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Intelligence : a very short introduction
Some people appear to be smarter than others, but how do we measure intelligence? Why do some people have better thinking powers than others? What does intelligence predict about people's health and social outcomes? This \"Very Short Introduction\" uses the best, large-scale psychological data to answer important questions about intelligence, such as how environment, genes, brain structure, gender, and age affect people's thinking skills. It asks whether intelligence increased over the 20th century. Ian Deary also considers the new field of cognitive epidemiology, which discovers links between higher intelligence and better health, lower rates of illness, and longer life. -- From publisher's description.
Book
The “Reading the Mind in the Eyes” Test in Autism-Spectrum Disorders Comparison with Healthy Controls: A Systematic Review and Meta-analysis
We conducted a meta-analysis of 18 studies to establish whether a relation exists between Reading the Mind in the Eye Test (RMET) performance and intelligence quotient (IQ) in individuals diagnosed with autism-spectrum disorders (ASD) and controls, taking into account relevant characteristics such as age, gender, and autism quotient. Our findings indicate that RMET performance was better in controls compared with those diagnosed with ASD. We found that RMET performance is dependent on full and verbal IQ and age in controls. However, RMET performance is negatively correlated with performance IQ in individuals diagnosed with ASD. These results suggest that the methodology applied by ASD when taking the RMET is different from control individuals and might depend less on verbal abilities.
Journal Article
Role of test motivation in intelligence testing
Intelligence tests are widely assumed to measure maximal intellectual performance, and predictive associations between intelligence quotient (IQ) scores and later-life outcomes are typically interpreted as unbiased estimates of the effect of intellectual ability on academic, professional, and social life outcomes. The current investigation critically examines these assumptions and finds evidence against both. First, we examined whether motivation is less than maximal on intelligence tests administered in the context of low-stakes research situations. Specifically, we completed a meta-analysis of random-assignment experiments testing the effects of material incentives on intelligence-test performance on a collective 2,008 participants. Incentives increased IQ scores by an average of 0.64 SD, with larger effects for individuals with lower baseline IQ scores. Second, we tested whether individual differences in motivation during IQ testing can spuriously inflate the predictive validity of intelligence for life outcomes. Trained observers rated test motivation among 251 adolescent boys completing intelligence tests using a 15-min \"thin-slice\" video sample. IQ score predicted life outcomes, including academic performance in adolescence and criminal convictions, employment, and years of education in early adulthood. After adjusting for the influence of test motivation, however, the predictive validity of intelligence for life outcomes was significantly diminished, particularly for nonacademic outcomes. Collectively, our findings suggest that, under low-stakes research conditions, some individuals try harder than others, and, in this context, test motivation can act as a third-variable confound that inflates estimates of the predictive validity of intelligence for life outcomes.
Journal Article
IQ and psychometric tests : assess your personality, aptitude and intelligence
A variety of tests for measuring IQ and other psychometrics.
Book
Assessing intelligence at autism diagnosis: mission impossible? Testability and cognitive profile of autistic preschoolers
Intelligence in minimally verbal children on the autism spectrum (AS) is at risk of being underestimated. The present study investigated testability and cognitive profile of preschool autistic children using conventional tools and strength-informed tools. Fifty-two AS children and fifty-four typical children matched on age (31–77 months) were assessed. Testability increased with age in both groups, was generally lower in AS children, but not related to their test performance. Typical children performed significantly better than AS children on conventional tools, but performance of both groups was similar on strength-informed tools. Differences of performance across tests were much greater in the AS group. These results emphasize the heterogenous, yet characteristic, cognitive profile in preschool children, and introduce the usefulness of flexible testing.
Journal Article
The Level and Nature of Autistic Intelligence
Autistics are presumed to be characterized by cognitive impairment, and their cognitive strengths (e.g., in Block Design performance) are frequently interpreted as low-level by-products of high-level deficits, not as direct manifestations of intelligence. Recent attempts to identify the neuroanatomical and neurofunctional signature of autism have been positioned on this universal, but untested, assumption. We therefore assessed a broad sample of 38 autistic children on the preeminent test of fluid intelligence, Raven's Progressive Matrices. Their scores were, on average, 30 percentile points, and in some cases more than 70 percentile points, higher than their scores on the Wechsler scales of intelligence. Typically developing control children showed no such discrepancy, and a similar contrast was observed when a sample of autistic adults was compared with a sample of nonautistic adults. We conclude that intelligence has been underestimated in autistics.
Journal Article
Artificial intelligence test: a case study of intelligent vehicles
To meet the urgent requirement of reliable artificial intelligence applications, we discuss the tight link between artificial intelligence and intelligence test in this paper. We highlight the role of tasks in intelligence test for all kinds of artificial intelligence. We explain the necessity and difficulty of describing tasks for intelligence test, checking all the tasks that may encounter in intelligence test, designing simulation-based test, and setting appropriate test performance evaluation indices. As an example, we present how to design reliable intelligence test for intelligent vehicles. Finally, we discuss the future research directions of intelligence test.
Journal Article