Raven's Progressive Matrices and inspection time: P200 slope correlates

D(& Vol. 18. No. I, pp.81-87. 1995 Copyright 0 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0191.8869/95 $9.50 + 0.00

PP~W~Z. tdivid.

Pergamon 0191-8869(94)00126-X

RAVEN’S

PROGRESSIVE MATRICES AND INSPECTION TIME: P200 SLOPE CORRELATES

Grant L. Morris and Mark B. Alcom Departmentof Psychology, University of Northern Colorado, Greeley, CO 80639, U.S.A (Received 19 March 1994)

Summary-The N I P2 complex is a component of cortical event related-potentials. It has been linked to intelligence as well as early processing and attention. This study examined a new measure--the slope of the NIP2 complexduring an inspection time task. The 49 participants also completed the Raven’s Standard Progressive Matrices as an intelligence index. P200 slope at the frontal and temporo-occipital, but not at temporal or occipital, recording sites was significantly correlated with both inspection time and Raven’s performance. This stud; supports earlier NIP2 links with intelligence, and refines the ERP measure and eliciting task.

INTRODUCTION

In 1969 Ertl and Schafer stated that “evoked potentials, which reflect the time course of information processing by the brain, could be the key to understanding the biological substrate of individual differences in behavioral intelligence” (p. 422). In the ensuing quarter of a century the literature relating to the speed of information processing and intelligence has swelled with findings that, while sometimes equivocal, have done nothing to temper the enthusiasm for Ertl and Schafer’s prediction (see Vernon, 1987). Inspection time (IT), the minimum exposure duration needed for reliable discrimination of a stimulus, has been widely found to correlate significantly with measures of intelligence (Vickers, Nettelbeck & Willson, 1972; Nettelbeck & Lally, 1976; Nettelbeck, 1982; Brand & Deary, 1982; Anderson, 1986, Longstreth, Walsh, Alcom, Szeszulski & Manis, 1986; Nettelbeck, Edwards & Vreugdenhil, 1986; Nettelbeck, 1987; Deary, Caryl, Egan & Wight, 1989; and Zhang, Caryl & Deary, 1989b). Vickers et al. (1972) described IT as placing an elemental constraint on initial perceptual stages of information processing. One explanation of this effect is that an individual’s rate of sampling of the target stimulus is limited, and that this sampling rate dictates how long the subject needs to be exposed to the target before sufficient information is accumulated for the subject to make an identification. This sampling rate varies across subjects and interacts with the complexity of the target stimuli and difficulty of the discrimination in determining the recognition threshold or Inspection Time for that subject (Vickers et al., 1972; Vickers & Smith, 1986). Inspection time is often assessed in a backward masking task in which the target to be identified is presented briefly (often, for less than 100 ms) and then replaced by an overwriting masking stimulus. The mask interrupts or erases iconic storage and precludes target identification from the iconic image after the target has physically offset. This task is well suited to the requirements for collecting average evoked potentials (AEPs). Just such a brief, discrete stimulus is necessary to be able to reliably link an evoked cortical response to its eliciting stimulus. Through repeated presentation of an evoking stimulus, and simultaneous recording of constant intervals, or “epochs,” of electroencephalographic (EEG) activity, a number of samples of EEG activity are collected. The beginning of all of these epochs is synchronous with the instant of target stimulus presentation. By “averaging” all of the epochs together, any systematic variation in electrocortical activity (the AEP) will emerge, while random, or irrelevant, EEG activity averages out toward zero. It has been suggested that inspection time in backward masking tasks way depend upon the speed with which information in the sensory register is transferred to short term memory (STM) (Nettelbeck, 1982; Saccuzo & Miller, 1977; and Vickers & Smith, 1986). Of the various components found in AEPs, Chapman, McCrary and Chapman (1978) have suggested that the P200 (or P2), a positivegoing peak occurring around 200 ms after the eliciting stimulus, may be a marker of transfer of information to STM. Converging lines of inquiry--IT and IQ along with correlates of IQ in 81

82

Grant L. Morris and Mark B. Alcom

AEPs-have led to our attempts, along with Zhang et al. (1989 a & b) to assess recognition accuracy through a backward masking procedure while simultaneously recording the P200 and relating the performance to a measure of intelligence. Haier, Robinson and Braden (1983) and Robinson, Haier, Braden and Krengel(l984) correlated Raven’s Advanced Progressive Matrices (RAPM) scores with NlP2 amplitude elicited by having their subjects passively view light flashes of various intensities. The Nl (or N150) is a negative going component which precedes the P2. Recorded from the vertex, the NlP2 amplitude was defined as the amplitude difference between the most negative and positive features of the AEP occurring within a time window bracketing the Nl P2 complex. This measure correlated significantly with RAPM scores at all light intensities with rXY= 0.69 being the largest reported. Their (1984) replication also found a significant correlation of rxy = 0.50, but for only one of the light intensity levels tested, and only after eliminating subjects who differed most in age from the sample in the first study, or who exceeded the EP measurement error level observed in the first study. The authors, nevertheless, remained confident that NlP2 amplitudes are related to individual differences in intelligence. Zhang er al. (1989b) in a replication and extension of earlier reported work (1989a) used an inspection time task to elicit AEPs. They intercorrelated IT, scores on the Alice Heim (AH) test of intelligence, and P200t, their measure of the NlP2 complex. The P200t was defined as the time from the point at which the upward slope of the N lP2 complex intersects the mean amplitude for the epoch to the peak of the P200. This definition avoids having to identify a sometimes obscure feature, the Nl. All AEPs were recorded from the vertex. Their inspection time task required the subjects to decide which of two vertical lines was longer. IT for a subject was determined prior to AEP recording. In the three experiments they report (1989b), a consistent and significant relationship was found between IT and the P200,, with a weighted mean correlation of rxy = 0.59. In their second experiment a significant IQ-P200, correlation was observed, but only with NlP2s elicited by a cue stimulus which signaled the response requirement in the backward masking trial which followed, and not with the P200, elicited by the target stimulus itself. The NlP2 complex, then, has gained support in independent research as an electrocortical correlate of IQ, even in tasks in which minimal cognitive demands are placed upon the observer. The measurement of the NlP2 has varied, however, with Haier et al. (1983) using the peak-to-peak amplitude of the NlP2 complex, while Zhang et al. (1989 a, b) used the ‘rise-time’ of the P200. One purpose of our study was to consolidate these different measurements of the NlP2 complex. We accomplished this by calculating the slope, in pV/ms, between the N150 and the P200. We will refer to this as the P200,. We measured the slope of the NlP2 complex by dividing the NlP2 amplitude by its latency difference: P200, = (P200 amplitude-N150 amplitude)/(P200 latency-N150 latency). We were also interested in assessing the IQ-NIP2 relationship in a (slightly) more complex IT task than the two-choice line discrimination frequently employed. Our subjects were asked to identify which of four letters was presented. Another purposes was to collect AEPs from 6 homotopic locations rather than from the vertex alone. An examination of the P200, as a function of recording site may allow further inferences regarding the relevant cognitive processes.

METHOD

Subjects. The subjects were 22 males and 27 female college students. All subjects displayed normal or corrected to normal vision as assessed by the Snellen Equivalent Visual Acuity Test. Evoked Potential Apparatus. Bilateral evoked potential recordings were obtained from four monopolar locations referenced to linked earlobes. The electrodes were placed on the scalp using an electro-cap (Electra Cap International). Eye movement artifact recording electrodes were placed above and to the lower temporal side of the right eye. The impedance for all recording sites was maintained below 5 KOhms. The sites that are reported in this paper are 0, (left occipital), Ts (left temporo-occipital), Tj (left temporal), and F7 (left frontal) of the International lo-20 System (Jasper, 1958). The waveforms generated during the backward masking task were amplified by a 10 channel Beckman Accutrace electroencephalograph. The amplifiers’ time constant (high pass filter) was 0.1 second, and the low pass filter was bypassed. Amplified and filtered EEG were transformed by a 12

83

~200, and IQ Table I. Average percent recognition accuracy by stimulus onSet asynchrony (n = 49) Stimulus onset asynchrony (ms)

M SD

17

34

50

67

84

100

44.8 17.3

72.0 19.8

83.0 16.5

89.3 14.8

92.8 10.0

94.5 7.8

bit resolution analog-to-digital converter (Scientific Solutions LabMaster). Stimulus presentation and data collection were microcomputer controlled. Individual epochs of EEG were digitized at 100 samples per second and stored for later averaging and analysis. Procedure. Subjects were given Raven’s Standard Progressive Matrices (Raven, 1938) and performed the backward masking task with simultaneous EEG collection. The backward masking task presented a letter-the target stimulus-for a variable interval before it was replaced by a masking stimulus that interrupted iconic storage of the target stimulus. Our subjects were required to identify one of four possible letters as it appeared on the computer screen, Those letters were all in lower case and were “b”, “p”, “ d ” , or “q.” The horizontal and vertical visual angles subtended by these stimuli were 1.67 and 2.38 degrees, respectively, at the test distance of 120 cm. The delays between target onset and mask onset (stimulus onset asynchrony; SOA) were 17, 34, 50,67, 84, and 100 ms. The mask which replaced the target was a random pattern of 5.4 mm by 3.7 mm blocks with a light intensity that matched the overall luminance of the target letters. The mask subtended the same height and width visual angles as the target letters. Subjects were instructed to respond with one of the four possible target letters on all trials. To familiarize the subjects with the procedure, as well as to minimize eye- and head-movement artifacts during the experimental trials, participants received a minimum of 13 practice trials with SOAs of 67 or more ms. After a subject demonstrated reliable letter recognition, eye and head movement artifact detection levels were individually determined. Trials during which eye or head movement artifact exceeded the preset rejection criterion were aborted and the feedback “eyes moved” and/or “head moved’ was displayed on the CRT. A trial started with a small fixation point projected on the center of the screen for a duration of 3 seconds. The target letter replaced the fixation cue and was presented at one of the SOAs, followed by the mask which remained on the screen until the end of the 1 second epoch. At this point the subjects were prompted by the word “Letter ?,” to identify a letter. The subjects responded orally. If the subject responded correctly on a given trial, the phrase “That’s Correct” was displayed for a period of 1 second. After the presentation of feedback, the screen went blank for a period of 1.5 seconds and then the next trial commenced with the display of the fixation point. Following an incorrect response the screen went blank for 1.5 seconds prior to the beginning of the next trial. During the one-second trial interval following target onset, EEG was digitized from the four bilateral homotopic electrodes sites at the rate of 100 samples per second. The subjects received 260 experimental trials: the four target letters were each presented 60 times in random order. SOA values were not randomized. Instead, trials were presented in series of increasing or decreasing SOAs (17-100 ms) with each SOA tested twice before moving on to the next higher or lower duration. Testing was interrupted at regular intervals to permit the subject a brief rest. RESULTS

Raven’s Standard Progressive Matrices. The average RSPM score of the 49 participants was 49.2 with a standard deviation of 4.8. This places our sample at the 75th percentile, with markedly attenuated variation. Recognition Accuracy. The average percentage correct recognition scores for the 6 SOAs are presented in Table 1. These recognition accuracies are collapsed across the four different target letters. These recognition accuracies (and RSPM-RA correlation reported below) are quite close to those reported by Longstreth er al. (1986) with a similar population, task and nonverbal, g-loaded test of psychometric intelligence (Cattell’s Culture Fair Intelligence Test).

84

Grant L. Morris and Mark B. Alcom

Evoked Potentials. We found that Nl and P2 latencies did not vary systematically across SOAs within a recording site. Consequently, the Nl and P2 amplitudes at the different SOAs were determined at the same latency for a particular electrode site in a subject. Zhang et al. (1989b) also found no significant variation in P200 latency across the SOA levels they employed. The AEP amplitudes we report are all relative to the average for an entire epoch. Analysis of homotopic electrodes sites did not reveal any systematic differences between the hemispheres. Therefore, we decided to report AEP data from the left hemisphere only. Table 2 lists the means and standard deviations of the AEP components for the various left hemisphere recording sites and SOAs. Recognition accuracy (RA) correlated significantly with RSPM scores at the 17 and 34 ms SOAs (17 ms: rxy = 0.44; 34 ms: rxr = 0.38; bothp < 0.01). The correlations of the longer SOAs with RSPM scores were progressively smaller and not significant. This is to be expected, given the “ceiling effect” truncation of RA variance at the longer SOAs. While recognition accuracy is not identical to inspection time, the two are highly correlated. IT is typically defined as the SOA at which a 90% RA is achieved. Therefore, RA accuracy at any SOA should go up as IT goes down. Given this linkage, it is reasonable to compare our RSPM-RA correlation of 0.44 with the estimated - 0.50 correlation between IT-IQ which Nettelbeck (1987) derived after his review of the literature, and with the - 0.54 correlation yielded by Kranzler and Jensen’s (1989) meta-analysis. Both of these estimates were increased by corrections for the high and restricted range of ability present in most samples, a characteristic of our sample as well. The first column of coefficients in Table 3 presents the correlations of RSPM with P200, for each SOA tested at the four recording sites. Nine of the 12 correlations involving the frontal and temporo-occipital were significant, while the temporal and occipital recording sites yielded not a single significant correlation. The frontal P200, measures show the greatest relationship with RSPM performance. These values compare well with the RAPM-NIP2 correlations reported by Haier et al. (1983). They found correlations ranging from 0.38 to 0.69 (all significant) across four light intensity levels in their visual evoked potentials recorded at the vertex. Further, a significant P200,-IQ (Alice Heim 5 test) correlation of - 0.339 was found by Zhang et al. (1989b). However, this significant relationship was found with the AEPs elicited by a warning cue, but not with those elicited by the IT stimulus. Recognition accuracy also shows modest but significant correlations with P200,. Given the colinearity between RSPM scores and RA described above, this is to be expected. Zhang et al. (1989b) reported significant correlations of 0.580 (combination of experiments 1 & 2) and 0.645 (experiment 3) between their vertex P200, rise-time measure and IT. Zhang et al. (1989b) found no significant change in their P2001 measure across the three levels of SOA they employed. Our results replicate theirs but only at the F7 and T3 electrode sites (F(5, 240) = 2.215, p > 0.05, F(5, 240) = 0.9, p > 0.05, respectively). However, at T5 a significant linear increase in P200, values was found with increasing SOA (F(5, 240) = 3.320, p < 0.01, first order (linear) polynomial test F( 1,48) = 12.7 p = 0.001, higher order polynomial tests not significant). The same significant linear increase in P200, across SOAs was found at 0, (F(5,240) = 5.482, p < 0.001, first order (linear) polynomial test F( 1, 48) = 9.158 p < 0.01, higher order polynomial tests not significant).

DISCUSSION

This research is further support for significant relationships among intelligence, recognition accuracy and a measure of the NlP2 complex. As such, it replicates findings reported by Zhang, Caryl and Deary (1989a, b). We found that, while P200, correlated significantly with our IQ measure, conventional latency and amplitude measures of either N 1 or P2 did not. Zhang et al. (1989b) reported identical findings. Further, when we used either Haier’s et al. NlP2 amplitude measure, or Zhang’s et al., P200,, correlations with our IQ measure were smaller than with the P200,. The differing degrees of correlation between Raven’s and the P200, across electrode sites suggest that the P200, is a multifocal rather than a far-field or pervasive cortical response. Further, the high P200,-Raven’s correlations from the frontal recording site has implications which can be further

P200, and IQ

85

Table 2. Evoked potential component average latencies and amplitudes (n = 49) AEP component SOA F) recording site (left frontal) lat. All (ms) 17 amp. (PV) amp. 34 W) amo. 50

(P6 amp.

SD 61

(PC

M

SD

amo.

84

amp.

100

(PC

M SD M SD M SD M

M

SD

(PV)

M

SD

TI recording site (left temporal) lat. All M SD (ms) 17 amp. M SD (PW amp. 34 M SD W) amp. 50 M SD W) amp. 67 M SD W) amp. 84 YD (PV) amp. 100 M SD W) Tr recording site (left temporo-occipital) lat. All M SD (ms) amp. 17 M

(IN amp. W) amp. W) amp. CUV) _. amp. WV) amp.

0)

SD 34 50 67 84 100

0, recording site (left occipital) lat. All (ms) amp. 17 W) amp. 34 W) amp. 50 (PW amp. 67 (YV) amp. 84 (UV) amp. 100 WV)

M SD M SD M SD M SD M

SD M SD

M SD M SD M SD M SD M SD M SD

Nl

P2

140.0 18.1 - 3.1 3.1 - 2.6 3.1 - 2.1 3.2 - 2.4 2.9 - 2.2 2.9 - 4.5 4.2

223.2 22.1 1.4 3.0 2.4 3.3 2.1 3.4 2.2 3.2 2.5 3.1 0.7 3.4

154.6 18.7 0.3 4.4 0.4 4.9 0.8 4.6 0.8 4.7 0.1 4.6 0.8 5.5

234.8 20.7 3.0 4.6 3.4 4.8 3.6 4.5 3.1 4.8 3.6 4.9 3.5 4.9

175.3 10.8 - 2.0 3.0 - 1.8 3.2 - 1.1 3.3 - 1.0 2.9 - 1.1 2.8 - 1.1 3.3

240.4 13.3 0.8 2.5 I .4 3.1 2.3 3.2 2.7 3.5 2.5 3.0 2.7 3.1

174.8 15.6 - 1.8 3.6 - 2.4 4.0 - 2.0 4.1 - 2.1 4.5 - 2.2 4.2 - 3.0 4.3

235.9 17.3 3.9 4.4 3.7 4.9 3.8 4.9 4.5 4.7 4.6 5.1 4.5 5.2

explored only through multiple site, rather than vertex only, recording. The extent to which our findings may be the result of a lateralized montage versus the midline recordings conventionally used can be resolved by simultaneous midline and lateralized recordings. Our apparatus precluded us from doing so. Without minimizing the theoretical significance of IT-IQ correlations derived when the backward masking task involves only a simple discrimination, we believe a better understanding of the IQ-IT-P200,s relationships would result from systematic manipulation of the cognitive demands of the IT task.

Grant L. Morris and Mark B. Alcom Table 3. Correlations of RSPM scores and recognition accuracies with P200 slope at all recording sites and stimulus onset asynchronies (SOA in ms; n = 49)

Site and

Recognition accuracy for

SOA of P200,

RSPM

F7 site SOA

61 84 100 17 34 50 61 84 100

- 0.20 - 0.26 -0.17 -0.15 -0.15 -0.10

TS site SOA

17 34 50 :: 100

* p < 0.05, +*p
0.11

-0.11 0.01 0.2 1 0.34* 0.22 0.07

0.24 0.39** 0.49*** 0.43** 0.28 - 0.08 - 0.03 0.05 0.00 0.07 - 0.03

0.32* 0.14 0.23 0.36* 0.37** 0.33*

:: 50 67 84 100 01 site SOA

SOA = 34

0.24 0.40* * 0.46*** 0.60**** 0.47*** 0.47***

50

T3 site SOA

SOA= 17

-

0.01 0.05 0.06 0.05 0.05 0.02

***p
0.14 0.21 0.23 0.36* 0.36* 0.36* - 0.01 0.04 0.20 0.28 0.20 0.25

- 0.02 0.00 0.03 0.04 0.07 - 0.02 - 0.01 -0.01 0.09 0.18 0.18 0.14 - 0.06 0.02 0.18 0.22 0.20 0.15

****p
Despite differences in method of research examining NlP2 links to psychometric intelligence, we find our results to be consistent with those reported in the literature. Further integration of IT and AEP paradigms should yield a better understanding of the psychophysiological bases of performance on cognitive tasks via an information processing model. Acknowledgements-We

thank William Gallagher, MA and Kevin Duff, MA for their assistance in collecting data.

REFERENCES Anderson, M. ( 1986). Inspection time and IQ in young children. Personality and Individual DifSerences, 7, 677-686. Brand, C. & Deary, I. (1982). Intelligence and ‘inspection time’. In A Modelfor Intelligence (Ed. by Eysenck, H. J.). Springer: Berlin. Chapman, R. M., McCrary, J. W. & Chapman, J. A. (1978). Short-term memory: The ‘storage’ component of the human response predicts recall. Science, 202, 121 I-1214. Deary, I. J., Caryl, P. G., Egan, V. & Wight, D. (1989). Visual and auditory inspection time: Their interrelationship and correlations with IQ in high ability subjects. Personality and Individual Differences, 10, 52.5-533. Ertl, J. P. & Schafer, E. (1969). Brain response correlates of psychometric intelligence. Nature, 223, 421422. Haier, R. J., Robinson, D. L. & Braden, W. (1983). Electrical potentials of the cerebral cortex and psychometric intelligence. Personality

and Individual

DifSerences,

4, 59 I-599.

Jasper, H. (1958). The ten-twenty electrode system of the International Federation. Neurophysiology,

Elecrroencephalography

and Clinical

IO, 37 1-375.

Kranzler, J. H. & Jensen, A. R. (1989). Inspection time and intelligence: A meta-analysis. Itzrelligence, 13, 329-391. Longstreth, L. E., Walsh, D. A., Alcorn, M. B., Szeszulski, P. A. & Manis, F. R. (1986). Backward masking, IQ, SAT and reaction time: Interrelationships and theory. Personality and Individual DifSerences, 7, 643-65 I. Nettelbeck, T. (I 982). Inspection time: an index for intelligence? Quarterly Journal of Experimental Psychology, 34a, 229-3 12. Nettelbeck, T. (I 987). Inspection time and intelligence. In Speed of Information-processing and Intelligence (Ed. Vernon, P. A.). Ablex, New Jersey. Nettelbeck, T., Edwards, C. & Vreugdenhil, A. (1986).Inspection time and IQ: evidence for a mental speed-ability association. Personality and Individual Differences, 7, 633-64 I. Nettelbeck, T. & Lally, M. (1976). Inspection time and measured intelligence. British Journal of Psychology, 67, 17-22. Raven, J. C. (1938).Progressive matrices: A perceptual rest of intelligence (individual form). London: Lewis. Robinson, D. L., Haier, R. J., Braden, W. & Krengel, M. (1984). Psychometric intelligence and visual evoked potentials: A replication. Personality and Individual DifSerences, 5, 487-489.

P200, and IQ

87

Saccuzo, D. P. & Miller, S. (1977). Critical interstimulus interval in delusional schizophrenics and normals. Journal of Abnormal Psychology, 86, 261-266. P. A. (Ed.). (1987). Speed of information processing

Vernon, Vickers, and Vickers,

and intelligence. Norwood, NJ: Ablex. D., Nettelbeck, T. & Willson, R. J. (1972). Perceptual indices of performance: the measurement of ‘inspection time’ ‘noise’ in the visual system. Perception, 1, 263-295. D. & Smith, P. L. (1986). The rationale for the inspection time index. Personality and Individual DifJerences, 7,

609-623.

Zhang, Y., Caryl, P. G. & Deary, I. J. (1989a). Evoked potential correlates of Inspection Time. Personality and Individual Differences,

IO, 379-384.

Zhang, Y., Caryl, P. G. & Deary, I. J. (1989b). Evoked potentials inspection time and intelligence. Personaliry and Individual Differences,

10, 1079-1094.