Theories of Intelligence

An individual’s ‘implicit theory of intelligence’ refers to an individual’s

fundamental underlying beliefs about whether intelligence or abilities can change or are static (Dweck, Chiu, & Hong, 1995; Dweck & Henderson, 1989). Individuals who believe

24

intelligence and ability are fixed and unchangeable subscribe to the ‘entity’ mindset

(sometimes referred to as a fixed mindset), while an individual who believes hard work and effort influences intelligence has an ‘incremental’ mindset (sometimes referred to as a growth mindset). These implicit theories influence behavior; individuals who subscribe to incremental theory perform better academically (Blackwell, Trzesniewski, & Dweck, 2007) and have stronger negotiating skills (Kray & Haselhuhn, 2007). Thus far, research on implicit theories of intelligence has focused on impacts on goal orientation and

motivation. Yet, there are reasons to believe that implicit theories of intelligence could also impact upon perceptions of other people’s intelligence, for example they may alter the accuracy of judgements of the intelligence of others.

2.1.1 Multiple intelligences

Individuals (including psychological researchers), differ in their definitions of intelligence. Some researchers have criticized the traditional conception of intelligence, as measured by standardized intelligence tests, by arguing that intelligence tests primarily measure how “book smart” an individual is. In turn, various researchers have proposed that there are multiple intelligences (Gardner, 1983, 2000; Sternberg, 1985, 2000). Guilford (1959, 1977) pioneered the concept of separate measures of intelligence, suggesting human mental abilities encompass about 150 different types. Later, Gardner (1983, 2000)

suggested that there are eight different types of intelligence: rhythmic, spatial, linguistic, mathematical, kinesthetic, interpersonal, intrapersonal, and naturalistic. Thus, while someone may be musically gifted, they may be less mathematically inclined. Theories of multiple intelligences have inherent appeal in that they imply that everyone is smart at something, even if the traditional intelligence test (e.g., Raven Standard Progressive Matrices, Wechsler Adult Intelligence Test, etc.) does not reflect it. Yet, less empirical evidence supports multiple intelligences theories compared to other theories of intelligence (Carroll, 1993; Gottfredson, 2003).

2.1.2 General factor g

Psychometrics and traditional psychology appreciates Spearman’s (1905) g factor of general intellectual ability, which is the underlying performance of an individual based on scores on a range of abilities. In essence, Spearman’s g suggests that different ability

25

tests are significantly intercorrelated; for example, someone who scores high on a verbal test will likely score similarly high on a numerical test.

Spearman (1927) developed a data-reduction procedure called factor analysis that examines the correlations among a series of measures to identify underlying patterns in the data that can be attributed to an unobservable variable (also called a latent variable). Through this method, Spearman (1927) found that performance on various aspects of cognitive ability could be represented by a single general underlying mental ability.

Spearman (1927)alsodeveloped statistical methods to correct for attenuation as a means to improve measurements of intelligence. This method accounts for measurement error when calculating the correlation between variables. Since cognitive/intelligence tests are not completely accurate in the measure they aim to assess (all tests have a certain level of measurement error), Spearman developed a formula that considers the unreliability of variables being correlated to improve estimates of the relationship between the variables (Spearman, 1927). This method of correcting for attenuation is widely used by

psychologists and is used in Chapter 4 and 6 of this thesis.

2.1.3 Fluid and crystalized intelligence

One of Spearman’s PhD students, Cattell (1963), extended Spearman’s (1905, 1927) findings to create the theory of crystallized and fluid intelligence. Using similar factor analysis techniques, Cattell (1963) argued that general intelligence can be

categorized into fluid intelligence and crystalized intelligence. Fluid intelligence enables an individual to perform well on nonverbal tasks and other measures of culture-free cognitive performance that do not require previous knowledge. Crystalized intelligence enables an individual to do well on verbal tasks and is substantially influenced by learning and previous knowledge. Chamorro-Prezmuzic (2007) described the difference between fluid and crystalized intelligence using an analogy with computers, saying that fluid intelligence is similar to the processor or hardware of the computer, while crystalized intelligence is more akin to the software on the computer. Testing Cattell’s (1963) theory, Horn and Cattell (1967) found that younger adults had higher fluid intelligence while older adults had higher crystallized intelligence, suggesting the negative effects of normal aging on fluid intelligence and the positive effects of additional learning and enculturation on crystalized intelligence. Horn and Cattell’s (1967) findings emphasize the importance of

26

breaking down intelligence by suggesting that, depending on what type of intelligence is being measured (fluid or crystalized), intelligence can either increase or decrease with age (p. 124).

There are various measures that assess fluid intelligence in a way that is aimed to be culturally unbiased (Cattell, Feingold, & Sarason, 1941; Cattell, 1940). Some of the most popular, privately owned tests are the Cattell Culture Fair IQ Test, the Raven’s Progressive Matrices, and the Wechsler Adult Intelligence Test. There are many types of intelligence test items and these have become much more sophisticated over time, with some of the first tests measuring only reaction time to a sound and time for naming colors (Cattell & Galton, 1890). Some of the most common subtests of general intelligence nowadays are: mental rotation tasks which attempt to measure spatial cognition and the rate of processing of spatial information; letter and number series that assess logical reasoning; matrix reasoning items, which assess nonverbal analytical ability; and verbal reasoning, which measures comprehension and critical reasoning (Chamorro-Premuzic, 2007). In the nature of open access science, Condon and Revelle (2014) developed the International Cognitive Ability Resource (ICAR), a 16-item intelligence test and made this resource freely available to researchers interested in measuring intelligence. ICAR is an unproctored and untimed assessment that has been shown to correlate moderately to strongly with other measures of cognitive ability and achievement and is used in Chapter 5. See Appendix 1 for examples on the different types of intelligence test items discussed (i.e., mental rotation, matrix reasoning, letter and number series, and verbal reasoning) and used in Chapter 5.

2.1.4 Defining intelligence

Theories on single general factor intelligence and multiple intelligences may both be correct; that is, they are not mutually exclusive. A single underlying intelligence may lead to strong performance on multiple measures, but these measurements may still represent distinct types of cognitive abilities. Nonetheless, varying perspectives,

measurements, and theories of intelligence have led to various definitions of intelligence. In turn, a group of researchers ranging in experience and opinions collaborated on a report (one of the most cited of its kind) entitled “Intelligence: Knowns and Unknowns” which

27

reviewed approaches to intelligence (Neisser et al., 1996). The following is an excerpt from the report:

“Individuals 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 overcome obstacles by taking thought. Although these individual differences can be substantial, they are never entirely consistent: a given person's intellectual

performance will vary on different occasions, in different domains, as judged by different criteria. Concepts of ‘intelligence’ are attempts to clarify and organize this complex set of phenomena. Although considerable clarity has been achieved in some areas, no such conceptualization has yet answered all the important questions, and none commands universal assent… Indeed, when two dozen prominent theorists were recently asked to define intelligence, they gave two dozen, somewhat different, definitions” (Neisser et al., 1996, p. 77).