Spatial Abilities and Individual Differences in Visual ... - Science Direct

INTELLIGENCE 15, 117-137 (1991)

Spatial Abilities and Individual Differences in Visual Information Processing JACQUES JUHEL

University of Rennes 2, France

This study explores individual differences in performance in visual memory and recognition tasks as a function of performance in spatial tests. One hundred students underwent a battery of spatial tests and laboratory tasks. Factor analysis conducted on the data followed by a Schmid-Leiman transformation (Schmid & Leiman, 1957) support (a) the validity of the distinction between a spatial visualization factor and a speeded rotation factor, and (b) the assumption of a relatively low overlap between spatial tests and cognitive tasks. Different groups of subjects were then contrasted according to visual memory and spatial visualization dimensions: Study of mean latencies of these groups in visual-spatial computerized tasks established that efficiency of encoding and information organization processes were reflected by the first dimension. The second dimension would particularly correspond with the quality of information transformation processes as well with strategic selections.

It is well k n o w n that the 1970s saw the emergence o f a strong current of psychological research involving the study of individual differences in cognitive aptitudes (e.g., Carroll, 1978; Glaser & Pellegrino, 1978; Pellegrino & Glaser, 1979). These works resulted in n u m e r o u s findings giving proof of fertility of methodological and theoretical views presented by an approach which integrated cognitive and differential aspects o f h u m a n behavior. (See, for instance, Kail & Pellegrino, 1985; Keating, 1984; Pellegrino & G o l d m a n , 1983.) A m o n g other aptitudes, spatial ability actually gives rise to n u m e r o u s studies (Carpenter & Just, 1986; Cooper & Mumaw, 1985; Hunt, Pellegrino, Frick, Farr, & Alderton, 1988; K y l l o n e n , L o h m a n , & Snow, 1984; K y l l o n e n , L o h m a n , & Woltz, 1984; L o h m a n & K y l l o n e n , 1983; M u m a w & Pellegrino, 1984; Mumaw, Pellegrino,

The present article is based on a doctoral dissertation (third chapter) submitted to the Department of Psychology of the University of Rennes 2. I wish to thank my dissertation advisor, A. Lieury, and comminee members, M. Reuchlin and G. Le Calv6, for their assistance and encouragement. 1 want particularly to thank J.B. Carroll for his valuable assistance throughout the course of this work and for attentive reading of the first draft of this article. 1 am grateful to F. Royer for his helpful comments, M. Bernoussi, C. Cossec for their help in collecting data, and D. Delaborde for his friendly technical assistance. Correspondence and requests for reprints should be sent to Jacques Juhel, University of Rennes 2, Laboratory of Experimental Psychology, 6, av. Gaston Berger, 35 043 Rennes Cedex, France. ll7

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Kail, & Carter, 1984; Pellegrino & Kail, 1982; Pellegrino, Mumaw, & Shute, 1985; Poltrock & Agnoli, 1986; Poltrock & Brown, 1984). Very early, investigators concerned with the nature and structure of intelligence had discovered the possibility of grouping in clusters tests requiring, in varied degrees, manipulation or transformation of spatial information. The large group factor associated with these spatial tests, named according to diffm'ent authors, S (Thurstone, 1938), gv (Horn & Cattell, 1966) or Vz (Guilford & Lacey, 1947), has been the subject of multiple endeavors trying to clarify its structure and characterize the lower components of the hierarchy; however, conclusions of some authors seem rather conflicting (Lohman, 1979; McGee, 1979). The reasons for the obvious heterogeneity of these findings, which resulted both from the nature, diversity, and complexity of the tasks, and from the wide range of factor analysis methodologies used by the investigators (Carroll, 1983) will not be reexamined in this article. Nevertheless, psychometric research has successfully established the existence of a spatial factor and a factorial structure of spatial ability, which as recently described by Carpenter and Just (1986) and Lohman (1988), gains some consensus. Briefly, this spatial group factor may be segregated into three factors of a lower order: a spatial orientation factor (SO), a spatial visualization factor (VZ), and a speeded rotation factor (SR). The main specifications of these factors whose characteristics are treated at greater length elsewhere (Carpenter & Just, 1986; Lohman, 1988; Pellegrino & Kail, 1982) are as follows:

1. The spatial orientation factor, which is well defined by the Guilford-Zimmerman Spatial Orientation Test (Guilford & Zimmerman, 1947), is usually associated with tests requiting subjects to imagine how information will come into view when seen from another position, thus implying that the subjects would have to build up a mental representation of the information's shift. Generally, factorial studies converge to a clear distinction between SO on the one hand and VZ and SR on the other hand. 2. The spatial visualization factor is often defined by paper-and-pencil tests requiring the examinee to mentally handle and transform figurally and spatially complex information; this is the case for the Minnesota Paper Form Board Test (MPFBT; Likert & Quasha, 1970), the Paper Folding Test (PFF; French, Ekstrom, & Price, 1963), or the spatial subtest of the Differential Aptitude Test (DAT; Bennett, Seashore, & Wesman, 1974). 3. The factor, called speeded rotation by Lohman (1988), is less significant and more specific: It is primarily defined by simple tests setting mental rotations of forms or objects in action. Because of their simplicity, the speed of rotation is of great importance in these tests; the Cards Rotation Test (CRT; French et al., 1963) or the Flags Test (FT; Thurstone & Thurstone, 1941) generally have high loadings with this factor.

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In fact, analysis of tests usually employed to determine these last two factors gives evidence that the difference between VZ and SR is reflected by two complementary but frequently separate dimensions of performance: speed and accuracy of information processing (Egan, 1978; Lohman & Kyllonen, 1983; Mumaw & Pellegrino, 1984). It is obvious that when the task is simple enough to allow very high accuracy (as for instance with CRT or FT), the speed of information processing, experimentally measured by means of latencies, becomes very discriminant. Conversely, when more complex tasks are used (for instance, the spatial subtest of the DAT), constraints are principally exerted on the accuracy of information processing, accordingly reducing the importance of speed. Of course, it is relatively difficult to discover what really contributes to the accuracy of spatial information processing, which is largely dependent on many interactive variables. But the efficiency of information-processing components (Sternberg, 1985), allowing visual image generation (Kosslyn, Brunn, Cave, & Wallach, 1983) and visualization activity in working memory (Baddeley, 1983) and, higher in the processing hierarchy, the planning and strategic choices, are undoubtedly determinant. Some results (Poltrock & Agnoli, 1986; Poltrock & Brown, 1984) showed that the relationship between spatial abilities and visual imagery is based on imagery processing efficiency and/or visual representation quality, directly or indirectly measured in the framework of the Kosslyn computational model (Kosslyn, 1981; Kosslyn et al., 1983). Other reports on differential capacity in the resolution of a spatial problem have been interpreted in terms of adaptability, efficiency, or availability of individual strategies viewed as processing sequences (Just & Carpenter, 1985; Kyllonen, Woltz, & Lohman, 1981; Snow, 1978). The aim of the present experiment was to try to understand how a subject's performances on visual-spatial tasks can be explained in terms of individual differences in processing information efficiency, in the quality of mental representations, and if necessary, in terms of preferential processing strategies. Supporting this work, the main idea was to evaluate individual differences varying with the nature of the comparison between groups of subjects. It w~s with this goal in mind that individuals of a single sample were compared depending on whether their performance concerned spatial tests or visual-spatial computerized tasks. METHOD Subjects The subjects were 100 psychology students at the University of Rennes 2. The 12 males and 88 females ranged in age from 18 to 35 years (M = 20.8, SD = 1.6). They served voluntarily in the experiment for course credit. The experiment was presented in two sessions (about 1V2 hr each); participants were tested individually for experimental tasks and by pairs for spatial tests.

! 20

JUHEL TEST ls D

(/yo .on. "~

Presentation Time ls FIG. 1. Sequential presentationof dots in the 5 x 5 matrix and illustration of the probe-testparadigm (ISI = lnterstirnulus interval).

Apparatus The computer-controlled tasks were administered using Atari 1040 ST computers with disk drives for test item and response storage and adapted keyboards for response entry (Figure 1), as well as high resolution black and white video monitors. Presentation timings of stimulus and items along with response latency and accuracy recordings were achieved with a program written in compiled GFA BASIC. Spatial Tests Participants were administered five paper-and-pencil tests particularly loaded with VZ and SR factors: the CRT and PFT (French et al., 1963); the Mental Rotations Test (MRT; Vandenberg & Kuse, 1978); the MPFBT (Likert & Quasha, 1970), presented in a revised version of 31 items; and the spatial subtest of the DAT Battery (Bennett et al., 1974). In each case, the dependent measure was the number of items answered correctly. For CRT and PFT, the score was the number of correct responses; for the MRT, the DAT spatial test, and the MPFBT, the number of incorrect answers (divided by 2 for the MPFBT) was subtracted from the number correct. Computer-Controlled Tasks Four experimental tasks were administered: Dot-in-Matrix Patterns Recall, Visual Memory Search, Recognition (R), and Recognition after Rotation (RR).

Dot-in-Matrix Patterns Recall. Four dots (diameter = 2 cm) were presented simultaneously in a 5 x 5 matrix (the size of a cell was 3.5 cm x 3.5 cm); the number of dots and presentation time had been selected in order to get a correct

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answer probability of .50 (Ichikawa, 1982; Juhel, 1988). Subjects recalled the pattern immediately after the presentation by marking the position of dots in 5 x 5 grids (size of a cell was 5 c m x 5 cm) with a pen. After the subjects were accustomed to a first set of 10 patterns, accuracy was recorded for 25 new totally different patterns. The score was the number of patterns correctly recalled (max = 25).

VisualMemory Search. Five dots were presented successively in the cells of a 5 x 5 matrix on the monitor screen. The distance from the subject's eye to the stimulus was about 60 cm. Subjects were required to memorize the different positions of these five dots. At a signal, the word Attention was presented for i s in the center of the matrix; after the signal disappeared, the first dot was presented for 1 s, followed by a blank interstimulus interval of 500 ms. The presentation of the four other dots was regulated in the same way. Memory accuracy was measured by a probe-test method: 1 s after the last dot of the set disappeared, another dot (the test) was presented in one of the matrix's cells. The subject, as quickly and accurately as possible, had to press one of two buttons ("yes" or "no") to indicate if one of the five dots was (or was not) located in the test's cell. Twenty items (or sets consisting of five dots each) were administered to subjects in order to familiarize them with the paradigm and the apparatus. The task itself was constituted with 50 different items and subjects were tested in a counterbalanced design: subjects designated "i" and "(50 + i)" saw item "i" first. Every dot of a set was tested 10 times, as a function of its rank (i.e., ordinal position): five "yes" answers and five "no" answers. The test on item "no" was always located in one of the cells adjacent to the dot's location. Accuracy and latencies of correct answers ("yes" and "no") were recorded. Recognition. In this visual memory task, a shape was first presented for 1 s on the monitor screen. After a blank interstimulus interval (600 ms), a recognition set of four shapes was presented (Figure 2, p. 122). Subjects were then required to recognize, as accurately and quickly as they could, the shape in the set. Two blocks of 20 items (shapes with 6, 8, 10, or 12 vertices; Attneave, 1957) were built; the presentation's rank of the item and stimulus localization in the recognition set were counterbalanced between subjects. In the first part, the task required subjects to recognize the shape in the same orientation as in the presentation. In the second part, subjects had to transform the shape (mental rotation of 90 ° clockwise, see Figure 2) before recognizing it in the correct orientation. Once their choice was made, the subjects then pressed one of the four response buttons placed in front of them. Subjects were familiarized with eight shapes for each block; this step was reproduced until instructions for the two tasks (R and RR) were perfectly understood. Latencies (time to answer from end of shape's presentation) and accuracy were recorded.

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Shape

Recognition set

1 2 3 4 FIG. 2. Illustrativeshape and recognition set associated. Subjectspressed button "3" in the Recognition task (R) and button "4" in the Recognition after Rotation task (RR). Finally, 90 subjects participated in the two sessions of this study; they were administered the Dot-in-Matrix Patterns Recall, CRT, MRT, and Visual Memory Search during one day, and the PFT, MPFBT, R task, RR task, and DAT spatial test on the other day. RESULTS AND DISCUSSION Mean Performance Analysis for All the Tasks Means and standard deviations (N = 90) computed for all the variables are displayed in Table 1. The spatial test scores were entirely consistent with those usually noted (for instance, Poltrock & Brown, 1984, for PFT and DAT; Vandenberg & Kuse, 1978, for MRT). However, Sholl (1988) obtained higher performances on CRT, PFT, and MRT with only 28 subjects. Correlations between accuracy scores for spatial tests and laboratory tasks are listed in Table 2. Though some of these correlations between spatial tests seemed rather low (particularly for the reduced MPFBT), our results were globally similar to others on the same topic. Poltrock and Brown (1984), for instance, found a .60 correlation between PFT and DAT, and Vandenberg and Kuse (1978) noted correlations from .34 (n = 172) to .58 (n = 3435) and .62 (n = 456) between CRT and MRT. It is to be noted that in Sholl's (1988) study, the correlation between these two tests was .61. Furthermore, correlations between DAT and MRT were also similar to Vandenberg and Kuse's (1978) results (.50). However, in contrast to Petrusic, Varro, and Jamieson (1978) who found a high correlation (.63) between MPFBT and CRT, I obtained a rather low one. This

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SPATIAL ABILITIES AND VISUAL INFORMATION PROCESSING TABLE I Descriptive Statistics for Psychometric Tests and Experimental Tasks (N = 90) Variable Cards Rotation Test Mental Rotations Test Paper Folding Test Paper Form Board Test Differential Aptitude Test Visual Span (max = 25) Visual Memory Search: true false Recognition Recognition after Rotation

CRT MRT PIT MPFBT DAT VS VMT VMF R RR

M

SD

108.46 13.66 10.84 14.48 46.78 13.71 82.72% 85.73% 91 % 79.39%

27.82 6.25 3.23 4.47 15.17 4.97 13.39 12.62 6.88 13.99

Note. All scores are raw scores except VMT, VMF, R, and RR (expressed in percentages).

was the case also between the PFT and CRT even though Sholl (1988) obtained .56. As planned, on account of high mean accuracy for computerized tasks, it was possible to analyze latencies for correct answers. It can noted that, except for the visual span (VS) measured by means of the Dot-in-Matrix Patterns Recall and the correct "true" answers for Visual Memory Search (VMT), correlations between laboratory tasks' accuracy scores were fairly weak.

TABLE 2 Correl~iom Among Spafi~ Tests andExperiment~ Tasks(N = ~ )

CRT MRT PFT MPFBT DAT VS VMT VMF R RR

CRT

MRT

PFT

.408 .156 .171 ,256 .131 .100 .239 .075 .051

.263 .247 .522 .190 .137 .089 -.050 .058

.249 .539 .429 .346 .132 .072 .252

Variables a MPFBT DAT

.297 .323 .269 .262 .129 .184

.334 .193 .158 .001 .191

VS

VMT

VMF

R

.523 .226 .240 .031

.258 .246 .241

.195 .197

.195

RR

aVariables names, in the order listed above, are: Cards Rotation Test, Mental Rotations Test, Paper Folding Test, Paper Form Board Test, Differential Aptitude Test (spatial subtask). Dot-inMatrix Patterns Recall (Visual Span), Visual Memory Search "True" and "False", Recognition, Recognition after Rotation.

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Visual Memory Search. Though accuracy for "yes" and "no" judgments was very high, the correlation between the two variables was not so strong (.258). The idea of within-individual consistency in strategic choices, being more or less efficient according to the nature of judgment, could explain this relatively low correlation. Indeed, true (or detection) judgments and false (or default) judgments could underlie different processes of various efficiencies (see, for instance, the distinction between the "identity reporter" and the "slow detector" described by Farrell, 1985). In another way, true correct answers for the first and the last dot of the set were on average faster (1280 ms) than for the other dots ( 1375 ms). This finding can be explained if one considers that the subject's decision is based on the strength of mnesic traces of the probed dot: the stronger the mnesic trace, the briefer the latency. (This is the case for the last dot of the set or for the dots bearing visualization activity.) Recognition of Shapes (Without and After Rotation). A moderately weak correlation between these two tasks, R and RR, gave support to the hypothesis that one or more processes, required by only one task, were sources of individual differences. As expected, mean latency reaction time (RT) strongly increased with the number of inflection points of the shape, emphasizing the analytic and sequential properties of one or more of the activated processes. In addition, R accuracy (regression equation: TR = 1375 + 116x, r = .974, SD = 42) was relatively less affected by information complexity as was RR accuracy (regression equation) TR = 1331 + 198x, r = .979, SD = 64). Factor Analysis of the Intercorrelation Matrix The purpose of this study was to compare different groups of subjects as a function of their performance to paper-and-pencil spatial tests and computerized visual-spatial tasks. To what extent such a distinction is justified was first checked. In order to determine the underlying factor structure, a common factor analysis of the intercorrelation matrix depicted in Table 2 was conducted. Because inferences and hypothesis testing are dependent in a large part on sample size and because confirmatory factor analysis like Lisrel-Modeling (J6reskog, 1978) requires the assumption of normality, this method was not computed. Considering the methodological criteria stated by Carroll (1985a), I preferred to conduct a common factor analysis performed by means of Carroll's factor analysis programs (Carroll, 1985b). In a few words, principal factor analysis is based on an iterated procedure of communalities estimation; the vector of squared multiple correlation (SMC) values, computed from the inverse of intercorrelation matrix, was entered in the diagonal of the matrix. Generally, the degree of accuracy of the iterated communalities is related to a convergence criterion; here a convergence to the level of .0005 was used. It is well known that determining the number of common factors is a difficult problem; statistical tests of the number of factors (maximum-likelihood method)

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SPATIAL ABILITIES AND VISUAL INFORMATION PROCESSING TABLE 3 Factor IA)adin~s With Variables Rotated Oblique (promax) First.Order Factor Matrix (N = 90)

Hierarchical Factor Matrice Factors

Factors

Variable

A

B

C

D

VS VMT MPFBT DAT PFq" MRT CRT VMF R RR

.856 a .476 a .211 -.012 .194 -.067 .008 .206 .276 -.075

.111 .072 .125 .672 a .543 a .342 -.015 -.!10 -.157 .213

-.032 -.010 .182 .141 -.101 .469 a .573 a .275 .043 -.070

-.138 .254 .186 .003 .123 -.113 .051 .326 a .305 ~ .606 a

i

2

3

4

.816 .454 .201 -.011 .185 -.064 .008 .196 .263 -.072

.085 .055 .096 .514 .415 .262 -.012 -.084 -.120 .163

-.030 -.009 .172 .133 -.095 .443 .542 .260 .041 -.066

-.138 .254 .186 .003 .123 -.113 .050 .326 .305 .606

1.07

.58

.62

.72

g .482 .349 .350 .636 .524 .487 .316 .230 .095 .226

Column sum squares:

1.61

h2

.925 .396 .236 .686 .506 .519 .397 .272 .187 .454 4.6

Factor Correlations 1

I 2 3 4

.337 .214 .188

2

.395 .114

3

4

.066

Isalient value for each variable. is dependant upon sample size with variables assumed to be normally distributed. Inspection o f roots greater than or equal to 1 (three roots > 1 and a fourth = .982) and percentage o f variance extracted was in favor o f solutions calling for at least three factors. Solutions with 3, 4, and 5 factors were c o m p u t e d but the four factor solution with a 4 6 . 0 2 % o f c o m m o n variance I accounted for wag retained. The factor matrix was first rotated by the varimax orthogonal method; an oblique p r o m a x rotation (k = 3) was then conducted in order to give the simplest structure possible (Table 3). Because the first-order factors (1, 2, 3, and 4) were correlated, a second-order factor analysis was c o m p u t e d , following a procedure o f S c h m i d - L e i m a n transformation (Schmid & L e i m a n , 1957; see also Reuchlin, 1955). With this orthogonalized hierarchical analysis, r e c o m m e n d e d by numerous investigators (e.g. Carroll, 1985a; Gorsuch, 1983; Jensen, 1986a) both firstand s e c o n d - o r d e r factors are defined by the original variables. Factors are o r t h o g o n a l i z e d to produce part correlations; each o f the factors ( p r i m a r y and

IThis fairly weak percentage of explained variance emphasizes relative independence between tasks included in this battery.

g

.470 .739 .502 .202

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JUHEL

second order) is uncorrelated with every other factor, both between and within all levels of the hierarchy. The final outcome presenting the orthogonal hierarchical factor matrix is displayed in Table 3.

Interpretation of the Results. The factor loadings with variables are given in Table 3; 34.78% of common variance was accounted for by factor g, being hierarchic and residual. This factor appeared to be a spatial general factor underlying not only all visualization tests but also the visual span task. The common feature for all of the tasks lies in their requirement to actively maintain and operate on visual-spatial representations in working memory (see Baddeley, 1983, and his conception of a "visual-spatial sketch-pad"). This general factor appears to be similar to the one often called Visualization (Lohman, 1988). For its part, 65.22% of the common variance was explained by the four first-order factors all together. These four factors could be alloted to two groups, common variance accounted for by each of them being equal; the first one (Factors 2 and 3) was associated with spatial tests, the second one (Factors 1 and 4) with visual memory computerized tasks. Visualization tasks (DAT spatial test and PFT) loaded on Factor 2. These tasks require transformations and movements performed on internal parts of mental representations, generated from static presented information (see Hunt & Pellegrino, 1985; Hunt et al., 1988, for dynamically presented information). Factor 3 seemed primarily to be associated with tests calling for rotation of a form or an object and could be labeled Spatial Relations (Lohman, 1979; McGee, 1979; Pellegrino & Glaser, 1979; Pellegrino & Kail, 1982). As previously stated, when information becomes more complex, distinction between factors, Spatial Relations and Visualization, becomes more subtle, as can be seen when reading the fairly high loadings of MRT with Factors 2 and 3. To the contrary, with very elementary information, this factor, Spatial Relations, is identical to Speeded Rotation, as described by Lohman (1988). The second group of factors appeared to fit in with visual memory computerized tasks with briefly presented information. Factor 1 seemed to be a Visual Memory factor because VS, and to a certain extent VMT and R, loaded on this factor. It could represent the ability to organize information encoding and to actively maintain mental representations in working memory. Factor 4 was more specific and relatively inexplicit; its importance, in terms of explained variance, was about the same before and after the Schmid-Leiman transformation (Schmid & Leiman, 1957). Tests with salient loadings (>.30) with this factor seemed to reflect visual-perceptive ability, concentration, or attention capacities. Finally, results showed once again how much it is necessary to distinguish computerized tasks such as the VS task, requiring the ability to process rapidly, organize efficiently, and maintain visual information in working memory, from paper-and-pencil tests such as the DAT spatial test, requiring the ability to manipulate and transform complex spatial information. These findings are consistent

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with the undoubted statement that operations underlying these two sorts of tasks are relatively independent at some level of a structural hierarchy (Carroll, 1986) or distant from each of the others in the radex space of cognitive tests (Snow, Kyllonen, & Marshalek, 1984). Thus, complex tasks such as PFT or the DAT spatial subtest require a particularly high level of processing and are, for that reason, good measures of a general spatial factor.

Between-Group Comparisons as a Function of Discriminant Dimension Retained Earlier and present reports of the low overlap between spatial tests and visual memory tasks justified examining the differences among classification of groups on the basis of two dimensions: visual memory and visualization. With regard to the first one, a stepwise discriminant analysis 2 was computed: a two-group a priori subject classification (N = 90) was performed, taking performance on the VS task into account (scores under or above mean). As independent variables, VMT, correct "false" answers for Visual Memory Search (VMF), R, and RR were then successively introduced in the analysis; original partition was in fact modified only a very little by the discriminant function. Introduction of VMT transferred 3 subjects from one group to the other; the three other explicative variables did not produce a significant variation of the generalized distance function (D z of Mahalanobis). Only 12 subjects for each group, being the more distant from the barycentric points on the discriminant axis (+.046) were retained in order to make contrast maximal. These two groups were to be later on labeled high visual memory (HVM) and low visual memory (LVM). Classification as a function of visualization was supported by performance on CRT, MRT, PFT, and the DAT spatial subtest. A principal component analysis with individuals represented in the principal plane (76% of total variance accounted for by the two first components) was conducted. It should be noted that the more discriminant tests were DAT spatial subtest, MRT, and PFT (in this order). Two groups were then constituted with the more contrasted subjects. This classification 3 was then tested by means of a new stepwise discrimina'nt analysis, but all subjects were well classified. As done previously, groups were labeled high spatial visualization (HVZ) and low spatial visualization (LVZ).

Visual Memory Search Task. The aim was to study mean latencies, in every group, for "true" correct answers as a function of the ordinal position of the probe test. It is obvious that the last position dot of the set is generally better

2Stepwise discriminant analysis is a principal components analysis in a space supplied with a distance defined by the inverse of the total covariance matrix (generalized distance of Mahalanobis). 31t is interesting to note that only 3 subjects belonged to both HVM and HVZ groups and 2 subjects to LVM and LVZ; in addition, I subject belonged to LVM and HVZ and 2 subjects to HVM and LVZ.

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memorized than the others, even if poststimulus duration is sufficiently long, as it was in this case (1500 ms), to weaken sensory traces. For this reason, regressions between mean latencies and probed test ranks have been computed only for the first four elements of the set. When between-group comparison was made along the visualization dimension, results were similar to the whole experimental group results; slopes of regression lines were fairly equal. It was interesting to note the obvious difference between intercepts: HVZ subjects seemed rather faster than LVZ subjects, even though mean accuracy differences were not significant (