Table of contents

Introduction. 1

Experimental data of cognitive evoked potential P300 measurements of Mantas Puociauskas. 2

Conclusions. 6

Theory background of P300 cognitive evoked potentials correlation to brain cognitive information processing speed 6

References. 26

Introduction

The cognitive evoked potential P300 was first described in 1964 (Davis) and has been studied extensively since that time. The response is represented by a large positive voltage wave (5 uV or greater) occurring at approximately 300 ms after presentation of an infrequent or rare auditory stimulus embedded within a sequence of standard stimuli.

The P300 latency time is generally accepted as a measure of speed of cognitive processing (Kutas et al., 1977), and its amplitude – to reflect the number of neurons allocated to the eliciting task (Wickens et al., 1983). It reflects cognitive brain functions of the subject.

The latency of the P300 event-related potential becomes prolonged in:

1. The course of neural changes that accompany aging;

2. In disorders associated with neural damage and degeneration;

3. The more difficult task prolongs the P300 latency.

Average P300 latency increase during aging (“aging coefficient”) usually is 0.9-1.8 ms/ per year. Standard error in this case is in limited range of 21-36 ms (Picton, 1992). P300 latency increase is due to information processing speed decrease and amplitude decrease is due to decrease of number of neurons involved in information processing. It is important to notice that the change of P300 latency of the maturing children differs from adults. P300 latency of the children decreases until 16-17 years and increases after that age (Picton, 1992).

By comparing P300 response of the subject with P300 response of normal population of the same age group, increased P300 latency reveals such kinds of disorders as various types of dementia, Alzheimer disease, schizophrenia, depression, obsessive-compulsive disorders, psychophathy; behavioral changes; lack of attention and also epilepsy in children.

In other words, virtually every disorder and condition has shown either a smaller P300 amplitude or increased P300 latency. Such lack of specificity has greatly hindered the potential usefulness of this measure as either a research or clinical tool, because it reveals only the fact of brain degeneration but not the cause of degeneration.

In summing, P300 latency:

· Index of speed of information processing;

· Correlates with reaction times;

· P300 latency correlates with various measures of intelligence

– P300 latency is shorter in more intelligent people

– Intelligent individuals make decisions faster.

The range of P300 latency values ever recorded in the world is from 250 ms to 1000 ms.

The measured P300 latency of Mantas Puociauskas is 274.0 ms, thus cognitive information processing speed is 99.97 % above of normal population and 20% faster than that of normal population. The probability of such cognitive information processing speed to be found in normal population is approximately 3 in 10,000.

Experimental data of cognitive evoked potential P300 measurements of Mantas Puociauskas

P300 potential measurements 19/03/2003 16:27:00 P300

view raw experiment data

Acoustic stimulator

Mode Type Pulse width (us) Rate (ms) Level (dB) Right Left

Click Alternate 140 2000 80 Signal Signal

Measure

Trace Label Lat. (s) Amp. (V) Area (V/s)

Cz-RF Rare P1 34.0m -1.88u

Cz-RF Rare N1 100.0m -12.1u

Cz-RF Rare P2 124.0m -11.0u

Cz-RF Rare N2 168.0m -18.1u

Cz-RF Rare P3 274.0m 14.9u

P300 potential measurements 19/03/2003 16:29:00 P300

view raw experiment data

Acoustic stimulator

Mode Type Pulse width (us) Rate (ms) Level (dB) Right Left

Click Alternate 140 2000 80 Signal Signal

Measure

Trace Label Lat. (s) Amp. (V) Area (V/s)

Cz-RF Rare P1 38.0m -2.80u

Cz-RF Rare N1 84.0m -11.0u

Cz-RF Rare P2 140.0m -11.3u

Cz-RF Rare N2 170.0m -18.1u

Cz-RF Rare P3 274.0m 19.5u

Table 1. Cognitive evoked potential P300 latency and amplitude values of normal population

(data from the studies conducted by Electrophysiological Research and Treatment Methods Department of Republican Vilnius Psychiatric Hospital, Lithuania)

Age, years	Amplitude (mean), µV	Standard deviation s of amplitude, µV	Latency (mean), ms	Standard deviation s of latency, ms
16-17	12.6	4.7	312	12.48
20-29	14.7	5.6	336.6	16.6
30-39	17.4	5.3	347.5	12.4
40-49	14.4	4	350	14
50-59	18	9	347	16.7

The Gaussian probability distribution with mean m and standard deviation s is a normalized Gaussian function of the form

where P(x)dx gives the probability that a variate with a Gaussian distribution takes on a value in the range [x, x + dx].

Substituting values from measurements of Mantas Puociauskas:

age = 26

mean m = 336.6

standard deviation s = 16.6

we get:

Picture 1. Distribution of P300 latency of normal population (age 20-30 years).

Substituting values from measurements gives:

when

x = 274.0

then

P(x)=0.0003100775841, 1-P(x)= 0.9996899224159,

i.e. probability is approximately 3 in 10,000.

Cognitive information processing speed

(336.6-274.0)/336.6*100% = 18.6% » 20%

faster than that of normal population.

Conclusions

1. Cognitive information processing speed of Mantas Puociauskas is 99.97 % above of normal population;
2. Cognitive information processing speed 20% faster than that of normal population;
3. The probability of such cognitive information processing speed to be found in normal population is approximately 3 in 10,000.

Theory background of P300 cognitive evoked potentials correlation to brain cognitive information processing speed

Evoked potentials (EPs) is a study of brain function. One of the EPs classifications involves their distinction into two different classes: exogenous and endogenous (event-related - ERPs) potentials. Exogenous potentials, first of all, depend on physical stimulus characteristics (e.g., the loudness of auditory stimulus) and are usually used as a means of identification of functional disability of the nervous system. In contrast, ERPs depend more on the inner subject interaction with the stimulus (his/her attention, motivation, etc.) and can reflect such processes as expectancy, attention, differentiation between meaningful and meaningless information, and remembering as well as storage of meaningful information. Therefore, ERPs can be effectively applied in psychiatric practice. One of them is cognitive evoked potential P300 (P3). It reflects cognitive brain functions of the subject. P300 can be used in such kinds of disorders as various types of dementia, Alzheimer disease, schizophrenia, depression, obsessive-compulsive disorders, psychophathy; behavioural changes; lack of attention and also epilepsy in children; in monitoring of pharmacotherapy and electroconvulsive therapy as well.

In 1965 Sutton, Braren, Zubin and John were the first to report on a positive-going ERP component with a peak latency of 300 msec. A substantial amount of subsequent research elaborated on the conditions that modulate both the amplitude and latency of this "P300" or "P3" (see Picton, 1992, for a recent review). Briefly, the amplitude of the P300 tends to increase as a function of at least two variables: declining stimulus probability or expectancy (e.g., Duncan-Johnson & Donchin, 1977) and increasing task relevance or value (e.g., Johnson, 1986). On the other hand, with the possible exception of response related processes, the peak latency of the P300 varies as a function of many of same factors that effect behavioral reaction time, (e.g., McCarthy & Donchin, 1981). This has lead some investigators to conclude that P300 latency is a measure of stimulus evaluation time.

P300 component is a late positive wave of the brain electrical activity associated to the detection of a rare target among standard stimuli according to the "oddball paradigm". The P300 latency is generally accepted as a measure of speed of cognitive processing (Kutas et al., 1977), and its amplitude is supposed to reflect the amount of attention allocated to the eliciting task (Wickens et al., 1983).

Individual differences in the amplitude and latency of P300s have been reported in a large number of studies (see Picton, 1992). Factors influencing this component include type of psychopathology, age, personality, diet and, in a few studies, intelligence. For example, McGarry-Roberts, Stelmack and Campbell (1992) reported that the latency of the P3b were negatively correlated with IQ in a group of 30 women, while P3b amplitude had a more complicated relationship with IQ (it was larger in some cases and smaller in others for subjects with higher IQs). One problem with this correlational approach to studying intelligence and to much of the clinical research on individual differences using ERPs, has been a general lack of specificity of findings. In other words, virtually every disorder and condition has shown either a smaller or later P300. Such lack of specificity has greatly hindered the potential usefulness of this measure as either a research or clinical tool.

The latency of the P300 event-related potential is prolonged in disorders associated with neural damage and degeneration and also becomes prolonged in the course of neural changes that accompany aging. We tested whether the rate of P300 latency increase with age was greater in male schizophrenic patients than in normal subjects because a steeper slope in schizophrenia would suggest a progressive neurodegenerative process.

The P300 response has been used less often clinically, but results have been reported in a variety of clinical populations. The P300 was first described in 1964 (Davis) and has been studied extensively since that time. The response is represented by a large positive voltage wave (5 v or greater) occurring at approximately 300 ms after presentation of an infrequent or rare auditory stimulus embedded within a sequence of standard stimuli. The term "endogenous" is often used to describe the P300 response and other AER components that are highly dependent on subject attention to certain auditory stimuli (as opposed to exogenous responses that are stimulus dependent). Although an exact generator site is unknown, human depth electrode studies provide evidence of medial temporal lobe (hippocampus) contribution to the response. Clinically, the P300 has been used for assessment of higher-level auditory processing in both children and adults.

It is reported that in acutely depressed patients the amplitude of P300 is significantly reduced (Blackwood et al., 1987). Its latency is prolonged pointing to "task-related slowing of perceptual decisions" (Bruder et al., 1991) and psychomotor retardation (el Massioui & Lesevre, 1988).

P300 (P3) is the most intensively studied component of the late positive complex. P300 is elicited under a certain experimental situation independently of the sensory modality or intensity of the stimulus. Task relevant ("attended") stimuli elicit P300 waves and its amplitude is larger for infrequent stimuli. The amplitude of the P300 rises as the probability of the rare stimulus is reduced. P300 latency may vary from 250 ms to 1000 ms — the more difficult task the longer the P300 latency. Late components of latencies about 300 ms produced by novel stimulus have been called "orienting response potential" (Ritter et al., 1968). P300 and EDA amplitudes are greater when stimuli are improbable and task relevant (Roth, 1980).

When subjects are presented with events that are both task relevant and rare, a prominent positive component with a latency of at least 300 msec is elicited. The literature concerned with the P300 is quite extensive (see Donchin et al., 1986; Pritchard, 1981; and Rossler, 1983, for reviews). Johnson (1988, in press) has summarized much of the evidence concerning its antecedent conditions and has concluded that the elicitation and amplitude of the P300 depends on a multiplicative relationship between the subjective probability of events (the rarer the event, the larger the P300) and the amount of information and the utility of the information to the subject (the more information, the larger the elicited P300). Donchin and his colleagues have interpreted these data within the context of a model that assumes that the P300 is a manifestation of the revision of mental models (see Donchin and Coles, in press; Donchin, 1981). Much empirical evidence supports a wide variety of applications of the P300, including the measurement of mental workload (Gopher and Donchin, 1986; Donchin et al., 1986), analyses of memory mechanisms (Neville et al., 1986; Karis, Fabiani, and Donchin, 1984), and concession making in bargaining situations (Druckman, Karis, and Donchin, 1983; Karis, Druckman, Lissak, and Donchin, 1984). The latency of the P300 has also proven to be of use. It can be shown to be relatively independent of response execution processes and can thus serve as a pure measure of mental timing (Kutas, McCarthy, and Donchin, 1977; McCarthy and Donchin, 1983).

Intelligence and brain myelination

Many observations concerning intelligence could be explained if much variance in intelligence reflects myelination differences. More intelligent brains show faster nerve conduction, less glucose utilization in positron emission tomography, faster reaction times, faster inspection times, faster speeds in general, greater circumference and volume, smaller standard deviation in reaction times, greater variability in EEG measures, shorter white matter T2 relaxation times, and higher gray-white matter contrast with magnetic resonance imaging. Also explainable are peculiarities of the increased reaction times and standard deviations with number of choices and complexity, reaction time skewness, the shorter latencies in evoked potentials, shorter latencies to the P300 wave, the high glial to neuron ratio in Einstein's brain, less glucose utilization per unit volume in large brains, certain results related to lipids, essential fatty acids, and cholesterol in adults and premature babies, and the survival of genes for lower intelligence. Children's improved performance with maturation might result from myelination. The slowing of response times with age, the decline in intelligence, and increased T1 relaxation times could be explained. Differential myelination in the mouse brain might be able to explain the heterosis observed for myelination, brain size, caudal nerve conduction velocity, and maze performance observed.

Various researchers have reported the following differences between the brains of the highly intelligent and the less intelligent.

1. Intelligence is subject to considerable genetic influence.
2. Large brains are more intelligent.
3. The brain size versus intelligence relationship is pleiotropic.
4. More intelligent brains use less energy.
5. Large brains use less energy per unit volume.
6. Brains with faster nerve conduction are more intelligent.
7. The more intelligent have shorter white matter T2 relaxation times.
8. White grey matter MRI contrast decreases with age.
9. Response times decline with age.
10. Intellectual performance declines with age.
11. Faster reaction times correlate with higher intelligence.
12. More variable reaction times correlate with lower intelligence.
13. Reaction time distributions are positively skewed.
14. The variability of choice reaction times increases more rapidly with complexity than the median reaction times.
15. In the elderly, variability increases more rapidly with complexity than in young adults.
16. The genes for low intelligence have survived, even though intelligence appears to improve performance in virtually all mental functions.
17. In the mouse, heterosis for brain myelination, brain size, caudal (tail) nerve conduction velocity, and various aspects of behavior exists.

What mechanism could make all these statements true? The high heritability for intelligence (recent references are provided by Hewitt & Last, 1984; Plomin & Loehlin, 1989; McCartny, Harris, & Bernieri, 1990; Bouchard, Lykken, McGue, Sega, & Tellegen, 1990; Bouchard, 1993; and Plomin, Chipuer, & Neiderhiser, 1994) is an important clue, since we know that genes code for biological differences. The inverse relation between size and energy use implies that much of the added matter in large brains uses little energy. Thus, it is probably not merely additional neurons. This added matter probably has a purpose (from evolutionary theory). Since conduction is faster in smart brains, the effect of this substance could be to speed up nerve conduction. The obvious candidate is myelin, since neurons sheathed with myelin have faster nerve conduction rates than those without myelin, and neurons with more myelin are also faster. Various features of the reaction time experiments could be explained by a process in which signals from one neuron accidentally cause signals in adjacent neurons, and these errors in transmission are more common where myelin was thinner.

The remainder of the paper will document the assertions made and explain how they could be explained by differences in myelination

Thicker myelin must increase reliability, or speed, or both. There is probably considerable variability in the thickness of myelin along a single nerve fiber, and a greater average thickness could increase the thickness at the thinnest spots or reduce the number of such thin spots, even if the average is adequate.

Myelination is known to occur sequentially in the brain, and the order and timing in which myelination occurs in the brain is consistent with it causing the improvement in intellectual functioning that occurs as infants and toddlers mature (Gibson, 1991; Konner, 1991).

Delay of myelination is associated with clinically delayed development of motor milestones (Dietrich & Hoffman, 1992, p. 1071). More speculatively, later myelination may be related to children's increased ability to think abstractly as they age.

Speed related evidence

There are several early reports of statistically significant correlations between the speed of nerve conduction in the patellar reflex arc or the Achilles tendon reflex arc and intelligence (Vernon, 1990). Vernon and Mori (1992) have reported a correlation of peripheral nerve conduction velocity (interpreted as a surrogate for direct measures of brain nerve conduction velocity) with intelligence, although Barrett, Daum, & Eysenck (1990) and Reed & Jensen (1991) failed to find such a correlation, and Wickett & Vernon (in press) failed to replicate. Differences in myelin thickness could produce these differences, since myelin-sheathed neurons are faster. For already myelinated fibers, the effect of thicker myelin is to decrease the membrane capacitance (Koester, 1991b, p. 1036) and hence speed up the propagation of the action potential by reducing the time required to create a potential difference across the axon membrane (Koester, 1991a, p. 101).Reed and Jensen (1989, 1992) have developed a measure which is a good surrogate for nerve conduction velocity over a well defined path. Their measure was the time to the first electrical potential (the P100 visual evoked potential latency at the occiput) on the scalp after a visual stimuli, divided by the subject's head length. They found this to be correlated with scores on an untimed intelligence test. Another speed-related variable known to be correlated with intelligence is visual inspection time, the minimum time required to determine the longer of two tachistoscopically presented lines. It measures the minimum time the brain needs for a particularly simple visual discrimination. A review and meta-analysis (Kranzler & Jensen, 1989) found the best estimate of the correlation between IQ and visual inspection time to be -.54. The negative sign indicated that brains with shorter inspection times were more intelligent. More recently, Deary (1993) showed a strong correlation with performance IQ measures, although not with verbal ones. Again a measure of speed correlates with intelligence. However, Reed and Jensen (1993) reported that in their 147 subjects that visual pathway nerve conduction velocity and choice reaction times lacked an appreciable correlation with each other. This goes against the theory that myelination differences cause correlation of both with intelligence. This leads them to suggest that the more intelligent have shorter total length of cortical pathways, a possibility that might explain the lower energy use in the brains of the more intelligent. In contract to the visual inspection time results, auditory inspection time appears to measure a specific ability and not to correlate with intelligence (Langsford, Mackenzie, & Maher, 1994).Jensen (1982, 1992, 1993) and others (Jensen, Schafer, & Crinella, 1981; Barrett, Eysenck & Lucking, 1986; Lynn, 1991b; Lynn & Holmshaw, 1990; Lynn & Shigehisa, 1991, Beauducel & Brocke, 1993, Schweizer, 1993, also see the papers in Vernon, 1987) have shown that reaction time in various simple tasks negatively correlates with intelligence (i.e., faster reacting brains tend to be more intelligent). They have even been found to correlate with school marks (Van de Vijver & Willemse, 1991).Reaction times and intelligence correlate within families, as well as between families (Jensen, Cohn, & Cohn, 1989). The smarter sibling has the faster reaction time. Since siblings share many aspects of their environment (i.e. nutrition) this suggests the effect is not merely due to a shared aspect of the environment affecting both intelligence and reaction times. More importantly, this within family correlation suggests that both are being influenced by common genes that affect both traits. Myelination is a very plausible candidate for this trait.McGary-Roberts, Stelmack, & Campbell (1992), using apparatus that measures both reaction time and event-related potentials for 6 simple tests of mental function, have confirmed that both short reaction times and a short latency of the P300 wave of an event related potential correlate with intelligence (although the effect is not statistically significant for some items). Reed and Jensen (1992) also review evidence that P300 latency is inversely related to IQ. More recently a study (Polich & Martin, 1992) found P300 latency to correlate (inversely) with university grade point average, but, surprisingly, not with Raven's matrices scores (a good measure of intelligence). That event related potentials are indeed measuring intelligence is suggested by the fact that correlations of a composite average evoked potential measure with the various subtests of the Wechsler Adult Intelligence Scale correlate highly (Spearman rho of .95) with the factor loadings of these subtests on the general factor, g (Eysenck & Barrett, 1985). Polich, Ladish, & Burns (1990) showed that the latency of the P300 wave decreased as children (4 years to 20 years) matured, and that children that did worse on the Digit Span subtest of the Wechsler Adult Intelligence Scale had longer latencies, with both effects appearing about equally important on their Figure 2,

and both being reported to be highly significant (at least one P<.001 for each electrode). The memory measure was statistically significant when age was controlled for. Unfortunately, results were not presented in such a way as to tell how much of age effect was left after the P300 latency (a speed measure) was controlled for. However, increased myelination is one of the features of brain maturation, and the simplest hypothesis to explain these results is that increased myelination increases the speed of brain processes, including the time to the peak of the P300 wave, and that this also somehow contributes to memory and intelligence. The Reed and Jensen nerve conduction velocity measurements, although over a specified nerve conduction pathway, are basically a variation of the latency studies made with evoked potential techniques. These show that certain waves are picked up earlier in the brains of the intelligent. The simplest interpretations of these results is that intelligent brains are faster. Many other simple measures of time required to do very simple tasks, such as determining whether a single digit was in a string of previously seen digits, or whether two words were the same or different, have been found to be correlated with intelligence (Vernon, 1983). The various measures of mental speed correlate with each other as well as with intelligence. Speed at the very simple task of drawing lines connecting numbers in order has a high correlation with IQ (Vernon 1993). Eysenck (1987, pp. 43-50) summarizes other studies correlating processing speed and intelligence. Vernon & Weese (1993) report a recent set of such experiments. Ho, Baker, & Decker (1988) found that 120 twin children's speed of naming of color, numbers, letters, and pictures was correlated with intelligence (statistically significant with r=.419), as was speed at symbol processing (also statistically significant with r=.418). Both intelligence and the speed measures were found to exhibit substantial heritabilities. Most importantly, correlated genetic effects appeared to underlie both intelligence and speed of processing, "lending support to the notion that speed and IQ may share some common biological mechanism" (p. 258).Most intelligence tests are timed, and reward speed. Thus, it might be thought that the correlation between speed and intelligence was due only to performance on timed tests benefitting from speed. However, Vernon (1983) shows that once general intelligence is controlled for, there is no significant correlation between mental speed and performance on the timed subtests of the intelligence test used. More importantly, Vernon & Kantor (1986) show that the reaction time and speed variables actually explain less of the variance of a timed intelligence test than that of an untimed administration of the same test.

Faster reaction times, faster nerve conduction, and shorter latencies for evoked potentials all represent doing the same things faster. Speed of simple operations and intelligence are found to be correlated, using several different methodologies. This suggests that a widespread property of the cerebral neurons is being measured, rather than just the ability of a particular part of the brain or nervous system to process information faster. Differences in the thickness of myelin layers or in the percentage of fibers myelinated are one possibility. Of course, other possibilities exist. Indeed, several independent factors derived from reaction times and related experiments appear to correlate with intelligence (Kranzler & Jensen, 1991; Vernon & Weese, 1993), suggesting there is more than a single physical difference.

Myelination in the human cortex continues throughout childhood (Yakovlev & Lecours, 1967), with the associative cortical areas showing increased amounts of myelin staining only by the second decade of life (Benes, 1989). Indirect evidence from interhemispheric coordination suggests that incomplete myelination of the corpus callasum leads to poorer childhood functioning on certain tasks (Hatta & Moriya, 1988). Because myelination is going on during childhood, myelination is a candidate (admittedly not the only one) to explain the improved intellectual functioning as children mature. Choice reaction times and their standard deviations, which are inversely correlated with intelligence, also decrease with age in children (Jensen, 1982, pp. 149-151). Gray-white matter contrasts increase as children mature (Schultz, 1991). In the elderly, reaction times and their standard deviations increase (Jensen, 1982), fluid intelligence decreases (Seligman, 1992), and gray-white matter contrast decreases (Schultz, 1991). Myelination could explain these changes with age.

It was discovered early in the study of intelligence that the ratio of mental age to chronological age (called the intelligence quotient or IQ) was approximately constant for an individual (Jensen, 1980, p. 105). This is consistent with there being some biological variable, such that children perform at the intellectual level of older children if their brain resembles that of older children with regard to the variable. Myelination is a plausible candidate for such a variable.

Kail & Bisanz (1992) showed that the time children took to do simple tasks declined at a decreasing rate with age, approaching asymptotically the adult levels. More interestingly, the decline was described by a formula in which the term (an exponential) describing the decline in time with age had the same value for five different tasks (mental rotation, name retrieval, memory search, visual search, and mental addition). Furthermore, in another experiment, the decline for six tasks was well described by the formula described in the earlier experiment, even though only two of the tasks were common to both experiments.

Kail (1990) has examined 72 studies of speed of mental processing that yielded 1,826 comparisons of children's average response times with adults' average response times for a specified task. He concluded that children take longer than adults on most tasks, and more strikingly, that children's response times increase linearly with the response times of adults on the same tasks. It appears that in doing a task both children's and adults' brains go through the same steps, but children do each step proportionately slower. As he points out, these results are consistent with age differences in processing time reflecting a general (non-task specific) component that changes rapidly during childhood and more slowly during adolescence. Myelination is a plausible candidate for the variable that causes the increased processing speed with maturation. Hale (1990) found similar results for 10, 12, and 15 year olds in comparison with adults.

This may be a good place to point out the remarkable resemblance between the performance of gifted 7th graders and Berkeley undergraduates on tests of information processing speed. Cohn, Carlson, & Jensen (1985) studied the speed of information processing in a group of gifted children who at ages of 12 to 14 years were succeeding in college courses in mathematics and science.

Very striking is their Fig. 2 (reproduced here as Figure 1), which shows the mean latency of various processing tasks for eight different tests in university students, gifted, and non-gifted students. The three lines are close to parallel, showing that the three groups have the same rank order and pattern of processing time for the different elementary processing tasks. The parallelism between university students and non-gifted children is an example of the similarity in processing times that Kailís (1990) literature analysis found to be common. More interestingly, the lines for the gifted students (12-14 years old) and the university students almost coincide with one another at the left hand side of the figure, with the line for the non-gifted 12-14 year olds being much further to the left, indicating processing times almost twice as great. The intraclass correlation between the 7th grade gifted and the university students was .98, the Pearson correlation was .99, and the Spearman rank correlation was .98 (p. 627). (Although Furneaux (1961) has argued that the log of response times may be better than absolute values, the similarity of the absolute values for the various groups suggests that such a transformation would have little effect. However, Hale, Myerson, & Wagstaff (1987) have shown that when response times for old adults are plotted against reaction times for young adults, a linear relationship is obtained if the logs are used, but not otherwise.)

figure 1 suggests that there is a basic neurological characteristic that determines processing speed, and that this characteristic changes systematically so as to decrease processing time as maturation occurs. This characteristic must essentially determine performance on these tests, and the gifted must be blessed with more of this characteristic. Life experience variables (such as culture) are unlikely to meet this condition, since the life experiences of 7th graders and university students are quite different.

Likewise, theories that hypothesize that variability in a particular part of the brain play a key role in explaining variability in certain of the tasks have difficulty in explaining the close resemblance between university students and the gifted 7th grade-aged students. For instance, since some tasks involve virtually no memory, some short term memory, and some accessing long term memory, the differences between the gifted and non-gifted are unlikely to result from factors that are peculiar to the memory mechanisms, or to parts of the brain that may be dedicated to memory (such as the hippocampus) or to overall planning (such as the frontal lobes). The gray matter of the brain appears to be highly differentiated, with different parts of the brain handling different functions.

It is unlikely that the differences between the gifted and the non-gifted involves the size of different parts of the gray matter or the anatomy of its neural connections, since it is hard to think of a mechanism that would consistently affect the gifted relative to the normal, while simultaneously having the same effect on university students relative to 7th grade students. The brain neurotransmitters appear to be highly differentiated, with different transmitters playing different roles in different areas and for different functions. Thus, their levels are unlikely to explain the pattern across tests.

Therefore, the difference between the gifted and non-gifted must be in a variable that affects, in a similar manner, many parts of the brain, and which also changes with age. One possibility is some unknown characteristic of the synapses, which causes them to act both faster and more consistently as children age, and which also exhibits considerable individual differences. The author knows of no such characteristics which change systematically with age during childhood (although such could exist).

However, the extent of myelination in the cerebral cortex is known to increase with age, and to continue increasing up to adulthood. It is plausible that myelination is greater in the intelligent, and presumably in the gifted. If the difference in the T2 relaxation time measurements indeed are due to differences in myelin content, as they seem to be, this would provide the evidence that myelination is greater in the gifted. It is plausible that the factors that determine the amount of myelin in one part of the brain are the same as those that determine it in other areas.

If children can't solve certain problems until certain areas have achieved a required degree of myelination, and children agree in the order of myelination (as they appear to) but differ in the speed, it would be possible to explain why children improve in ability as they age, and why mental age can explain children's ability in a wide range of areas. Those who achieve a certain pattern of myelination earlier would be able to perform as well as those older. This might explain why gifted 7th graders display a pattern of speeds that so closely resembles those of the Berkeley undergraduates whose intellectual performance they equal. Once myelination stops, the children whose brains were more myelinated would be those who had higher intelligences as adults. This would explain the correlation of childhood IQís with adult IQís. The inability to predict adult intelligences from those at very young ages (three or earlier) would be because the relevant areas had not begun to myelinate in any children, and hence there were no myelin related performance differences to observe.

If thicker myelin contributes to intelligence, and if some children have thicker myelin as adults because they accumulated myelin more rapidly as children, those children who will become the most intelligent will develop sufficient myelinization to lay down and retain memories at an earlier age. Rabbit & McInnis (1988) have shown that the more intelligent among the old recall memories from an earlier age than do the less intelligent.

Measures of variability in brain processes

A correlation (also negative) between intelligence and the intraindividual standard deviation of reaction time has been found in most of the reaction time and intelligence research cited above. The correlation is negative because the less intelligent display more variability in their reaction times. Indeed, the inverse correlation between intelligence and standard deviation is usually stronger than that between intelligence and the median or mean reaction time (see Jensen, 1992, for references; Beauducel & Brocke, 1993, provide a more recent report of an inverse intelligence versus reaction time correlation).Within families, intelligence correlates with the intraindividual standard deviation of reaction times (Jensen, Cohn, & Cohn, 1989). The smarter sibling has the more consistent reaction time. This within family correlation suggests that both are being influenced by common genes that affect both traits. An inverse correlation between intelligence and the standard deviation (calculated across trials) of a measure of nervous system functioning has also been found in average evoked potential research (a review is in Deary & Caryl, 1993). The standard deviation of potential evoked by an auditory stimulus (Hendrickson & Hendrickson, 1980; Hendrickson, 1982; Barrett & Eysenck, 1992) displays a statistically significant inverse correlation with intelligence. Also, data from averaged sensory nerve action potentials (Barrett, Daum & Eysenck, 1990) measured in the hand and wrist showed that speed variability correlates -.44 with psychometric IQ. A later study (Barrett & Eysenck, 1993) partially replicated these results. The fact that several experimental methodologies show that subjects with lower intelligence display more variability in the actions of their nervous systems suggests that a "noisy" system displays less intelligence, possibly because long trains of thought cannot be carried on without error. The Hendricksons (1980) built a plausible theory in which greater reliability in neural transmission increases intelligence.

The hypothesis that greater myelination improves mental performance was first suggested by Case (1985, pp. 377-381). In essence, he argued that less myelination led to greater cross-talk along neurons. Such cross-talk caused by transmission of signals across thin spots in the myelin layer could very well be the source of the random errors that both lower intelligence and raise the standard deviations for various measures of nervous system functioning. While Caseís hypothesis is a developmental one, proposed to explain why older children can do more than younger ones, it is easily extended to explain why children of the same age differ in intelligence (although he did not do so).Errors in transmission may explain the finding of Larson & Alderton (1990) that the highest correlations of intelligence with reaction time were found for the subject's worst performances, rather than with their best ones. Also (see their Figure 3), the difference in reaction time between the high intelligence and low intelligence subjects was minor for the quickest reactions, but much larger for the slowest ones (which took at least twice as long). Kranzler (1992a) also found that correlations with intelligence were greatest for the slowest reaction times for a variety of other reaction time tests. Jensen had earlier (1982) reported similar results.

Reaction time variability increases rapidly with complexity

However, the above model has a less obvious prediction. For background, consider a model where the more complex tasks involve more steps (such as in Jensen, 1982). Now suppose the delays at each step are independent (such as might be true if they occurred only at synapses, and the cause of delays at one synapse did not affect delays at another). It would frequently happen that unusually large delays at one stage would be offset by unusually small delays at another. With such offsetting errors, the standard deviation would increase less than proportionately with the number of steps. More precisely, it would increase the square root of the number of steps, if all steps were identical. In the pyramidal system lying behind Hick's law (see illustration in Jensen, 1982, p. 128) in which the brain at each step reduces the uncertainty in the response by a factor of two (making the delay time proportional to the number of steps, and hence the number of bits of information involved), the standard deviation would increase less than proportionately with the number of bits. The data falsified the prediction. Even more surprising, the standard deviation increases more rapidly than median delay times (Jensen, 1982, 1992; Cohn et al., 1985, Fig. 3 and 4). As Jensen recognizes, this is a puzzle. He offers no solution to it. Even if the delays are perfectly correlated at each stage of the process, the standard deviations would increase only in proportion to the number of steps. Figure 2 shows the pattern which regularly emerges, using data kindly supplied from the relevant theses by Professor Jensen for physically active and educated elderly (aged 51-85, mean 68) (Ananda, 1985), and for university students (Vernon, 1983). The standard deviations are plotted versus the reaction times. The resulting curve is concave upwards, while it would be concave downwards if the reaction times were proportional to the number of steps required to solve the problem, and the times at each step were independent.

Somehow, additional variability is introduced in more complex processing, and this additional variability is more than proportional to the additional processing required. If errors in the early steps of processing actually lower the accuracy at the later steps, the rapid increase in variability with complexity could be explained. Furthermore, if the more error prone brains also had a tendency to introduce more errors into later steps, the more rapid increase in standard deviation in the less intelligent could be explained. Jensen (1982) has found that this is one of the most conspicuous differences between the more and less intelligent. Leakage of signals across myelin layers is one of the few mechanisms by which errors in early steps of processing could increase the standard deviation of later steps. Notice that the above leads to a different theory of why speed at elementary cognitive tasks is correlated with intelligence, as measured by the ability to successfully perform complex tasks. Jensen (1982, p. 122) has argued that short term memory traces in the brain decay rapidly, and a successful solution to a complex problem may require keeping several elements simultaneously in working memory. People whose brains work slowly will forget part of the problem before a solution is found. Thus, successful performance of complex tasks requires a fast brain. The chief problem with this theory is that the differences in reaction time between groups differing in intelligence are relatively small. For instance, Jensen reports (1987, p. 115) that the average 3 bit choice reaction time was 412 msec for gifted 7th graders, versus 523 msecs for average 7th graders. The average take 27% longer than the gifted. This might imply that the gifted could handle problems 27% more complex. Yet the difference in performance between the gifted and the average appears much greater than this. While the calculation is only illustrative, it is hard to explain how small speed differences produce large differences in ability to solve complex problems. Using intraindividual variability improves the situation somewhat, since average students' standard deviation was 118 msecs, versus 78 for the gifted (Jensen, 1987,p. 136). The average students' performances are 51% more variable than those of the gifted. This appears to be a more powerful effect, and if one interprets variability in aggregate times as indicating variability in individual steps, it becomes easier to imagine how the gifted derive their advantage. However, the complexity the gifted can handle still seems more than 51% above that which the average student can handle. Indeed, if this were all that was involved, one would expect average students to be able to solve the hardest problems the gifted can solve, although slower. Yet there are tasks the gifted can do that the average fail to do, even after repeated trials.The proposal that errors in processing at early steps somehow produces errors at later steps could produce an exponential increase in aggregate errors with problem complexity (just as the standard deviations of reaction times increase exponentially with problem complexity). This could explain why only a few individuals can solve the most complex problems. The leakage across the myelin layers is one of the few mechanisms which might produce this exponential increase in errors with complexity. There is one other implication of a system in which errors take the form of creation of erroneous signals. This is that total energy use will rise in brains that are more error prone. If these are in turn the less intelligent (as they are believed to be), the testable implication is that the brains of the less intelligent will use more glucose. This, as has been discussed above, is precisely what has been found. This provides a second mechanism (besides reducing the packing density of neurons) by which thicker myelin reduces energy use per unit volume.

Other reaction time considerations

Other reaction time considerations Jensen (1982, p. 115) notes that a satisfactory theory must account for the close relationship between a subject's fastest reaction times and his mean or median (an effect which has also been reported by Larson & Alderton, 1990, and by Kranzler, 1992a). He notes that correlations have been reported as high as .96 between the mean RT over 100 trials, and the average time for the fastest ten. This appears to be the same phenomenon as the linear relationship between standard deviations and median reaction times found by Myerson (undated). At first glance, this creates a problem for any theory that treats the standard deviation as measuring error proneness or noise, while treating the mean as measuring speed, since speed and error proneness need not be that well correlated if they are caused by different mechanisms. Thus, it appears that one mechanism must account for both, or at least explain a large common variance. Myelination can also explain, on a physical basis, a correlation between the fastest reaction times and the standard deviations. It appears to be the most plausible factor that could vary among individuals, and affect both processing errors and speed. Thicker myelin could both speed up impulse transmission and reduce the number of cross-talk errors. However, there is another mechanism that could explain a correlation between speed (as measured in the fastest reactions) and variability. The optimal criteria for a neuron to fire depends on how noisy the signals are. In a generally low noise system, a small difference between two signals can be safely interpreted as evidence of a real difference. Thus, low noise systems are likely to have the critical criteria set low, so that they respond quickly. Given individual variation in the noisiness of circuits, a well designed brain would have some mechanism for adjusting the criteria to the level of noise. High noise systems will have the criteria set higher. Even on those rare occasions when no errors occur, the receiving neurons will wait for the preset difference to accumulate (since they have no way of knowing that this time there were no errors). Hence, the noisier brains will have slower reaction times, even on the occasions when none of the possible random errors that cause delays occur. Thus, the correlation between the fastest reaction times and the standard deviations can be explained.

Aging and myelin

Once adulthood is reached, reaction times start increasing again. An analysis of a large number of published studies, (Hale, Myerson, & Wagstaff, 1987) showed that response times could be well described by a general slowing of brain processes with age. They show the effect with Brinley plots (Brinley, 1965). These show the time the old take for a specific task versus the time young adults take for the same task. The times for the old are then found to be well approximated by a near linear (actually slightly curved) function of the times for young adults (usually university students). The times taken by young adults is usually interpreted as a measure of task complexity. It is generally found that the old take longer for all tasks, and that the lengthening of reaction times with age increases with complexity.

What is striking is that the time for old adults on a wide variety of tasks can be predicted so well from young adultsí times, even when the tasks differ in their apparent demands for short term memory or access to long term memory, or to resources localized in different parts of the brain. For instance, the times needed for mental rotations (which correlate with skills believed to be typically centered in the right hemisphere) and letter and word matching (which correlate with verbal skills believed to be typically centered in the left hemisphere) can be predicted from the same function. That such a good fit can be obtained by such a simple model, suggests some type of generalized slowing at the neurological level, rather than slowing limited to particular structures.

The relationship between measures of simple processing speed and fluid intelligence that is observed in the young is also found in the old. Speed on simple tasks correlates with mental performance on more complex tests of fluid intelligence well enough that most of the deterioration with age disappears if speed is controlled for (Hertzog, 1989; Salthouse, 1992; Salthouse & Babcock, 1991; Schaie, 1989).

Salthouse (1993c) has provided a table summarizing six major studies, all of which show reductions in the impact of age on cognitive performance after controlling for speed. All but one study reports that for all the tests given that at least half of the age related variance is eliminated by controlling for perceptual speed. (The exception is Salthouse and Mitchell, 1990, who report on spatial tests where controlling perceptual speed reduced the effect of age by from 20.3% to 92.9%.) Salthouse summarizes these studies by pointing out that, on average, age explains 15.8% of the variance, but only 3.5% after statistically controlling for the variation in a composite measure of perceptual speed. Thus, almost 80% of the age related variance in certain measures of fluid cognition is associated with variations in perceptual speed.

Since that review, Linderberger, Mayr, & Kliegl (1993) have reported that virtually all of the age related decline in intelligence (g) was mediated by measures of simple processing speed, and that 98% of the age related variance in general ability was shared with speed. Also in 1993, Salthouse (1993a) reported that the same equation could be used for both the young and old to predict performance on simple verbal tasks from speed and knowledge. Age seemed to have little independent effect, not mediated by speed.

The latency to P300 event-related brain potential is another measure of mental speed that correlates with intelligence (inversely). Interestingly, the latency increases with age in adults (Polich & Luckritz, in press), while it decreases as children mature. Thus, the pattern over the lifespan for P300 latency parallels that for other measures of mental speed.

The explanatory power of speed suggests that changes in some neurological variable both slow the brain and produce the poorer performance. Myelin degradations that increase at an accelerated rate with age is a plausible explanatory variable. It is not necessary that the myelin thickness actually decreases. Aging could impair the functioning of the myelin layers so as to increase errors. Other possible explanations exist. In particular, Cerella (1990) points out the cognitive slowing data is well explained by any mechanism that destroys or weakens random links in a neural network. Many age related changes in the brain are known (Rogers & Styren, 1987; Welford, 1984), most of which could affect speed or intelligence.

Age related myelin failures would be accompanied by increased intra-individual standard deviation in reaction times, and this has indeed been found (Salthouse, 1993b; Fozard, Thomas, & Waugh, 1976). A sample of the elderly (university educated) that averaged 59.19 ms which was appreciably above a weighted average of 34.61 ms from 11 university student samples that which had been obtained using the same methodology (calculated from Table 14 in Jensen, 1987).

An even more interesting feature is that the reaction times increase more rapidly with complexity in the old than in the young. Figure 3 shows reaction times versus number of bits for two samples measured in the same laboratory (that of Jensen, who was kind enough to send the data). The elderly sample (Ananda, 1985) consisted of 26 males, 50 females aged 51 to 87 (mean 68), physically active with a mean number of years of education of 15.25. The young sample consisted of 100 university students (Vernon, 1983). The elderly and the young differ very little in simple reaction times. However, they differ much more in choice reaction times, especially choice reaction times involving 8 choices (3 bits of information). As was discussed above, both samples are upwardly concave with complexity, an increase which is more than proportional to the time required to solve the problems and the presumed number of steps involved. However, the increase with complexity is much larger for the elderly. This is consistent with the myelin layers of the old permitting the creation of more erroneous signals. Each of these signals propagates creating additional delays at later stages.

Raz, Millman, and Sarpel (1990) using MRI have reported age-related prolongation in the white matter T1 decay time (Wahlund, et al 1990 have also reported this) which Raz interprets as reflecting age related declines in brain myelination. T1 measures the rate of decay of the magnetic signal due to interaction with nearby protons, and is usually interpreted as being an indirect measure of the water content. Myelin is a dense material relatively low in water. In particular, Raz reported that the grey-white matter contrast declined with age, virtually disappearing in the oldest individuals. The white matter T1 times showed a significant negative correlation with scores on the Cattell Culture Fair Test, considered a measure of fluid intelligence. Of course, even if a change in average T1 can be attributed to changes in myelin, MRI measurements lack the resolution needed to separate the decline in myelin accompanying neuronal death from a decline due to thinning or alteration of the layers surrounding particular neurons.

Another white matter change seen on MRI in healthy elderly persons correlates with attention and speed of mental processing (Ylikoski, Ylikoski, Erikinjuntti, Sulkava, Raininko, & Tilvis, 1993). The patchy or diffuse white matter changes known as leukoaraiosis correlates .48 (p<.001) with one measure of speed and attention (sum of Trail Making time and reading color names on the Stroop test), as well as with the Block Design of the WAIS-R and a language comprehension test. Since leukoaraiosis increased with age, its correlation with speed may merely result from both being a product of aging. However, the fact that leukoaraiosis still had a statistically significant effect on the above speed measure (r=.31) with age controlled for, suggests there may be a causal relationship here. Demyelination is one of several conditions stated to be related to leukoaraiosis. At a minimum, the Ylikoski et al. results suggests that white matter deterioration with aging can produce cognitive slowing.

General slowing with age could be explained by slowing within the axon of the neurons, at the synapses, by age related increases in the lengths of paths (presumably caused by certain links having disappeared due to neuronal death or deterioration), by the next neuron in a chain requiring a longer time to accumulate sufficient information for it to fire, or by a combination of these. A generalized slowing from any of these causes could adequately explain the data. Thus, the various hypotheses can not be distinguished by tests of functioning.

There is one aspect of the data that is not predicted by a model involving only across the board slowing. If the time required for a signal to traverse a neuron, or to cross a synapse was simply increased by a constant amount, the aging caused increase in delays with complexity would be proportional to the number of steps done. The number of steps might then be measured by the time required by the young (or another reference group) to do the task. This would predict a linear Brinley plot, with the time required by the old being a linear function of the time required by the young.

However, the slope of the Brinley plot exhibits a clear curvature, which disappears when the logarithms of the times replace the actual times (see diagrams in Hale et al., 1987). This suggests something more is going on than a simple slowing of all cognitive processes. A model is needed in which the delay per step increases with the number of steps involved. The above described model in which leakage across the myelin sheaths causes delays at subsequent steps is such a model. Later neurons must wait longer for the cumulated difference between two signals to indicate a statistically significant difference, at which point the neuron fires. The leakage across myelin layers model given above can be viewed as a version of information-loss model of Myerson, Hale, Wagstaff, Poon, & Smith (1990). They put forth a mathematical model with information loss (such as could be produced by leakage across the myelin layers) that accurately fits the data. Thus, the proposed model where brains differ in their ability to prevent propagation of errors across myelin layers can explain the age-related changes about aging that Myerson, et al. (1990) explain with their information loss theory. As put forth by Myerson, et al. (1990), the information loss theory does not make predictions about the standard deviations of response times, although a later paper did discuss them (Myerson, undated). Conceivably, the loss in information could induce a constant increase in time at each step, but with the increases being the same for all trials. This would leave the standard deviations unchanged. However, there is another possibility. Neurons might fire only when the accumulated surplus of positive signals over negative signals exceeded some amount. Stray signals emitted at random intervals (possibly due to leakage across myelin layers) would then increase the average time required for the neurons to fire. However, since the stray signals were emitted randomly, the magnitude of the delay would vary. As discussed above, the errors at different steps will be correlated with each other, thus causing the standard deviations to increase more rapidly with the number of processing steps than would be predicted from a simple model with random and independent delays at each step. By the time the later neurons in a more complex task were reached, many stray signals would have been created. However, the variability in the number of such signals would be quite large, causing the standard deviation to increase rapidly with complexity. Deterioration with age would then result in the intraindividual standard deviation increasing more rapidly with complexity for the old than for the young. Unfortunately, there is not enough published information on intraindividual standard deviations in aged individuals to fully test this prediction against alternative models with uncorrelated delays at each step. Few published reports of elderly reaction times provide intraindividual standard deviations.However, Smith, Poon, Hale, & Myerson (1989) provide some indirect evidence on standard deviations in an aged sample. They show a linear relationship between old adultsí reaction times and those of young adults for the 10th, 25th, 50th, 75th, and 90th percentiles of each individualís trials. All of these different points lie on a single straight line. This very striking finding is inconsistent with the variances at each stage being independent. The argument is as follows. The xth percentile time for an individual is the sum of his mean time plus a fraction of his standard deviation (assuming a normal distribution for reaction times on each task). Smith et al. show that the mean reaction times of the old increase linearly with the times of the young on the same tasks. Since there is a linear relationship between the xth percentile of reaction times for the old and mean times of the young, the standard deviations must be a linear function of the reaction times of the young. As pointed out above this is independent with independent delays at each stage, or with aging raising the variability at each step in when the delays at different steps are uncorrelated. The problem is to find an aging process that causes the aging related variability in processing time at each step to increase with the required number of steps in the required manner. Cascading errors from leakage across the myelin layers is one of the few processes that could produce the observed effects. Deterioration in the myelin layers with age could produce the hypothesized increase in variability with age. While the reaction time increase with age is well documented, as is the fact that this increase statistically explains the observed decline in performance on many tests, the magnitude of the observed slowing with age (1.5 to 2) appears too little to produce the observed decline in the ability to solve more complex cognitive problems. However, if longer reaction times are merely a sign of increased errors which, once created, propagate, it is very plausible that the increased error rate in the older, noisier systems could cause the last stages of processing to suffer from such high error rates that the most complex problems cannot be solved. This could explain the dramatic deterioration in the aged's ability to solve complex problems, as well as the rapid increase in their latencies with complexity. It is known that the complexity as measured in young adults by time taken to solve a problem correlates well with the percentage of young children who can solve the problem. Presumably, the same effect would be observed in comparing young adults with very old adults. Admittedly, other processes could produce the observed standard deviation increases with complexity and with age. Possibly, a task can be done by several sequences of neural actions, with some being quicker than others. Whether a quicker or longer route is used for each trial may depend on some random factor, such as whether certain neurons are ready to fire, or are still in the recovery period from having last fired. If the routes through the network are equivalent in the young and the old, but there is a general slowing, the result would be a spreading out of the distribution proportional to the degree of slowing. Myerson's recent showing (undated) that measures of intraindividual dispersion in response times are a linear function of median latencies, with the function appearing the same for both young adults and old adults is important, and is consistent with such a process. It is clearly inconsistent with a process where the delays at different stages are independent of each other, or where the older take longer because their brains go through more steps.

While there are several theories that could explain slowing with age, not all of them imply changes observable by MRI methods. However, changes in the myelin sheath, or disappearance of neurons and their myelin sheaths, are consistent with the prolongation of the T1 times described above. Thus, it is possible that myelin deterioration may help explain the age related declines in speed and intelligence.

Autopsy data has revealed that the myelin content of the human brain declines with age (Berlet & Volk, 1980). Ansari & Loch (1975) show that myelin basic protein (which accounts for about 30% of the proteins in adult myelin) in 7 individuals aged 72-78 was lower than in 7 individuals aged 45-65. The effect was quite striking, since the highest level in the old individuals was lower than the lowest in the middle aged individuals. However, some part of the age related decline in myelin may reflect an age related neuronal death, causing the disappearance of their myelin sheaths.

Thus, it is very plausible that changes in myelin could explain much of the age related decline in cognitive functioning. This could explain the general slowing, the increase in reaction times, the longer visual evoked potential latencies, the long P300 latencies, and the fact that measures of speed explain so much of the decline in cognitive performance. Not only are the MRI effects with age consistent with a myelin based theory, but the MRI measurements correlate with a test of intelligence.

Other evidence

In multiple sclerosis (caused by partial demyelination), reaction times increase (Arena, Mazzoni, Moretti, & Lepori, et al., 1986; Rao, St. Aubin-Faubert, & Leo, 1989) and intelligence declines (Medaer, de Smedt, Swerts, & Geutjens, 1984; Matthews, Compston, Allen, Martyn, 1991, pp. 56-57). Also, P300 latencies increase (i.e., slower processing times as in healthy subjects with poor intelligence test performance) (Rao, 1990, p. 175; Polich, Romine, Sipe, Aung, & Dalessio, 1992). Also, Polich, et al, (1992) showed that the visual evoked potential latency (which appears somewhat similar to the measurements that underlay the Reed and Jensen [1992] nerve conduction velocity measurements, which correlated with intelligence in university students) was significantly slowed (P<.001) in the multiple sclerosis patients. As discussed above, these latencies are a measure of cognitive speed which correlates with intelligence. The slower processing in those with long latencies for the P300 wave could be due to slower nerve conduction, or due to delays at the synapses. The fact that the demyelinating disease, multiple sclerosis, lengthens the latencies makes it more plausible that the variations observed between the bright and the less bright, the increase in adults with age, and the decreases observed as children mature, also reflects differences in the performance of the myelin sheath. Patients with abnormalities of later event-related potentials often have significantly delayed reaction times, interpreted as suggesting that lack of white matter integrity can affect both event-related potentials and reaction times (Newton, Barrett, Callanan, & Towell, 1989). Although multiple sclerosis involves only demyelination at particular spots, the effects of this selective demyelination, and the resulting interference between adjacent axons, may be qualitatively similar to the effects resulting from occasional naturally occurring thin spots in the myelin.Volpe (1991, p. 277) has hypothesized that "myelination could be the crucial process impaired in very-low-birth-weight infants with subsequent cognitive deficits." The evidence for this includes delayed myelination in infants after neonatal injury to the white matter, and that microcephaly due to perinatal injury in full term infants appears at a mean age of approximately nine months. Obviously, this hypothesis requires that the extent of myelination affect later intellectual performance. If adverse effects on myelination from injury lower intelligence, it is plausible that poorer myelination from other sources would have similar effects. Also, mentally handicapped children over 14 years of age, classified as autistic have significantly slower central conduction times (McClelland, Eyre, Watson, Calvert, & Sherrard, 1992). Because of the age distribution, this has been interpreted as being due to a maturational defect in myelination.

Diamond et al. (1985) reported that one area of Albert Einsteinís brain had an exceptionally high ratio of glial cells to neurons. Since glial cells create and maintain myelin (Vom Muralt, 1972, p. 5; Kandel, 1991, p. 22), this would be consistent with thicker myelin contributing to greater intelligence. Also, suggesting that a real effect exists, Diamond (1988) found that rats reared in an enriched environment had an elevated ratio of glial cells to neurons.

A number of different observations concerning intelligence could be explained if individuals with greater myelination are also more intelligent. These include faster nerve conduction in the more intelligent, less glucose utilization in the brains of the more intelligent, less glucose utilization per unit volume in the large brains, the higher glial to neuron ratio in the brain of Einstein, the intraindividual skewness in reaction times, the differential increase of standard deviations and means of reaction times with the number of bits, and the shorter T2 relaxation times in the white matter of the more intelligent. Other facts about child development and aging could be explained by increased myelination over time up to adulthood, and then myelin deterioration afterwards.

References

1. Ananda, S. M. (1985). Speed of information processing and psychometric abilities in later adulthood. Unpublished doctoral dissertation, University of California, Berkeley.

2. Andreason, N. C., Flaum, M., Swayze, V., O. Leary, D. S., Alliger, R., Cohen, G., Ehrhardt, and J., Yuh, W. T. C., (1993). Intelligence and brain structure in normal individuals. American Journal of Psychiatry, 150, 130-134.

3. Anisman, H. (1975). Task complexity as a factor in eliciting heterosis in mice: Aversely motivated behaviors. Journal of Comparative and Physiological Psychology, 89, 976-984.

4. Ansari, K. A. & Loch, J. (1975). Decreased myelin basic protein content of the aged human brain. Neurology, 25, 1045-1050.

5. Arena, R., Mazzoni, M., Moretti, P., Lepori, P. et al., (1986). Reaction times to lateralized visual stimuli in multiple sclerosis. Acta Neurologica, 8, 545-553.

6. Barrett, P. T. & Eysenck, H. J., (1992). Brain evoked potentials and intelligence: The Hendrickson paradigm. Intelligence, 16, 361-383.

7. Barrett, P. T. & Eysenck, H. J., (1993). Sensory nerve conduction and intelligence: A replication. Personality and Individual Differences, 15, 249-260.

8. Barrett, P. T., Daum, I & Eysenck, H. J., (1990). Sensory nerve conduction and intelligence: A methodological study. Journal of Psychophysiology, 4, 1-13.

9. Barrett, P. T., Eysenck, H. J., & Lucking S. (1986). Reaction time and intelligence: A replicated study. Intelligence, 10, 9-40.

10. Beauducel, A. & Brocke, B. (1993). Intelligence and speed of information processing: further results and questions on Hickís paradigm and beyond. Personality and Individual Differences, 15, 627-636.

11. Benes, F. M. (1989). Myelination of cortical-hippocampal relays during late adolescence. Schizophrenia Bulletin, 15, 585-593.

12. Benton, D. (1994a). Do low cholesterol levels slow mental processing. unpublished manuscript.

13. Benton, D. (1994b). Eating fatty foods is associated with quicker reaction times. Unpublished manuscript.

14. Berent, S., Giordani, B., Lehtinen, S., Markel, D., Penney, J. B., Buchtel, H. A., Starosta-Rubinstein, S., Hichwa, R. & Young, A. B. (1988). Positron emission tomographic scan investigations of Huntington's disease: Cerebral metabolic correlates of cognitive function. Annals of Neurology, 23, 541-546.

15. Berlet, H. & Volk, B. (1980). Studies of human myelin proteins during old age. Mechanisms of Aging and Development, 14, 211-222.

16. Bermon, S., & Magnie, P300 and acute aerobic exercise, M.N. Department of Physiology, Medical School, University of Nice-Sophia Antipolis, avenue Valombrose, 06107 Nice CEDEX 02, FRANCE

17. Bishop, G. H. & Smith, J. M. (1964). The sizes of nerve fibers supplying cerebral cortex. Experimental Neurology, 9, 583-502.

18. Bouchard, T. J., Jr., (1993). The genetic architecture of human intelligence. In Vernon, P. A. (Ed.). The biological basis of intelligence, Norwood: Ablex, 33-94.

19. Bouchard, T. J., Jr., Lykken, D. T., McGue, M., Sega, N. L., & Tellegen, A. (1990). Sources of human psychological differences: The Minnesota study of twins reared apart. Science, 250, 223-250.

20. Bourre, J., Francois, M., Youyou, A., Dumont, O., Piciotti, M., Pascal, G., & Durand, G. (1989). The effects of dietary a-linolenic acid on the composition of nerve membranes, enzymatic activity, amplitude of electrophysiological parameters, resistance to poisons and performance of learning tasks in rats. Journal of Nutrition, 119, 1880-1893.

21. Brinley, J. F. (1965). Cognitive sets, speed and accuracy of performance in the elderly. In Welford, A. T. & Birren, J. E. (eds), Behavior, aging, and the nervous system. Springfield: Charles C. Thomas.

22. Carlson, S. E. (1993). Lipid requirements of very-low-birthweight infants for optimal growth and development. In Dobbing, J. (Ed.) Lipids, learning, and the brain: Fats in infant formula, report of the 103rd Ross Conference on pediatric research, Columbus: Ross Laboratories, 188-206.

23. Carlson, S. E., Werkman, S. H., Peeples, J. M., Cooke, R. J., and Tolley E. A. (1993). Arachidonic acid status correlates with first year growth in preterm infants. Proceedings of the National Academy of Science (USA), 90, 1073-1077.

24. Case, R, (1985). Intellectual development: Birth to adulthood. New York: Academic Press.

25. Cerella, J. (1990). Aging and information-processing rate. in Handbook of the psychology of aging, Birren, J. E. and Schaie, K. W., 3rd Ed., Academic Press, 201-219.

26. Churchill, J. A., Ayers, M. A., & Caldwell, D. F. (1967). Intelligence of children whose mothers have biliary tract disease. Journal of the American Medical Association, 201, 222-225.

27. Cohn, S. J., Carlson, J. S., & Jensen, A. R. (1985). Speed of information processing in academically gifted youths. Personality and Individual Differences, 6, 621-629.

28. Commission on Behavioral and Social Sciences and Education. National Research Council. NATIONAL ACADEMY PRESS, Washington, DC 1989

29. Courchesne, E., Hillyard, S.A., & Galambos, R. (1975). Stimulus novelty, task relevance and the visual evoked potential in man. Electroencephalography and Clinical Neurophysiology, 39, 131-143.

30. Cremer, R., & Zeef, E. (1987). What kind of noise increases with age? Journal of Gerontology, 42, 515-518.

31. Crusio, W. E. & Schwegler, H. (1987). Hippocampal mossy fiber distribution covaries with open-field habituation in the mouse. Behavioural Brain Research, 26, 153-158.

32. Daniel Druckman and John I. Lacey, Editors. Brain and Cognition, Some New Technologies. Committee on New Technologies in Cognitive Psychophysiology

33. Davis, H. (1964). Enhancement of evoked cortical potentials in humans related to a task requiring a decision. Science, 145:182-183.

34. Deary, I. J. & Caryl, P. G. (1993). Intelligence, EEG, and evoked potentials. In Vernon, P. A. (Ed.). The biological basis of intelligence, Norwood: Ablex, 259-316.

35. Deary, I. J. (1993). Inspection time and WAIS-R IQ subtypes: A confirmatory factor analysis study. Intelligence, 17, 223-237.

36. Dewille, J. W. & Horrocks, L. A. (1992). Synthesis and turnover of myelin phospholipids and cholesterol. In Martenson, R. E., Myelin: Biology and chemistry, Boca Raton, CRC Press, 213-236.

37. Diamond M. C. (1988). Enriching heredity: The impact of the environment on the anatomy of the Brain. New York: The Free Press.

38. Diamond M. C., Scheibel, A. B., Murphy, Jr..G. M., & Harvey, T. (1985). The brain of a scientist: Albert Einstein. Experimental Neurology, 88, 198-204.

39. Dietrich, R. B. & Hoffman, C. H. (1992). Myelination and dsymyelination. In Stark, D. D. & Bradley, W. G. (Ed s). Magnetic resonance imagining. 1057-1080.

40. Dobbing, J. (1993). Commentary. In Dobbing, J. (Ed.) Lipids, learning, and the brain: Fats in infant formula, report of the 103rd Ross Conference on pediatric research, Columbus: Ross Laboratories, 210.

41. Donchin, E., & Coles, M. G. H. (1988). Is the P300 component a manifestation of context updating? Behavioral and Brain Science, 11, 355-372.

42. Donchin, E., and M.G.H. Coles . in press Is the P300 component a manifestation of context updating? Behavioral and Brain Science.

43. Donchin, E., D. Karis, T. Bashore, M. Coles, and G. Gratton. 1986 Cognitive psychophysiology and human information processing. In M.G.H. Coles, E. Donchin, and S. Porges (Eds.), Psychophysiology: Systems, Processes, and Applications. New York: Guilford Press.

44. Donchin, E.. 1981 Surprise? ... Surprise! Psychophysiology 18:493-513.

45. Druckman, D., D. Karis, and E. Donchin . 1983 Information-processing in bargaining: Reactions to an opponent's shift in concession strategy. In R. Tietz (Ed.), Aspiration Levels in Bargaining and Economic Decision Making. Berlin-Heidelberg-New York: Springer.

46. Duncan-Johnson, C. C. & Donchin, E. (1979). On qunatifying surprise: The variation of event-related potentials with subjective probability. Psychophysiology, 14, 456-467.

47. Edward M. Miller. Intelligence and Brain Myelination: A Hypothesis. Personality and Individual Differences, Vol 17, (December 1994) No. 6, 803-833.

48. Eysenck, H. J. & Barrett, P. T. (1985). Psychophysiology and the measurement of intelligence. In Reynolds, C. R. & Willison, V. L. (Eds.) Methodological and statistical advances in the study of individual differences. New York: Plenum, 1-44.

49. Eysenck, H. J. (1987). Speed of information processing, reaction time, and the theory of intelligence. In Vernon, P. A. (Ed.). Speed of information-processing and intelligence, Norwood: Ablex.

50. Fabsitz, R., Feinleib, M., & Garrison, R. (1978). An evaluation of the intrapair relationship for personal attributes in the NHBLI twin study. In Nance W. E., Ed. Twin research: Part B, biology and epidemiology. New York: Liss, 71-80.

51. Farquharson, J., Cokburn, F., Patrick, W. A., Jamieson, E. C., & Logan, R. W., (1992). Infant cerbral cortex phsopholipid fatty-acid composition and diet. Lancet, 340, 810-813.

52. Finch, C. E. (1988). Neural and endocrine approaches to the resolution of time as a dependent variable in the aging processes of mammals. The Gerontologist, 28, 29-50.

53. Fozard, J. L., Thomas, J. C. & Waugh, N. C. (1976). Effects of age and frequency of stimulus repetitions on two-choice reaction time. Journal of Gerontology, 31, 556-562.

54. Friede, R. L. & Samorajski, T. (1967). Relation between the number of myelin lamellae and axon circumference in fibers of vagus and sciatic nerves of mice. The Journal of Comparative Neurology, 130, 223-231.

55. Furneaux, W. D., (1961). Intellectual abilities and problem solving behaviors. In Eysenck, H. J. (Ed.) Handbook of abnormal psychology: An experimental approach, New York: Basic Books, 167-192.

56. Gibson, K. R. (1991). Myelination and behavioral development: a comparative perspective on questions of neoteny, altricialty and intelligence. In Gibson, K. R. & Peterson, A. C., (Ed.) Brain maturation and cognitive development. (pp. 29-63). New York: Aldine de Gruyter.

57. Gibson, R. (1992, August 3). Food maker turn to blends of fat, fakes. Wall Street Journal, p. B1.

58. Goodall, J. (1986). The chimpanzees of Gombe. Cambridge, Mass.:Harvard University Press.

59. Gopher, D., and E. Donchin. 1986 Workload-An examination of the concept. In K.R. Boff, L. Kaufman, and J.P. Thomas (Eds.), Handbook of Perception and Human Performance, Vol II. Cognitive Processes and Performance. New York: Wiley & Sons.

60. Hack, M, Breslau, N.,Weisman, B., Aram, D., Klein, N., & Borawski, E. (1991). Effect of very low birth weight and subnormal head size on cognitive abilities at school age. New England Journal of Medicine, 325, 231-237.

61. Haier, R. J. (1993). Cerebral glucose metabolism and intelligence. In Vernon, P. A. (Ed.). The biological basis of intelligence, Norwood: Ablex, 317-332.

62. Haier, R. J., Chueh, D., Touchette, P., Lott, I., MacMillan, D., Sandman, C., LaCasse, L., Friedman, G., & Sosa, E. (1993). Brain size and cerebral glucose metabolic rate in non-specific mental retardation and Down's syndrome. Unpublished manuscript, Irvine: Department of Psychiatry, University of California.

63. Haier, R. J., Siegel, B. V., Nuechterlein, K. H., Hazlett, E., Wu, J. C., Paek, J., Browing, H. L., & Buchsbaum, M. S. (1988) Cortical glucose metabolic rate: correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence, 12, 199-217.

64. Haier, R. J., Siegel, B., Tang, C., Abel, L., & Buchsbaum, M. S. (1992). Intelligence and changes in regional cerebral glucose metabolic rate following learning. Intelligence, 16, 415-426.

65. Hale, S., (1990). A global developmental trend in cognitive processing speeds. Child Development, 61, 653-663.

66. Hale, S., Myerson, J., & Wagstaff, D. (1987). General slowing of nonverbal information processing: Evidence for a power law. Journal of Gerontology, 42,131-136.

67. Hatazawa, J., Brooks, R. A., Di Chiro, G., & Bacharach, S. L. (1987). Glucose utilization rate versus brain size in humans. Neurology, 37, 583-588.

68. Hatta, T., & Moriya, K. (1988). Developmental changes of hemisphere collaboration for tactile sequential information. International Journal of Behavioral Development, 4, 451-465.

69. Hegmann, J. P. (1972). Physiological function and behavior genetics. I. Genetic variation for peripheral conduction velocity in mice. Behavior Genetics, 2, 55-67.

70. Hegmann, J. P. (1979). A gene-imposed nervous system difference influencing behavioral convariance. Behavior Genetics, 9, 165-175.

71. Henderson, N. D., (1972). Relative effects of early rearing environment and genotype on discrimination learning in house mice. Journal of Comparative and Physiological Psychology, 79, 243-253.

72. Hendrickson, A. E., (1982). The biological basis of intelligence, part 1: Theory. In Eysenck, H. J., (Ed.). A model for intelligence. New York: Springer-Verlag, 151-196.

73. Hendrickson, D. E., & Hendrickson, A. E. (1980). Biological basis of individual differences in intelligence. Personality and Individual Differences, 1, 3-33.

74. Hertzog, C. (1989). Influences of cognitive slowing on age differences in intelligence. Developmental Psychology, 25, 636-651.

75. Hewitt, J. K., & Last, K. A. (1984). Twin studies of intelligence: Recent data, new questions. In Turner, C. J., &, Miles, H. B. (Eds.). The biology of human intelligence, Nafferton: Nafferton Books Limited.

76. Ho, H., Baker, L. A., & Decker, S. N. (1988). Covariation between intelligence and speed of cognitive processing: Genetic and environmental influences. Behavior Genetics, 18, 247-261.

77. Inagawa, K., Watanabe, S., Tsukada, Y., & Mikoshiba, K. (1988). The role of myelination in learning performance observed in two strains of myelin-deficient mutant mice. Behavioral and Neural Biology,50, 184-192.

78. Jacobs, B., Schall, M. & Scheibel, A. B. (1993). A quantitative dendritic analysis of Wernickeís area in humans. II Gender, hemispheric, and environmental factors. The Journal of Comparative Neurology, 327, 97-111.

79. Jensen, A. R. & Johnson, F. W. (1994). Race and sex differences in head size and IQ. Intelligence, 18 .

80. Jensen, A. R. (1980). Bias in mental testing. New York: The Free Press.

81. Jensen, A. R. (1982). Reaction time and psychometric g. In Eysenck, H. J. (Ed.). A model for intelligence. PP. 93-132). New York: Springer-Verlag.

82. Jensen, A. R. (1987). Individual differences in the Hick paradigm. In Vernon, P. A. (Ed.). Speed of information-processing and intelligence, Norwood: Ablex.

83. Jensen, A. R. (1992). The importance of intraindividual variation in reaction time. Personality and Individual Differences, 13 , 869-881.

84. Jensen, A. R. (1993). Why is reaction time correlated with intelligence? Current Directions in Psychological Science, 2, 53-56.

85. Jensen, A. R., and Sinha, S. N. (1993). Physical correlates of intelligence. In Vernon, P. A. (Ed.). The biological basis of intelligence. Norwood: Ablex, 139-142.

86. Jensen, A. R., Cohn, S. J., & Cohn, C. M. (1989). Speed of information processing in academically gifted youths and their siblings. Personality and Individual Differences, 10, 29-33.

87. Jensen, A. R., Schafer, E. W. P., & Crinella, F. M. (1981). Reaction time, evoked brain potentials, and psychometric g in the severely retarded. Intelligence, 5, 179-197.

88. Johnson, F. W. (1991) Biological factors and psychometric intelligence: a review. Genetic, Social, and General Psychology Monographs, 117 (2), 313-357.

89. Johnson, F. W. (1991a). Within-family analysis of cognitive and physical variables in sibs. Unpublished manuscript, University of California, Berkeley, School of Education.

90. Johnson, F. W. (1991b). Within-family analysis of cognitive and physical variables in twins. Unpublished manuscript, University of California, Berkeley, School of Education.

91. Johnson, R. (1986). A triarchic model of P300 amplitude. Psychophysiology, 23, 367-384.

92. Johnson, R., Jr. 1987 The amplitude of the P300 component of the event-related potential: Review and synthesis. In P.K. Ackles, J.R. Jennings, and M.G.H. Coles (Eds.), Advances in Psychophysiology, Vol III. Greenwich, Conn.: JAI Press.

93. Johnson, R., Jr., and P. Fedio. 1986 Pre- and post-surgical event-related potentials in epilepsy patients. Psychophysiology 23:444-445.

94. Juhel, J. (1993). Should we take the shape of reaction time distributions into account when studying the relationship between RT and psychometric intelligence. Personality and Individual Differences, 15, 357-360.

95. Kail, R. & Bisanz, J.(1992). The information-processing perspective on cognitive development in childhood and adolescence. In Sternberg, R. J. & Berg, C. A. (Eds.), Intellectual development. Cambridge: Cambridge University Press.

96. Kail, R. (1990). Development change in speed of processing during childhood and adolescence. Psychological Bulletin, 109, 490-501.

97. Kalakowski, D., & Malina, R. M. (1974). Spatial ability, throwing accuracy and manís hunting heritage. Nature, 251, 410-412.

98. Kandel, E. C. (1991). Nerve cells and behavior. In Kandel, E. C., Schwartz, J. H., & Jessell, T. M. (Eds.). Principles of neural science. New York: Elsevier, 18-32.

99. Karis, D., D. Druckman, R. Lissak, and E. Donchin. 1984 A psychophysiological analysis of bargaining: ERPs and facial expressions. In R. Karrer, J. Cohen, and P. Tueting (Eds.), Brain and Information- Annals of the New York Academy of Sciences, Volume 425.

100. Karis, D., M. Fabiani, and E. Donchin. 1984 P300 and memory: Individual differences in the von Restorff effect. Cognitive Psychology 16:177-216.

101. Koenig, S. H., (1991). Cholesterol of myelin is the determinant of gray-white contrast in MRI of brain. Magnetic Resonance in Medicine, 20, 285-291.

102. Koenig, S. H., Brown III, R. D., Spiller, M & Lundbom, N. (1990). Relaxometry of brain: why white matter appears bright in MRI. Magnetic Resonance in Medicine, 14, 482-495.

103. Koester, J. (1991a). Passive membrane properties of the neuron. In Kandel, E. C., Schwartz, J. H., & Jessell, T. M. (Eds.). Principles of neural science. New York: Elsevier, 95-103.

104. Koester, J. (1991b). Current flow in neurons. In Kandel, E. C., Schwartz, J. H., & Jessell, T. M. (Eds.). Principles of neural science. New York: Elsevier, 1033-1039.

105. Koletzko, B. & Braun, M. (1989). Annales of Nutrition and Metabolism, 35, 128-131.

106. Koletzko, B. (1992). Fats for brains. European Journal of Clinical Nutrition 46, Supplement, 1, S51-S62.

107. Konner, M. (1991). Universals of behavioral development and myelination. In Gibson, K. R. & Peterson A. C., (Eds.). Brain maturation and cognitive development. (pp. 181-223). New York: Aldine de Gruyter.

108. Kranzler, J. H. & Jensen, A. R. (1989). Inspection time and intelligence: A meta-analysis, Intelligence, 13, 329-347.

109. Kranzler, J. H. & Jensen, A. R. (1991). The nature of psychometric g: unitary process or a number of independent processes? Intelligence, 15, 397-422.

110. Kranzler, J. H. (1992a). A test of Larson and Aldertonís (1990) worst performance rule of reaction time variability. Personality and Individual Differences, 13, 255-262.

111. Kranzler, J. H. (1992b). The skewness of the distribution of RT trials does not correlate with psychometric g. Personality and Individual Differences, 13, 945-946.

112. Kutas, M., G. McCarthy, and E. Donchin. 1977 Augmenting mental chronometry: The P300 as a measure of stimulus evaluation time. Science 197:792-795.

113. Kutas, M., MacCarthy, G., & Donchin, E. (1977). Augmenting mental chronometry : The P300 as a measure of stimulus evaluation. Science, 197, 792-795.

114. Laming, D.. (1988). Some boundary conditions of choice reaction performance. In Hindmarch, I., Aufdembrinke, B. and Ott, H. (Eds.) Psychopharmacology and reaction time, Chichester: John Wiley & Sons.

115. Langsford, P. B., Mackenzie, B. D., Maher, D. P. (1994). Auditory inspection time, sustained attention, and the fundamentality of mental speed. Personality and Individual Differences, 16, 487-497.

116. Larson, G. E. & Alderton, D. L. (1990). Reaction time variability and intelligence: A ìworst performanceî analysis of individual differences. Intelligence, 14, 309-325.

117. Leamy, L. (1985). Morphometric studies in inbred and hybrid house mice. VI. A genetical analysis of brain and body size. Behavior Genetics, 15, 251-263.

118. Lee, R. B. (December 1969). Eating Christmas in the Kalahari. Natural History, 78(10), 14.

119. Linderberger, U., Mayr, U., & Kliegl, R. (1993). Speed and intelligence in old age. Psychology and Aging, 8, 207-220.

120. Lucas, A., Morley, R., Cole, T. J., Lister, G., & Leeson-Payne, C. (1992). Breast milk and subsequent intelligence quotient in children born preterm. Lancet, 339, 261-264.

121. Lynn, R. & Holmshaw, M. (1990). Black-white differences in reaction time and intelligence. Social Behavior and Personality, 18, 299-308.

122. Lynn, R. & Shigehisa, T. (1991). Reaction time and intelligence: a comparison of Japanese and British children. Journal of Biosocial Science, 23, 409-416.

123. Lynn, R. (1990a). New evidence on brain size and intelligence: a comment on Rushton and Cain and Vanderwolf. Personality and Individual Differences, 11, 795-797

124. Lynn, R. (1990b). The evolution of brain size and intelligence in man. Human Evolution, 5, 241-244.

125. Lynn, R. (1990c). The Role of nutrition in the secular increase in intelligence. Personality and Individual Differences, 10, 273-285

126. Lynn, R. (1991a). The evolution of racial differences in intelligence. The Mankind Quarterly, XXXII (1-2), 99-121.

127. Lynn, R. (1991b). Reaction times and intelligence in Chinese and British children. Perceptual and Motor Skills, 72, 443-452.

128. Lynn, R. (1993). Nutrition and intelligence. In Vernon, P. A. (Ed.). The biological basis of intelligence. Norwood: Ablex, 243-258.

129. Matthews, M. A. (1968). The electron microscopic study of the relationship between axon diameter and the initiation of myelin production in the peripheral nervous system. The Anatomical Record, 161 (3), 337-345.

130. Matthews, W. B., Compston, A., Allen, I. V., & Martyn, C. N. (1991). McAlpineís multiple sclerosis, Edinburgh: Churchill Livingstone.

131. McCarthy, G., & Donchin, E. . ((1981)). A metric for thought: A comparison of P300 latency and reaction time. Science, 211, 77-79.

132. McCarthy, G., and E. Donchin. 1983 Chronometric analyses of human information processing. Pp. 251-268 in A.W.K., Gaillard and W. Ritter (Eds.), Tutorials in Event- Related Potential Research: Endogenous Component,. Advances in Psychology, Vol.10, G.E. Stelmach and PA. Voon (Eds.). Amsterdam: North Holland Publishing Co.

133. McCartny, M., Harris, O. J. & Bernieri, F. (1990). Growing up and growing apart: a developmental meta-analysis of twin studies. Psychological Bulletin, 107, (2), 226-237.

134. McClelland, R. J., Eyre, D. G., Watson, D., Calvert, G. J., & Sherrard, E. (1992). Central conduction time in childhood autism. British Journal of Psychiatry, 160, 659-663.

135. McGarry-Roberts P.A., Stelmack, R.M., & Campbell, K.B. (1992). Intelligence, reaction time and event-related potentials. Intelligence, 16, 289-313.

136. McGary-Roberts, P. A., Stelmack, R. M., & Campbell, K. B., (1992). Intelligence, reaction time, and event-related potentials. Intelligence, 16, 289-313.

137. Medaer, R., de Smedt, L, Swerts, M., & Geutjens, J. (1984) Use of rating scales to reflect cognitive and mental functioning in multiple sclerosis. Acta Neurologica Scandinavica, 70 (Supp. 101), 65-67.

138. Miller, E. M, (1991). Climate and Intelligence. Mankind Quarterly, XXXII, 127-132.

139. Miller, E. M, (1994). Paternal provisioning versus mate seeking in human populations. Personality and Individual Differences, 691-719.

140. Myerson, J., Hale, S., Wagstaff, D., Poon, L. W., & Smith, G. A. (1990). The information-loss model: A mathematical theory of age-related cognitive slowing. Psychological Record 97, 475-487.

141. Myerson, J., Widaman, K., & Hale, S. (undated). Theoretical implications of the variability of speeded information processing. manuscript, Department of Psychology, Washington University, St. Louis, Mo. USA.

142. Neville, H.J., M. Kutas, G. Chesney, and A.L. Schmidt. 1986 Event-related brain potentials during initial encoding and recognition memory of congruous and incongruous words. Journal of Memory and Language 25:75-92.

143. Newton, M. R., Barrett, G., Callanan, M. M., & Towell, A. D. (1989). Cognitive event-related potentials in multiple sclerosis. Brain, 112, 1637-1660.

144. Olivero, A., Castellano, C, & Messeri, P. (1972). Genetic analysis of avoidance, maze, and wheel-running behavior in the mouse. Journal of Comparative and Physiological Psychology, 79, 459-473,

145. Ongari, M. A., Ritter, K. M., Orchard, M. A. Waddell, K. A., Blair, I. A., & Lewis, P. J. (1984). Correlation of prostacyclin synthesis by human umbilical artery with status of essential fatty acid. American Journal of Obstetrics and Gynecology, 149, 455-460.

146. Parks, R. W., Loewenstein, D. A., Dodrill, K. L., Barker, W. W., Yoshii, F., Chang, J. Y., Emran, A., Apicella, A., Sheramata, W. & Duara, R., (1988). Cerebral metabolic effects of a verbal fluency test: A PET scan study. Journal of Clinical and Experimental Neuropsychology,10, pp. 565-575.

147. Paylor, R., Baskall, L., & Wehner, J. M. (1993). Behavioral dissociations between C57BL/6 and DBA/2 mice on learning and memory tasks: A hippocampal-dysfunction hypothesis. Psychobiology, 21, 11-26.

148. Picton, T. W. (1992). The P300 wave of the human event-related portential. Electroenchephalography and Clinical Neurophysiology, 9, 456-479.

149. Plomin, R., & Loehlin, J. C. (1989). Direct and indirect IQ heritability estimates: a puzzle. Behavior Genetics, 19, (3), 331-342.

150. Plomin, R., Chipuer, H. M., & Neiderhiser, J. M. (1994). Behavior genetic evidence for the importance of nonshared environment. In Hetherington, E. M., Reiss, D., & Plomin, R. Eds. Separate social worlds of siblings: The impact of nonshared environment on development. Hillsdale: Lawrence Erlbaum.

151. Polich, J., & Luckritz, J. Y. (in press). Electrophysiological assessment of young, middle-age, and elderly adults. In Mergener, M., Brocklehurst, J. & Kanowski, S. (Eds.). Aging, Health, and Healing. New York: Springer.

152. Polich, J., & Martin, S. (1992). P300, cognitive capacity, and personality: A correlational study of university graduates. Personality and Individual Differences, 13, 533-543.

153. Polich, J., Ladish, C., & Burns, T. (1990). Normal variation of P300 in children: age, memory span, and head size. International Journal of Psychophysiology. 9, 237-248.

154. Polich, J., Romine, J. S., Sipe, J. C., Aung, M., & Dalessio, D. J. (1992). P300 in multiple sclerosis: a preliminary report. International Journal of Psychophysiology. 12, 155-163.

155. Pritchard, W.S.. 1981 Psychophysiology of P300. Psychological Bulletin 89:506-540.

156. Project Leader: Lars-Hakan Thorel. Electrodermal and Electrocardiac Reactivity, Event Related Potentials in EEG, Cognitive Style, Personality and Central Nervous System Enzymes and Receptors in Suicidal and Non-Suicidal Depressive Patients and Non-Clinical Control Subjects

157. Rabbit, P. & McInnis, L. (1988). Do clever old people have earlier and richer first memories? Psychology and Aging 3, 338-341.

158. Rao, S. M. (1990). Multiple sclerosis. In Cummings, J. L. (Ed.) Subcortical Dementia. New York: Oxford University Press, 164-180.

159. Rao, S. M., St. Aubin-Faubert, P., & Leo, G. J. (1989). Information processing speed in patients with multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 11, 471-477.

160. Raz, N., Millman, D., & Sarpel, G. (1990). Cerebral correlates of cognitive aging: gray-white-matter differentiation in the medial temporal lobes, and fluid versus crystallized abilities. Psychobiology, 18, (4), 475-481.

161. Raz, N., Torres, I., Spencer, W., Millman, D., & Sarpel, G. (1993). Neuroanatomical correlates of age-sensitive and age-invariant cognitive abilities: an in vivo MRI investigation. Intelligence, 17, 407-422.

162. Reed, T. E., & Jensen, A. R. (1989). Short latency visual evoked potentials (VEPs), visual tract speed, and intelligence: Significant correlations. Behavior Genetics, 19, 772-773.

163. Reed, T. E., & Jensen, A. R. (1991). Arm nerve conduction velocity (NCV), brain NCV, reaction time, and intelligence. Intelligence,15, 33-47.

164. Reed, T. E., & Jensen, A. R. (1992). Conduction velocity in a brain nerve pathway of normal adults correlates with intelligence level. Intelligence, 16, 259-272.

165. Reed, T. E., & Jensen, A. R. (1993). Choice reaction time and visual pathway nerve conduction velocity both correlates with intelligence but appear not to correlate with each other: Implications for information processing. Intelligence, 17, 191-203.

166. Rodgers, B, (1978). Feeding in infancy and later ability and attainment: a longitudinal study. Development Medical Child Neurology, 20, 421-26.

167. Rogers, J. & Styren, S. D. (1987). Neuroantomy of aging and dementia. Review of Biological Research in Aging 3, 223-253.

168. Rossler, S. 1983 Endogenous ERPs and cognition: Probes, prospects, pitfall in matching pieces for the mind-body puzzle. Tutorials in ERP Research; Endogenous Components. Amsterdam: North Holland Publishing.

169. Rushton, J. P. (1990). Race, brain size, and intelligence: A reply to Cernovsky. Psychological Reports, 66, 659-666.

170. Salthouse, T. (1992). Mechanisms of age-cognition relations in adulthood, Hillsdale, New Jersey: Lawrence Erlbaum Associates.

171. Salthouse, T. A. & Babcock, R. L. (1991). Decomposing adult age differences in working memory. Developmental Psychology, 27, 763-776.

172. Salthouse, T. A. & Mitchell, D. R. (1990). Effects of age and naturally occurring experience on spatial visualization performance. Developmental Psychology, 26, 845-854.

173. Salthouse, T. A. (1992). Why do adult age differences increase with task complexity? Developmental Psychology, 28, 905-918.

174. Salthouse, T. A. (1993a). Speed and knowledge as determinants of adult age differences in verbal tasks. Journals of Gerontology, 48, P29-P36.

175. Salthouse, T. A. (1993b). Attentional blocks are not responsible for age related-slowing. Journals of Gerontology, 48, P263-P270.

176. Salthouse, T. A. (1993c). Speed mediation of adult age differences in cognition. Developmental Psychology, 29, 722-738.

177. Schaie, K. W. (1989). Perceptual speed in adulthood: Cross-sectional and longitudinal studies. Psychology and Aging, 4, 443-453.

178. Schultz, R. (1991). The relationship between intelligence and gray-white matter image contrast: A MRI study of healthy college students, Unpublished doctoral dissertation, Department of Psychology, University of Texas.

179. Schultz, R. (1993). Presentation at the behavior genetics association.

180. Schultz, R., Willerman, L., Rutledge N., & Bigler, E. (1992). The relationship between intelligence, gray-white image contrast, and brain size: An MRI study of healthy college students. Proceedings of the 20th Annual Meeting of the International Neuropsychological Society, San Diego, California. Abstracted and published in Journal of Clinical and Experimental Neuropsychology, 14, 32.

181. Schweizer, K. (1993). Verbal ability and speed of information processing. Personality and Individual Differences, 15, 645-653.

182. Seligman, D. (1992). A question of intelligence: The IQ debate in America. New York: Carol Publishing.

183. Seyfried, T. & Yu, R. (1980). Heterosis for brain myelin content in mice. Biochemical Genetics, 18, 1229-1237.

184. Seyfried, T., Glaser, G. & Yu, R. (1979). Genetic variability for regional brain gangliosides in five strains of young mice. Biochemical Genetics, 17, 43-55.

185. Smith, G. A., Poon, L. W., Hale, S., & Myerson, J., (1989). A regular relationship between young and old adultsí latencies on their best, average and worst trials. Australian Journal of Psychology, 40, 195-210.

186. The Audiology Primer for Students and Health Care Professionals contributions from and compilation by the VA Audiology staff at Mountain Home, Tennessee with contributions from the VA Audiology staffs at Long Beach, California, West Los Angeles, California, Nashville, Tennessee, San Francisco, California, Department of Veterans Affairs, Summer, 1997

187. Upchurch, M. & Wehner, J. M. (1989). Inheritance of spatial learning ability in inbred mice: A classical genetic analysis. Behavioral Neuroscience, 103, 1251-1258.

188. Van de Vijver, F. & Willemse, G. R. (1991). Are reaction time tasks better suited for cultural minorities than paper-and-pencil tests? In Bleichrodt, N. and Drenth, P. J. D. (Eds.) Contemporary issues in cross-cultural psychology, Amsterdam: Swets & Seitlinger, 450-464.

189. Vernon, P. A. & Kantor, L. (1986). Reaction time correlations with intelligence test scores obtained under either timed or untimed conditions. Intelligence, 10, 315-330.

190. Vernon, P. A. & Mori, M. (1992). Intelligence, reaction times, and peripheral nerve conduction velocity. Intelligence, 16. 273-288.

191. Vernon, P. A. & Weese, S. E. (1993). Predicting intelligence with multiple speed of information-processing tests. Personality and Individual Differences, 14, 413-419.

192. Vernon, P. A. (1983). Speed of information processing and general intelligence. Intelligence, 7, 53-70.

193. Vernon, P. A. (1990). The use of biological measures to estimate behavioral intelligence. Educational Psychologist, 25, 293-304.

194. Vernon, P. A. (1993). Der Zahln-Verbindungs-Test and other trail-making correlates of general intelligence. Personality and Individual Differences, 14, 35-40.

195. Vernon, P. A. (Ed.). (1987). Speed of information-processing and intelligence, Norwood: Ablex.

196. Vickers, D., Nettelbeck, T., & Willson, R. J. (1972). Perceptual indices of performance: the measurement of ëinspection timeí and ënoiseí in the visual system. Perception, 1, 263-295.

197. Volpe, J. (1991). Cognitive deficits in premature infants. New England Journal of Medicine, 325, 276-277.

198. Vom Muralt, A. (1972). The developing brain and the damage inflicted by malnutrition: an introduction, In Ciba Foundation, Lipids, malnutrition & the developing brain, Amsterdam: North Holland.

199. Wahlsten, D. (1975). Genetic variation in the development of mouse brain and behavior: evidence from the middle postnatal period. Developmental Psychobiology, 8, 371-380.

200. Wahlsten, D. (1975). Genetic variation in the development of mouse brain and behavior: Evidence from the middle postnatal period. Developmental Psychobiology, 8, 371-380.

201. Wahlund, L., Agartz, I, Almqvist, O, Basun, H., Forssell, L., Saaf, J., & Wetterberg (1990). The brain in healthy aged individuals: MR imaging. Radiology, 174, 675-679.

202. Weiss, V. (1984). Psychometric intelligence correlates with interindividual different rates of lipid peroxidation. Biomedica Biochimica Acta, 43, 755-763.

203. Welford, A. T. (1984). Between bodily changes and performance: Some possible reasons for slowing with age. Experimental Aging Research, 10, 73-88.

204. Wendy C. West, Timothy O’Rourke, Phillip J. Holcomb. Event-Related Brain Potentials and Language Comprehension: A Cognitive Neuroscience Approach to the Study of Intellectual Functioning. Tufts University

205. Wickens, C., Kramer, A., Vanasse, L., & Donchin, E. (1983). The performance of concurrent tasks: A psychophysiological analysis of the reciprocity of information processing resources. Science, 221, 1080-1082.

206. Wickett, J. C., & Vernon, P. A. (in press). Peripheral nerve conduction velocity, reaction time, and intelligence: An attempt to replicate Vernon and Mori (1992). Intelligence .

207. Wickett, J. C., Vernon, P. A. & Lee, D. H. (in press). In vivo brain size, head perimeter, and intelligence in a sample of healthy adult females. Personality and Individual Differences.

208. Willerman, L., Schultz, R., Rutledge, J. N. & Bigler, E. D. (1991). In vivo brain size and intelligence. Intelligence, 15, 223-228.

209. Willerman, L., Schultz, R., Rutledge, J. N. & Bigler, E. D. (1992). Hemisphere size asymmetry predicts relative verbal and nonverbal intelligence differently in the sexes: an MRI study of structure-function relations. Intelligence, 16, 315-329.

210. Willerman, L.. Schultz, R., Rutledge, J. N. & Bigler, E. D. (1994). Brain structure and cognitive function. in Reynolds, C. R. Cognitive Assessment: A multi-disciplinary assessment. New York: Plenum, 35-55..

211. Wilson, R. S. (1983). The Louisville twin study: Develpmental synchronies in behavior. Child Development, 54, 298-316.

212. Witelson, S. F., & McCulloch P. B. (1991). Premortem and postmortem measurement to study structure with function: A human brain collection. Schizophrenia Bulletin, 17, 581-591.

213. Woodworth, R. S. & Schlosberg H. (1954). Experimental Psychology. New York: Henry Holt.

214. Yakovlev, P. & Lecours, A. (1967). In Minkowski, A. (Ed.) Regional development of the brain in early life, Oxford, Blackwell, 3-70.

215. Yeo, R. A., Turkheimer, E., Raz, N., & Bigler, E. D. (1987). Volumetric asymmetries of the human brain: intellectual correlates. Brain and Cognition, 6, 15-23.

216. Ylikoski, R., Ylikoski, A., Erikinjuntti, T., Sulkava, R., Raininko, R. & Tilvis, R. (1993). White matter changes in healthy elderly persons correlate with attention and speed of mental processing. Archives of Neurology, 50, 818-824.

217. Yoshii, F., Barker, W., Chang, J. Y., Lowenstein, D., Apicella, A., Smith, A., Boothe, T., Ginsberge, M., Pascal, S., & Dura R. (1988). Sensitivity of cerebral glucose metabolism to age, gender, brain volume, brain atrophy, and cerebral risk factors. Journal of Cerebral Blood Flow and Metabolism, 8, 654-661.