Jensen (1998) argues that biological correlates of IQ scores establish the substantiveness of general intelligence (g). This review critically examines three pieces of evidence Jensen adduces to support this claim: event-related potentials, brain nerve conduction velocity, and inspection time. It is argued that Jensen's conclusions are premature and therefore unwarranted.
2. Jensen (1998, pp.152-157) discusses four measures derived from ERPs that have shown significant correlations with IQ. However, the situation with each of these measures is that the relationships reported in the literature do not provide strong evidence for the status of g as a biological phenomenon. The first of these measures, number of zero-crossings, has been convincingly dismissed by Verleger (1999). The second, latency of ERP response, no doubt correlates with various measures of cognitive ability at about 0.3. The interpretation of this outcome is not clear-cut: such results merely support the view that differences in physiological speed (to the extent that physiological speed is manifested as ERP latencies) form a component of cognitive abilities; our level of understanding of the bases of individual differences in cognitive abilities has not been greatly advanced. The third, amplitude of the ERP waveform, is discussed selectively by Jensen. A more comprehensive treatment (Burns, Nettelbeck & Cooper, 1997) identified 28 studies as having calculated some comparison of ERP amplitude measure and intellectual ability; 17 studies reported a positive although not necessarily large or statistically significant relationship and the remaining 11 studies reported a negative although not necessarily large or statistically significant relationship. While it could be argued that methodological differences between studies account for the inconsistency in these correlations between ERP amplitude measures and IQ, it is unlikely that these inconsistent findings are a statistical artefact. The possible confounding factors that can affect scalp recorded amplitude measures (skull thickness and inter-electrode impedences to name but two) would add to error variance and increase the possibility of Type II error. While it is difficult to see how this would reverse any effect that might exist, the inconsistency in the relationships is not surprising. The fourth ERP measure Jensen (1998) mentions, waveform complexity (i.e., the string measure), can be dealt with by noting that both a proponent for g (Robinson, 1997) and the current author (Burns et al., 1997) independently concluded on the basis of their empirical studies that this measure was useless for understanding the mechanisms that link ERPs and IQ scores. To summarise: such ERP correlates of IQ that prove to be replicable, likely only latency measures, do not speak to the fundamentality of g.
3. Turning now to relationships of cognitive abilities with measures of brain nerve conduction velocity (i.e., the speed of axonal conduction of action potentials). This discussion centres on Reed and Jensen's (1992) use of ERPs to derive estimates of nerve conduction velocity within the brain. The rationale for their experiment rested on the assumption that, when studying the response of the visual system, almost all of the latency between the retina and the cortex is axonal conduction time. The visual ERP procedure adopted by Reed and Jensen was chequerboard pattern reversal stimulation; this VERP consists of two deflections, N70 and P100, with latencies of about 70 and 100 ms, respectively. Two visual pathway NCV estimates were calculated by dividing the subjects' head length by the relevant latency. While these estimates were of subcortical NCV, it was argued that they should correlate highly with cortical NCV. Reed and Jensen offered no argument as to why this relationship should hold. It could be argued that NCV would be a general property of the brain but much of the activity flow in the cortex involves radial flow through columns of neurons (i.e., local circuits) rather than through longer distance cortico-cortical connections. Therefore, the notionally measurable subcortical NCV may not relate meaningfully to transmission in the cortex.
4. Visual ERP latency, I argue, cannot be considered to be mainly nerve conduction time (as required by Reed and Jensen's, 1992, rationale). The implication of this argument is that it is not nerve conduction velocity that is being estimated by their procedure. Rather, it is a measure of VERP latency corrected for head size, this interpretation being consistent with the fact that evaluations of clinical data on pattern reversal ERPs recommend the adoption of different normal ranges of latencies for males and females because of gender differences in head size. As such, the findings of Reed and Jensen should be interpreted in light of Paragraph 2, above. Furthermore, there are other reasons why the use of visual ERP latencies to estimate visual pathway NCV is not straightforward (which is not to say that it is impossible).
5. The first reason is based on a consideration of some functional properties of the visual pathways. The estimation of visual pathway NCV from visual ERP latency requires the assumption that the pathway from retina to visual cortex is a unidirectional one. That is, the effect of a stimulus impinging on the retina is taken to be the propagation of nerve impulses along the visual pathway to the visual cortex from whence they are passed on for higher processing. However, there is evidence that visual processing is not so linear. For example, there are more projections back from the visual cortex to the thalamus than there are forward from the thalamus to the visual cortex and there is evidence that these projections exert feedback control from the visual cortex on activity in the lateral geniculate bodies. Such a consideration should not affect the very first response of the cortex to the afferent input but it may affect the later responses (i.e., P100). Furthermore, knowledge of the interconnectedness of areas within the visual cortex and of extrastriate input to the visual cortex modifies the simple notion of information passing from the visual cortex to higher processing stations. It is plausible that these projections act as modulators of activity within the visual cortex and visual pathway and that this modulation reflects the effects of activity within the visual pathway itself. These types of considerations mean that there is no simple beginning or end point in brain processing. In other words, the assumption that ERP latency solely reflects NCV may be an oversimplification because the delay from retina to visual cortex may not be a constant which is solely dependent on axonal conduction of action potentials.
6. The second reason is methodological. The chequerboard pattern reversal ERP and the cortical origins and characteristics of the deflections recorded are not as simple as the description given by Reed and Jensen (1992) implied. Factors that influence pattern reversal ERP latency are well known and include the luminance of the stimulus, cheque size, and sex and age of subject. Moreover, the shape of the primary visual cortex is known to differ from individual to individual and this can lead to problems in obtaining standardised single-electrode recordings. These factors may not have directly affected Reed and Jensen's results but if they had used differently configured stimuli then their estimates of NCV would have been different. They also treated retinal transduction time as a constant; the validity of this is an open question.
7. The third reason is anatomical. The visual pathway consists of the retinal receptors and ganglionic cells, the optic nerves (which unite and cross at the optic chiasm then proceed as the optic tracts), the lateral geniculate bodies of the thalamus (there are also projections to the superior colliculus), and the optic radiations (which terminate in the visual cortex). At even the most basic level of analysis, this complexity renders simple interpretation of visual ERP latency as NCV problematic. This is because structurally, the optic nerve, the visual tract and the optic radiation vary in terms of the diameter of the axons involved. This difference means that axonal conduction velocity will vary along the pathway. Conduction velocity will be fastest in the optic nerve and tract which are of larger diameter and will be slowest in the optic radiation which is of smaller diameter. Whether the ratios of these diameters is constant across individuals is not known.
8. The final issue I wish to comment on is the correlations of cognitive abilities measures with inspection time (IT). The IT task was designed to measure the periodicity of an hypothesised sampling mechanism of the brain. Latterly, it has been claimed to measure the speed with which the brain transduces simple stimuli (speed of apperception). Jensen's (1998) treatment of this area trivialises the problems of interpretation that exist. Nothing is known of what IT is measuring and little is known of what established relationships mean. Briefly, the pattern of relationships has been that IT correlates most strongly with performance IQ rather than with verbal IQ from the Wechsler scales, and with tests of g such as Ravens matrices. One interpretation of this pattern of relationships has been that performance IQ and matrices tests measure fluid ability and therefore IT provides an index of the biological substrate of g. An alternative interpretation is that IT shares variance only with measures of general processing speed and general visualisation ability. Moreover, there are suggestions that IT indexes even more specific abilities such as the ability to detect motion in noise. It seems reasonable to suppose that IT is but one of a series of similar tasks (i.e., tasks incorporating a backward masking procedure) which together would define a lower-order factor within a hierarchy of abilities that define general speed of processing. Jensen appears unaware of the complexities of the topic; the interested reader is referred to White (1993, 1996), Burns, Nettelbeck & White (1998) and Burns, Nettelbeck & Cooper (1999). Mackintosh (1998) also discusses some of these issues; his discussion accords with my contention that if we are to use psychometric test scores as criterion variables, which is what we do when we correlate IT with IQ, then we should be using a multi-dimensional model of intelligence. Such a contention does not fit well with Jensen's unidimensional description of human abilities.
9. It is extremely doubtful that the non-specialist would be able to glean from reading Jensen (1998) that much of the evidence he adduces is highly controversial. While the whole appears comprehensive and consistent, many of his conclusions are unwarranted. More particularly, since much of his argument on heritability and genetic determination of g rests to a degree on evidence the like of which has been dissected here, strong conclusions on anything, let alone the basis of group differences in IQ scores, are premature.
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