supported by psychometric findings suggesting that fluid intelligence is the best predictor of performance in situations that involve human intelligence, including performance at school, at university, and in cognitively demanding occupations (Gottfredson 1997)....
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...Nevertheless, it is clear that the variance common to even a relatively small battery of such tests taps a general intellectual ability with substantial relevance to a wide variety of life outcomes (Gottfredson 1997; Jensen 1998; Lubinski 2004)....
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TL;DR: The culture-only and the hereditarian models of the causes of mean Black-White differences in cognitive ability are compared and contrasted across 10 categories of evidence: the worldwide distribution of test scores, g factor of mental ability, heritability, brain size and cognitive ability, transracial adoption, racial admixture, regression, related life-history traits, human origins research, and hypothesized environmental variables as discussed by the authors.
Abstract: The culture-only (0% genetic–100% environmental) and the hereditarian (50% genetic–50% environmental) models of the causes of mean Black–White differences in cognitive ability are compared and contrasted across 10 categories of evidence: the worldwide distribution of test scores, g factor of mental ability, heritability, brain size and cognitive ability, transracial adoption, racial admixture, regression, related life-history traits, human origins research, and hypothesized environmental variables. The new evidence reviewed here points to some genetic component in Black–White differences in mean IQ. The implication for public policy is that the discrimination model (i.e., Black–White differences in socially valued outcomes will be equal barring discrimination) must be tempered by a distributional model (i.e., Black–White outcomes reflect underlying group characteristics).
423 citations
Cites background from "Why g matters: The complexity of ev..."
...Because test scores are the best predictor of economic success in Western society (Schmidt & Hunter, 1998), these group differences have important societal outcomes (R. A. Gordon, 1997; Gottfredson, 1997)....
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...In today’s technological society, everyday life itself is a type of IQ test (see R. A. Gordon, 1997; Gottfredson, 1997; Herrnstein & Murray, 1994)....
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References
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