Of course, if no single source of variance was common to all tasks, a factor analysis might reveal no common factor at all.Accordingly, intelligence is best seen as a general ability that can in uence performance on a wide range of cognitive tasks.
IQ (the intelligence quotient) is the quanti cation of an individual s intelligence relative to peers of a similar age. IQ is one of the most heritable psy- chological traits, and an individual s score on a modern IQ test is a good predictor of many life outcomes, including educational and career suc- cess, health, longevity, and even happiness (Gottfredson 1998 ). Like humans, several species of animals express a general cognitive ability that in uences performance on broad and diverse cognitive tasks, and moreover, animals exhibit a wide range of individual variations in this ability. Intelligence and Intelligence Testing (IQ) in Humans It has long been recognized that intelligence varies across individuals. Colloquially, we refer to someone as brilliant or comment that our dog is a little dull. In 1995, a committee of the American Psychological Association stated that Individ- uals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to over- come obstacles by taking thought. Concepts of intelligence are attempts to clarify and organize this complex set of phenomena (Neisser et al. In an article in the Wall Street Journal (December 13, 1994) signed by 52 intelligence researchers, it was asserted that intelligence was a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from expe- rience. It re ects a broader and deeper capability for comprehending our surroundings. In this regard, the quanti cation of intelligence is best relegated to performance on psychometric tests. Springer International Publishing AG 2017 J. V onk, T.K. Shackelford (eds.), Encyclopedia of Animal Cognition and Behavior. In fact, psychometric tests (e.g., the Stanford-Binet, the Wechsler or W AIS, and the Raven s Progressive Matrixes or RPM) do differ in their content and structure. For instance, the Stanford-Binet includes questions that are cultur- ally relevant and thus is best suited to predict performance in a particular culture s school sys- tem. The W AIS is less culturally biased but, like the Stanford-Binet, includes categories of ques- tions that are presumed to re ect domains of abil- ities (verbal comprehension, working memory, perceptual reasoning, processing speed). An indi- vidual s performance on tests within a particular domain (e.g., reasoning) tends to be highly corre- lated, while performance on tests across domains (e.g., a reasoning task and a spatial task) is usually less correlated. Nevertheless, positive correlations are observed between performance on all tests in the battery. This is in line with the conclusion that all cognitive abilities are regulated (to varying degrees) by one general factor, or Spearman s g, while other speci c abilities might in uence performance within a particular domain. These kinds of observations have led to the development of hierarchical models regarding the structure of intelligence, where g in uences domains of spe- ci c abilities, which in uence tasks within those domains. An illustration of a hierarchical model is provided in Fig. Since many studies on intelligence use factors analyses, a brief explanation of this technique is warranted. Brie y, a factor analysis is a statistical method which reduces a large number of correla- tions into as few explanatory factors as possible. If, for example, all of the correlations across sev- eral tests of cognitive ability are strongly positive, the factor analysis recognizes that a common source of variance contributed to performance on all tasks, and this would be described as a general factor. In reality, the outcome of such an analysis can be much more complicated, and of course we might be interested in large numbers of cognitive tasks, some of which represent clusters of what are presumed to be specialized abilities. In these cases, the factor analysis might extract a general factor, as well as secondary factors, which explain relationships between only subsets of the tasks being considered.
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