Individual differences in cognition: British contributions over a century.
Humans differ in their powers of mental work: that much has been recognized from antiquity (Huarte, 1575/1969). Since the beginnings of scientific psychology three principal questions have been asked about human cognitive abilities: Which domains of mental performance can be delineated? Does level of mental ability predict anything about real life achievement? What are the causes of cognitive ability differences? The bulk of this article relates the century's progress in addressing these questions. Discussing human intelligence differences, though it can focus disinterestedly on these scientific questions, tends to involve the controversies of the topic. Thus, Britain's contribution may be seen unhelpfully as a series of battles, instead of a cumulative contribution to knowledge.
Three differences about human ability differences
Differential vs. experimental: the human norm or human differences?
First, a major British disagreement concerned the relative usefulness of experimental and individual differences approaches to human cognitive functions. These were represented in Britain by the difference between the Cambridge (Bartlett, 1932) and London (Spearman, 1927) schools of psychology, respectively. Bartlett paid tribute to the London school's forerunner Galton as 'a brilliant and original investigator', dubbing him 'the father of experimental psychology in England' (p. 7). He further lauded the contribution made by differential psychologists, 'as all the psychological world knows, in the extremely important work of Prof. C. E. Spearman' (p. 7). But Bartlett worried that
Such statistical treatment gives, not, indeed, the mode of determination of the individual reaction, but a picture of trends of response and their interrelations ... Largely by direct influence, but probably also because Galton's outlook contains something that is peculiarly attractive to the English temperament, the methods initiated by him have become very widely used in English psychology, and have been greatly developed by his successors [p. 7] ... In this book there will be no statistics whatever [p. 9].
Some commentators bemoaned the separation of differential and experimental psychology, insisting that it is important to know both the modal structure and function of cognition as well as the parameters governing individual differences. Thus, Spearman (1904, 1923) insisted that the understanding of human ability differences must be founded upon valid variables delivered by experimental psychologists, and that the study of intelligence differences must be preceded by an understanding of the 'principles of cognition'. To bridge the gap, Spearman, prior to writing his statistics-strewn The abilities of man (1927), wrote The nature of intelligence and the principles of cognition (1923) which, some latter-day Cambridge school devotees might be surprised to hear, was deemed by Gustafsson (1992) the first textbook in cognitive psychology. Sporadically, as the 20th century matured, the calls to combine experimental (or cognitive) and differential approaches to human ability and its differences have echoed more or less strongly (Cronbach, 1957; Eysenck, 1967, 1995; R. J. Sternberg, 1978), with the result being an intermittently satisfying but rather desultory affair (Deary, 1997).
Measuring vs. understanding human cognitive abilities
A second apparent battle was that between the British, London-school approach to intelligence differences and that of Binet and Simon. The former aimed to 'understand' intelligence differences in terms of elementary psychological processes, such as sensory discrimination, whereas Binet's approach was to construct a 'hotchpot' (Spearman's epithet) of higher-level tasks to gain a 'measurement' of ability. The latter's aim was practical prediction, the former's was explanation of the phenomenon. There was certainly a trace of sour grapes in Spearman's (1927) celebration of Binet's practical achievement even though he disavowed any such practical intention himself. As he discussed various approaches to assessing intelligence he noted (1927, pp. 60-61):
... up to now the trend that we have been examining has always been towards finer and finer analysis. Seemingly flouting all this, the procedure dictated by this correlational theory [developed from his own discoveries in the paper of 1904] was to take almost at random very numerous tests quite different from each other and to throw all the scores for them indiscriminately into one common pool. A little more than a year afterwards appeared the great work of Binet and Simon. Here, this paradoxical recommendation to make a hotchpot was actually adopted in practice. Nevertheless the elaborate correlational theory which had in point of fact generated the idea, and had supplied the sole evidence for its veracity, was now passed over. The said authors employed a popular substitute. 'Intelligence,' as measured by the pool was depicted as a 'general level' of ability. So far as doctrine is concerned this is the only thing introduced by them that was novel. And most surprisingly Binet, although in actual testing he took account of this 'general level' alone, still in all his theoretical psychology continued to rely altogether upon his old formal faculties, notwithstanding that these and the 'general level' appear to involve doctrines quite incompatible with each other.
The contrast between Spearman's and Binet's approaches is often portrayed as a disagreement about the best way to measure human mental abilities (see Deary, 1994, for an historical review and discussion). The above quotation demonstrates that they agreed on the matter of how to measure, with Spearman detectably miffed at his hard graft being ignored in Binet's atheoretical success. They parted on two other matters. Binet, according to Spearman, preferred a faculty (modular) structure for mental abilities, whereas Spearman sought a structure that could incorporate g. Secondly, Spearman worried that the hotchpot approach to measurement (bash on with measurement and never mind theory or explanation) would curtail the necessary work of understanding the general factor. Though Binet's tests might measure the general factor, its nature was still mysterious:
But notice must be taken that this general factor g, like all measurements anywhere, is primarily not any concrete thing but only a value or magnitude. Further, that which this magnitude measures has not been defined by declaring what it is like, but only by pointing out where it can be found. It consists in just that constituent--whatever it may be--connected by the tetrad equation. This way of indicating what g means is just as definite as when one indicates a card by staring on the back of it without looking at its face. [Spearman, 927, p. 75]
To see g or not to see g, that is the question
The most wearisome of the three battles that Spearman engaged in was the controversy over the existence and nature of g. The previous quotation illustrates that Spearman did not reify this ubiquitous (see Jensen, 1998a, for documentation) empirical finding, though he did speculate about possible physical bases for g. The place of g in a descriptive structure of mental ability differences is discussed at length below.
Human cognitive abilities: some of the 20th century's key moments
Before proceeding to discuss progress on some major questions about ability differences, and the British contribution to addressing them, some of the 20th century's main events and themes related to cognitive ability differences might be reviewed. By way of warning rather than apology, note that the list is idiosyncratic. Spearman's (1904) demonstration of g and Binet's (1905) construction of a usable mental test began the field in earnest, after some suggestions by Galton (1883) and false starts by Sharp (1898-99) and Wissler (1901). Binet's influence (some of his original items persist in today's mental tests) might have been much less but for Goddard (Zenderland, 1998). Goddard was a teacher who retrained as a psychologist and became interested in the diagnosis of 'feeblemindedness'. He exhausted medical, psychological and social approaches to diagnosing this condition in the USA and went on a 'fact finding tour' through European countries in 1908. As Zenderland (pp. 91-92) tells it,
Seeking new directions for his own research, he spent sixty-two days traveling through England, France, Italy, Germany, Austria, Switzerland, Holland, and Belgium. During his travels, he sought advice from prominent psychologists while also visiting doctors and teachers working in nineteen different institutions and ninety-three special classes. Although he met many of the most distinguished institutional physicians of his day, his most provocative find was a short list of mental tests given to him by a Belgian doctor and special educator, Ovide Decroly. The tests had been published three years earlier by Alfred Binet, a renowned French psychologist, and his assistant, Theodore [sic] Simon, a physician. Beginning with Binet and Simon's 1905 article pointedly entitled 'Upon the necessity of Establishing a Scientific Diagnosis of Inferior States of Intelligence,' Goddard found his first answer to the problems plaguing both psychologists and physicians. Years later, in explaining the event that dramatically reoriented his career for the remainder of his life, Goddard again denied any initiative of his own and credited only luck. 'My getting hold of Binet's work was the result of a series of lucky accidents,' he explained. 'Somehow there came into my hands a single printed sheet by an unknown Belgian by the name of M. C. Schuyten,' he reported. 'Luckily I did not throw it in the waste basket.' Schuyten had been active in the European Child-Study movement, and he referred Goddard to others interested in similar problems. Through these contacts, Goddard was taken to meet Decroly, a physician who ran an experimental school in Brussels, and who closely followed the literature on Child Study. 'When Dr. D. came to the door,' Goddard recalled, 'I said I am Mr. Goddard from America. Quick as a flash, he said, "Dr Henry Goddard? You have written an article on the ideals of German children. My wife has translated it into French." HE told me about the Binet tests. ALL PURE CHANCE.'
This event does not mark any theoretical or practical advance, but in the land of mental ability differences it is Stanley's meeting Livingstone. Thus, by chance, in 1908, mental testing took off from this fragile concatenation of events and, as told in Zenderland's (1998) encyclopaedic and disinterested account, expanded via the training schools and mass testing movements of World War I to the domains of education, medicine and work. By professionals adopting a mental test rather than a medical examination to diagnose feeblemindedness, there was a return to the view of the condition held in English common law since the 16th century:
An 'idiote, or a naturall foole,' it argued, was a person of lawful age and yet 'so witlesse that he cannot number to twentie, can he tell what age he is of, nor knoweth who is his father, or mother, nor is he able to answer to any such easie question.' [Zenderland, 1998, p. 74]
From the 1930s to the 1960s the landmark theories and studies included American revisionist accounts of the descriptive structure of psychometric intelligence differences, which either minimized or erased Spearman's g from their taxonomies (Guilford, 1956; Horn & Cattell, 1966; Thurstone, 1938). Spearman's imperative--to investigate the nature of mental ability differences--lay pretty well quiescent from World War I until the late 1970s when Eysenck's (1967) call for a remarriage of differential and experimental psychology was answered by the rise of cognitive psychology and the information processing agenda of the new-look psychologists involved in founding the journal Intelligence. Both psychological and biological approaches to the origins of human ability differences prospered in the last two decades of the century (Deary, 1997, 2000, and Deary & Caryl, 1997, reviewed these topics). In the 1980s and 1990s arguments about the structure of psychometric intelligence were settled largely by the application of structural modelling techniques (Gustafsson, 1984) and Carroll's (1993) mammoth reanalyses of existing data sets. Publication of The bell curve (Herrnstein & Murray, 1994) saw the recrudescence of a decades-old acrimonious debate about group differences in mental abilities (Jensen, 1969), much of it missing the point that, within groups, mental ability scores might have significant predictive value for some real-life outcomes. The century ended with an unexpected consensus, grown from post-bell curve confusion. The American Psychological Association's task force, formed in the aftermath of The bell curve, gathered a group of authoritative and differently minded intelligence researchers to set out and sign a document stating what, at that date, might be considered known and still unknown about psychometric intelligence. Headed by the doyen of cognitive psychology, Ulric Neisser, it proclaimed qualified good news about the resolving of the psychometric structure of abilities, about the progress made in investigating the origins of ability differences and about the usefulness of psychometric intelligence as one predictor of real-life outcomes (Neisser et al., 1996). Jensen's (1998a) monument to g, The g factor, collected a century's evidence for the centrality of Spearman's (1904) discovery in human mental life. The publisher Wiley's withdrawing Brand's (1996) book of the same name was a fin de siecle reminder of the controversy that attends some issues related to mental ability differences (Caryl and Deary (1996) noted the threat to academic freedom).
The structure of human intelligence differences
Five of the greatest books in the history of psychometric intelligence and mental measurement were written by Britons in the first half of the 20th century: Spearman (1923, 1927), Thomson (1939), Burt (1940) and P. E. Vernon (1950). What staggers one on reading these books is the authors' impressiveness on a number of fronts: their erudition and knowledge of disparate research literature, their ability to devise complex novel statistical methods, their empirical contribution (this must be qualified for Burt), and their contribution to theory. Moreover, they have lasted the course. Among them they emphasized the facts about psychometric intelligence that emerged in the next 50 years: that mental ability differences may be described as a hierarchy of more or less specific packets of variance with g on top; that psychometrics will never explain intelligence differences; and that ability factors, especially g, should be treated as discoveries to be explained rather than things in the brain.
Though there seemed to be a UK-USA argument about the existence of a general factor, the cognoscenti knew very early on that there was no substantial difference in results obtained across the Atlantic; it was one of emphasis rather than substance. Those (e.g. Gould, 1981, 1997) who still retain the erroneous notion that, somehow, Thurstone (1938) rid the scene of g or that g is an arbitrary artefact or statistical whim should note two things. First, even Thurstone was aware very early on that his data contained a g factor (Eysenck, 1939). Secondly, Gould's (1981) incorrect comments on the psychometric nature of abilities have been corrected (Carroll, 1995). Gustafsson (1984) explained clearly why g does not go away with different factor analytic approaches, and Humphreys (1979, p. 107) commented:
The neglect in the United States of the general factor in human abilities has arisen from the popularity of the group factor model and the almost universal restriction of that model to factors in the first order only. Investigators who prefer orthogonal rotations hide the general factor in the predominance of small positive loadings of measures that are supposedly in the hyperplane. Investigators who prefer oblique rotations reveal the general factor in the intercorrelations of their factors, but these correlations are typically not interpreted.
Today the converging consensus about mental ability differences incorporates ideas from Thurstone (concerning primary-level mental abilities), Burt and P. E. Vernon (concerning a hierarchy of intelligence factors ranging from specific abilities to g with group factors in between) and Spearman (concerning specific factors and g; in his 1927 book he was rather dismissive about group factors). Only Guilford (1956), of the major psychometric intelligence theorists, fails to be included (Gustafsson, 1984). Thus, whether one examines the analyses of diverse mental test batteries given to large, discrete samples of participants (Bickley, Keith, & Wolfle, 1995; Carretta & Ree, 1995; Gustafsson, 1984; Undheim, 1981a, 1981b), or considers Carroll's (1993) standardized reanalyses of hundreds of mental test data sets gathered throughout the 20th century, the result is similar: human mental ability differences show near universal positive correlations; the packets of covariance in a heterogeneous mental test battery given to a broad sample of adults or children can be arranged into correlated group factors; and a g factor can be extracted that accounts for around 50% of the variance among individuals. Gustafsson referred to 'this unifying model' of mental abilities (p. 193) and summarized its characteristics as follows:
The Spearman, Thurstone, and [R. B.] Cattell-Horn models may, in a structural sense at least, be viewed as subsets of the HILI [Hierarchical, LISREL-based] model: the Spearman model takes into account variance from the third-order factor; the Thurstone model takes into account first-order variance; and the Cattell-Horn model takes into account both first- and second-order variance. The Vernon model comes close to the proposed model: The g-factor is included in both models, and at the second-order level v:ed [P. E. Vernon's (1950) verbal--educational factor)] closely corresponds to Gc [crystallised intelligence], and k:m [Vernon's spatial--mechanical factor] corresponds to Gv [general visualization].
The 'three-stratum' (Carroll, 1993) account of human ability differences is sometimes, nowadays, referred to as a 'theory' (Bickley et al., 1995; Bouchard, 1998). It is nothing of the kind. It is a taxonomy that construes covariance into different-sized packages that serve the purposes of providing predictive validity and the substrate for explanatory science. Thus, Burt (1940, p. 251) warned:
So far as it seeks to be strictly scientific, psychology must beware of supposing that these principles of classification can forthwith be treated as 'factors in the mind,' e.g. as 'primary abilities' or as 'mental powers' or 'energies.' [It is interesting to see equal criticism thus aimed specifically at both Thurstone and Burt's London-school forerunner Spearman.]
Similarly, Vernon (1961, pp. 138-139) was concerned that:
the best-established factors, such as Thurstone's, represent the external qualities or materials of the tests--verbal, numerical, spatial, etc.--rather than central mental functions. It may be that statistical analysis alone is incapable of yielding these more fundamental functional components of the mind.
The exact same factors do not appear from every analysis, nor would one expect that, given the variation in test batteries, and the possibility--first suggested by Spearman and Burt--that human abilities might be structured slightly differently at different levels of ability (Deary et al., 1996). Bickley et al. (1995, pp. 324-326) concluded that:
the existence of g is difficult to dispute ... [M]any alternative models are possible within the boundaries of this three-stratum theory (Carroll, 1993; Gustafsson, 1984; Undheim & Gustafsson, 1987). In fact, one of the reasons it is such a useful and appealing model is its flexibility. As a point of reference, however, this study provides compelling evidence that the three-stratum theory may form a parsimonious model of intelligence. The fact that it is grounded in a strong foundation of vast, previous research also lends strong support for the acceptance of the model ... [T]his model of intelligence provides a sound theoretical and empirical base to continue research in the field of mental abilities.
Burt's (1940) mid-century 'four-factor' (the three strata model plus error variance, essentially) solution to human ability differences was very similar to, perhaps even more general than, the model that attracted some consensus half a century later:
Four kinds of factors may be formally distinguished--(i) general, (ii) group or bipolar, (iii) specific, and (iv) error factors, i.e. those possessed by all the traits, by some of the traits, by one trait always, or by one trait on the occasion of its measurement only ... From the four-factor theorem (as it may be termed) all the familiar factor theories may be derived. [Burt, 1940, p. 249-250]
The diamond jubilee of Burt's (1940) suggestion witnesses many psychometricians unwittingly reconverging on his conclusion. Others continue to differ to some degree (see Neisser et al., 1996). Among these was the quondam Briton Raymond Cattell whose influential theory of fluid and crystallized intelligences recognized correlated general factors in human ability but, by diktat rather than data, never quite accommodated Spearman's g (Cattell, 1998; Horn, 1998; Horn & Cattell, 1966).
The predictive validity of human intelligence differences
The testing of human mental abilities began as a practical enterprise, whether one dates it to Huarte's (1575) suggestions for vocational assessment or Binet's (1905) test for the ability to benefit from normal schooling. It might fairly be said that the original British (Galton-Spearman) tendency to worry about the origins of mental ability differences was swamped for the greater part of the century by the spread of testing into educational, occupational and medical settings. The usefulness of mental tests, apparent and real, was perhaps their most obvious characteristic over the 20th century. The application and misapplication of mental tests in the immediate wake of the Binet test's export to the USA in Goddard's handbaggage is well told by Zenderland (1998). Conceived as a predictor of educational aptitude, mental tests were pressed to provide a variable to account for crime, drunkenness and prostitution. The tests became a 'property' and power in the unseemly scraps among teachers, doctors and psychologists as they fought for the professional high ground in looking after the 'feebleminded'. The tests were tried out at Ellis Island to assist the harrowing and impossible job of a few doctors as they tried to implement the demand of the USA's immigration authorities to spot the 'feebleminded'. After noting that 1 726 966 US Army recruits had been mentally tested by 31 January 1919, Zenderland (1998) commented that, no matter what testing had done for the army, the army brought about a unification and professionalization of mental testers (Wechsler among them).
Among the 'knowns' concerning human intelligence differences decided upon by the American Psychological Association's task force (Neisser et al., 1996, pp. 82-83) was the tests' ability moderately well to predict educational and occupational outcomes:
The relationship between test scores and school performance seems to be ubiquitous. Wherever it has been studied, children with high scores on tests of intelligence tend to learn more of what is taught in school than their lower-scoring peers. There may be styles of teaching and methods of instruction that will decrease this correlation [typically about .5], but none that consistently eliminates it has yet been found ... Scores on intelligence tests predict various measures of job performance: supervisor ratings, work samples, etc. Such correlations, which typically lie between r = .30 and r = .50, are partly restricted by the limited reliability of those measures themselves. They become higher when r is statistically corrected for this unreliability: in one survey of relevant studies (Hunter, 1983), the mean of the corrected correlations was .54 ... Psychometric intelligence is negatively correlated with certain socially undesirable outcomes ... The correlations for most 'negative outcome' variables are typically smaller than .20.
Schmidt and Hunter (1998) reviewed the best part of the 20th century's work on the predictive validity of mental tests in occupational settings. Their meta-analysis concluded that the predictive validity coefficient of general mental ability tests in job selection was just over .5 (compared with .02 for graphology). The tests' applications across many job types, their low cost and their theoretical background made them more attractive than other options, though prediction could be improved significantly by adding other measures. With regard to other real-life outcomes, The bell curve (Herrnstein & Murray, 1994) offered some information. In analyses carried out within the white population, scores on the Armed Forces Qualification Test taken in the late teens or early twenties had some limited predictive power for differences in some social factors over 10 years later (e.g. poverty, school success, marriages lasting, welfare dependency, children's health, and crime).
The areas mentioned above tend to be the main focus for psychometricians who point to the practical applications of tests. Clinical psychologists, and those with a medical background, will know that mental tests find huge and growing application as indicators of brain integrity in uncountable medical conditions. To end this section, however, two British contributions are recorded. The age pattern of industrialized countries has shifted to containing more elderly people. Short of frank dementia, the issue of cognitive decline has become a major medical research topic since it is known that retention of cognitive function predicts quality of life and survival (Korten et al., 1999). In addition to testing current cognitive function, it is important to know the relative level of people's previous mental ability. This is almost never available. In cases where early life mental ability differences have been retrieved, even though the estimates are poor and the population sample rather unusual, they have proved invaluable (e.g. in the 'Nun' study; Snowdon et al., 1996). Therefore, the British innovation of estimating 'premorbid' ability levels, by assessing the ability to read words that do not comply with normal phonological rules, stands as a major achievement in mental testing (Nelson, 1982; O'Carroll, 1995). Nelson's National Adult Reading Test combines the simplicity of conception and widespread application that put it on a par with Binet's original test in providing a quick-and-dirty answer to a pressing practical need.
Perhaps an even greater achievement will be a uniquely Scottish one. Godfrey Thomson (1939) was rightly famous for his statistical innovations and debates with Spearman about the interpretation of the general factor in mental ability. However, he played a major part in the Scottish Council for Research in Education's (1933) national surveys of psychometric intelligence. In the first of these surveys, in 1932, the entire nation of children born in 1921 sat a version of the Moray House Test on 1 June. This test was like the general reasoning section of the '11-plus' tests, which were used in the UK between about the 1930s and the 1960s to select children for different types of secondary school education. Small pockets of 11-plus testing remain in the UK today. The number of children tested was 87 498, representing well over 90% of the 1921-born population. As it turned out at the time, this proved mostly to be a descriptive achievement. However, the data were retained and, therefore, within Scotland are held high quality mental test data for an entire birth cohort. Our research team followed up 101 of the Scottish 1921-borns 66 years to the day after the original test. We gave them the same test using the same instructions and the same time limit. Comparing the 1932 and the 1998 results on the Moray House Test gave a correlation of .63 (.73 when corrected for attenuation) (Deary, Whalley, Lemmon, Crawford, & Starr, 2000). Future, follow-up research with larger numbers from the cohort will examine the predictors of healthy cognitive ageing. This, though, is promise rather than achievement and, although it is not my intention substantively to address the issue of cognitive ageing, two better-established British contributions should not be omitted here. Among other cohort studies worldwide the UK has contributed some large-scale studies of cognition and ageing (e.g. Rabbitt, Donlan, Bent, McInnes, & Abson, 1993). On the theoretical side, R. B. Cattell's (1998) ideas of fluid and crystallized intelligence find much application in the contemporary study of ageing and intelligence differences.
Some causes of human intelligence differences
Spearman's (1904) first investigations examined the association between psychometric intelligence and sensory discrimination, an idea that had been suggested and tried by others, Galton (1883) among them. Review and reanalyses of largely British studies before and during World War I showed that there was a small, significant correlation between visual and auditory discrimination and mental test scores (Deary, 1994). These hold up in more recent investigations, and Raz, Willerman, and Yama (1987, p. 209) commented that:
no matter what the exact mechanisms of information processing underlying intelligence, Galton's (1883) suggestion of an important link between 'the avenues of the senses' and good sense may not be as far-fetched as previously supposed.
However, correlations between any one information-processing index and psychometric intelligence are not large, and there are few current researchers who search after the 'Holy Grail' (Hunt, 1980) of a single information-processing index that will explain g or other abilities in the psychometric hierarchy.
Currently, the lively but heterogeneous research activity that seeks the causes of psychometric intelligence has the following agenda. It examines associations between psychometric test scores and indices of brain function at putatively lower levels of reduction than the test scores themselves. When correlations are obtained and replicated it then considers the possible mechanisms of the associations and the validity and tractability of the brain indices. Properly self-critical investigators consider the possibility that, in some cases, the cause of any correlation might be the reverse of that which is supposed (i.e. better performance on supposedly lower-level brain indices might be caused by, rather than be the cause of, psychometric intelligence differences). Collections of this type of research may be found in Eysenck (1982), P. A. Vernon (1987, 1993) and Deary (2000). For the purposes of a resume, it is convenient to describe the state of this area by descending through different levels of reduction. Thus, brain indices have been sought at, arguably, psychometric, cognitive, psychophysical, psychophysiological, physiological and biological levels.
One approach to understanding ability test score differences is to pick apart performance on the mental tests themselves to reveal the stages of processing which the brain runs through in completing them. Some writers would call this approach cognitive rather than psychometric (e.g. R. J. Sternberg (1978) called this a 'cognitive components' approach); nevertheless, I have called it psychometric because it is psychometric tests that provide the tasks from which components are distilled. Likewise, in the sections that follow this one, the mental components or information-processing parameters or biological indices are drawn from, respectively, cognitive-experimental, psychophysical and biological procedures. Thus, sections are labelled to indicate the level of description of the task or tasks used by researchers to yield parameters relevant to the brain's processing of information.
R. J. Sternberg (1977, 1985) executed inventive experiments using psychometric tasks. Developing a legacy from Spearman and Burt, Sternberg dissected, using a partial cueing technique and regression models, analogical reasoning performance into 'mental components'. He chose analogical reasoning because so many past researchers on psychometric intelligence had placed this type of reasoning near to the centre of their thinking about psychometric intelligence differences. Sternberg's models of component function accounted successfully for performance differences on reasoning tasks. Sternberg's components of reasoning bore strong resemblances to Spearman's 'principles of cognition' (1923), especially the eduction of relations and correlates. And they also suffered the same problems as Spearman's principles/components: they were brought into being from the armchair and not the lab; they were never validated outside the rarefied world of the mental test item; and it was never finally established whether they were components of mind or merely components of mental test items (Deary, 1997, 2000).
Successors to R. J. Sternberg have also concentrated on reasoning ability and have applied newer analytic techniques. Carpenter, Just, and Shell's (1990) analysis of performance success on Raven's matrices used participants' verbal reports and eye-tracking information to construct computer models of average and good performers on the task. Raven's (1938) Matrices, a British-built task based on Spearman's (1923) principles of cognition, is widely acknowledged as just about the best single, group test of g (Marshalek, Lohman, & Snow, 1983). It is not easy to decide whether Carpenter and colleagues got beneath the psychometric skin of performance of the Raven task or just elaborately redescribed Raven's own task-building principles, but the key processes involved in task success were rule-finding (like Spearman's eductions of relations and correlates) and keeping track of multiple goals in working memory (like Spearman's (1927) mental span). Indeed, it seems that more and more accounts of reasoning performance are appealing to the British construct of working memory (Baddeley, 1986, 1992) as a basis for performance differences. Kyllonen and Christal's (1990) structural equation models of thousands of US armed forces applicants' test scores found reasoning and working memory to be near inseparable constructs. They were not able to decide which of the two had causal precedence over the other (see also Kyllonen, 1996). However, if working memory is so closely related to psychometric intelligence, then researchers in the latter field would be well advised to make use of the extensive clinical, cognitive and biological information about working memory in thinking about the elements of mental ability differences. (This would make a nice meeting ground for a proper reconciliation of the Cambridge and London school approaches to cognition.) In addition to working memory, another strong British contender as an explanatory factor for g has been frontal lobe function as suggested by Duncan, Emslie, and Williams (1996) from their study of the 'second side instruction' task.
Also in the mode of R. J. Sternberg's componential approach, Embretson (1995) used multicomponent latent trait models to decompose reasoning performance. She found that reasoning performance differences were well accounted for by two latent traits derived from the psychometric tests she had devised: general control processing and working memory. Her opinion was that this modern methodology was rediscovering some of Spearman's ideas:
General control processing, Spearman's mental energy, is the conative directing of attention, whereas working memory capacity parallels Spearman's mental span concept [p. 184].
Appeals to cognitive variables in an attempt to account for variance in human ability differences have leant heavily on various reaction time procedures. Buried within Galton's unanalysed data from his anthropometric laboratory in South Kensington was some indirect evidence to link faster reactions with higher mental ability (Johnson et al., 1985). Famously, Wissler (1901) found no significant association between reaction time and ability among American undergraduates, though his study was inadequate (Deary, 1994). By 1933, Beck's review of many studies suggested a small but significant association, especially involving choice reaction time. But concerted research began in the 1970s with differential psychologists' borrowings of procedures from cognitive psychology that offered an account of the processing stages between stimulus and response. Measuring individual differences in isolable processing stages and correlating them with individual differences in psychometric test scores was seen as the 'cognitive correlates' approach to intelligence (R. J. Sternberg, 1985).
The most researched of the reaction time procedures is that first described by the British psychologist Hick (1952). He described the linear increase in reaction times as a function of the log of the number of stimulus alternatives in a choice reaction time procedure. His epither for the slope's psychological importance was that it might represent the participant's 'rate of gain of information'. Beginning with the German psychologist Roth (1964), differential psychologists alighted on the possibility that individual differences in this slope parameter might account for some of the individual differences in psychometric intelligence. However, three decades on from Roth's pioneering study something rather surprising has emerged. Along with other favoured reaction time procedures, especially the S. Sternberg (1966) memory scanning task and the Posner (Posner & Mitchell, 1967) letter matching task, the Hick task does indeed throw up significant correlations with psychometric intelligence differences (see Jensen, 1987; Neubauer, 1997; Vernon, 1987, for reviews). Galton was correct: higher test scorers do have faster reactions. They also have less variable reactions. In all three procedures the effect sizes are small-to-medium (i.e. enough to be interesting but not enough to 'explain' what it is to have high psychometric intelligence). But in all three procedures the elementary processing stage that attracted the differential psychologists failed to have any special association with psychometric intelligence. Thus, the slope in the Hick task, the speed of memory scanning in the S. Sternberg task and the speed of access to long-term memory in the Posner task are outshone by the prosaic indices assessed in the intercept and variability of the reaction times (Neubauer, 1997).
Two general responses are made to this state of affairs. First, there is clearly a need to try to explain the association between reaction times and their variabilities and psychometric intelligence, because the two sets of variables appear to derive from different levels of description. Suggestions that high-level factors, such as motivation, attention or strategy formation, are responsible for the association find little support (Neubauer, 1997), though some of the association between Hick slope and psychometric intelligence may occur because of faster learning among higher psychometric test scorers (Widaman & Carlson, 1989). Neubauer's (1997) review concluded that the best guess on the evidence available is that the correlation is caused by some lower-level, processing efficiency factors measured by reaction time procedures that correlate with ability test scores. The nature of these factors is obscure, though support for this conclusion comes from finding that the reaction time--ability test score correlation is almost entirely owing to genetic factors (Neubauer, Spinath, Riemann, Borkenau, & Angleitner, in press). Thankfully, glib formulations about the correlations being caused by some otherwise unspecified 'mental speed' or 'speed of information processing' are in decline and being replaced by empirical studies of the mechanisms of this interesting association.
The second response is to examine the reasons why processing kernels from reaction time procedures fail to correlate with psychometric ability test scores and, moreover, why such theoretically important features of cognitive tests deliver unreliable parameter estimates. Jensen (1998b) made various psychometric suggestions for improving the reliability of the slope measure in the Hick reaction time task. His expectation was that this repair kit will reveal the true, higher correlation with psychometric intelligence. Time will tell, but that will still leave for explanation the fact that so many other aspects of the task correlate with mental test scores too. Lohman (1994, 1999), more gloomily for cognitive and differential psychologists, diagnosed a fatal flaw in the marriage between cognitive and differential psychology (1994, p. 1):
We can decompose overall individual differences in performance on a task or ability factor into component scores that reflect individual differences in mental processes ... I claim, however, that these component scores do not decompose and therefore cannot explain individual differences in overall performance on such tasks. Rather, component scores salvage systematic individual variance from the error term. This may be a useful activity, but it does not help explain the main source of individual differences on the task.
In summary, there are successful attempts to correlate psychometric intelligence with reaction time indices. However, those aspects of reaction time performance that correlate significantly with mental test scores are typically the non-theoretically interesting parts.
At what seems to common sense like a lower level of reduction, still, than reaction times comes the study between psychometric intelligence and indices related to sensory processing. Galton (1883) hypothesized that people with higher levels of mental ability had finer powers of discrimination. But a more prescient lead was J. McKeen Cattell's (1886a, 1886b) discovery, in Wundt's lab, that the minimum stimulus duration required to make an accurate discrimination might be related to ability level (Deary, 1986). Also, Burt (1909-10), in his first empirical study, found a strong association between tachistoscopic recognition and imputed intelligence level.
In the modern era, a mass of research has accumulated around a procedure termed 'inspection time', and this research suggests that the efficiency of the early stages of sensory processing have a moderate association with psychometric intelligence (the present author here declares an interest). Inspection time was developed in Australia by Vickers, a student of the British psychologist Welford (Vickers, Nettelbeck, & Willson, 1972). The procedure fits into the mould of backward-masking techniques that assess processes related to iconic memory storage and the passage of information to short-term memory and decision processes (White, 1996). In the typical task participants, after a cue, see a stimulus composed of two vertical lines of markedly different lengths. The duration of the stimulus is controlled by the experimenter. This is followed by a backward mask to prevent further processing. The participant must judge which of the two lines was longer. Responses are made at leisure and only the correctness of each response is noted. The association between stimulus duration and probability of a correct response is well described by a cumulative normal ogive (Burns, Nettelbeck, & White, 1998). Qualitative reviews, meta-analyses and single large studies agree that there is a correlation of about .4 between inspection times and psychometric intelligence (Crawford, Deary, Allan, & Gustafsson, 1998; Deary & Stough, 1996; Kranzler & Jensen, 1989; Nettelbeck, 1987). The correlation is stronger, in adult samples at least, between performance-type ability measures than verbal. Mechanisms to explain this association between a psychophysical task and psychometric test scores have yet to be uncovered, though explanations in terms of higher-level processing such as strategies and motivation have been unsuccessful (Deary, 1996) and there are some interesting leads in terms of links with the P200 wave of the event-related response (Caryl, 1994). Simple auditory processing efficiency likewise appears to associate with psychometric intelligence, but it is not clear whether auditory and visual processing tasks assess the same brain limitations (Deary, 1999).
Psychophysiological, physiological and biological
A gallimaufry of biological indices correlate significantly with mental test scores (Deary & Caryl, 1997; Deary, 2000). Two barriers, though, stand in the way of answering Mackintosh's (1986) question: 'The biology of intelligence?' First, the processing construct(s) measured by the biological index is often unclear. Secondly, in some cases, it is unclear whether the biological differences are causes or consequences of psychometric intelligence differences.
The idea that bigger brains might be found in cleverer people goes back to antiquity (Huarte, 1575, provided an early review), though Fuller (1648, p. 75) wrote equivocally about mental ability and brain size:
Generally nature hangs out a sign of simplicity in the face of a fool; and there is enough in his countenance for a hue and cry to take him on suspicion: or else it is stamped on the figure of his body; their heads sometimes so little, that there is no room for wit, sometimes so long, that there is no wit for so much room.
Early research on the hypothesis was carried out in Britain by Galton (1888) and Pearson (1906-07) who examined head size and academic achievement. It was clear by then that measuring head size would not prove a practical way of assessing mental ability; Pearson (p. 105) concluded:
(a) that there is a sight correlation between size of head and general intelligence, (b) that this correlation is not sensibly increased by allowing for the size of the body relative to the size of the head, (c) that the correlation is so small that it would be absolutely idle to endeavour to predict the intellectual ability of an individual from his or her head measurements.
The availability of in vivo measurements of brain volumes, using structural magnetic resonance imaging, has revitalized the idea that there might be some association between brain size and mental ability. Several studies now more or less agree that there is a correlation between .3 and .5 between brain size and mental ability test scores (Rushton & Ankney, 1996). Newer studies concentrate on those areas of the brain whose volumes correlate most highly with ability test scores (Flashman, Andreasen, Flaum, & Swayze, 1998). Understanding this association has reached the stage of investigators suggesting the possible brain variables that might bring it about. The following have been nominated: more cortical columns; greater number of stem cells; different rates of neuronal death; dendritic expansion; number of synapses; thickness of myelin; metabolic efficiency; greater number of nerve cell bodies; greater number of processing elements; more extensive connectivity in the left hemisphere; neuronal quantity or myelinization; and millions of excess neurones for some individuals. This list was compiled from suggestions by Andreasen et al. (1993), Egan et al. (1994), Raz et al. (1993), Wickett, Vernon, and Lee (1994), and Willerman, Schultz, Rutledge, and Bigler (1991), and it is arguably not a very different list from suggestions made over four centuries ago (Huarte, 1575; see Deary, 2000).
With respect to understanding its causal constructs, nerve conduction velocity has an advantage over brain size. However, if it exists at all, the several reports of correlations between psychometric intelligence and nerve conduction velocity point to a small but as yet noisy effect size. No greater than .2 and perhaps quite a bit lower would be a defensible guess to date (Rijsdijk & Boomsma, 1997, review the field). This small correlation does seem to be mediated almost entirely by genetic factors (Rijsdijk & Boomsma, 1997). Partialling out differences in nerve conduction velocity from correlations between reaction times and psychometric test scores does not lower the latter's correlations, refuting suggestions of a continuity of 'mental speed' from nerves to cognition (Reed & Jensen, 1993; P. A. Vernon & Mori, 1992).
One of Britain's best-known, and regrettable, contributions to the question of the environmental and genetic contributions to intelligence was Burt's dubious twin data (Hearnshaw, 1979). Striking them from the experimental canon was the only prudent option. The other accumulated family, adoption and twin (reared together and apart) data allow the following conclusions: the heritability of psychometric intelligence is moderately high, as is the environmental contribution; the heritability is probably higher in adulthood and even more so in old age; non-shared environment makes a larger contribution than family upbringing; much of the heritability of psychometric intelligence is via Spearman's g; and some group factors of ability are more heritable than others (Bouchard, 1998; Devlin, Daniels, & Roeder, 1997; McClearn et al., 1997; Petrill et al., 1998; Plomin, DeFries, McClearn, & Rutter, 1997; Plomin & Petrill, 1997).
These findings point to sources of variance in ability test scores. They do not offer tractable accounts of the biological mechanisms of ability differences. A start on this longer and more interesting road is being made by the application of molecular genetic research to psychometric intelligence differences (McGuffin & Martin, 1999; Plomin & Crabbe, 2000). British research features prominently, because of the IQ QTL (quantitative trait loci) project at the Institute of Psychiatry in London and collaborating institutions. Though mental handicaps were previously the focus of genetic research into ability levels, this has shifted to include normal and exceptional ability (Daniels, McGuffin, Owen, & Plomin, 1998). Some allelic associations with psychometric intelligence have already been reported, and some have been replicated (Chorney et al., 1998). However, these typically represent searches on parts of single chromosomes (e.g. three associations were found and replicated between general ability in children and markers on chromosome 4; Fisher et al., 1999). The aim of the IQ QTL project is to search for associations using over 3000 genetic markers and to replicate any findings in other samples (Plomin et al., 1995; Petrill et al., 1996). No one should doubt the difficulties of such a project: there is an unknown number of genetic influences on different abilities; some smaller or greater proportion of these might have effect sizes too small feasibly to be detected; even finding an association just announces the start of a tortuous job of unravelling the route between gene effect and ability level; and there are so many loci to investigate that Type I errors will be common (Petrill et al., 1997; Wahlsten, 1999). On a more positive note, commentators agree that progress in this field will include cognitive constructs related to information processing at some level of explanation between psychometric phenotypes and biology as revealed (if it is) by molecular genetic associations (Gottesman, 1997). Plomin and Crabbe (2000) call this process of investigation 'behavioural genomics'. Another hopeful sign is that research focus is on understanding human differences in cognitive ageing, rather than on brave new world scenarios of increasing mental ability in normal people (MacLullich, Seckl, Starr, & Deary, 1998).
The descriptive structure of human mental ability differences is a neat-looking pyramid from a distance though, up-close, one can see imperfections and uncompleted work. It is a converging consensus about the taxonomy of psychometric test materials and their intercorrelations. It is a monument to the achievements of British psychologists: Spearman's g sits at the top, R. B. Cattell (with Horn, 1966) provided some of the major group factors, and Burt and P. E. Vernon were the architects of the structure. Eysenck kept the wreckers away. The American Thurstone provided many lower-level, correlated factors, though Guilford's suggestions find no clear instantiation. A revived, major activity within mental ability differences is to seek an explanatory account of this hierarchy of differences, marking a return to the long-ignored emphases of Galton and Spearman. Unexpected and interesting associations, as yet unexplained, are found between psychometric intelligence and a catalogue of variables from cognitive and biological theatres of measurement (Deary, 2000). The moderately high genetic contribution to ability differences supports the search for the brain's processing features that can account for these associations.
For the foreseeable future mental tests will find application, though the origins of individual differences in test scores will remain largely, but progressively less, obscure. Predictions about the coming of 'biological tests of intelligence' are unwarranted (Matarazzo, 1992); the correlations between psychometric intelligence and cognitive/biological indices are simply not strong enough, the ease of use of mental tests compared with biological measures is high, and the predictive validity of the tests is not matched by any brain indices (Deary, Caryl, & Austin, 2000). But the main points on which to end are that the psychometric structure of intelligence is becoming clearer, with British discoveries prominently settled in place; mental tests have proven predictive validity; and the London school agendum of seeking some of the causes of mental ability differences is attracting much attention and gathering replicable findings. That is why Neisser (1997), a cognitive psychologist visiting the broad arena of psychometric intelligence research, was able to conclude, a near-century after Spearman and Binet, that 'As of now, the study of intelligence looks promising' (p. 80).
Andreasen, N., Flaum, M., Swayze, V., O'Leary, D. S., Alliger, R., Cohen, G., Erhardt, J., & Yuh, W. T. C. (1993). Intelligence and brain structure in normal individuals. American Journal of Psychiatry, 150, 130-134.
Baddeley, A. D. (1986). Working memory. Oxford: Clarendon.
Baddeley, A. (1992). Working memory. Science, 255, 556-559.
Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge: Cambridge University Press.
Beck, L. F. (1933). The role of speed in intelligence. Psychological Bulletin, 30, 169-178.
Bickley, P. G., Keith, T. Z., & Wolfle, L. M. (1995). The three-stratum theory of cognitive abilities: Test of the structure of intelligence across the life span. Intelligence, 20, 309-328.
Binet, A. (1905). New methods for the diagnosis of the intellectual level of subnormals. L'Annee Psychologique, 12, 191-244. (Translated in 1916 by E. S. Kite in The development of intelligence in children. Vineland, NJ: Publications of the Training School at Vineland.)
Bouchard, T. J. (1998). Genetic and environmental influences on adult intelligence and special mental abilities. Human Biology, 70, 257-279.
Brand, C. R. (1996). The g factor. Chichester: Wiley. (This book was withdrawn by the publisher shortly after publication, making it difficult to obtain.)
Burns, N. R., Nettelbeck, T., & White, M. (1998). Testing the interpretation of inspection time as a measure of speed of sensory processing. Personality and Individual Differences, 24, 25-39.
Burt, C. (1909-10). Experimental tests of general intelligence. British Journal of Psychology, 3, 94-177.
Burt, C. (1940). The factors of the mind. London: University of London Press.
Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical account of processing in the Raven's Progressive Matrices Test. Psychological Review, 97, 404-431.
Carretta, T. R., & Ree, M. J. (1995). Near identity of cognitive structure in sex and ethnic groups. Personality and Individual Differences, 19, 149-155.
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor analytic studies. Cambridge: Cambridge University Press.
Carroll, J. B. (1995). Reflections on Stephen Jay Gould's The mismeasure of man (1981): A retrospective review. Intelligence, 21, 121-134.
Caryl, P. G. (1994). Event-related potentials correlate with inspection time and intelligence. Intelligence, 18, 15-46.
Caryl, P. G., & Deary, I. J. (1969). IQ and censorship (correspondence). Nature, 381, 270.
Cattell, J. McK. (1886a). The inertia of eye and brain. Brain, 8, 295-381.
Cattell, J. McK. (1886b). The time taken up by cerebral operations. Mind, 11, 220-242, 377-392, 524-538.
Cattell, J. McK. (1890). Mental tests and measurements. Mind, 15, 373-381.
Cattell, R. B. (1998). What is intelligence? Some answers from the triadic theory. In J. J. McArdle & R. W. Woodcock (Eds.), Human cognitive abilities in theory and practice (pp. 29-38). London: Erlbaum.
Chorney, M. J., Chorney, K., Seese, N., Owen, M. J., Daniels, J., McGuffin, P., Thompson, L. A., Detterman, D. K., Benbow, C., Lubinski, D., Eley, T., & Plomin, R. (1998). A quantitative trait locus associated with cognitive ability in children. Psychological Science, 9, 1-8.
Crawford, J. R., Dreary, I. J., Allan, K. M., & Gustafsson, J.-E. (1998). Evaluating competing models of the relationship between inspection time and psychometric intelligence. Intelligence, 26, 27-42.
Cronbach, L. J. (1957). The two disciplines of scientific psychology. American Psychologist, 12, 671-684.
Daniels, J., McGuffin, P., Owen, M. J., & Plomin, R. (1998). Molecular genetic studies of cognitive ability. Human Biology, 70, 281-296.
Deary, I. J. (1986). Inspection time: Discovery or rediscovery? Personality and Individual Differences, 7, 625-631.
Deary, I. J. (1994). Sensory discrimination and intelligence: Postmortem or resurrection? American Journal of Psychology, 107, 95-115.
Deary, I. J. (1996). Reductionism and intelligence: The case of inspection time. Journal of Biosocial Science, 28, 405-423.
Deary, I. J. (1997). Intelligence and information processing. In H. Nyborg (Ed.), The scientific study of buman nature: Tribute to Hans Eysenck at eighty (pp. 282-310). Oxford: Elsevier.
Deary, I. J. (1999). Intelligence and visual and auditory information processing. In P. L. Ackerman, P. C. Kyllonen, & R. D. Roberts (Eds.), Learning and individual differences: Process, trait, and content determinants (pp. 111-133). Washington, DC: American Psychological Association.
Deary, I. J. (2000). Looking down on buman intelligence. Oxford: Oxford University Press.
Deary, I. J., & Caryl, P. G. (1997). Neuroscience and human intelligence differences. Trends in Neurosciences, 20, 365-371.
Deary, I. J., Caryl, P. G., & Austin, E. J. (2000). Measuring versus understanding human intelligence. Psychology, Public Policy and Law, 6, 180-190.
Deary, I. J., Egan, V., Gibson, G. J., Brand, C. R., Austin, E., & Kellaghan, T. (1996). Intelligence and the differentiation hypothesis. Intelligence, 23, 105-132.
Deary, I. J., & Stough, C. (1996). Intelligence and inspection time: Achievements, prospects and problems. American Psychologist, 51, 599-608.
Deary, I. J., Whalley, L. J., Lemmon, H., Crawford, J. R., & Starr, J. M. (2000). The stability of individual differences in mental ability from childhood to old age: Follow-up of the 1932 Scottish Mental Survey. Intelligence, 28, 49-55.
Devlin, B., Daniels, M., & Roeder, K. (1997). The heritability of IQ. Nature, 388, 468-471.
Duncan, J., Emslie, H., & Williams, P. (1996). Intelligence and the frontal lobe: The organisation of goal-directed behavior. Cognitive Psychology, 30, 257-303.
Egan, V., Chiswick, A., Santosh, C., Naidu, K., Rimmington, J. E., & Best, J. J. K. (1994). Size isn't everything: A study of brain volume, intelligence and auditory evoked potentials. Personality and Individual Differences, 17, 357-367.
Embretson, S. E. (1995). The role of working memory capacity and general control processes in intelligence. Intelligence, 20, 169-189.
Eysenck, H. J. (1939). Primary mental abilities. British Journal of Educational Psychology, 9, 270-275.
Eysenck, H. J. (1967). Intelligence assessment: A theoretical and experimental approach. British Journal of Educational Psychology, 37, 81-97.
Eysenck, H. J. (Ed.) (1982). A model for intelligence. Berlin: Springer-Verlag.
Eysenck, H. J. (1995). Can we study intelligence using the experimental method? Intelligence, 20, 217-228.
Fisher, P. J., Turic, D., Williams, N. M., McGuffin, P., Asherson, P., Ball, D., Craig, I., Eley, T., Hill, L., Chorney, M. J., Benbow, C. P., Lubinski, D., Plomin, R., & Owen, M. J. (1999). DNA pooling identifies QTLs on chromosome 4 for general cognitive ability in children. Human Molecular Genetics, 8, 915-922.
Flashman, L. A., Andreasen, N. C., Flaum, M., & Swayze, V. W. (1998). Intelligence and regional brain volumes in normal controls. Intelligence, 25, 149-160.
Fuller, T. (1648, reprinted 1936). Of natural fools. In R. Vallance (Ed.), A hundred English essays (pp. 75-77). London: Nelson.
Galton, F. (1883). Inquiries into human faculty. London: Dent.
Galton, F. (1888). Head growth in students at the University of Cambridge. Nature, 38, 14-15.
Galton, F. (1890). Remarks on 'Mental tests and measurements' by J. McK. Cattell. Mind, 15, 380-381.
Gottesman, I. I. (1997). Twins: En route to QTLs for cognition. Science, 276, 1522-1523.
Gould, S. J. (1981; 2nd ed. 1997). The mismeasure of man. Harmondsworth: Penguin.
Guilford, J. P. (1956). The structure of intellect. Psychological Bulletin, 53, 266-293.
Gustafsson, J.-E. (1984). A unifying model for the structure of mental abilities. Intelligence, 8, 179-203.
Gustafsson, J.-E. (1992). The relevance of factor analysis for the study of group differences. Multivariate Behavioral Research, 27, 239-247.
Hearnshaw, L. S. (1979). Cyril Burt: Psychologist. London: Hodder and Stoughton.
Herrnstein, R. J., & Murray, C. (1994). The bell curve. New York: Free Press.
Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4, 11-26.
Horn, J. L. (1998). A basis for research on age differences in cognitive capabilities. In J. J. McArdle & R. W. Woodcock (Eds.), Human cognitive abilities in theory and practice (pp. 57-91). London: Erlbaum.
Horn, J. L., & Cattell, R. B. (1966). Refinement and test of the theory of fluid and crystallised general intelligences. Journal of Educational Psychology, 57, 253-270.
Huarte, J. de San Juan (1575/1969). Examen de ingenios, or, a triall of wits (the examination of mens wits). Amsterdam: Da Capo Press, Theatrum Orbis Terrarum. (This facsimile edition from a copy held in the Bodleian Library, Oxford, is an English translation by R. Carew, from an Italian translation by M. Camilio Camiili of the original Spanish. Published by Richard Watkins, London, 1594.)
Humphreys, L. G. (1979). The construct of general intelligence. Intelligence, 3, 105-120.
Hunt, E. (1980). Intelligence as an information processing concept. British Journal of Psychology, 71, 449-474.
Hunter, J. E. (1983). A causal analysis of cognitive ability, job knowledge, job performance, and supervisor ratings. In F. Landy, S. Zedek, & J. Cleveland (Eds.), Performance measurement and theory (pp. 257-266). Hillsdale, NJ: Erlbaum.
Jensen, A. R. (1969). How much can we boost IQ and scholastic achievement? Harvard Educational Review, 39, 1-123.
Jensen, A. R. (1987). Individual differences in the Hick paradigm. In P. A. Vernon (Ed.), Speed of information processing and intelligence (pp. 101-175). Norwood, NJ: Ablex.
Jensen, A. R. (1998a). The g factor: The science of mental ability. New York: Praeger.
Jensen, A. R. (1998b). The suppressed relationship between IQ and the reaction time slope parameter of the Hick function. Intelligence, 26, 43-52.
Johnson, R. C., McClearn, G. E., Yuen, S., Nagoshi, C. T., Ahern, F. M., & Cole, R. E. (1985). Galton's data a century later. American Psychologist, 40, 875-892.
Korten, A. E., Jorm, A. F., Jiao, Z., Letenneur, L., Jacomb, P. A., Henderson, A. S., Christensen, H., & Rogers, B. (1999). Health, cognitive, and psychosocial factors as predictors of mortality in an elderly community sample. Journal of Epidemiology and Community Health, 53, 83-88.
Kranzler, J. H., & Jensen, A. R. (1989). Inspection time and intelligence: A meta-analysis. Intelligence, 13, 329-347.
Kyllonen, P. C. (1996). Is working memory capacity Spearman's g? In I. Dennis & P. Tapsfield (Eds.), Human abilities: Their nature and measurement (pp. 77-96). Hillsdale, NJ: Erlbaum.
Kyllonen, P. C., & Christal, R. E. (1990). Reasoning ability is (little more than) working memory capacity?! Intelligence, 14, 389-433.
Lohman, D. F. (1994). Component scores as residual variation (or why the intercept correlates best). Intelligence, 19, 1-11.
Lohman, D. F. (1999). Minding our p's and q's: On finding relationships between learning and intelligence. In P. L. Ackerman, P. C. Kyllonen, & R. D. Roberts (Eds.), Learning and individual differences: Process, trait, and content determinants (pp. 55-72). Washington, DC: American Psychological Association.
Mackintosh, N. J. (1986). The biology of intelligence? British Journal of Psychology, 77, 1-18.
MacLullich, A. M. J., Seckl, J. R., Starr, J. M., & Deary, I. J. (1998). The biology of intelligence: From association to mechanism. Intelligence, 26, 63-73.
Marshalek, B., Lohman, D. F., & Snow, R. E. (1983). The complexity continuum in the radex and hierarchical models of intelligence. Intelligence, 7, 107-127.
Matarazzo, J. D. (1992). Psychological testing and assessment in the 21st century. American Psychologist, 47, 1007-1018.
McClearn, G. E., Johansson, B., Berg, S., Pedersen, N. L., Ahern, F., Petrill, S. A., & Plomin, R. (1997). Substantial genetic influence on cognitive abilities in twins 80 or more years old. Science, 276, 1560-1563.
McGuffin, P., & Martin, N. (1999). Behaviour and genes. British Medical Journal, 319, 37-40.
Neisser, U. (1997). Never a dull moment. American Psychologist, 52, 79-81.
Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci, S. J., Halpern, D. F., Leohlin, J. C., Perloff, R., Sternberg, R. J., & Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51, 77-101.
Nelson, H. E. (1982). National Adult Reading Test (NART): Test manual. Windsor: NFER, Nelson.
Nettelbeck, T. (1987). Intelligence and inspection time. In P. A. Vernon (Ed.), Speed of information processing and intelligence (pp. 295-346). Norwood, NJ: Ablex.
Neubauer, A. C. (1997). The mental speed approach to the assessment of intelligence. In J. Kingma & W. Tomic (Eds.), Advances in cognition and education: Reflections on the concept of intelligence (pp. 149-173). Greenwich, CT: JAI Press.
Neubauer, A. C., Spinath, F. M., Riemann, R., Borkenau, P., & Angleitner, A. (in press). Genetic and environmental influences on two measures of speed of information processing and their relation to psychometric intelligence: Evidence from the German Observational Study of Adult Twins. Intelligence.
O'Carroll, R. (1995). The assessment of premorbid ability: A critical review. Neurocase, 1, 83-89.
Pearson, K. (1906-07). On the relationship of intelligence to size and shape of head, and to other physical and mental characters. Biometrika, 5, 105-146.
Petrill, S. A., Ball, D., Eley, T., Hill, L., Plomin, R., McClearn, G., Smith, D. L., Chorney, K., Chorney, M. J., Hershz, M. S., Detterman, D. K., Thompson, L. A., Benbow, C., Lubinski, D., Daniels, J., Owen, M. J., & McGuffin, P. (1997). Failure to replicate a QTL association between a DNA marker identified by EST00083 and IQ. Intelligence, 25, 179-184.
Petrill, S. A., Plomin, R., Berg, S., Johansson, B., Pedersen, N. L., Ahern, F., & McClearn, G. E. (1998). The genetic and environmental relationship between general and specific cognitive abilities in twins age 80 and older. Psychological Science, 9, 183-189.
Petrill, S. A., Plomin, R., McClearn, G. E., Smith, D. L., Vignetti, S., Chorney, M. J., Thompson, L. A., Detterman, D. K., Benbow, C., Lubinski, D., Daniels, J., Owen, M. J., & McGuffin, P. (1996). DNA markers associated with general and specific cognitive abilities. Intelligence, 23, 191-203.
Plomin, R., & Crabbe, J. (2000). DNA. Psychological Bulletin, 126, 806-828.
Plomin, R., DeFries, J. C., McClearn, G. E., & Rutter, M. (1997). Behavioral genetics. New York: W. H. Freeman.
Plomin, R., McClearn, G. E., Smith, D. L., Skuder, P., Vignetti, S., Chorney, M. J., Kasarda, S., Thompson, L. A., Detterman, D. K., Petrill, S. A., Daniels, J., Owen, M., & McGuffin, P. (1995). Allelic associations between 100 DNA markers and high versus low IQ. Intelligence, 21, 31-48.
Plomin, R., & Petrill, S. (1997). Genetics and intelligence: What's new? Intelligence, 24, 53-77.
Posner, M. I., & Mitchell, R. F. (1967). Chronometric analysis of classification. Psychological Review, 74, 392-409.
Rabbitt, P., Donlan, C., Bent, N., McInnes, L., & Abson, V. (1993). The University of Manchester Age and Cognitive Performance Research Centre and North-east Age Research longitudinal programs 1982 to 1987. Zeitschrift fur Gerontologie, 26, 176-183.
Raven, J. C. (1938). Progressive matrices. London: Lewis.
Raz, N., Torres, I. T., Spencer, W. D., Millman, D., Baertschi, J. C., & Sarpel, G. (1993). Neuroanatomical correlates of age-sensitive and age-invariant cognitive abilities: An in vivo MRI investigation. Intelligence, 17, 407-422.
Raz, N., Willerman, L., & Yama, M. (1987). On sense and senses: Intelligence and auditory information processing. Personality and Individual Differences, 8, 201-210.
Reed, T. E., & Jensen, A. R. (1993). Choice reaction time and visual pathway nerve conduction velocity both correlate with intelligence but appear not to correlate with each other: Implications for information processing. Intelligence, 17, 191-203.
Rijsdijk, F. V., & Boomsma, D. I. (1997). Genetic mediation of the correlation between peripheral nerve conduction velocity and IQ. Behavior Genetics, 27, 87-98.
Roth, E. (1964). Die Geschwindigkeit der Verabeitung von Information and ihr Zusammenhang mit Intelligenz. Zeitschrift fur Experimentelle und Angewandte Psychologie, 11, 616-622.
Rushton, J. P., & Ankney, C. D. (1996). Brain size and cognitive ability: Correlations with age, sex, social class, and race. Psychonomic Bulletin and Review, 3, 21-36.
Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124, 262-274.
Scottish Council for Research in Education (1933). The intelligence of Scottish children: A national survey of an age group. London: University of London Press.
Sharp, S. E. (1898-99). Individual psychology: A study in psychological method. American Journal of Psychology, 10, 329-391.
Snowdon, D. A., Kemper, S. J., Mortimer, J. A., Greiner, L. H., Wekstein, D. R., & Markesbery, W. R. (1996). Linguistic ability in early life and cognitive function and Alzheimer's disease in late life: Findings from the Nun study. Journal of the American Medical Association, 275, 528-532.
Spearman, C. (1904). 'General intelligence' objectively determined and measured. American Journal of Psychology, 15, 201-293.
Spearman, C. (1923). The nature of intelligence and the principles of cognition. London: Macmillan.
Spearman, C. (1927). The abilities of man. London: Macmillan.
Sternberg, R. J. (1977). Intelligence, information processing, and analogical reasoning: The componential analysis of human abilities. Hillsdale, NJ: Erlbaum.
Sternberg, R. J. (1978). Intelligence research at the interface between differential and cognitive psychology. Intelligence, 2, 195-222.
Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. Cambridge: Cambridge University Press.
Sternberg, S. (1966). High speed scanning in human memory. Science, 153, 652-654.
Thomson, G. H. (1939). The factorial analysis of human ability. London: University of London Press.
Thurstone, L. L. (1938). Primary mental abilities. Psychometric Monographs, No. 1.
Undheim, J. O. (1981a). On intelligence II: A neo-Spearmanian model to replace Cattell's theory of fluid and crystallised intelligence. Scandinavian Journal of Psychology, 22, 181-187.
Undheim, J. O. (1981b). On intelligence IV: Toward a restoration of general intelligence. Scandinavian Journal of Psychology, 22, 251-265.
Undheim, J. O., & Gustaffson, J.-E. (1987). The hierarchical organisation of cognitive abilities: Restoring general intelligence through the use of linear structural relations (LISREL). Multivariate Behavioral Research, 22, 149-171.
Vernon, P. A. (Ed.) (1987). Speed of information processing and intelligence. Norwood, NJ: Ablex.
Vernon, P. A. (Ed.) (1993). Biological approaches to the study of human intelligence. Norwood, NJ: Ablex.
Vernon, P. A., & Mori, M. (1992). Intelligence, reaction times, and peripheral nerve conduction velocity. Intelligence, 16, 273-288.
Vernon, P. E. (1950). The structure of human abilities. London: Methuen.
Vernon, P. E. (1961). The structure of human abilities (2nd ed.). London: Methuen.
Vickers, D., Nettelbeck, T., & Willson, R. J. (1972). Perceptual indices of performance: The measurement of 'inspection time' and 'noise' in the visual system. Perception, 1, 263-295.
Wahlsten, D. (1999). Single-gene influences on behavior. Annual Review of Psychology, 50, 599-624.
White, M. (1996). Interpreting inspection time as a measure of the speed of sensory processing. Personality and Individual Differences, 20, 351-363.
Wickett, J. C., Vernon, P. A., & Lee, D. H. (1994). In vivo brain size, head perimeter, and intelligence in a sample of healthy adult females. Personality and Individual Differences, 16, 831-838.
Widaman, K. F., & Carlson, J. S. (1989). Procedural effects on performance on the Hick paradigm. Intelligence, 13, 63-86.
Willerman, K. F., Schultz, R., Rutledge, J. N., & Bigler, E. D. (1991). In vivo brain size and intelligence. Intelligence, 15, 223-228.
Wissler, C. (1901). The correlation of mental and physical tests. Psychological Review: Monograph No. 3.
Zenderland, L. (1998). Measuring minds: Henry Herbert Goddard and the origins of American intelligence testing. Cambridge: Cambridge University Press.
Ian J. Deary*
University of Edinburgh, UK
* Requests for reprints should be addressed to Professor Ian J. Deary, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK (e-mail: I.Deary@ed.ac.uk).
|Printer friendly Cite/link Email Feedback|
|Author:||Deary, Ian J.|
|Publication:||British Journal of Psychology|
|Date:||Feb 1, 2001|
|Previous Article:||Human rationality and the psychology of reasoning: where do we go from here?|
|Next Article:||Social cognition: categorical person perception.|