Rolf Verleger (1999) The g Factor and Event-related EEG Potentials. Psycoloquy: 10(039) Intelligence g Factor (2)

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PSYCOLOQUY (ISSN 1055-0143) is sponsored by the American Psychological Association (APA).
Psycoloquy 10(039): The g Factor and Event-related EEG Potentials

Book Review of Jensen on Intelligence-g-Factor

Rolf Verleger
Department of Neurology
Medical University
D 23538 Luebeck



The empirical basis provided by Jensen for his claim that the psychometric g factor has evoked potentials as its "biological correlate" is critically considered in the light of current knowledge about event-related EEG potentials.


behavior genetics, cognitive modelling, evoked potentials, evolutionary psychology, factor analysis, g factor, heritability, individual differences, intelligence, IQ, neurometrics, psychometrics, psychophyiology, skills, Spearman, statistics
1. One central statement in Jensen's (1999) rationale for soliciting commentary about the g factor is: "[g] has many biological correlates, including brain size, evoked potentials, nerve conduction velocity, and cerebral glucose metabolic rate during cognitive activity." There is an even stronger claim in the Abstract of the Precis: "Psychometric g also has more direct biological correlates than any other independent source of test variance, for example brain size, brain evoked potentials, nerve conduction velocity, and the brain's glucose metabolic rate during cognitive activity." Having spent more than 20 years studying "evoked potentials" (viz. event-related EEG potentials, ERPs, this term is generally preferred), I became curious about the empirical basis for Jensen's claim that evoked potentials are biological correlates of the g factor.

2. It comes as a surprise that out of the 596 pages of the book under review (Jensen 1998), only seven (including 8 notes) are devoted to this topic, with headings: "Electrochemical Activity in the Brain", "Spontaneous EEG Correlates of g", and "Averaged Evoked Potential Correlates of g". Thus, about 1% of the book is eligible to support the strong claims about ERPs. This does suggest that there might be a disproportion between the description of the hard facts and their suggested relevance.

3. Going on to inspect the one figure (6.1) depicting an event-related potential, one is amazed to find Jensen call this a "typical... record" (p. 153). It is in fact highly atypical, in several respects. Although it is usually easy to tell the stimulus modality from looking at graphs of recorded ERPs, this is not possible with this figure. Jensen tells us that the recording was from Cz against earlobes (p. 153, top), evoked by an auditory stimuli -- probably clicks as described on p. 155. If this is indeed an auditory evoked potential, then there are several problems with this curve. This can be easily seen when comparing this figure to published figures in the literature, e.g., figures 1, 6, and 11 in Naatanen's (1990) review, which are likewise averages of single subjects, like the present figure 6.1.

4. (i) The largest negative peak in an auditory ERP (usually called

    "N1") usually reaches its maximum at 100 ms. In figure 6.1, the
    largest negative peak is at about 150 ms.

    (ii) The positive peak preceding the N1 (usually called "P1")
    usually has much smaller absolute amplitude than N1, but the
    positive peak in figure 6.1 that precedes the largest negative peak
    is about the same size as the negative peak.

    (iii) P1 usually peaks at 50 ms; the positive peak in figure 6.1
    has its maximum at about 110 ms.

    (iv) Following the N1 peak, there is usually a positive peak ("P2")
    at about 180-200 ms, i.e., about 80-100 ms after N1. Such a peak
    can indeed be seen in figure 6.1 at about 240 ms (i.e., about 90 ms
    after N1, which would preserve the correct temporal relation).

    (v) Usually, there are no components between N1 and P2. However, in
    figure 6.1, the P2 at 240 ms is preceded by a positive-negative
    complex, with the positive peak having its maximum at about 180 ms
    (30 ms after N1), the negative peak at about 210 ms (60 ms after

    (vi) Usually, no fast-frequency waves occur after P2. But here,
    another negative-positive wave follows, with peak latencies at
    about 70 ms and 130 ms after P2, followed by smaller waves of
    similar frequency. Matters are further confused for the reader
    because the numbering of peaks in figure 6.1 does not correspond to
    the usual convention. What I called here N1, is N3 in the figure;
    similarly, P1 is P2 in the figure, and P2 is P4.

5. More generally, there are two problems with figure 6.1, one with the signal and one with the noise. The signal problem (summarising the above features I-IV) is that the P1-N1-P2 complex (P2-N3-P4 in figure 6.1) is about 50 ms later in figure 6.1 than usual, and further that the P1 (P2 in figure 6.1) is unusually large. Both features would make sense if the person whose ERP was shown in this figure had been six years old (e.g., Bruneau et al., 1997) but not an adult. Unfortunately, the only stated characteristic of this person is that this is a "person with above-average IQ" (legend of Figure 6.1), so this question cannot be pursued further.

6. The noise problem is that this waveform is obviously contaminated by a regular background rhythm (summarising the above features 5-6). Beginning with what is called "P3" or "N4" in the figure, this regular rhythm continues until well after 500 ms. The latency difference between peaks is about 110 ms, so this is a 9 Hz background rhythm, in the lower alpha band. Most probably, this rhythm also overlaps the larger earlier peaks and is therefore also responsible for the peaks called "P1" and "N2" in the figure. It is not surprising that an ERP is contaminated by such a background rhythm but the problem is that the assumption in the book that this is part of the signal; thus all these peaks were numbered together with the signal peaks and their "zero-crossings" were counted.

7. Jensen appears to be too optimistic about what happens with background EEG when ERPs are averaged: "whatever is random (i.e., 'noise') is averaged out" (p.152) when "hundreds" (p.152) or "a great many" (p.153) trials are averaged together. In reality, whatever is random does not get averaged out but gets attenuated, by a factor of the square root of the number of trials (e.g., Mocks et al., 1988; Turetsky et al., 1988). Thus, for example, if someone has a large alpha rhythm (being well relaxed in the experimental situation) of 40 microV baseline-to-peak, there will be 4 microV left in the average of 100 trials, and 2 microV left in the average of 400 trials. For comparison: The largest peak in Figure 6.1 is 10 microV.

8. Remnants of background noise are likewise responsible for part of the original result reported by Ertl and Schafer (1969) thirty years ago with a necessarily primitive technique and necessarily insufficient knowledge about ERP components. In their visual potentials, what makes the difference between IQ 130 and IQ 80 children is that the former have at least two small peaks before the first large peak. This large peak is at 80 ms, and although the latency would be rather early for children, we might assume for argument's sake that this peak corresponds to the visual C1 component (e.g., Clark & Hillyard, 1996; Miniussi et al.,1998), which reflects activity of the primary visual cortex. It is then altogether unclear and physiologically uncertain what these earlier components are supposed to be, with their latencies of 30 ms and 50 ms on average (Fig. 2 in Ertl & Schafer, 1969).

9. Jensen's primary criterion for the intelligence of a person is the number of zero crossings; for example, there are twelve zero crossings in the intelligent person's ERP depicted in figure 6.1 within 500 ms, and still nine within the first 300 ms. In contrast, the auditory ERPs shown in Naatanen's (1990) Figure 14 have only 3 to 5 zero crossings within the first 300 ms, yet that subject was able, and is still able, to write several important scientific papers. There is simply less noise in the latter figure.

10. Besides that early work from 25-30 years ago, Jensen mentions some promising research using the "string" measure, i.e., calculating the length of the contour of the waveform. This measure combines the "noisiness" of the waveform, likewise quantified in terms of the number of zero crossings, with the size of component amplitudes. It is apparent that researchers tried this measure because the older impressive correlations could not be replicated using zero crossings alone. Although Jensen does not seem to be aware of it, the "string" results too met with methodological criticisms and proved either not replicable at all, or only to a lesser degree (review: Robaey et al., 1995, p. 57).

11. In many respects ERPs are no doubt very sensitive indicators of cognitive processes. They can indicate the timing and extent of visual attention (e.g., Heinze et al., 1990; Wauschkuhn et al., 1998), the smallest language-specific noticeable difference in receptive language (Naatanen et al., 1997; Winkler et al., 1999), the implicit sex ascription people make to gender-neutral nouns (Osterhout et al., 1997), the tendency to prepare a wrong response (Gratton et al., 1988; Wascher et al., 1999), the implicit response when an error is detected (Falkenstein et al., 1991; Coles et al., 1995) and much more. There are accordingly no doubt also relationships between ERPs and intelligence. ERPs can differ in complex ways between adults differing in intelligence (Pelosi et al., 1992) or working memory capacity (Gunter et al., 1995), and also between children differing in intelligence (Robaey et al., 1995). But these differences probably reflect cognitive strategy differences (Pelosi et al., 1992), depending on the task (Gunter et al., 1992) or on the way intelligence is measured (Robaey et al., 1992). So it appears that these results do not support the idea that ERPs provide a simple biological basis for the measurement of intelligence.


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