Mental speed is not the ‘basic’ process of intelligence

Person. hdicid.

Pergamon SO191-8869(96)00163-8

MENTAL

SPEED IS NOT THE ‘BASIC’ PROCESS INTELLIGENCE* Lazar Stankovt

Department

Diff. Vol. 22, No. I, pp. 69-84. 1997 Copyright Q 1997 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0191-8869/97 $17.00+0.00

of Psychology,

and Richard The University

OF

D. Roberts

of Sydney,

Sydney, Australia

(Received 26 Jaanuar~ 1996) Summary-It is argued that a great deal of human intelligence research has unjustly overemphasised the role played by mental speed. While it is accepted that mental speed is an important aspect of intelligence, determination of this as the basic process underlying individual differences is a consequence of flawed logic. This may be attributed to several questionable research practices and/or theoretical shortcomings. These include: (a) The adoption of a narrow neo-Spearmanian model of intelligence; (b) Selective interpretation of the available empirical evidence wherein correlations between mental speed and intelligence measures are actually mediocre and certainly of no greater order of magnitude than many other elementary cognitive processes; (c) A failure to realise that the factorial composition of mental speed may be as complex as that for number correct (i.e. level) measures; (d) The acceptance of two main paradigms in the literatureChoice Reaction Time (CRT) and Inspection Time (IT)-both of which contain a number of unresolved controversies; (e) A tendency to examine in post hoc fashion those parameters of CRT and IT tasks that show correlations with measures of intelligence; and (f) The absence of a satisfactory explanatory model to account for the correlations between mental speed and intelligence. Copyright % 1997 Elsevier Science Ltd.

INTRODUCTION The contemporary psychological literature has been inundated by a large number of studies that focus upon the biological bases of human intelligence (for recent reviews, see e.g. Jensen, 1987; Juhel, 1991; Kranzler & Jensen, 1989; Vernon, 1987 1992). Particularly widespread has been the study of cognitive (or mental) speed. While reasons for this interest are no doubt diverse, powerful influences appear to have been exerted by a combination of sociological and technological factors. On the one hand, this situation has arisen because of influences exerted by social policies that place emphasis on the material (i.e. biological) bases of behaviour (see e.g. Herrnstein & Murray, 1994). As a consequence the study of intelligence has tended towards reductionism at the expense of ‘holistic’ approaches. On the other hand, new methodologies (guided in particular by advances in computer technology) have allowed researchers the opportunity to investigate the role played by previously inaccessible aspects of timed performance. Against this setting, there would appear to be a need for a more balanced approach to the study of human intelligence-one which views mental speed as being of peripheral rather than of critical importance. This undertaking is given added impetus in the light of several cautionary notes punctuating the individual differences literature (see e.g. Carroll, 1993, p. 506; Horn, 1987; Juhel, 1991; Marr & Sternberg, 1987), each of which appears largely to have been ignored. Whilst a cogent review of the scientific literature examining the relationship between mental speed and human cognitive abilities is perhaps overdue, the present paper, in attempting to redirect the emphasis of this research program, principally suggests that efforts to equate mental speed (or related constructs) to intelligence have injudiciously driven much of the contemporary empirical and theoretical developments in this field. An historical overview of much of the earlier literature examining mental speed, with its shortcomings and prospects, is provided in Berger (1982). Notwithstanding, many of the caveats surrounding mental speed, which Berger draws in this excellent treatise, appear presently to have gone unheeded. In the search for parsimonious models of both speed of mental processing and human intelligence, intellectual rigour has sometimes been sacrificed,

*We are grateful to N. Todorov who asked us to put these thoughts about mental speed on paper. Several colleagues including J. Alexander, R. Heath, R. Kerr. T. Nettelbeck, G. Pallier. and J. Taplin also provided useful written comments. t To whom all correspondence should be addressed. 69

Lazar Stankov

70

and Richard

D. Roberts

with no available theoretical edifice left to support a variety of alternative explanations. As such, mental speed constructs, once fully understood, may serve to provide an account of some of the

behaviours considered present paper provides

SPEED

AS

to constitute ‘intelligence’, but almost certainly not the vast majority. a series of arguments in support of these various assertions.

BASIC:

A PROMISING INDIVIDUAL

PARADIGM DIFFERENCES?

FOR

THE

SCIENCE

The

OF

In a contemporary issue of Intelligence, Eysenck (1995) has contributed an Editorial article that concludes by acknowledging: “The recent explosion of experimental studies into the speed of mental processes and the psychophysiological bases of cognitive behaviour (Barrett & Eysenck, 1992; Eysenck, 1986a, 1986b) promises to lead to a more theory-oriented and experimental approach to the whole problem of intelligence” (p. 225). Whilst the notion of an explosion of research might seem to represent a gross exaggeration, this claim should be evaluated in the light of the following facts. Extensive research investigating cognitive speed has now been conducted worldwide (e.g. Agrawal & Kumar, 1993; Deary, Gary, Egan & Wight, 1989; Jensen, 1992a; Lubbin & Fernandez, 1986; Nettlebeck, 1987; Neubauer & Freudenthaler, 1994; Schweizer, 1993), encompassing diverse racial and ethnic groups (e.g. Jensen & Whang, 1993; Lynn, Chan & Eysenck, 1991; Saccuzzo, Johnson & Guertin, 1994) across samples varying widely in age (e.g. Anderson, 1988; Cerella, 1985; Jenkinson, 1983; Myerson, Wagstaff & Hale, 1994; Nettelbeck & Rabbitt, 1992; Salthouse, 1994; Smith, Poon, Hale & Myerson, 1988; Smith & Stanley, 1983; Tomer & Cunningham, 1993), and embracing varied socio-economic (e.g. Jensen, 1987) and clinical status (e.g. Gold, Deary, MacLeod & Frier, 1995; Kirby & Thomas, 1989; Wade, Newell & Wallace, 1978; Zahn, Kruesi, Leonard & Rapoport, 1994). The paradigms employed also vary widely-from those such as the ZahlenVerbindung’s-Test which are administered in paper and pencil format (e.g. Oswald & Roth, 1987; Vernon, 1993; Vernon & Weese, 1993) to the well known Hick paradigm and its derivatives (e.g. Barrett, Eysenck & Lucking, 1986; Beh, Roberts & Prichard-Levy, 1994; Diascro & Brody, 1994, Frearson & Eysenck, 1986; Jensen, 1979) and on through the ‘new and improved’ computerised tasks such as the ‘frequency accrual speed test’ (i.e. FAST, see Vickers, 1995, Vickers & McDowell, in press, Vickers, Pietsch & Hemingway, in press). Across this array of research, the prominence given to a unitary conception of cognitive speed remains pervasive (e.g. Bors & Forrin, 1995; Hale & Jansen, 1994; Kail, 1992, 1993; Miller & Vernon, 1992; Neubauer & Bucik, in press; Salthouse, 1985, 1994; Sliwinski, Buschke, Kuslansky, Senior & Scarisbrick, 1994; Vernon & Jensen, 1984). While, as suggested already, it is not the intention of the current paper to review this literature in specific detail, the preceding commentary serves both to illustrate the extent of mental speed research and to set a scenario for (and justification of) the critique that is to follow.

TOWARD AN INTELLIGENCE:

EXPLANATION THE CASE BASIC

FOR INDIVIDUAL FOR ELEMENTARY

DIFFERENCES AS OPPOSED

IN TO

PROCESS(ES)

Over the past 20 years there have been several shifts in the direction which empirical research into human intelligence has been targeted. Some proponents of the resurgent cognitive psychology in the mid-1970s made emphatic anti-psychometric statements. It was claimed, for example, that all psychological tests based on factor-analytic studies should be replaced by experimental tasks derived from the prevalent theoretical stance that employed an information processing metaphor (Voss, 1976). Over time, a much less confrontationist view prevailed. Several important contributors, who had the same theoretical roots in experimental psychology but a more positive attitude towards traditional psychometric studies of intelligence, enriched the field by probing deeper into the microstructure of typical individual difference measures. Perhaps the best known of these efforts involved research conducted within each of the so-called ‘cognitive components’ (e.g. Sternberg, 1977) and ‘cognitive correlates’ (e.g. Hunt, 1978) frameworks. Whilst focusing on elementary cognitive processes and operations (see Carroll, 1976; Stankov, 1980), these investigators envisaged their work to constitute an exploratory search for a link between theory-based cognitive psychology

Mental speed is not basic

71

and what they perceived as the largely atheoretical psychometric approach. In this research programme, measures of timed performances were viewed as sensitive dependent variables that provided information about the microstructure of human cognition; speed per se was neither the construct of interest nor of particular theoretical significance. By and large, psychometricians welcomed such contributions from the experimentally-oriented colleagues since descriptive factors could be given a deeper, more conceptually sophisticated meaning. Arguably, the main difference between these early studies of elementary processes and those that are in vogue at the moment, resides in the fact that the former approach was open to accepting relatively complex processes (e.g. ‘justification’, ‘retrieval from the long-term store,’ etc.) as being elementary. In terms of this latter framework, research involves finding not so much elementary, but rather some ‘basic’ process of intelligence. In contemporary parlance the term ‘basic’ is used in the reductionist sense, and as such provides a link with psychological and genetic influences (cf. Brody, 1992, Chapter 3). This ‘basic’ process is perceived to be more fundamental than any other elementary mechanism. At the moment, cognitive speed appears to be the favoured candidate for this basic process. This is quite dissimilar to views held previously. In contrast, many of those setting the trend in individual differences research during the 1970s have moved in what is largely an antithetical direction over the last decade. For instance, Sternberg’s (1985) Triarchic Theory of intelligence is clearly influenced by anthropological views held by various cross-cultural psychologists. As a consequence, Sternberg’s ‘experiential’ and ‘contextual’ subtheories have gained equal standing with his original ‘componential’ subtheory of intelligence. Moreover, this latter subtheory lays claim to being neither elementary nor basic. Similarly, Hunt (1994) has continued his longstanding interest in issues related to global aspects of performance (i.e. psycholinguistics, cognitive aspects of the teaching of physics, and so forth) that clearly span diverse areas of human cognition. Another means of conceptualising differences between past and contemporary approaches is to point out that in the former, measures of time were treated purely and simply as dependent variables. As such these parameters were used in psychological research mainly because of their greater sensitivity relative to traditional ‘number-correct’ (or level) scores. Within this overall framework ‘speed of lexical access’, ‘speed of memory search’, and various other speed constructs indicate the functional role of some underlying cognitive mechanism. More recent studies of timed performance seem to gloss over important distinctions ‘within’ the processes under investigation by appearing instead to assert that there exists a general speed construct whose function is intrinsically ‘basic’.

THE SEARCH IMPLICATIONS

FOR FROM

A BASIC PROCESS IS ‘UNNATURAL’: MULTIPLE INTELLIGENCE(S) THEORY

There are conflicting scientific opinions about the nature and structure of human intelligence (cf. Horn & Nell, 1994). In the main, contemporary studies can be divided roughly into two traditional groups: (a) those that emphasise the importance of a single construct (i.e. psychometric g; see e.g. Jensen, 1979, 1992a, 1993; Jensen & Weng, 1994; Vernon, 1990) and (b) those that find it more useful to consider intellect as a composite of several broad classes of cognitive abilities (see e.g. Gardner, 1983; Horn & Hofer, 1992). For convenience, theories postulating more than one (global) mental ability may be referred to as multiple intelligence(s) models. While there are in fact a number of varieties of this latter framework, almost all can be incorporated within the hierarchical theory of fluid and crystallized intelligence (GJG,; cf. Carroll, 1993, p. 624 ff.; Gustafsson, 1992; see also Messick, 1992). For those researchers advocating a single factor conceptualisation of intelligence it is natural (and indeed efficacious) to seek some basic underlying construct. For obvious reasons, the existence of a single basic process causes tension within the framework of multiple intelligence(s). This is not simply a direct consequence of questioning the importance of the general factor (positive manLold, after all, represents a well-replicated empirical fact) but rather represents a tendency by proponents of multiple intelligence(s) theory to direct attention upon particularly complex psychological measures. While it may be useful to conceive of a basic process if attention is focussed on tasks that have low loadings exclusively on a general factor (as proponents of a single-intelligence theory are compelled to do), the existence of such a cognitive mechanism appears less acceptable to those who study tasks

12

Lazar Stankov and Richard D. Roberts

with higher g loadings where more complex processes of intellectual functioning are emphasised (cf. Horn & Noll, 1994). The faceted theory of intelligence proposed by Guttman (1992) and supported by the work of Snow (1989) and his collaborators is consistent with the concept of multiple-intelligence(s) (cf. Stankov, Boyle & Cattell, 1995). Collectively these investigators have demonstrated that manipulations of complexity within one cognitive domain (e.g. quantitative) differ from those within all others (e.g. either the verbal or spatial domains). It is difficult to envisage the existence of a single basic process under such conditions. Whatever makes one test a good measure of fluid intelligence has to be different from the psychological process (or processes) that makes another test a good measure of crystallized intelligence. Moreover, when two ability tests are administered simultaneously to a group of people, these so-called competing tasks tend to exhibit higher correlations among themselves than do the same tests given singly (Stankov, 1983, 1988a)-a finding that is similarly observed for dual task presentation of elementary cognitive tasks (Matthews & Dorn, 1995; Roberts, Beh & Stankov, 1988). Whilst it may be justifiable to assert that the increase in correlation points to the existence of a limited capacity system and that this capacity may be basic to intelligence, such notions about the nature of capacity are not analogous to the concept of a basic process of intelligence. In fact, this view of capacity is consistent with the proposition that there are many abilities [of similar or differing kinds (cf. Spilsbury, 1992; Stankov & Crawford, 1993)] contained within a particular ‘cognitive vessel’. Of course, this notion may also be argued to be consistent with a single mental energy perspective. However, those who subscribe to the idea of a basic process tend to glorify one (or, at the very best, a few) of the many components involved in any given cognitive act (Stankov et ul., 1995). The concept of multiple intelligence(s) is more logically consistent than any other model of intelligence from both the standpoint advocating the existence of many elementary cognitive processes [akin to the bonds of Thompson’s (1939) theory] and the notion that these processes are organised into subpools. Increasingly complex tasks tend to draw from larger and more diverse components of these subpools (cf. Stankov & Crawford, 1993; Stankov & Cregan, 1993; Wickens, 1980). The purpose of experimental manipulations of tasks’ characteristics is that these should result in either an increase or a decrease in the strength of the relationships between a given cognitive measure and the underlying experimental treatment. Contemporary individual difference research within the G,/Gc paradigm, aims to discover those cognitive processes that will reliably affect these empirical relationships in a psychologically meaningful way. How many of these processes will be uncovered? Arguably it is in the fulfilment of this objective (more than anything else) that future research into human intelligence ‘should’ be directed. Given the above propositions, it would seem pointless to select a given process and lay claim to its status being more (or less) basic to human intelligence. The major problem for any single researcher wishing to make such an assertion would be in deciding which psychological process to focus upon. In turn, this poses serious questions for those intelligence researchers who currently argue that a central role be afforded to the construct of mental speed.

MENTAL

SPEED

IS NOT BASIC: EVIDENCE THEORY AND PRACTICE

FROM

RESEARCH,

Even the most cursory examination of the current literature on intelligence would convince any reader that a substantial number of empirical studies exclusively investigate the relationship between mental speed and intelligence. The reader may even gain an impression that there is a consensus that individual differences in speed of performance may constitute a feasible explanation for individual differences in intelligence. It is doubtful whether such a consensus, if real, should have been reached.

SPEED

IS NOT

SPECIAL:

THE

CASE

FOR

OTHER

PROCESSES

In Spearman’s (1904) seminal paper, it was suggested that auditory abilities (i.e. those involved in music, and especially differential thresholds for sound frequencies) might constitute the basic process of intelligence. More recently, remarkably similar claims have been made by several com-

Mental speed is not basic

13

mentators examining audition, including Raz (see e.g. Raz, Willerman & Yama, 1987) and Deary (1994). However, mental processes involved in any of the sensorimotor tasks proposed by Cattell (1890) or Galton (1883) are, in principle, equally worthy candidates for this process. All of these tasks are known to share ‘low’ but positive correlations with complex measures of intelligencecorrelations, incidentally, of the same order of magnitude as those of simple mental speed measures (cf. Lindenberger & Baltes, 1994). Nevertheless, it seems counterproductive that people interested in intelligence should be willing to spend vast resources on psychological tasks that have such demonstrably low correlations! The latest important push in this direction was alluded to earlier in the present paper. In a summary of the 1970s effort to link components and correlates to intelligence, Hunt (1980) mentions a 0.30 barrier for the correlations between measures of intelligence and elementary cognitive tasks. In so doing, Hunt simply joined a series of many, many researchers this century who have reached a comparable conclusion (see Brody, 1992; Cattell, 1987; Horn & Noll, 1994). Hunt also notes that, whilst for some investigators this might mean ‘the bottle is half full’, for others this compels acknowledgement that ‘the bottle is half empty’. Thus, unless working with extreme groups, censoring data, employing ‘corrections for the restriction in range and for unreliability’, and/or using similar statistical devices, on balance, the main conclusion concerning the correlational barrier for elementary cognitive tasks (including those assessing mental speed) remains unchanged. Notwithstanding, recent meta-analysis of inspection time studies (cf. Kranzler & Jensen, 1989) suggest that the correlation of this mental speed measure and intelligence may have reached the 0.30 barrier, or perhaps even moved this point marginally higher. Because of this experimental finding, the possibility might still be entertained that future empirical studies will move this barrier upwards-at least for a subset of simple cognitive tasks. Nevertheless, an indeterminate number of studies that obtained non-significant correlations between intelligence and either sensory or speed measures remain unpublished, such that the preceding scenario would seem most doubtful. The present authors have several data sets of this kind on file.* Of course, the picture painted by mental speed research changes when cognitive complexity is added to the task, as is evidenced whenever the odd-man out reaction time paradigm, competing tasks, variation in bit levels, and the like are investigated (see e.g. Diascro & Brody, 1994; Roberts et al., 1988; Stankov et al., 1995). Conceivably, some kind of attentional process [or perhaps working memory (cf. Kyllonen & Christal, 1990)] may be responsible for the observed linkage between complexity and intelligence (see Stankov & Crawford, 1993). However, there are notable difficulties in using cognitive capacity notions (e.g. ‘pool of attentional resources’) to account for this empirical phenomenon (cf. Stankov, 1994). The picture also changes with elderly subjects. Recent evidence indicates that the correlations between different sensorimotor abilities among people aged 65 and over tend to be high, suggesting the presence of a single sensorimotor factor in the elderly. This factor has a high correlation with measures of intelligence for this age group, a correlation which is not evidenced in normal adult populations (see Anstey, Stankov & Lord, 1993). A similar situation may also exist in childhood development, at least into early adolescence (Nettelbeck, personal communication, 1995).

THE

FACTORIAL

COMPOSITION

OF MENTAL

SPEED

IS NOT

SIMPLE

Even though ostensibly there are many different types of speed measures of mental operations, it does not appear that they correlate highly amongst themselves. In fact the possibility cannot be ruled out that there may be as many disparate mental speed factors as there are factors among measures based on accuracy scores (cf. Carroll, 1993, Chapter 11). Furthermore, the factorial structure of speed may provide the same type of hierarchical organisation that has been established with number correct (i.e. level) scores (Roberts & Stankov, 1994; Stankov, Roberts & Spilsbury,

* Note that this scepticism would seem also to extend to those researchers working intensively within For example, comments in an earlier version of this paper were obtained from two prominent who had studied the correlation between inspection time and intelligence for some time. Both 0.30 barrier. One of them said that he believes that the true correlation between inspection time lies within the 0.40 and 0.50 range. The other one admitted that he discovered the 0.30 barrier

mental speed paradigms. Australian psychologists made a remark about the and intelligence probably “the hard way”.

Lazar Stankov and Richard D. Roberts

14

1994). In the light of claims that submit that mental speed is the basic process underlying intelligence, it would seem useful to consider the situation emerging from a growing factor analytic literature involving parameters that are based on both speed and level scores. Historically, the earliest formulations of G,/G, theory contained a broad speediness factor, denoted by G,. For some considerable time this remained one of the less well understood and poorly explored broad cognitive factors of this psychological theory. Within this early formulation, the overall structure of abilities resembled that depicted in Fig. 1 (see e.g. Carroll, 1993, Figure 15.1, p. 626; Horn, 1987). Three points of relevance to the current critique emerge from relationships implied by this approximate representation of the hierarchical structure of human cognitive abilities. First, it cannot be claimed that mental speed is more important than any other broad cognitive ability construct. Thus, when considering what might constitute the ‘basic’ process of intelligence, it follows that processes contributing to individual differences in G,, G,, SAR, (and so forth) share equal status with performance speed (i.e. G,). Second, the interpretation of factors at a given stratum is psychologically meaningful. For instance, the existence of a separate (and independent) broad visualization factor (GY) indicates that even though a measure of intelligence (e.g. the Raven’s Progressive Matrices Test) must inevitably draw upon visual processes to some extent, the perceptual processes captured by this operational measure are effectively partialled out through factor analytic procedures. In other wore’s, since a separate G, factor exists, the resultant G, factor is in a sense ‘purified’. Consequently, Gr stands as a factor that is separate and distinct from G,. Third, the size of nonzero correlations among the broad factors and their loadings on the general factor indicate the strength of relationship between different psychological processes. These correlations reflect the relative role of, say, G, in Gr. While at this stage something is known of the correlations shared between G,, Gr, G, and G, (Horn & Stankov, 1982; Stankov, 1978) it is still too early in this research programme to say anything definitive about the correlation between G, and most other broad cognitive ability factors. However, one broad factor encapsulated under the framework of Gr/Gc theory is an important exception. It would appear that crystallised intelligence (G,) does not share significant correlation with G, (see in particular, Roberts, 1995; Roberts, Beh, Spilsbury & Stankov, 1991; Roberts & Stankov, 1994; Stankov et al., 1994). Indeed, the extent to which this is evidenced in a recent study is compelling. Roberts (1995) had 179 Ss perform a battery of 25 cognitive ability tests and 11 chronometric tasks, and noted that, of over 500 measures that were possible within various treatment conditions of mental speed tasks, not one correlation exceeding 0.30 was found with a well-defined G, factor, and that over 80% of these correlation coefficients ranged between -0.10 and 0.10. Accordingly, it would appear that individual differences in the ability to use an educationallyacquired body of knowledge is not related to typical measures of mental speed. This poses an interesting problem for those subscribing to a single-factor model of intelligence. Retaining the status of speed as a basic process requires that the verbal abilities of G, not be considered part of human intelligence-a situation that is very much at odds both with faceted theories of intelligence

Gf

Gc

SAR

TSR

Gv

Ga

Gs

Fig. 1. The hierarchical structure of cognitive abilities showing the highest-stratum general factor (G) and broad factors of fluid intelligence (Gr), crystallized intelligence (G,), short-term acquisition and retrieval function (SAR), tertiary storage and retrieval function (TSR), broad visualisation (G,), broad auditory function (G,), and broad speediness function(G,).

Mental speed is not basic

75

(see Marshalek, Lohman & Snow, 1983) and Carroll’s (1993, Chapter 15) comprehensive re-analysis of the psychometric literature. A similar problem emerges if consideration is given to the factor loadings of speeded versus non-speeded tasks. For instance, it has been shown that loadings on the general factor are almost always higher for non-speeded tasks (Jensen, 1987, p. 417). If speed of information processing is important for intelligent thinking, why is it not the case that factor loadings are higher in tasks that depend critically on speed of performance? Moreover, recent elaborations of the organisation of human cognitive abilities suggest that the construct of mental speed should be viewed as having a complex hierarchical structure. Proponents of G,/G, theory now at least partially recognise the existence of two broad (second-order) speed factors-Perceptual/Clerical Speed and Speed of Test-Taking (see Horn & Noll, 1994). Evidence from a more recent study (Roberts & Stankov, 1994) verifies the existence of these two broad cognitive speed factors and identifies two additional factors reflecting Movement Time and Decision Time. In that study, all four factors cut across different putative measures of mental speed. A similar conclusion regarding the factorial structure of performance speed was also reached by Carroll (1993) when re-analysing the many data sets leading to his Three Stratum model of intelligence (see in particular Figure 15.1, p. 626). Furthermore, these various speed factors tend to define a single factor at the highest order of analysis. However, the strength of this general factor varies from study to study and is sometimes found to be particulary weak. For example, in the above mentioned research conducted by Roberts (1995) the first principal component of 33 measures derived from a battery of 11 chronometric tasks and assorted other speed measures accounted for only 25% of the total variance. These research findings raise several interesting questions. For example, is the highest order factor encompassing the set of all speed measures akin to a ‘g’-speed factor or should it be viewed as a second stratum G, factor? (The latter would cause the four other speed factors to be moved to a lower rung in the proposed hierarchy). Are all strata sufficient to account for the structure of mental speed? Are all strata equally important and/or psychologically meaningful? Alternatively, is it possible to identify additional broad speed factors, such as natural tempo, coincidence timing, inspection time and the like? These issues seem to be of no consequence to most contemporary researchers interested in finding the basic process of intelligence by recourse to performance speed measures. Yet surely the answers to such questions influence both the way mental speed is conceptualised and its proposed status as the basic process. It is perhaps necessary at this point to also draw to the reader’s attention both the theoretical implications of, and conceptual difficulties engendered by, having a complex factorial structure underlying the construct of mental speed. These reinforce the central argument of this paper-that speed is not basic. For instance, traditional views concerning the organisation of cognitive abilities place sensory processes at the bottom of a hierarchy, perceptual and memory processes on the middle rungs, and thinking processes at the top (see e.g. Horn, 1987). It might similarly be predicted that the factor structure of mental speed would resemble this hierarchical organisation. Given four broad factors of mental speed these should, in theory, be ordered with respect to the degree of cognitive complexity intrinsic to each construct’s composition. ‘Speed of Test-taking’ might be viewed as the most In elaboration of the above proposition, complex mental speed factor, since it reflects the global time needed for a person to solve demanding tests of intelligence. Below this should reside the ‘Perceptual/Clerical Speed’ factor-deriving as it does from tasks of trivial difficulty that all Ss, in theory, would get correct if given unlimited time (i.e. search tasks). Then, at the next (even) lower rung of the mental speed hierarchy should be found the factor of ‘Decision Time’-reflecting aspects of simple and choice response to stimulus information. Finally, the ‘Movement Time’ factor should be located at the bottom of this hierarchy, since it derives largely from psychomotor performance rather than cognitive processing. Indeed, it would appear logical to conclude that decision time is an aspect of search time and that both of these entities are components of test-taking speed. This would be analogous to suggesting that the perceptual processes involved in vision are a part of the thinking processes involved in solving complex problems such as those forming a Matrices test. However, at present it is doubtful that this ordering of a broad cognitive speed factors is supported by the available empirical evidence. This would require that measures of Speed of Test-taking share higher loading on a general mental speed factor than do measures of Perceptual/Clerical, Decision

Lazar Stankov and Richard D. Roberts

76

or Movement Speed. Contrary to this, results reported by Roberts and Stankov (1994) indicate that decision times measured by card-sorting procedures show the highest loading on the general speed factor (see also Tomer & Cunningham, 1993). This finding indicates that intuitive analyses are perhaps flawed; that early notions of complexity reflected in performance speed measures require reconceptualisation. These results also suggest that the Decision Time factor acts in a similar fashion to Gr in traditional studies where it is sometimes difficult to distinguish a general factor from G, (cf. Gustafsson, 1984). Interestingly, this emerging structure also resembles findings with other cognitive abilities in the sense that manipulations of speed measures from the more complex tasks measuring decision time have correlations with fluid intelligence scores (Roberts et al., 1991; Roberts & Stankov, 1994). The factor structure of further, as yet to be systematically investigated, mental speed measures is also likely to differ from what has traditionally been found in studies of human cognitive abilities. Thus, while it would appear unlikely that there would be a separate speed factor corresponding to verbal tasks (or G, measures), another corresponding to visual tasks (or G, measures), another corresponding to memory tasks (SAR) and so forth, it is possible that there exist cognitive speed factors that are linked to some particular primary mental abilities. For example, there may exist a separate Inductive Reasoning ability speed factor (Roberts, 1995; see also Horn & Hofer, 1992). Obviously the complex nature of cognitive speed which has begun to be uncovered implies that some mental speed factors may be important in one area of cognition whilst other mental speed factors may be important in other areas of human cognition. This is largely to be expected. Equally, a complex structure within performance speed does not challenge the view that mental speed is important, but neither does it imply that this is the most important process of intelligence. Indeed, rather than a simplistic conceptualisation that finds operationalisation in a few well-known experimental paradigms, research should focus on more elaborate notions of mental speed. The preceding is no small point. It has become commonplace to use a particular timed measure and generalise to a general speed factor. Witness, for example, the interest given to mental speed within the aging literature where paradigms clearly invoking a construct linked to clerical/perceptual speed have been linked to findings obtained with other measures of speed to derive a comprehensive theory (cf. Lindenberger & Baltes, 1994; Lindenberger, Mayr & Kliegl, 1993).

MENTAL

SPEED

IN RELATIONSHIP

TO THE

HIGHEST-STRATUM

FACTOR

Two salient features of the theory of fluid and crystallized intelligence that follow from the hierarchical structure depicted in Fig. 1 sometimes pass unrecognized. First, from the substantive point of view, the highest-stratum factor, G, is no more psychologically meaningful than any of the factors immediately below it. Secondly, from the factor-analytic point of view, all lower-order broad factors (i.e., G, to G, in Figure 1) are equally important. One way to describe this particular position is by pointing out that ‘intelligence’ may be considered to be the sum-total of ‘all’ cognitive abilities. There is, of course, an alternative theoretical position that ascribes a paramount role to the highest-stratum factor, G. Comprehensive analyses conducted by Carroll (1993; 1994) led to a rather sceptical conclusion about the importance of mental speed measures to the third-stratum genera1 factor, G. Thus: Pending further research, one can only say that the general factor appears to have its highest loadings for factors and variables that involve levels of complexity [Carroll’s italics] at which individuals are able to handle basic processes of induction, deduction, and comprehension. I do not believe that the general factor pertains in any essential way to the speed with which individuals handle these processes. As I read the evidence thus far, cognitive speed has only a very low correlation with general intelligence, if any at all (Carroll, 1994, p. 62). THE OPERATIONALISATION INHERENT PROBLEMS

AND

OF MENTAL SPEED: CONTRADICTIONS

Two types of experimet. .a1 tasks have gained in prominence in the individual differences literature during the past couple of decades-the so-called Choice Reaction Time and Inspection Time

Mental

speed

is not basic

77

paradigms. Proponents of these research frameworks often lay claim to deriving the basic process of intelligence. However, measures obtained from these two types of experimental tasks do not have high intercorrelation (see e.g. Bates & Eysenck, 1993). Furthermore, research involving these tasks has impoverished the definition of intelligence since too often a single measure of intellectual functioning (i.e. the Raven’s Progressive Matrices Test), that plausibly assesses only fluid ability, is employed (see Juhel, 1991). From the Choice Reaction Time paradigm, scores representing median decision (DT) and movement time (MT) and a host of other variables (e.g. slope and intercept of individual DTs, intraindividual variability, ‘best performance’ scores, etc.) may be derived. Experimental and statistical independence of such measures is often assumed without empirical proof. Although most of this research is of a reasonable standard, many unresolved problems and controversies remain. In particular, not a single investigator seems particulary perturbed by the fact that ‘slope’ measures [which originally spawned interest in this paradigm (see Roth, 1964)] do not always show higher correlation with intelligence measures than do movement time, standard deviation measures or the like. This has led to a most curious state of affairs where intelligence has variously been shown to correlate most highly with median DT (e.g. Barrett et al., 1986) median MT (e.g. Buckhalt, Reeve & Dornier, 1990; Neubauer, 1990; Telzrow, 1983) slope of DT (e.g. Roth, 1964) and intraindividual variability in DT (e.g. Jensen, 1992b; Larson & Alderton, 1990). How a basic process might be inferred from this series of seemingly diverse studies appears difficult to fathom. Yet, in the majority of empirical studies investigating the relationship between intelligence and choice response, this is precisely what is claimed when the results are finally interpreted. The state of affairs described above should be a cause for concern to those researchers examining individual differences in mental speed-more especially since acceptance of an alternative reaction time parameter seems all too often to have involved ad hoc justification. This may have been further exacerbated if the selected measures were chosen on the basis of post hoc rationale (i.e. after the inspection of a large list of correlations between intelligence test scores and alternative measures derived from a given chronometric task). Whilst it is hoped that these questionable research practices do not occur, their possibility must be entertained seriously, as such an impression is easily gained from a perusal of the available psychological literature (cf. e.g. Jensen, 1987 and in particular, Table 25, pp. 158-159). Added to this, is the fact that in spite of a large number of studies (some of which are designed specifically for the purpose of clarification), it has yet to be determined whether the effects of practice, strategies, response bias, visual angle effects and so forth influence correlations between the many parameters extracted from Choice Reaction Time tasks and intelligence (see e.g. Bors, Macleod & Forrin, 1993; Longstreth, 1984, 1986; Widaman & Carlson, 1989). Indeed, the effects of many other factors known to influence choice response, including stimulus-response compatibility, sequential dependency, temporal uncertainty and the size and intensity of the stimulus (see Teichner & Krebs, 1974) seem not to have even been considered (Roberts, 1995). Further still, modelling of reaction time tasks according to information theory principles has been questioned-Hick’s law may not represent the best way of conceptualising the relationship between stimulus information and latency measures in all Choice Reaction Time tasks (Longstreth, El-Zahhar & Alcorn, 1985) nor is it entirely clear that Hick’s law is as robust a phenomenon within the individual as has been proposed. In regard to the latter assertion, Roberts (1995) found that up to 80% of Ss failed to provide acceptable indices of model fit in a series of choice reaction time tasks that employed between five and eight data points.* Similarly, intraindividual variability measures do not appear as lawful as has sometimes been suggested (see e.g. Jensen, 1987). Thus Roberts (1995) failed to find one simplex pattern across four data sets in which this measure was obtained. In sum, given confounding influences on choice response and difficulties in modelling subject’s performance, it is highly problematic whether or not any basic cognitive process is actually being assessed (cf. Detterman, 1987). The second of these often used mental speed paradigms-Inspection Time (IT)-provides information about the ‘basic’ process that is reflected in the minimal duration needed to detect the *Consistent with this assertion, the degree of model fit for each task is as high as any reported four data points are examined.

in the literature

when only

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78

difference between two simple stimuli. In the opinion of some researchers, IT is to be preferred over Reaction Time paradigms because of the absence of speed-accuracy trade-offs in the former’s methodology. In retrospect, IT measures represent yet another example in a long series of efforts to link simple perceptual tasks to cognitive processes. Perhaps the most successful, up to its emergence, was the link with field dependence/independence (Witkin, 1962). Some of the other attempts included critical flicker frequency (Barratt, Clark & Lipton, 1962; Jensen, 1983) and perceptual illusions (Thurstone, 1944) but, by and large, these latter measures did not succeed as well as field dependence/independence. Problems with IT research abound. These include the adequacy of the masking procedure, practice effects, accuracy of measurement, the appropriateness of various psychological techniques, and the need to eliminate Ss who do not provide useful data (3040% in some auditory IT experiments). Moreover, the fact that visual IT does not correlate with auditory IT should be a cause for concern to its proponents rather than, as has been claimed “a spur for further research” (Deary, 1992). Further still, no proponent of IT has an acceptable theory about the process captured by this simple task (see Levy, 1992). It should not go unnoticed too, that proponents of IT seem relatively oblivious (or at best seem loathe to cite) a fairly extensive literature examining response speed to differential exposure intervals (see e.g. Christ, 1970; Kaswan, Young & Nakamura, 1965; Raab & Fehrer, 1964; Raab, Fehrer & Hershenson, 1961) and the largely conflicting results that occur at exposure times exceeding 128 msec (see for example, Kaswan & Young, 1965a, 1965b).* Before leaving this section one final paradigm gaining prominence in the individual differences literature needs to be mentioned. Emanating from the ‘Erlangen school’ in Germany, proponents of this research program claim to have discovered the basic information processing unit (BIP, Lehrl & Fischer, 1988, 1990). As it turns out, the task used to measure BIP involves an identified Reading Speed primary factor (Carroll, 1993, p. 462) interpreted in relation to principles adopted from information theory. Despite claims to the contrary (e.g. Jensen, 1993) the intercorrelation between this task and measures of ‘decision time’ is low (Roberts, Pallier & Stankov, in press). Moreover, when correlated with various intelligence factors the task shares moderate correlation only with verbal abilities--a result that makes intuitive sense but that is largely inconsistent with the general literature involving mental speed and intelligence (Draycott & Kline, 1994; Roberts et al., in press). The fact that cognitive psychologists are aware that several complex cognitive processes underlie reading speed makes it difficult to envisage how this paradigm might measure some basic psychological process. For example, Roberts et al. (in press), have linked BIP to Speed of Articulation (or diadochokinetic ability) (see Carroll, 1993, p. 536) because of these authors’ finding that the measure employed to assess BIP correlates moderately with a series of movement time (MT) measures.7

THE

PROBLEMATIC AS

BASIC:

NATURE SOME

OF

THEORIES

EVOLUTIONARY

UNDERLYING AND

ENGINEERING

MENTAL

SPEED

ISSUES

In keeping with the view that performance speed is intrinsically basic to human intelligence, researchers have extended empirical findings to derive theoretical models. These models aim to explain errors on intelligence test items in relation to a unitary concept of mental speed. These explanations are expressed with reference to an information processing metaphor and presumed physiological substrata underlying mental functions. Generally these models seem to be underdeveloped and/or conceptually flawed. Thus, according to Jensen (1993, see also Lehrl & Fischer, 1990), the importance of speed of information processing derives from the fact that the brain has limited capacity for processing information and that there is a need to process information quickly before it decays within the central processing unit (which is most often linked to working memory). The foundations of this

* Even one of the individuals prominent in the development of the IT paradigm has now become sceptical about its usefulness in studies of intelligence (see Vickers, 1995) t Two other parameters on which the Erlangen school place particular emphasis are the duration of presence (Ts) and capacity of short-term storage (Ks, which is simply the product of T, and BIP). Since Ts involves a well-known marker for SAR and the Roberts er al. (in press) study shows KK largely to be redundant (in relation to the BIP), the claims of the Erlangen school (which gain considerable favour in the writings of Jensen and Eysenck in particular) must be viewed with suspicion.

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claim rest on an assumption that individual differences in the availability of memory traces are unimportant. However, it seems equally plausible to assert that speed is irrelevant if it is assumed that smarter people have memory traces that last longer in working memory. Whilst acknowledging the existence of individual differences in properties or working memory, Jensen (1993) focuses on the role of mental speed in intelligence. Nevertheless, at this stage in the development of the science of psychology, current knowledge is simply insufficient for a definitive statement as to whether speed or some other feature of the cognitive system provides the best account of individual differences in measures of intelligence. Within this context it seems unlikely that a task could be constructed that does not depend. on working memory but correlates with intelligence measures (see Kyllonen & Christal, 1990; Necka, 1992; Stankov, 1983). Despite this problem, Jensen (1993) gathers support for his claims by positing a link between nerve conduction velocity (NCV) and intelligence. However, reported findings are quite inconclusive (see e.g. Reed & Jensen, 199 1, 1993). Indeed, they are likely to remain inconclusive given current difficulties and problems associated with the interpretation of the available data. The later concept demands that developmental or ontogenetic data become an essential aspect of biological accounts of intelligence. While there are developmental data that point to the importance of mental speed for cognitive functioning in both growing children (see Kail, 1986) and in aging adults (see Rabbitt, 1988; Salthouse, 1994) there is no evidence to suggest changes in NCV within either child or adult populations. In fact, much of this developmental research focuses on speed of performance in relatively complex (i.e. Perceptual Speed measures) as opposed to simple tasks and, as mentioned earlier in this paper, the available evidence points to speed having a particular complex factorial structure. In any case, tests of Perceptual Speed are analogous to measures of the attentional processes involved in successfully completing Search tasks (Stankov, 1983). These processes are known to reflect age-related changes in fluid intelligence (Stankov, 1988b). Thus, attention (and not mental speed per se) may be the information processing mechanism critically affected by cognitive development. Biological accounts of intelligence also rely on phylogenetic comparisons (see Burt, 1941, 1955). Within this context, analogies are often drawn between young children’s problem solving abilities and the abilities of non-human species. Again, since NCV is much the same in other primates (and ‘lower’ animals) as it is in humans, caution should be exercised in relating this to intelligence. Recent developments within the biological metaphor also point to the relationship between brain size and intelligence. Bigger brains should conceivably be faster brains largely because longer distances need to be transversed between the different areas involved in solving a complex task. Egan, Cheswick, Santosh and Wickett (1995) calculated correlations between brain volume (measured using magnetic resonance imaging procedures), intelligence measures, and auditory evoked potential (AEP). The AEP task provided an estimate of mental speed in the form of the latency of the P3 component which is known to be sensitive to processing load (see Bates, Stough, Mangan & Pellett, 1995; Stough, Nettlebeck & Cooper, 1992; for a rationale). The results obtained indicate that the P3 latency was not related to intelligence test performance nor was it related to brain volume. Notably, the correlation between brain volume and intelligence was 0.47. Clearly, this evidence suggests that mental speed shares a little in common with either intelligence or brain volume.* Impetus for another theoretical model derives from studies reporting high correlations between measures involving the standard deviation of individual RTs (i.e. scores reflecting the variability of a person’s speed) and intelligence test performance (see Jensen, 1992a). This model states that ‘noise’ within the information processing system [due to errors in synaptic transmission (Eysenck, 1987)] is the critical phenomenon contributing to individual differences in intelligence. Since good thinking depends on the system’s ability to make as few mistakes as possible, this explanation of the link between measures of intraindividual variability and intelligence has some intuitive appeal. However, in a recent study (Stankov, et al., 1994) and in reanalyses conducted by Carroll (1993, p. 485ff.) the correlation between central tendency and measures of intra-individual variability of RT was found to be high, such that it is impossible to claim that these processes are different (see also, Roberts,

*While the AEP is not a ‘direct’ measure of mental speed, it has been inextricably linked to this construct in a number of recent papers (see Sough et al., 1992; Bates et al., 1995). Moreover, the arguments contained in this passage have also been highlighted by Rabbitt (1996) in similar vein with measures of visual evoked potentials.

80

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1995). Plausibly, the same factor is being measured by both parameters obtained in mental speed paradigms (cf. Miller, 1994, p. 815). Moreover, there exist other ways of examining the relationship between intra-individual variability and intelligence than those currently in vogue. For instance. a motor-variability hypothesis put forward by Schmidt, Zelaznik, Hawkins and Quinn (1979) states that “running a motor programme results in muscle contractions (in turn, causing a particular pattern of movement), and that the mechanisms involved in this chain of events introduce noise (within-subject variability)” such that “limitations in a subject’s capacity to move quickly is an indirect result of the variability produced by the force- and time-production mechanisms” (pp. 416, 424). Notwithstanding, the intercorrelation between measures of MT and variability in MT is near zero as is the correlation between intraindividual variability in MT and intelligence (Jensen, 1987; Roberts, 1995). This would seem to provide a major difficulty for any account that focuses on consistency of performance. However, of all the attempts to develop a theoretical model relating mental speed to intelligence, the most feasible involves the notion that ‘neural oscillation’ is the important physiological process underlying intelligence (Jensen, 1979). Importantly, several well-known physiologists subscribe to a similar model, including the Nobel Laureate, Francis Crick (1993) of the double helix fame. Even so, it remains unclear as to why ‘neural oscillation’ need be connected to the speed of mental processes or to measures of intra-individual variability in speed processes. Why, instead, is ‘neural oscillation’ not connected to measures of speech production, or some other non-speeded aspect of cognitive performance? Drawing an analogy, faster computers (i.e. those oscillating at 66 MHz as opposed to 32 MHz) do many things faster but they nonetheless make errors. Good thinking implies fewer errors and speed cannot always be beneficial in this regard. Perhaps a better image resolution or finer sound reproduction can be achieved with faster computers-speed of doing computations is just one of the consequences of faster ‘neural oscillation’. It is not the only function affected by these oscillations. The operations of a cognitive system depend not only on the speed of neural oscillation, but also on the wiring of its components. By themselves, the oscillations do not determine the efficacy of the cognitive system-this conceivably also depends on many hardware and software features and the interface between these (a relationship currently described in humans as ‘wetware’) (cf. Sternberg, 1986).

MENTAL SPEED AND INTELLIGENCE: SOME ALTERNATIVE INTERPRETATIONS There exist non-reductionist accounts of typical observations made of performance in reaction time tasks. The focus on these explanations is not on linking mental speed per se with intelligence but rather on trying to account for salient features of reaction time tasks, i.e. task difficultly and task complexity. It is to be expected that different types of reaction time tasks will have different complexity functions (see Mayr & Kliegl, 1993). A psychological model of these tasks emphasising the effects of stimulus-response (S-R) compatibility (Kornblum, 1994; Kornblum, Hasbroucq & Osman, 1990) has yet to find ready application in the individual differences domain but serves to make several anomalies in the literature interpretable (Roberts, 1995). Note that this model has an interesting consequence for those researchers seeking the basic process of intelligence-psychophysiological evidence suggests high S-R compatibility tasks may affect different neural processes than low S-R compatibility tasks (Georgopoulos, Lurito, Petrides, Scwartz & Massey, 1989). Seeking a better understanding of the features that show different complexity functions is potentially more important than a premature search for specific biological accounts of mental speed.

CONCLUSION As factor analysis of various constructs related to timed performance confirms, mental speed is important in the study of human cognitive abilities. On the balance of available evidence, mental speed would appear factorially complex. Moreover, various types of performance speed are obviously processes of importance to some broad cognitive ability factors (e.g. Gr). However, determination of mental speed as the basic process is a consequence of flawed logic. It was not the

Mental speed is not basic

81

original intention of cognitive approaches to find such basic processes-the structure of intelligence is far too intricate for this. Nor in fact can it be claimed that existing paradigms allow mental speed to be assessed in an accurate fashion. Theories that have attempted to construct an argument around the observed correlation between mental speed measures and intelligence are, as this paper shows, likewise hawed. In the search for understanding of the mechanisms underlying the various strata of intelligence it must be acknowledged that many different processes (whose number is currently undetermined) probably contribute to each factor of ‘intelligence’. The arguments contained herein demonstrate that it is improbable that any one facet of human cognitive abilities should be considered dominant in determining ‘intelligence’. This will probably remain the case in the foreseeable future, provided that healthy debate over an acceptable definition of ‘intelligence’ continues among psychologists.

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