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Psychomotor movement and IQ
Personality and Individual Differences 37 (2004) 523–531 www.elsevier.com/locate/paid
Psychomotor movement and IQ Margaret McRorie *, Colin Cooper School of Psychology, QueenÕs University of Belfast, Belfast BT7 1NN, Ireland Received 30 September 2002; received in revised form 8 September 2003; accepted 29 September 2003 Available online 19 November 2003
Abstract This study tested the Eysenck/Jensen Ôspeed of neural processingÕ theory of general ability by examining the relationship between general mental ability and speed of performing a simple, repetitive movement. Seventy psychology students were tested for intelligence using RavenÕs Advanced Progressive Matrices and the Wechsler Adult Intelligence Scale-III. Motor speed was assessed by means of a tapping task in which two fingers tapped alternately. The expected positive correlations between speed scores and cognitive measures were obtained, with significant associations demonstrated between tapping speed and traditional Full-scale IQ, Verbal IQ and RavenÕs Matrices scores. There was little evidence however of any association between motor speed and Performance IQ. These findings are consistent with other research, although the lack of correlation with WAIS-III Performance scores is surprising. 2003 Elsevier Ltd. All rights reserved. Keywords: Motor speed; Tapping speed; Performance IQ; Verbal IQ; Speed of processing
1. Introduction Renewed consideration of GaltonÕs and SpearmanÕs hypotheses have been reflected in the renewed search for basic processes which might underlie mental ability (e.g. Eysenck, 1982; Jensen, 1993; Vernon, 1987). Such theories suggest that intelligent behaviour is the result of a biological speed factor which is itself reflected in the time taken to carry out basic psychological tasks (Bates & Stough, 1997), typically reaction time or inspection time paradigms. These experiments are important in that they attempt to explore the hypothesis that speed and efficiency of brain functioning may be indirectly measured within simple perceptual and reaction time tasks which are themselves related to g. *
Corresponding author. Tel.: +353-28-90274177; fax: +353-28-90664144. E-mail address: [email protected] (M. McRorie).
0191-8869/$ - see front matter 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2003.09.023
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M. McRorie, C. Cooper / Personality and Individual Differences 37 (2004) 523–531
Whilst some of the variation in mental ability differences is linked to information processing capacity, there remains a need for an understanding of the actual biological mechanisms by which this is achieved (Hunt, 1999). With little known about the design of the brain in relation to cognition (Hillyard & Picton, 1987), one suggestion for the basis of the RT-IQ relationship is that speed comes from faster conducting neurons (e.g. Barrett & Eysenck, 1993; Reed & Jensen, 1991, 1992, 1993; Vernon, 1991; Vernon & Mori, 1992). These studies stem from ReedÕs (1984, 1988) hypothesizing that the genetic basis of g partly results from individual differences in nerve conduction velocity. Jensen and Sinha (1993) review investigations which have reported associations between physical correlates and mental ability, and Deary and Caryl (1993, 1997) review biological correlates, such as averaged evoked potentials and cerebral glucose metabolic rate. These studies generally support the hypothesis that greater mental ability is associated with a faster, more efficient central nervous system. 1.1. Psychomotor movement The definition of motor speed within the literature relates to the speed with which distinct movements of the fingers, hands, etc. can be carried out. It is strictly concerned with the speed with which a movement is executed, and not with initiation speed of the movement. One of the simplest possible motor tasks is finger tapping (Halstead, 1947), and studies in both the clinical and individual differences literature have investigated both paced tapping, i.e. at regular speeds, or instructions to tap as quickly as possible (speed of tapping). Finger tapping is a fine motor task, and is often used as a clinical neuropsychological test of controlled sequential responses. This is in contrast to reaction time which requires a controlled single response. Reitan and Wolfson (1993) have stated that Ôwe cannot emphasise enough that the finger tapping test is not a reaction time testÕ (p. 231). The ability to perform such a fine sensitive task presumably affects the rate of performance, and recent PET scans and MRI studies have shown that higher rates of finger tapping are reflected in stronger motor cortex signals, in terms of both the area and the strength of activity. This is thought to reflect increased neural activity associated with increased motor control commands during faster finger movements. J€ ancke, Specht, Mirzazade, and Peters (1999) suggest that rate and movement task effects on cerebrellar activation are differentially sensitive to the movements of dominant and non-dominant hands. In their investigation of trade-offs between finger tapping and verbal memory, Friedman, Polson, and Dafoe (1988) have reported that tapping hand differentially affects verbal recall performance, with performance on the verbal task poorer when tapping with the right hand. Positive correlations have been demonstrated between tapping speed and intelligence (Wilson, Tunstall, & Eysenck, 1971), and in a later investigation of associations between verbal skills, fine motor tasks and cognitive tempo (impulsivity, time judgement), Stanford and Barratt (1996) have reported that finger tapping is related to verbal skills in adolescents, measured by the Wechsler Intelligence Scale for Children-Revised (WISC-R; Wechsler, 1974), the Wide Range Achievement Tests (WRAT; Jastak & Jastak, 1978), and the Gray Oral Reading Test (GORT; Gray, 1963). However despite these scattered but generally encouraging results, this is not a paradigm which currently appears in any of the intelligence research literature specifically in relation to Ôspeed of neural processingÕ theory. Given the hypothesis that speed of processing underlies individual
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differences in intelligence, positive associations between tapping speed scores and the cognitive measures were expected.
2. Method 2.1. Participants The sample consisted of 70 psychology students (32 male and 38 female), with ages ranging between 18 and 43 (M ¼ 22:96, SD ¼ 6.27). The majority (N ¼ 54) were aged between 18 and 24. The age range for males was from 18 to 42 years, with a mean age of 22.47 and SD of 5.96. Age range for females was similarly between 18 and 43, with a mean age of 23.37 and SD of 6.56. Participants were recruited via contact within the Psychology Department of the QueenÕs University of Belfast, and were paid £5 for their participation. All those participating were in apparent good health, and those who normally used corrective glasses wore them during testing. Personal data gathered included age, health status, handedness (preferred hand for writing), finger dexterity, height, weight and armspan (arms horizontal, fingertip to fingertip). 2.2. Measures of IQ In order to assess as wide a range of ability as was feasible, both the Ravens Advanced Progressive Matrices (Raven, 1965) and the Wechsler Adult Intelligence Scale-III (WAIS-III; Wechsler, 1997) were administered. The RAPM was chosen because it provides a good measure of SpearmanÕs g, and as per the standardized procedure, Set 2 was administered under a time constraint of 40 min. The WAIS-III has the advantage of providing traditional IQ scores, and also yields index measures based on more specific cognitive functioning. This test was administered according to the standard instructions and most participants were able to complete it in one and a half hours. 2.3. Apparatus and testing procedure Motor performance has usually been examined by assessing the speed of tapping of one (index) finger over a set period of time (e.g. Stanford & Barratt, 1996). An obvious problem with this approach is that some individuals may lift their fingers higher than others, and so it seems likely that this will be confounded with speed of tapping. We therefore assessed motor speed by means of a simple finger tapping task involving the alternation of the index and middle fingers. This was designed in an attempt to (a) minimize the build-up effects of muscle fatigue caused by repeatedly tapping one finger, and (b) reduce variation in the extent to which participants lift their fingers. The apparatus consisted of two metal sensors on a pivot (points of contact), housed within a metal rectangular block (11 · 19 cm), and interfaced to a PC computer. The mechanism was designed to provide the least mechanical resistance to participantsÕ tapping force as possible, and operated on the principle of a fulcrum effect, i.e. two keys hitting either end of a pivoted plate so that when one was up the other was down. An optical encoder on the pivoting shaft counted the number of contacts. The distance between two keys (2.5 cm) enabled index and middle fingers of
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varying hand sizes to rest comfortably. The travel of each key was 0.8 cm. The participant rested the heel of his/her hand on the box and responded by tapping on the points of contact with index and middle fingers. Wilson et al.Õs (1971) investigation suggested that relationships between individual difference variables and performance may be affected by the time spent on the task. Early trials reflect adjustment periods and are thus unreliable in the measurement of overall speed (Getty, 1976). Barratt, Patton, Olsson, and Zucker (1981) discarded the first four taps in each trial, and likewise the first two seconds in Friedman et al.Õs (1988) study were treated as a warm-up period and the data not used in the analysis. Similarly, a pre-programmed time mechanism in this investigation discarded the first two seconds of each trial from the overall count. Some studies have chosen to operate a clamping system, whereby the hand is held in a constant position (e.g. Wilson et al., 1971). Clamping is designed to prevent participants from countering the build-up of inhibition by transferring work-load to different muscles, so that fatigue effects would be manifest more quickly. Due to the short length of each current trial session it was considered that the instruction to keep the heel of the hand resting on the block throughout would minimize hand movement, adequately preventing any major adjustment in position. Accuracy of motor movement is generally not considered, however the mechanism of the equipment eliminated the possibility of errors, in that it was impossible for both fingers to make contact at the same time. Participants were seated in front of the equipment and advised that the task would measure motor speed. They were instructed to keep the heel of their hand resting on top of the block (to minimize hand movement), and to place their index and middle fingers on the two sensors (points of contact). A few minutes were provided for participants to accustom themselves to the equipment, i.e. to get a feel for the placement of their hands, and to practice tapping on the response keys. Starting with their right hand, participants were instructed to commence tapping as fast as they could at the onset of a two-second high-pitched tone, to continue tapping while this tone sounded, and to finish tapping at the onset of a lower pitched tone. The whole trial lasted 12 s, the first two-seconds of which was not analysed. There was a 10 s rest period between trials. Then they repeated the process with their left hand, and continued alternating hands between trials. Scores for each hand are represented by the mean of 5 trials of 10 s each.
3. Results The data used for analysis was the sum of the means for both hands and the intra-individual variability between trials. Speed of tapping in respect of the dominant and non-dominant hands were examined separately to further investigate the possibility of related gender differences. Tan and TanÕs (1995) investigation relating to gender lateralization of peripheral nerve conduction velocity reported no significant differences in males, yet mean sensory velocity was demonstrated to be significantly faster in the left than in the right hand in females. The data were initially scrutinized for potential outliers, and any extreme values, i.e. falling outside three times the interquartile range, were omitted. Whilst a negative skew may have been expected due to possible lapses in attention, etc., the distributions of mean tapping totals for both hands (skewness, )0.073, kurtosis, )0.360), and for the non-dominant hand (skewness, 0.045,
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kurtosis, )0.392) demonstrate normal symmetry, with the mean scores for the dominant hand (skewness, )0.325, kurtosis, )0.298) showing an acceptable level of symmetry. The distributions for total tapping variability within subjects and variability of dominant and non-dominant hands are as expected positively skewed (skewness, 1.427, kurtosis, 2.670; skewness, 1.551, kurtosis, 2.951; skewness, 1.000, kurtosis, 0.481, respectively). Table 1 presents the mean number of taps and standard deviations between subjects for all ten trials and for the trials relating to the dominant and non-dominant hands. Mean taps are slightly higher than those reported in the literature for the same time duration. This was somewhat expected in that the majority of studies have been based on Ôone finger tappingÕ as opposed to the alternating taps used here. The standard deviations were higher than expected at 17.8 and 18.5 for the dominant and non-dominant hands respectively, in comparison to the standard deviations of 6.6 and 5.6 reported by Stanford and Barratt (1996). A gender by handedness by dominance mixed-model ANOVA on mean tapping performance revealed a massively significant gender effect (F1;66 ¼ 11:27, p < 0:001) indicating that males outperformed females. There was a significant dominance by handedness interaction (F1;66 ¼ 4:14, p < 0:05) indicating that the non-dominant-hand performance of the right-handed individuals was particularly poor, but no other variables approached significance. A similar analysis on intraindividual variability between trials indicated no significant effects. Mean tapping scores showed no correlation with age (r ¼ 0:06, ns). A regression analysis was performed with tapping speed as the dependent variable. When gender was entered first, with height, weight, head width and armspan being entered together in a second block, there was no significant increase in R as a result of adding the measures of body size (R increased from 0.44 to 0.54; F4;63 ¼ 2:01, ns). The correlations between motor speed and measures of IQ are presented in Table 2. There are significant correlations between the RavenÕs Matrices scores and mean total tapping speed and mean tapping speed for the dominant and non-dominant hands. There are no significant associations between variability of tapping speed and these measures of IQ. There was a significant correlation between tapping speed and traditional WAIS Full-scale IQ (FSIQ) and the WAIS Verbal IQ scale (VIQ) and Verbal Comprehension Index (VCI). (Correcting these correlations for restriction of range suggests that a correlation of 0.23 might be as high as 0.38 in the general population.) There was no significant correlation with Performance IQ. However the correlation between tapping and Verbal IQ was not significantly larger than that between tapping and Performance IQ. There were no significant relationships between abilities and within-subject variability in tapping either before or after correction for restriction of range.
Table 1 Average number of taps per 10 s period (five ten-second blocks per hand)
Dominant hand Non-dominant hand Average
Males (N ¼ 32)
Females (N ¼ 38)
All
Mean
SD
Mean
SD
Mean
SD
101.84 92.22 96.41
11.58 11.85 12.31
83.22 78.95 80.80
11.10 9.09 9.86
91.73 85.02 87.94
17.81 18.59 17.52
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Table 2 Correlations between psychomotor speed and ability scores, including corrections for range restriction Both hands
Non-dominant hand
0.256 0.413
0.267 0.431
0.225 0.367
WAIS-III Full Scale IQ Corrected r
0.233 0.380
0.258 0.416
0.184 0.305
WAIS-III Verbal IQ Corrected r
0.290 0.461
0.332 0.516
0.242 0.392
WAIS-III Performance IQ Corrected r
0.075 0.127
0.075 0.127
0.046 0.077
WAIS-III Verbal Comprehension Index Corrected r
0.281
0.319
0.232
0.448
0.499
0.378
WAIS-III Perceptual Organization Index Corrected r
0.182
0.183
0.150
0.301
0.303
0.251
WAIS-III Working Memory Index Corrected r
0.135 0.227
0.160 0.267
0.119 0.203
)0.148 )0.248
)0.125 )0.211
)0.187 )0.310
WAIS-III Processing Speed Index Corrected r
Dominant hand
RavenÕs Matrices Corrected r
p < 0:05 (1-tailed),
p < 0:01 (1-tailed).
Table 3 Factor analysis of WAIS subtests, Raven APM scores, and mean tapping speed scores (both hands combined) Component 1 Picture completion Vocabulary Digit symbol Similarities Block design Arithmetic Matrix reasoning Digit span Information Picture arrangement Comprehension Symbol search Letter–number sequencing Raven APM scores Mean tapping speed
0.286 0.516 )0.198 0.481 0.611 0.566 0.751 0.303 0.671 0.449 0.502 0.215 0.524 0.723 0.324
A principal factor analysis (unrotated) of all of the independent subtests of the WAIS-III (not including any of the composite scores), along with the RAPM scores and the mean tapping speed
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scores (both hands combined) was performed. The loading of tapping speed on the PF1, indicating its correlation with psychometric g, was 0.324 (Table 3).
4. Discussion The results reported above show clearly that 1. Fast tapping speed is reflected in above-average performance on RavenÕs matrices, for both males and females. 2. Higher rates of finger tapping are also reflected in above average performance on the traditional WAIS Full-scale IQ, Verbal IQ, and Verbal Comprehension Index. 3. There is no relationship between fast tapping speed and high scores on the WAIS Performance IQ scale. This investigation examined the extent of the association between a simple measure of motor speed, and general mental ability. This behavioural measure of speed contained virtually no cognitive component, thus (unlike the reaction time paradigm) there was no real possibility for strategy use. The specific hypotheses were that there would be positive correlations between mean estimates of tapping speed, and intelligence test scores. Negative correlations between these cognitive measures and tapping standard deviation were also hypothesized, in that less variability in motor speed would reflect a more efficient system. Within the full sample, mean tapping values for the dominant hand were greater, i.e. tapping speed was faster. The possibility that females might demonstrate faster motor speed for the nondominant hand as reported by Tan and TanÕs (1995) conduction velocity study was not substantiated, with greater values reported for the dominant hand. The positive correlations found between motor speed and g (estimated by the RAPM), and between motor speed and FSIQ, VIQ and VCI were within the range expected. These associations are as hypothesized and support the relationships between tapping speed and intelligence previously reported by Wilson et al. (1971) and Stanford and Barratt (1996). The significant correlations between mean motor speed and IQ provide a very consistent set of results. However the failure to find any relationship between motor speed and Performance IQ is surprising, given the associations demonstrated between motor speed and the verbal tests. These results do nonetheless substantiate previous findings in relation to verbal ability and Performance IQ. Hendrickson (1982) reported higher correlations for verbal than non-verbal tests, and likewise although visual inspection time is more highly correlated with performance tasks, auditory inspection time has at times been found to demonstrate higher correlations with verbal than with non-verbal IQ (Deary, Head, & Egan, 1989; Irwin, 1984). Somewhat similar findings have been reported in relation to the concept of a basic information processing unit (BIP). Lehrl and Fischer (1988, 1990) formulated the capacity of working memory in terms of the speed and duration of neural traces, and reported correlations between capacity and untimed verbal test scores at 0.67 (n ¼ 672) and 0.88 (n ¼ 66). Kline, Draycott, and McAndrew (1994), also Draycott and Kline (1994) later replicated these findings. However Stankov and Roberts (1997) point out that when correlated with various intelligence factors, the
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BIP procedure shares a moderate correlation only with verbal abilities. This is largely inconsistent with the general literature regarding mental speed and intelligence, although Jensen (1998) has suggested that the measure of processing speed may be factorially too narrow, containing a specific verbal factor. The findings currently reported support Stankov and Roberts (1997) –– within a behavioural paradigm containing no cognitive component whatsoever. In conclusion, although the consistent results between mean motor speed and measures of IQ provide tentative support for a speed theory of intelligence, it is particularly strange that motor speed is not predicted by Performance IQ. Given that performance tasks are heavily timed, one would think that if the relationship was just based on speed an association would have been found.
References Barratt, E. S., Patton, J. H., Olsson, N. J., & Zucker, G. (1981). Impulsivity and paced tapping. Journal of Motor Behavior, 13, 286–300. Barrett, P. T., & Eysenck, H. J. (1993). String length, attention and intelligence: focused attention reverses the string length-IQ relationship. Personality and Individual Differences, 15, 363–371. Bates, T., & Stough, C. K. (1997). Processing speed, attention, and intelligence: effects of spatial attention on decision time in high and low IQ subjects. Personality and Individual Differences, 23, 861–868. Deary, I. J., & Caryl, P. G. (1993). Intelligence, EEG, and evoked potentials. In P. A. Vernon (Ed.), Biological approaches to the study of human intelligence (pp. 259–315). Norwood, NJ: Ablex. Deary, I. J., & Caryl, P. G. (1997). Neuroscience and human intelligence differences. Trends in Neuroscience, 20, 365– 371. Deary, I. J., Head, B., & Egan, V. (1989). Auditory inspection time, intelligence and pitch discrimination. Intelligence, 13, 135–147. Draycott, S. G., & Kline, P. (1994). Further investigation into the nature of the BIP: a factor analysis of the BIP with primary mental abilities. Personality and Individual Differences, 17, 201–209. Eysenck, H. J. (Ed.). (1982). A model for intelligence. Berlin: Springer-Verlag. Friedman, A., Polson, M. C., & Dafoe, C. G. (1988). Dividing attention between the hands and the head: performance trade-offs between rapid finger tapping and verbal memory. Journal of Experimental Psychology: Human Perception and Performance, 14, 60–68. Getty, D. J. (1976). Counting process in human timing. Perception and Psychophysics, 20, 191–197. Gray, W. (1963). Gray oral reading test manual. Wilmington, DE: Jastak Associates, Inc. Halstead, W. C. (1947). Brain and intelligence: a quantitative study of the frontal lobes. Chicago: University of Chicago Press. Hendrickson, A. E. (1982). The biological basis of intelligence. Part 1: Theory. In H. J. Eysenck (Ed.), A model for intelligence. Berlin: Springer-Verlag. Hillyard, S. A., & Picton, T. W. (1987). Electrophysiology of cognition. In V. B. Mountcastle (Ed.), Handbook of physiology: Sec. 1. The nervous system: Vol. 5. Higher functions of the brain. Bethesda, MD: American Physiological Society. Hunt, E. (1999). Intelligence and human resources: past, present and future. In P. L. Ackerman, P. C. Kyllonen, & R. D. Roberts (Eds.), Learning and individual differences: process, trait, and content determinants (pp. 3–28). Washington, DC: American Psychological Association. Irwin, R. J. (1984). Inspection time and its relation to intelligence. Intelligence, 8, 47–65. J€ ancke, L., Specht, K., Mirzazade, S., & Peters, M. (1999). The effect of finger-movement speed of the dominant and subdominant hand on cerebellar activation: a functional magnetic resonance imaging study. NeuroImage, 9, 497– 507. Jastak, J. F., & Jastak, S. (1978). Wide range achievement test manual. Wilmington, DE: Jastak Associates, Inc.
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Jensen, A. R. (1993). SpearmanÕs hypothesis tested with chronometric information-processing tasks. Intelligence, 17, 47–77. Jensen, A. R. (1998). The g factor. New York: Praeger. Jensen, A. R., & Sinha, S. N. (1993). Physical correlates of human intelligence. In P. A. Vernon (Ed.), Biological approaches to human intelligence. Norwood, NJ: Ablex. Kline, P., Draycott, S. G., & McAndrew, V. M. (1994). Reconstructing intelligence: a factor analytic study of the BIP. Personality and Individual differences, 16, 259–536. Lehrl, S., & Fischer, B. (1988). The basic parameters of human information processing: their role in the determination of intelligence. Personality and Individual Differences, 9, 883–896. Lehrl, S., & Fischer, B. (1990). A basic information psychological parameter (BIP) for the reconstructions of concepts of intelligence. European Journal of Personality, 4, 259–286. Raven, J. C. (1965). Advanced progressive matrices. London: H.K. Lewis. Reed, T. E. (1984). Mechanism for heritability of intelligence. Nature, 311, 417. Reed, T. E. (1988). A neurophysiological basis for the heritability of intelligence. In H. J. Jerison & I. Jerison (Eds.), Intelligence and evolutionary biology. Berlin: Springer. Reed, T. E., & Jensen, A. R. (1991). Arm nerve conduction velocity (NCV), brain NCV, reaction time and intelligence. Intelligence, 15, 33–47. 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. Reed, T. E., & Jensen, A. R. (1993). Choice reaction time and visual pathway nerve conduction velocity both correlate with intelligence but appear not to correlate with each other: implications for information processing. Intelligence, 17, 191–203. Reitan, R. M., & Wolfson, D. (1993). The Halstead–Reitan neuropsychological test battery: theory and clinical interpretation. Tucson, AZ: Neuropsychology Press. Stanford, M. S., & Barratt, E. S. (1996). Verbal skills, finger tapping, and cognitive tempo define a second order factor of temporal information processing. Brain and Cognition, 31, 35–45. Stankov, L., & Roberts, R. D. (1997). Mental speed is not the ÔbasicÕ process of intelligence. Personality and Individual Differences, 22(1), 69–84. Tan, M., & Tan, U. (1995). Possible lateralization of peripheral nerve conduction associated with gender. Perceptual and Motor Skills, 81, 939–943. Vernon, P. A. (1987). Speed of information-processing and intelligence. Norwood, NJ: Ablex. Vernon, P. A. (1991). Studying intelligence the hard way. Intelligence, 15, 389–395. Vernon, P. A., & Mori, M. (1992). Intelligence, reaction times and nerve conduction velocity. Intelligence, 16, 273–288. Wechsler, D. (1974). Manual for the Wechsler intelligence scale for children-revised. New York: The Psychological Corporation. Wechsler, D. (1997). Manual for the Wechsler adult intelligence scale III. New York: Psychological Corporation. Wilson, G. D., Tunstall, O. A., & Eysenck, H. J. (1971). Individual differences in tapping performance as a function of time on the task. Perceptual and Motor Skills, 33, 375–378.
Psychomotor movement and IQ Margaret McRorie *, Colin Cooper School of Psychology, QueenÕs University of Belfast, Belfast BT7 1NN, Ireland Received 30 September 2002; received in revised form 8 September 2003; accepted 29 September 2003 Available online 19 November 2003
Abstract This study tested the Eysenck/Jensen Ôspeed of neural processingÕ theory of general ability by examining the relationship between general mental ability and speed of performing a simple, repetitive movement. Seventy psychology students were tested for intelligence using RavenÕs Advanced Progressive Matrices and the Wechsler Adult Intelligence Scale-III. Motor speed was assessed by means of a tapping task in which two fingers tapped alternately. The expected positive correlations between speed scores and cognitive measures were obtained, with significant associations demonstrated between tapping speed and traditional Full-scale IQ, Verbal IQ and RavenÕs Matrices scores. There was little evidence however of any association between motor speed and Performance IQ. These findings are consistent with other research, although the lack of correlation with WAIS-III Performance scores is surprising. 2003 Elsevier Ltd. All rights reserved. Keywords: Motor speed; Tapping speed; Performance IQ; Verbal IQ; Speed of processing
1. Introduction Renewed consideration of GaltonÕs and SpearmanÕs hypotheses have been reflected in the renewed search for basic processes which might underlie mental ability (e.g. Eysenck, 1982; Jensen, 1993; Vernon, 1987). Such theories suggest that intelligent behaviour is the result of a biological speed factor which is itself reflected in the time taken to carry out basic psychological tasks (Bates & Stough, 1997), typically reaction time or inspection time paradigms. These experiments are important in that they attempt to explore the hypothesis that speed and efficiency of brain functioning may be indirectly measured within simple perceptual and reaction time tasks which are themselves related to g. *
Corresponding author. Tel.: +353-28-90274177; fax: +353-28-90664144. E-mail address: [email protected] (M. McRorie).
0191-8869/$ - see front matter 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.paid.2003.09.023
524
M. McRorie, C. Cooper / Personality and Individual Differences 37 (2004) 523–531
Whilst some of the variation in mental ability differences is linked to information processing capacity, there remains a need for an understanding of the actual biological mechanisms by which this is achieved (Hunt, 1999). With little known about the design of the brain in relation to cognition (Hillyard & Picton, 1987), one suggestion for the basis of the RT-IQ relationship is that speed comes from faster conducting neurons (e.g. Barrett & Eysenck, 1993; Reed & Jensen, 1991, 1992, 1993; Vernon, 1991; Vernon & Mori, 1992). These studies stem from ReedÕs (1984, 1988) hypothesizing that the genetic basis of g partly results from individual differences in nerve conduction velocity. Jensen and Sinha (1993) review investigations which have reported associations between physical correlates and mental ability, and Deary and Caryl (1993, 1997) review biological correlates, such as averaged evoked potentials and cerebral glucose metabolic rate. These studies generally support the hypothesis that greater mental ability is associated with a faster, more efficient central nervous system. 1.1. Psychomotor movement The definition of motor speed within the literature relates to the speed with which distinct movements of the fingers, hands, etc. can be carried out. It is strictly concerned with the speed with which a movement is executed, and not with initiation speed of the movement. One of the simplest possible motor tasks is finger tapping (Halstead, 1947), and studies in both the clinical and individual differences literature have investigated both paced tapping, i.e. at regular speeds, or instructions to tap as quickly as possible (speed of tapping). Finger tapping is a fine motor task, and is often used as a clinical neuropsychological test of controlled sequential responses. This is in contrast to reaction time which requires a controlled single response. Reitan and Wolfson (1993) have stated that Ôwe cannot emphasise enough that the finger tapping test is not a reaction time testÕ (p. 231). The ability to perform such a fine sensitive task presumably affects the rate of performance, and recent PET scans and MRI studies have shown that higher rates of finger tapping are reflected in stronger motor cortex signals, in terms of both the area and the strength of activity. This is thought to reflect increased neural activity associated with increased motor control commands during faster finger movements. J€ ancke, Specht, Mirzazade, and Peters (1999) suggest that rate and movement task effects on cerebrellar activation are differentially sensitive to the movements of dominant and non-dominant hands. In their investigation of trade-offs between finger tapping and verbal memory, Friedman, Polson, and Dafoe (1988) have reported that tapping hand differentially affects verbal recall performance, with performance on the verbal task poorer when tapping with the right hand. Positive correlations have been demonstrated between tapping speed and intelligence (Wilson, Tunstall, & Eysenck, 1971), and in a later investigation of associations between verbal skills, fine motor tasks and cognitive tempo (impulsivity, time judgement), Stanford and Barratt (1996) have reported that finger tapping is related to verbal skills in adolescents, measured by the Wechsler Intelligence Scale for Children-Revised (WISC-R; Wechsler, 1974), the Wide Range Achievement Tests (WRAT; Jastak & Jastak, 1978), and the Gray Oral Reading Test (GORT; Gray, 1963). However despite these scattered but generally encouraging results, this is not a paradigm which currently appears in any of the intelligence research literature specifically in relation to Ôspeed of neural processingÕ theory. Given the hypothesis that speed of processing underlies individual
M. McRorie, C. Cooper / Personality and Individual Differences 37 (2004) 523–531
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differences in intelligence, positive associations between tapping speed scores and the cognitive measures were expected.
2. Method 2.1. Participants The sample consisted of 70 psychology students (32 male and 38 female), with ages ranging between 18 and 43 (M ¼ 22:96, SD ¼ 6.27). The majority (N ¼ 54) were aged between 18 and 24. The age range for males was from 18 to 42 years, with a mean age of 22.47 and SD of 5.96. Age range for females was similarly between 18 and 43, with a mean age of 23.37 and SD of 6.56. Participants were recruited via contact within the Psychology Department of the QueenÕs University of Belfast, and were paid £5 for their participation. All those participating were in apparent good health, and those who normally used corrective glasses wore them during testing. Personal data gathered included age, health status, handedness (preferred hand for writing), finger dexterity, height, weight and armspan (arms horizontal, fingertip to fingertip). 2.2. Measures of IQ In order to assess as wide a range of ability as was feasible, both the Ravens Advanced Progressive Matrices (Raven, 1965) and the Wechsler Adult Intelligence Scale-III (WAIS-III; Wechsler, 1997) were administered. The RAPM was chosen because it provides a good measure of SpearmanÕs g, and as per the standardized procedure, Set 2 was administered under a time constraint of 40 min. The WAIS-III has the advantage of providing traditional IQ scores, and also yields index measures based on more specific cognitive functioning. This test was administered according to the standard instructions and most participants were able to complete it in one and a half hours. 2.3. Apparatus and testing procedure Motor performance has usually been examined by assessing the speed of tapping of one (index) finger over a set period of time (e.g. Stanford & Barratt, 1996). An obvious problem with this approach is that some individuals may lift their fingers higher than others, and so it seems likely that this will be confounded with speed of tapping. We therefore assessed motor speed by means of a simple finger tapping task involving the alternation of the index and middle fingers. This was designed in an attempt to (a) minimize the build-up effects of muscle fatigue caused by repeatedly tapping one finger, and (b) reduce variation in the extent to which participants lift their fingers. The apparatus consisted of two metal sensors on a pivot (points of contact), housed within a metal rectangular block (11 · 19 cm), and interfaced to a PC computer. The mechanism was designed to provide the least mechanical resistance to participantsÕ tapping force as possible, and operated on the principle of a fulcrum effect, i.e. two keys hitting either end of a pivoted plate so that when one was up the other was down. An optical encoder on the pivoting shaft counted the number of contacts. The distance between two keys (2.5 cm) enabled index and middle fingers of
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varying hand sizes to rest comfortably. The travel of each key was 0.8 cm. The participant rested the heel of his/her hand on the box and responded by tapping on the points of contact with index and middle fingers. Wilson et al.Õs (1971) investigation suggested that relationships between individual difference variables and performance may be affected by the time spent on the task. Early trials reflect adjustment periods and are thus unreliable in the measurement of overall speed (Getty, 1976). Barratt, Patton, Olsson, and Zucker (1981) discarded the first four taps in each trial, and likewise the first two seconds in Friedman et al.Õs (1988) study were treated as a warm-up period and the data not used in the analysis. Similarly, a pre-programmed time mechanism in this investigation discarded the first two seconds of each trial from the overall count. Some studies have chosen to operate a clamping system, whereby the hand is held in a constant position (e.g. Wilson et al., 1971). Clamping is designed to prevent participants from countering the build-up of inhibition by transferring work-load to different muscles, so that fatigue effects would be manifest more quickly. Due to the short length of each current trial session it was considered that the instruction to keep the heel of the hand resting on the block throughout would minimize hand movement, adequately preventing any major adjustment in position. Accuracy of motor movement is generally not considered, however the mechanism of the equipment eliminated the possibility of errors, in that it was impossible for both fingers to make contact at the same time. Participants were seated in front of the equipment and advised that the task would measure motor speed. They were instructed to keep the heel of their hand resting on top of the block (to minimize hand movement), and to place their index and middle fingers on the two sensors (points of contact). A few minutes were provided for participants to accustom themselves to the equipment, i.e. to get a feel for the placement of their hands, and to practice tapping on the response keys. Starting with their right hand, participants were instructed to commence tapping as fast as they could at the onset of a two-second high-pitched tone, to continue tapping while this tone sounded, and to finish tapping at the onset of a lower pitched tone. The whole trial lasted 12 s, the first two-seconds of which was not analysed. There was a 10 s rest period between trials. Then they repeated the process with their left hand, and continued alternating hands between trials. Scores for each hand are represented by the mean of 5 trials of 10 s each.
3. Results The data used for analysis was the sum of the means for both hands and the intra-individual variability between trials. Speed of tapping in respect of the dominant and non-dominant hands were examined separately to further investigate the possibility of related gender differences. Tan and TanÕs (1995) investigation relating to gender lateralization of peripheral nerve conduction velocity reported no significant differences in males, yet mean sensory velocity was demonstrated to be significantly faster in the left than in the right hand in females. The data were initially scrutinized for potential outliers, and any extreme values, i.e. falling outside three times the interquartile range, were omitted. Whilst a negative skew may have been expected due to possible lapses in attention, etc., the distributions of mean tapping totals for both hands (skewness, )0.073, kurtosis, )0.360), and for the non-dominant hand (skewness, 0.045,
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kurtosis, )0.392) demonstrate normal symmetry, with the mean scores for the dominant hand (skewness, )0.325, kurtosis, )0.298) showing an acceptable level of symmetry. The distributions for total tapping variability within subjects and variability of dominant and non-dominant hands are as expected positively skewed (skewness, 1.427, kurtosis, 2.670; skewness, 1.551, kurtosis, 2.951; skewness, 1.000, kurtosis, 0.481, respectively). Table 1 presents the mean number of taps and standard deviations between subjects for all ten trials and for the trials relating to the dominant and non-dominant hands. Mean taps are slightly higher than those reported in the literature for the same time duration. This was somewhat expected in that the majority of studies have been based on Ôone finger tappingÕ as opposed to the alternating taps used here. The standard deviations were higher than expected at 17.8 and 18.5 for the dominant and non-dominant hands respectively, in comparison to the standard deviations of 6.6 and 5.6 reported by Stanford and Barratt (1996). A gender by handedness by dominance mixed-model ANOVA on mean tapping performance revealed a massively significant gender effect (F1;66 ¼ 11:27, p < 0:001) indicating that males outperformed females. There was a significant dominance by handedness interaction (F1;66 ¼ 4:14, p < 0:05) indicating that the non-dominant-hand performance of the right-handed individuals was particularly poor, but no other variables approached significance. A similar analysis on intraindividual variability between trials indicated no significant effects. Mean tapping scores showed no correlation with age (r ¼ 0:06, ns). A regression analysis was performed with tapping speed as the dependent variable. When gender was entered first, with height, weight, head width and armspan being entered together in a second block, there was no significant increase in R as a result of adding the measures of body size (R increased from 0.44 to 0.54; F4;63 ¼ 2:01, ns). The correlations between motor speed and measures of IQ are presented in Table 2. There are significant correlations between the RavenÕs Matrices scores and mean total tapping speed and mean tapping speed for the dominant and non-dominant hands. There are no significant associations between variability of tapping speed and these measures of IQ. There was a significant correlation between tapping speed and traditional WAIS Full-scale IQ (FSIQ) and the WAIS Verbal IQ scale (VIQ) and Verbal Comprehension Index (VCI). (Correcting these correlations for restriction of range suggests that a correlation of 0.23 might be as high as 0.38 in the general population.) There was no significant correlation with Performance IQ. However the correlation between tapping and Verbal IQ was not significantly larger than that between tapping and Performance IQ. There were no significant relationships between abilities and within-subject variability in tapping either before or after correction for restriction of range.
Table 1 Average number of taps per 10 s period (five ten-second blocks per hand)
Dominant hand Non-dominant hand Average
Males (N ¼ 32)
Females (N ¼ 38)
All
Mean
SD
Mean
SD
Mean
SD
101.84 92.22 96.41
11.58 11.85 12.31
83.22 78.95 80.80
11.10 9.09 9.86
91.73 85.02 87.94
17.81 18.59 17.52
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Table 2 Correlations between psychomotor speed and ability scores, including corrections for range restriction Both hands
Non-dominant hand
0.256 0.413
0.267 0.431
0.225 0.367
WAIS-III Full Scale IQ Corrected r
0.233 0.380
0.258 0.416
0.184 0.305
WAIS-III Verbal IQ Corrected r
0.290 0.461
0.332 0.516
0.242 0.392
WAIS-III Performance IQ Corrected r
0.075 0.127
0.075 0.127
0.046 0.077
WAIS-III Verbal Comprehension Index Corrected r
0.281
0.319
0.232
0.448
0.499
0.378
WAIS-III Perceptual Organization Index Corrected r
0.182
0.183
0.150
0.301
0.303
0.251
WAIS-III Working Memory Index Corrected r
0.135 0.227
0.160 0.267
0.119 0.203
)0.148 )0.248
)0.125 )0.211
)0.187 )0.310
WAIS-III Processing Speed Index Corrected r
Dominant hand
RavenÕs Matrices Corrected r
p < 0:05 (1-tailed),
p < 0:01 (1-tailed).
Table 3 Factor analysis of WAIS subtests, Raven APM scores, and mean tapping speed scores (both hands combined) Component 1 Picture completion Vocabulary Digit symbol Similarities Block design Arithmetic Matrix reasoning Digit span Information Picture arrangement Comprehension Symbol search Letter–number sequencing Raven APM scores Mean tapping speed
0.286 0.516 )0.198 0.481 0.611 0.566 0.751 0.303 0.671 0.449 0.502 0.215 0.524 0.723 0.324
A principal factor analysis (unrotated) of all of the independent subtests of the WAIS-III (not including any of the composite scores), along with the RAPM scores and the mean tapping speed
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scores (both hands combined) was performed. The loading of tapping speed on the PF1, indicating its correlation with psychometric g, was 0.324 (Table 3).
4. Discussion The results reported above show clearly that 1. Fast tapping speed is reflected in above-average performance on RavenÕs matrices, for both males and females. 2. Higher rates of finger tapping are also reflected in above average performance on the traditional WAIS Full-scale IQ, Verbal IQ, and Verbal Comprehension Index. 3. There is no relationship between fast tapping speed and high scores on the WAIS Performance IQ scale. This investigation examined the extent of the association between a simple measure of motor speed, and general mental ability. This behavioural measure of speed contained virtually no cognitive component, thus (unlike the reaction time paradigm) there was no real possibility for strategy use. The specific hypotheses were that there would be positive correlations between mean estimates of tapping speed, and intelligence test scores. Negative correlations between these cognitive measures and tapping standard deviation were also hypothesized, in that less variability in motor speed would reflect a more efficient system. Within the full sample, mean tapping values for the dominant hand were greater, i.e. tapping speed was faster. The possibility that females might demonstrate faster motor speed for the nondominant hand as reported by Tan and TanÕs (1995) conduction velocity study was not substantiated, with greater values reported for the dominant hand. The positive correlations found between motor speed and g (estimated by the RAPM), and between motor speed and FSIQ, VIQ and VCI were within the range expected. These associations are as hypothesized and support the relationships between tapping speed and intelligence previously reported by Wilson et al. (1971) and Stanford and Barratt (1996). The significant correlations between mean motor speed and IQ provide a very consistent set of results. However the failure to find any relationship between motor speed and Performance IQ is surprising, given the associations demonstrated between motor speed and the verbal tests. These results do nonetheless substantiate previous findings in relation to verbal ability and Performance IQ. Hendrickson (1982) reported higher correlations for verbal than non-verbal tests, and likewise although visual inspection time is more highly correlated with performance tasks, auditory inspection time has at times been found to demonstrate higher correlations with verbal than with non-verbal IQ (Deary, Head, & Egan, 1989; Irwin, 1984). Somewhat similar findings have been reported in relation to the concept of a basic information processing unit (BIP). Lehrl and Fischer (1988, 1990) formulated the capacity of working memory in terms of the speed and duration of neural traces, and reported correlations between capacity and untimed verbal test scores at 0.67 (n ¼ 672) and 0.88 (n ¼ 66). Kline, Draycott, and McAndrew (1994), also Draycott and Kline (1994) later replicated these findings. However Stankov and Roberts (1997) point out that when correlated with various intelligence factors, the
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BIP procedure shares a moderate correlation only with verbal abilities. This is largely inconsistent with the general literature regarding mental speed and intelligence, although Jensen (1998) has suggested that the measure of processing speed may be factorially too narrow, containing a specific verbal factor. The findings currently reported support Stankov and Roberts (1997) –– within a behavioural paradigm containing no cognitive component whatsoever. In conclusion, although the consistent results between mean motor speed and measures of IQ provide tentative support for a speed theory of intelligence, it is particularly strange that motor speed is not predicted by Performance IQ. Given that performance tasks are heavily timed, one would think that if the relationship was just based on speed an association would have been found.
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