Central Conceptions of Intelligence Research Paper

In 1921, a classical symposium convened by the editors of the Journal of Educational Psychology was held to discuss three questions: (a) What is intelligence?; (b) How can it be best measured?; (c) What are the most crucial next steps in research? (Thorndike et al. 1921). Among the 17 leading researchers who participated, 14 different answers were given! About one fourth of the participants suggested elementary processes (i.e., perception, sensation, and attention) as primary attributes of intelligence. Another fourth of the experts thought that physiological mechanisms of the brain should be the determining factors, and there were still others who contented that overt adaptive behavioral responses are the key features of intelligence. Sixty-five years later, a succeeding effort with 27 experts was arranged to address the exact same questions (Sternberg and Detterman 1986); however, the degree of lack of consensus remains similar to that was in 1921. Experts of the 1986 forum offered two dozen definitions.

Aside from the diverse conceptions among researchers, two popular books also sparked heated debates in the general public. The views range from renouncing the idea of ‘measuring’ individuals along the dimension of psychometrically-defined intelligence (S. J. Gould, The Mismeasure of Man, 1981) to the belief in a general intelligence factor (R. J. Herrnstein and C. Murray, The Bell Cur e, 1994). Reacting to these controversial debates, in 1995 the Board of Scientific Affairs of the American Psychological Association called for an another authoritative report from a dozen experts in the field to discuss the ‘knowns and unknowns’ of intelligence (Neisser et al. 1996). The diversities and controversies surrounding conceptions of intelligence led some researchers to contend that the multiple determinants (and their relations) contributing to individual differences in intelligence could only be understood within integrative frameworks.

2. Some Lasting Trends And An Attempt Of Integration

In order to highlight a few continuing trends hidden behind the overwhelming diversities, organizers of the 1986 forum compared the main issues raised in their discussions with those that were discussed in 1921 and showed that there were some general agreements across the two efforts (Sternberg and Detterman 1986). Specifically, conceptions including attributes such as adaptation to the environment, basic cognitive processes, and higher-order thinking (i.e., reasoning, problem solving, and decision-making) were prominent in both discussions. In addition, the recurring themes of central debates seemed to surround issues about (a) whether intelligence is unitary or manifold, (b) the relative contributions of nature and nurture to individual differences in intelligence, and (c) the relative emphases on the process, content, and product aspects of intelligence.

As an attempt to compile the two dozen different views, an integrative framework (Sternberg and Detterman 1986) with multiple levels was also proposed in the report of the 1986 forum. At the behavioral level, theoretical conceptions that originated within the psychometric tradition focus on measuring the ‘products’ of information processing and on identifying the structure(s) of intelligence. These theories could be compared mainly in terms of whether the structure of intelligence is, more or less, considered to be unitary or pluralistic. At the processing level, the theories attempt to integrate psychometrically-defined ‘products of intelligence’ with various concepts of information processing. In this regard, the various conceptions differ in terms of their differential emphases either on elementary cognitive processes or higher-order cognition involving metacognition, planning, and decision-making. At the contextual level, some theories of intelligence stress the dynamics of human-ecology interaction and the concepts of situated intelligence. Another version of the contextualist theories emphasizes the dynamic contexts of lifespan development from birth into old age. These contextualist theories evolved primarily in conjunction with a focus on the nature of intelligence as adaptive capacity.

Still there have been other theories focusing on genetic and neurobiological influences on intelligence, which are not within the purviews of this research paper. In the following sections, we review and compare main theoretical conceptions of intelligence with respect to their differences along the cross-level product-process, the contexts of human-ecology, and the lifespan developmental orientation; as well as their relative emphases on interactions across these dimensions.

3. Measuring The Products Of Intelligence: From Unitary To Pluralistic And Hierarchical Views

Ever since Alfred Binet’s success in devising tests to distinguish mentally retarded children from those with behavioral problems, the psychometric approach to quantify individual differences in cognitive competence using standardized intelligence scales (e.g., the Stanford–Binet test or the Wechsler test) played a dominant role in intelligence research. These scales tend to measure scholastic performance with tasks over broad areas (e.g., memory, analytical reasoning, numerical skills, verbal fluency, and practical knowledge). Thus, overall sum scales of such tests predict school performance well. Perhaps not surprisingly because of their emphasis on school-related performances, their predictive validity drops when predicting task performance outside of the classroom, such as everyday problem-solving, vocational competence (Dixon and Baltes 1986), or other more expertise-like outcomes of real-world intellectual functioning (Ericsson and Smith 1991).

With a more general aim to capture a broader spectrum of human cognitive abilities and their interrelations in addition to the applied orientation of predicting academic performance, a number of psychometric theories have been proposed over last century. Scholars such as Burt, Cattell, Guilford, Spearman, and Thurstone were the main proponents. At the methodological level, these theories differ with respect to the type of factor analytic methods they adopt and whether these methods are applied on the empirical exploratory or theoretical-confirmatory basis. At the substantive level, they differ in terms of whether they orient more towards the general factor or multidimensional views of intelligence.

3.1 Two-Factor Theory

Although subtests of the intelligence scales are often designed to measure specific domains of cognitive performance (like numerical skill and verbal fluency), performances across different tests tend to be positively correlated with each other (i.e., the so-called positive manifold ). Spearman (1904) first presented a theory of general intelligence which he later expended into what is now known as the two-factor theory. He developed the first statistical model for explaining the positive manifold among task performances. The underlying assumption of this model is that individual differences in a given test can be decomposed into two components. One variance component is common to all other cognitive tasks—a general factor of intelligence (commonly known as g), whereas the other component is specific to each task (Spearman 1927). With this method, Spearman showed with several data sets that g accounted for all positive correlations among the test performances (see Jensen 1998, for a modern review on g). Since then, hundreds of other studies have also found that g accounts for rather large portions of the common variance among a broad selection of cognitive tasks.

3.2 The Primary Mental Ability Theory

Since the discovery of g, a main issue in psychometric intelligence research has been whether the structure of intelligence is unitary with one general common factor or pluralistic with multiple specific factors reflecting domain-specific abilities. Among the first to question the unitary view was Thurstone, who developed multiple factor analytic techniques and conducted studies that included a broad range of paper-and-pencil items found in most other intelligence scales. In these studies, Thurstone (1938), for instance, was able to identify seven distinct factors reflecting spatial, perceptual speed, numerical, verbal, fluency, memory, and reasoning abilities. However, his assumption of these primary mental abilities being completely orthogonal to each other was not empirically supported. Instead, multiple common factor models later proposed by others showed that the positive manifold emerges again at the level of higher-order factors.

3.3 The Gf-Gc Theory And Related Theories

Building on the multiple primary abilities identified by Thurstone, one influential conceptual model since then has been the Gf-Gc theory (Cattell 1971, Horn 1982). Based on results from higher-order factor analyses, this theory distinguishes general fluid (Gf ) abilities reflecting the individual’s basic problem solving, reasoning, learning, and selective attending capabilities from general crystallized (Gc) abilities reflecting the acquired knowledge and their utilization. Unlike Thurstone’s theory, which assumes independent primary mental abilities, Cattell proposed the ontogenetic in estment theory to specify a possible mechanism relating Gf to Gc. He postulated that the culture-, experience-, and knowledge-based crystallized abilities arise, in part, as a result of a process in which the more biology-, maturationand process-based fluid abilities are been invested into suitable learning experiences and contexts as the individual develops. The Gf and Gc distinction is often compared with Hebb’s (1949) differentiation between innate potentials for information processing (Intelligence A) and the acquired level of performance and comprehension (Intelligence B), and Baltes’ (1987) juxtaposition of the cognitive mechanics and the cognitive pragmatics (see more details in a later section). Besides these two central broad factors, Cattell and Horn were also able to identify several other factors reflecting visual processing ability (G ), short-term memory (Gsm), quantitative abilities (Gq), associative storage-retrieval (Glr), novel reasoning (Gr), and processing speed (Gs). These factors have been further verified and captured by comprehensive psychometric test batteries developed by others, such as the Stanford–Binet, Wechsler, and Woodcock– Johnson batteries.

3.4 The Structure Of Intellect Model

Moving even further away from the unitary pole of g and working towards better delineating the process and product aspects of intelligence, Guilford (1967) proposed the structure of intellect (SOI) model, which specifies the (a) operation, (b) content, and (c) product components of intellectual abilities. The component of mental operation contains five general intellectual processes: cognitive, memory, divergent production, convergent production, and evaluation. The content component encompasses four broad areas (i.e., figural, symbolic, semantic, and behavioral) of information to which operations are applied. The product component entails six possible results (i.e., units, classes, relations, systems transformations, and implications) of applying particular operations in particular contents. Applying factor analytic techniques in a confirmatory manner to test this model deductively, Guilford expected the structure of intellect to result in 120 single independent factors of different operation content- product (5×4×6) combinations. However, in later modifications he acknowledged that the first-order factors could be correlated, therefore giving rise to multiple higher-order factors, but he still denied the existence of a general factor (Guilford 1982). On the one hand, the strength of the SOI theory is its more specific and refined definition of ability structure across dimensions capturing three distinct aspects of information processing. On the other hand, the main critiques of the model targeted at the fact that this model was derived through the use of a certain factor rotation technique (i.e., the procrustean rotation) which later was criticized to be subjective to confirmatory bias and lacking empirical support for most of the factors specified.

Another theoretical model akin to Guilford’s facetted conceptualization is the Berlin model of intelligence structure (BIS) by Jager (1984). This model cross-classifies each cognitive test with respect to an (a) operation and a (b) content facet. Operation is further categorized into four ability groups (reasoning, memory, creativity, and speed), whereas content is divided into three groups (figural, numerical, and verbal). Conceptually, the 12 operation content (4×3) combinations taken as a whole represents general intelligence. However, by factoring groups of variables that are homogeneous within one of the two facets but are heterogeneous in the other facet, one can either extract the four operational or the three content factors. Specified as such, methodologically this model implies that any given mental ability is a linear combination of the two factors, and conceptually it simultaneously specifies a mental ability with a process and a content aspect.

3.5 The Three-Stratum Theory

Reanalyzing 477 available covariance matrices of cognitive ability tasks, Carroll (1993) integrated the findings from the different theoretical schools into a hierarchical structure with three stratums. At the lowest stratum, more than 60 narrow first-order abilities could be found. Given these narrow abilities are positively correlated with each other to varying degrees, a second stratum of broad abilities, vich closely resemble the primary mental abilities identified by Thurstone and by Cattell and Horn, could also be extracted. At the highest stratum, a general factor of intelligence captures the positive manifold among the broad ability factors at the second level. The hierarchy with all three stratums then describes the overall structure of intelligence. In terms of empirical breadth, this model comprehensively integrates the empirical basis of human cognitive abilities observed at the behavioral level. In terms of theoretical integration, it shows that diverging psychometric views of intelligence can be understood in terms of their differential focus on one of the stratums within the overall structure, selections of indicator tasks defining the lowest level of the structure, and adoptions of factor analytical techniques.

Regarding the behavioral psychometric approach to intelligence research as a whole, a key issue that remains is whether factors derived by factor analysis are entities with causal status or organizational concepts capturing a system of the products of intelligence. Defining the structure of intelligence with multiple levels, going from multiple low-level specific abilities to intermediate broad abilities, and, finally, to a general factor at the highest level, has advantages for relating to other theoretical conceptions. Various conceptual connections from psychometric measures of intelligence to other theoretical conceptions that either emphasize inward cross-level integration with information-processing mechanisms and their biological correlates, outward integration with real-world everyday knowledge and problem solving skills, or lifespan developmental integration can be more easily drawn.

4. Integrating Psychometric Intelligence With Cognitive Processes

Starting from late 1970s, there has been a development towards specifying the processing aspects of intelligence in hierarchical models within the psychometric tradition and the development of investigating information-processing mechanisms in cognitive science in general. With the parallel developments in both fields, researchers of intelligence started to integrate information-processing concepts into the theoretical conceptions of intelligence. Some theories focus on integrating the measurement and explanation of intelligence by looking inwardly and downwardly within the individual for elementary information-processing correlates (e.g., processing speed and working memory) of intellectual abilities (e.g., Hunt 1980, Resnick 1976) and their neurobiological correlates (Eysenck 1982, see Deary 2000 for recent overview). Other theories focus on relating intelligence with aspects of information processing that are more closely related to real-world competence, such as complex reasoning, metacognition, planning, knowledge, vocational skills, and expertise (e.g., Ackerman 1996, Sternberg 1985, 1999).

4.1 Mental Speed Theory

The notion that information-processing speed may underlie individual differences in intelligence goes back all the way to Galton, who tried to measure intelligence by simple sensory and reaction time tasks. Researchers dedicated to the mental speed approach have taken up Galton’s initial idea to investigate the relation between intelligence and reaction times measured in a broad range of simple elementary cognitive tasks (see Jensen 1998, Vernon 1987 for reviews). Performances in these tasks tend to be perceptual based and do not require any semantic or practical knowledge. Thus, it is perhaps no surprise that the correlations between the reaction times measured with such simple tasks and intelligence have proven to be only moderate (i.e., hovering around the so called 0.3 barrier, Hunt 1980). Proponents of the elementary information processing theory in general and the mental-speed theory in particular admit that simple reaction time measures are inferior to more complex composite measures derived from mental tests for the measurement and prediction of intelligence. However, they argued that the moderate but consistent correlations between measures derived from simple elementary information-processing tasks and performance on mental tests may be important for unraveling the information processing aspects of intelligence. Variations in the observed correlations across studies and their theoretical interpretations have been the issues of much discussion. The interpretations range from proposing nerve conduction velocity as the basic biological parameter for all cognitive functioning, to several alternative explanations postulating higher-level constructs like attention, motivation, personality factors, or speed-accuracy trade-offs, to be the causes for a spurious relationship between intelligence and mental speed measured with simple de-contextualized tasks.

4.2 Working Memory Theory

Another aspect of cognitive function that has been deemed as a likely candidate information-processing correlate of intelligence is working memory (Baddeley 1986). Working memory reflects people’s ability to briefly maintain information, while simultaneously carrying out other mental operations on the same or other information. Empirical evidence suggests that tasks requiring greater working memory loads tend to correlate more highly with intelligence. Furthermore, it has also been found that working memory capacity correlates highly with psychometric reasoning (Kyllonen and Christal 1990). Taken these findings together, proponents of this theory suggest that working memory might be a better candidate process for explaining fluid intelligence than mental speed (Engle et al. 1999, Suß et al. in press).

4.3 The PPIK Theory

Staying with the general sentiment of relating intelligence with information processing, but orienting more towards issues on knowledge representation in artificial intelligence systems rather than the experimental cognitive approach, Ackerman (1996) proposed an integrative framework characterizing adult intellectual development as complex interactions between cognitive processes, personality traits, broad interest and knowledge domains. This framework can be seen as a starting point for disentangling the complex relations between the different aspects of intellectual functioning and personality-motivational modulators (Ackerman and Heggestad 1997). For instance, there is initial evidence indicating that personality traits, such as degrees of openness to experience and typical intellectual engagement, are correlated with the crystallized knowledge, but not with the general fluid abilities. Such findings motivate further intelligence research to explore the roles of broad domains of occupational and avocational knowledge in adult intelligence, which has not been captured sufficiently by the traditional assessment of Gc.

4.4 The Triarchic Theory

Sternberg’s triarchic theory of intelligence (1985) is another example of an integrative theory that seeks to combine traditional psychometric approach with information-processing perspectives at the level of higher-order cognition, rather than elementary cognitive processes. Three different kind of information processing components are distinguished in the triarchic theory. MetaCognitive components are higher order control processes used for executive planning, monitoring, and evaluating task performance. Performance components are lower-order processes that deal with the execution of a task itself. For instance, in an inductive reasoning task, the performance components may involve encoding the test word pairs, comparing the word pairs, and retrieve from memory information about the test items. The knowledge acquisition components are processes involved in learning, retaining and integrating new information with old experiences. More recently, Sternberg (1999) had extended his original theory to encompass a broader spectrum of issues regarding successful everyday real-world competence. For instance, in everyday context, successful intelligent individuals are likely to invoke metacognitive higher-order regulatory thought processes to discern their own strengths and weaknesses, and device strategies to maximize their strengths but compensate for their weaknesses. While this theory is more integrative and ecologically valid in nature, details of many components of the theory await further specification and empirical testing.

5. Bioecological And Lifespan Theories Of Intellectual Development

Traditionally, develop mentalists have also been among the researchers of intelligence. Piaget, the pioneer of modern developmental psychology, studied the development of mental processes through which knowledge is constructed during ontogenesis (Chapman 1988). The Piagetian view of intelligence focuses more on the processes of intelligence, rather on the products of intelligence and the individual differences therein. Specifically, central to Piaget’s theory of intelligence (1960) is the view that adaptive behavior and knowledge are constructed through an intimate interplay between the child and the environment. Intellectual development results from the child generalizing her current behavior and knowledge to new situations (assimilation) and also adaptively modifying her current behavior and knowledge through new experiences (accommodation). Influenced by Piaget’s conceptions, two main variants of later theories of intellectual development share common endorsements of gene environment (or biology-culture) co-constructivist and contextualist views.

5.1 The Bioecological Theory

With its main focus on understanding the complex interactions between biology and ecology in shaping child development, the bioecological theory (Bronfenbrenner and Ceci 1994) proposes that aside from genetic effects individual differences in intellectual functioning are the result of variations in the bioecological interactions a child experiences during development. The theory has three main tenets. First, there are multiple innate abilities, as opposed to a single ability. Second, innate abilities serve as a range of possibilities that may develop, or fail to develop depending on their interactions with mechanisms and processes in the proximal developmental contexts (e.g., such as parent–child interactions and schooling). Third, motivation plays important modulatory roles in determining how much the environmental context aids the actualization of the individual’s innate potential for intellectual development. The notion that interests and motivational factors play a role in intellectual development has also recently been considered in the PPIK theory (see above).

5.2 The Dual-Process Theory Of Lifespan Cognitive Development

While also being co-constructive and contextual in nature, the dual-process theory of lifespan cognitive development focuses on understanding the biology– cultural interplay in the gains and losses of intellectual development across the lifespan from early childhood into old age (e.g., Baltes et al. 1999). The lifespan developmental theory has been developed by combining both the psychometric and lifespan developmental approaches.

Combining Hebb’s Intelligence A and Intelligence B distinction (see above) and the Cattell–Horn Gf-Gc framework with perspectives from cognitive and evolutionary psychology, the dual-process theory of lifespan cognitive development highlights two distinct but interacting aspects of intellectual functioning: the biologically-driven cognitive mechanics of basic information processing, and the contextually-driven cognitive pragmatics of culture-based bodies of acquired factual, procedural, and profession-related skills and knowledge. Furthermore, with an emphasis on the dynamic of lifespan development, the dualprocess theory also focuses on how these two aspects of intellectual functioning interact with each other, develop, maintain, and decline throughout the lifespan. In line with Cattell’s investment theory, the dual-process theory proposes differential lifespan trajectories for the cognitive mechanics and pragmatics. The biologically-driven cognitive mechanics display an early growth pattern which is then invested into the acquisition of pragmatic skills and knowledge. While most abilities of the cognitive mechanics start to decline in midlife, the efficacy of culture-, experience-, and expertise-based pragmatics remain, well into old age; or, as suggested by Ackerman (1996), lifelong occupational and avocational knowledge could still be maintained or even increase up to late adulthood. However, in old age the role of biology-based cognitive mechanics in regulating the culture-based cognitive pragmatics increases.

6. Concluding Remarks

As reviewed above, there is a continuing effort in intelligence research at evolving its subject matter in terms of which mental representation and phenotypic behavioral expressions of intelligence are part of the theoretical conceptions. A few other themes, although deviating somewhat from the more traditional conventions, have been evolving in parallel with the theoretical conceptions reviewed thus far, and merit acknowledgements. For instance, there have been growing emphases on various forms of expertise, such as professional skills or cognitive skills associated, e.g., with chess, medicine, or computer programming, as part of the core of intelligence (see Ericsson and Lehmann 1996, Newell and Simon 1972 for reviews). Other examples are the various lines of research on extending the predominately cognition-oriented conceptions of intelligence to social intelligence, emotional intelligence (e.g., Davies et al. 1998, Ford 1992, Mayer and Geher 1996), and other presumably separable intelligences, such as musical, spatial, and interpersonal intelligence (i.e., the Multiple Intelligence Theory, Gardner 1993).

As it is evident from the debates and controversies since Galton and Binet, it is not fruitful for intelligence research to continue into the new millennium with questions such as: is intelligence unitary or pluralistic? Is the goal of intelligence research to measure the end product of intellectual functioning and to predict performance, or is it to explain the processes of intelligence? Which plays a greater role in the development of intelligence, nature or nurture? Looking at the terrain of current intelligence research and its related fields, it is probably more fruitful to ask questions that highlight the differential utility of the various conceptions of intelligence in furthering our understanding of the complex dynamic interplay between neurobiology and sociocultural contexts in shaping intellectual development. For instance, given existing data at the different levels, which facets of intelligence maybe more biologically-driven and which are more context and culture-based? Which aspects of intelligence are primarily implemented through elementary information processing mechanisms and their genetic and neurobiological correlates, which other aspects are open to influences from metacognitive, personality and motivational factors? Which aspects of intelligence are of more direct relevance to real-world problem solving? What are some aspects of intelligence that might be culture-invariant, and which other aspects are culture dependent?

Integrating most of the central conceptions of intelligence developed over the last century reveals that intelligence as a phenomenon has multiple facets and levels, and is dynamic both in terms of information processing and in terms of the varying contexts in which it is expressed during individual ontogeny. Couched within an integrated framework, intelligence may be viewed as biologically-implemented information-processing capabilities together with knowledge and expertise giving rise to adaptive behaviors in different domains of functioning that are co-constructed through the interplay between socio-cultural contextual influences and the genetic and neurobiological mechanisms across lifespan development. A growing Zeitgeist is more and more in the orientation of biocultural co-constructivism.

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