Measuring Working Memory With Digit Span and the Letter-Number ...

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Measuring Working Memory With Digit Span and the Letter-Number Sequencing Subtests From the WAIS-IV: Too Low Manipulation Load and Risk for Underestimating Modality Effects ab

Jens Egeland a

Division of Mental Health & Addiction, Vestfold Hospital Trust, Tønsberg

b

Institute of Psychology, University of Oslo, Oslo, Norway Published online: 24 Apr 2015.

To cite this article: Jens Egeland (2015): Measuring Working Memory With Digit Span and the Letter-Number Sequencing Subtests From the WAIS-IV: Too Low Manipulation Load and Risk for Underestimating Modality Effects, Applied Neuropsychology: Adult, DOI: 10.1080/23279095.2014.992069 To link to this article: http://dx.doi.org/10.1080/23279095.2014.992069

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APPLIED NEUROPSYCHOLOGY: ADULT, 0: 1–7, 2015 Copyright # Taylor & Francis Group, LLC ISSN: 2327-9095 print/2327-9109 online DOI: 10.1080/23279095.2014.992069

Measuring Working Memory With Digit Span and the Letter-Number Sequencing Subtests From the WAIS-IV: Too Low Manipulation Load and Risk for Underestimating Modality Effects Jens Egeland

Downloaded by [University of Oslo] at 00:44 27 April 2015

Division of Mental Health & Addiction, Vestfold Hospital Trust, Tønsberg, and Institute of Psychology, University of Oslo, Oslo, Norway

The Wechsler Adult Intelligence Scale (WAIS) is one of the most frequently used tests among psychologists. In the fourth edition of the test (WAIS-IV), the subtests Digit Span and Letter-Number Sequencing are expanded for better measurement of working memory (WM). However, it is not clear whether the new extended tasks contribute sufficient complexity to be sensitive measures of manipulation WM, nor do we know to what degree WM capacity differs between the visual and the auditory modality because the WAIS-IV only tests the auditory modality. Performance by a mixed sample of 226 patients referred for neuropsychological examination on the Digit Span and Letter-Number Sequencing subtests from the WAIS-IV and on Spatial Span from the Wechsler Memory Scale-Third Edition was analyzed in two confirmatory factor analyses to investigate whether a unitary WM model or divisions based on modality or level/complexity best fit the data. The modality model showed the best fit when analyzing summed scores for each task as well as scores for the longest span. The clinician is advised to apply tests with higher manipulation load and to consider testing visual span as well before drawing conclusions about impaired WM from the WAIS-IV.

Key words:

attention/perception, cognitive/learning, Digit Span, Letter-Number Sequencing, tests, WAIS, working memory

The concept of working memory (WM) has attained widespread significance among psychologists working in different fields. WM is related to fluid intelligence (Shelton, Elliot, Matthews, Hill, & Gouvier, 2010) and might mediate state-dependent decrements in learning (Egeland et al., 2003) and attention (Andersen, Hovik, Skogli, Egeland, & Øie, 2013). It is also closely linked to executive function and memory (Unsworth & Spillers, 2010). Deficits in WM are associated with most clinical states. Reflecting this close relationship, the concept has been more closely integrated into the Wechsler intelligence scales with successive Address correspondence to Jens Egeland, Ph.D., Division of Mental Health & Addiction, Vestfold Hospital Trust, P. O. Box 2267, Tønsberg, 3103 Norway. E-mail: [email protected]

revisions of the tests. In Norway, the Wechsler Adult Intelligence Scale (WAIS) is the only test used by a majority of practicing psychologists (Vaskinn & Egeland, 2012), and it is also rated as the most frequently used test among European psychologists (Evers et al., 2012). It thus seems reasonable to assume that this test is also the most widespread measure of WM. Historically, the Wechsler tests were divided into two factors, performance and verbal IQ. A third factor called “freedom from distractibility” had already been identified in the Wechsler-Bellevue Scale in 1952 (Cohen, 1952), although it did not become clinically relevant until the publication of Kaufman’s (1979) book, Intelligent Testing With the WISC-R. The third factor was later further developed and divided into separate measures


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of Processing Speed and Working Memory. In the fourth revision of the WAIS (Wechsler, 2008), both the Digit Span test and the Letter-Number Sequencing subtest were further developed to provide a better assessment of the manipulation element in WM. WM is still only tested in the auditory modality. The question asked here is whether the changes made are sufficient for considering the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) a relevant tool for measuring WM. In the Letter-Number Sequencing subtest in the WAISIV, participants now receive 3 training tasks and the number of tasks are extended from 21 to 30 by introducing 6 tasks involving only one letter and number and by extending the number of three-letter tasks to 9 instead of 3. Sequential Span was introduced as a new subtask in the WAIS-IV Digit Span, adding to Digit Forward and Backward to increase manipulation WM load. The base-rate figures are offered based on the maximal span for each of the tasks as are base rates for difference scores between the different conditions. Originally, the concept of WM was introduced by Baddeley and Hitch (1974) and was referred to as the multimodal model of WM. Two modality-specific slave systems were posited: the phonological loop and the visual-spatial scratchpad. The slave systems were equivalent to attention span or short term-memory capacity and were involved in simple storage of information for a short period of time. Whenever the information had to be manipulated in some way, a more complex overarching non-modality-specific system called the central executive had to be activated. The unique contribution of the model was the postulation of the central executive because short-term memory was already an established concept (Miller, 1956). When referring to WM today, it is unclear whether one refers to the central executive element solely or whether the slave systems are considered as well. Engle (2010) argue that it is the complex span tests that are important for variability in WM. Individual differences appear mainly when there is demand for some distracting activity or retrieval from secondary memory (Unsworth & Engle, 2007). According to this view, only the complex tests have external validity in the sense of being able to account for differences in learning ability specifically (Unsworth & Engle, 2007) or other cognitive and adaptive functions more broadly (Engle, 2010). This is in line with Miake’s (2001) view, calling for the use of WM span tasks that are different from simple short-term memory tasks such as digit span by imposing dual-task demand on the participant (i.e., some sort of simultaneous mental manipulation while processing information). In the Engle (2010) view of WM, the phonological loop and visual-spatial scratchpad posited by Baddeley and Hitch (1974) become less central, as they are considered merely two of what could be a dozen modality- or material-specific stores. The distinction between simple storage and complex manipulation tasks seems now to be well established and

is often used in clinical research. One example is the finding of Martinussen, Hayden, Hogg-Johnson, and Tannock (2005) that participants with attention-deficit hyperactivity disorder (ADHD) are more impaired in manipulation compared with simple storage. On the other hand, possible domain-specific impairments have continued to be of interest within research in dyslexia, specific language impairment, and nonverbal learning disorders. Shah and Miyake (1996) demonstrated that reading span correlated well with reading comprehension measures but not with spatial ability measures, whereas the spatial span showed the opposite pattern. However, Miake (2001) emphasized that the correlations between reading comprehension and WM are higher for complex reading span than for digit span, although these measures also correlate. Reduced phonological awareness, which is highly associated with reduced digit span performance, is characteristic of dyslexia. In a meta-analysis of 53 studies, Melby-Lervåg, Lyster, and Hulme (2012) found that participants with dyslexia performed 0.71 standard deviations below healthy controls on auditory short-term memory measures, including Digit Span Forward. Of studies comparing impairment within both the auditory and visual domains, Gooch, Snowling, and Hulme (2011) found that digit recall was impaired in dyslexia but not in ADHD, while visual span was impaired only in ADHD. Witruk, Ho, and Schuster (2002) found no support for a general, modality-independent deficit in children with dyslexia, as found in those with ADHD. The previously cited study by Martinussen et al. (2005) also revealed a modality effect in ADHD, with larger effect sizes in both visuospatial storage and manipulation tasks compared with the corresponding auditory WM tasks. Preßler, Krajewski, and Hasselhorn (2013) identified groups of children characterized by either visual or phonological WM deficits and found that good phonological WM was favorable for the later acquisition of reading, writing, and mathematics, whereas good visual WM was a prerequisite for arithmetic skills. These studies indicate that modality differences in WM capacity may be important when considering reading and writing impairments. Aboitiz, Aboitiz, and Garcia (2010) claimed that the phonological loop is vital both for the phylogenetic and ontogenetic development of language. In their meta-analysis of brain mechanisms underlying WM, Nee et al. (2013) found that a content division (i.e., modality) was more prominent than functional divisions (i.e., storage and manipulation). Summing up, it seems to be a convergence among researchers that complex span tasks are best for detecting test variability and to account for daily life variability in performance. The first question posed here, however, is whether the changes made in both the Digit Span and Letter-Number Sequencing subtests in the WAIS-IV are sufficient for differentiating them from the simple forward condition measuring short-term memory capacity.

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The importance of modality differences for variability in WM is less clear. Modality-specific impairments may play a role in dyslexia, particularly when measured with simple span tasks, but they may perhaps account for less variability in more complex manipulation tasks. The next research question is therefore related to the amount of modality-specific variability in the span tests in the WAIS-IV. In the clinic, we were interested in whether it is sufficient to test WM only in the auditory modality as is done in the WAIS-IV. In the technical and interpretative manual of the WAISIV (Wechsler, 2008, p. 15), Digit Span Forward is described as a test involving “attention, encoding and auditory processing,” while the term WM is reserved for the Backward and Sequencing tasks as well as the Letter-Number Sequencing subtest. There is no reference to modality specificity in describing these tasks. The question for the clinician is whether (a) to limit interpretations of impaired performance to a possible modality-specific impairment, or (b) to interpret impairment in the latter three WM tests as a level effect in manipulation specifically or (c) as a general effect. This could be crucial when examining participants for either reading or language deficits or a more general deficit expected, for example, in ADHD or schizophrenia. How often this leads to problems of interpretation of the test findings when test participants exhibit a range of different cognitive problems is not clear. This could be rephrased as a question of what is the most potent dimension of WM when using the span tests offered in the Wechsler tests and whether it could be tested in a confirmatory factor analysis (CFA). The last two revisions of the WAIS have been standardized together with new versions of the Wechsler Memory Scale (WMS). This scale offers a visual analogue to the Digit Span subtest in the WAIS, the Spatial Span subtest, which is equivalent to the Corsi Block Test. A CFA of the WMS-Third Edition (WMS-III) showed a separate WM factor composed of Spatial Span and Letter-Number Sequencing (Millis, Malina, Bowers, & Ricker, 1999). A joint WMS-III and WAIS-Third Edition (WAIS-III) CFA also showed a WM factor composed of Spatial Span together with the three WAIS-III tests comprising the WM index in that test. A CFA of the WAIS-IV and the WMS-Fourth Edition (Holdnack, Zhou, Larrabee, Millis, & Salthouse, 2011) showed two models that fit the data equally well: a seven-factor model with separate auditory and visual WM factors and a five-factor solution with a unitary WM factor. In the current study, we analyzed the raw scores of the WMS-III Spatial Span Forward and Backward tasks together with the WAIS-IV Digit Span and Letter-Number Sequencing raw scores to determine whether a unitary WM factor, level, or modality factors provide the best fit to the data. If a unitary factor or a level model offers the best fit, one could claim that the testing of visual WM may be

relevant only in special cases but not necessarily in standard assessments because it does not posit a fundamental threat to generalizing the auditory tests to WM in general. A best fit for the unitary model would be taken as an indication that the Backward, Sequencing, or Letter-Number Sequencing tasks are not sufficiently complex to be differentiated from the simple Forward task. If a level model gives best fit to the data, the WAIS has accomplished what was intended with the revision of the WM tasks from the third to the fourth revision. If the modality model gives the best fit, one must be careful in inferring both a general WM deficit and an impairment in the executive element as well. The WAIS-IV offers both measures of length of best span or total score earned for each condition. It is an empirical question as to which set of measures best reflects the underlying structure of WM or whether they show the same structure. In the CFA presented here, the three hypothesized models are tested both with the total points for each subtask and with the maximal length of each task. For the Spatial Span in the WMS-III, no such division between span length and total scores is offered.

METHODS Participants Two hundred and twenty-six sequentially collected test protocols were taken from the author’s part-time private practice. Permission was granted by the Norwegian Data Inspectorate. The participants had been referred for specialized neurocognitive assessment. Table 1 shows the composition of the sample divided according to diagnosis or cause of referral/tentative diagnosis before assessment to give the reader a picture of the heterogeneity of the cognitive and psychological conditions involved. The age of the participants ranged from 16 to 82 years old (Mage ¼ 38.1 years, TABLE 1 Cause of Referral/Tentative Diagnosis

Attention-deficit hyperactivity disorder Learning disorders Mild cognitive impairment Cerebrovascular accidents Traumatic brain injury Acquired cognitive dysfunction of unclear origin Addiction Affective disorders Toxic solvent exposure Anxiety disorders/posttraumatic stress disorder Fatigue Schizophrenia Mental retardation Other Total

N

%

55 45 12 12 12 33 14 11 6 6 10 5 1 4 226

24.3 20.0 5.3 5.3 5.3 14.6 6.2 4.9 2.7 2.6 4.4 2.2 0.4 1.8 100.0

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SD ¼ 14.3). Fifty-three percent of participants were men and 47% were women. The mean General Ability Index was 92 (SD ¼ 15) and ranged from 57 to 141.


Measures From the WAIS-IV Digit Span subtest, the following measures were collected: Digit Span Forward, Backward, and Sequencing total scores, as well as longest span for each task. Total score and longest score were also collected from the Letter-Number Sequencing subtest. To give background information as to the overall intellectual functioning of the participants, the General Ability Index was computed based on the three subtests comprising the Verbal Comprehension Index (Similarities, Vocabulary, and Information) and the three subtests comprising the Perceptual Reasoning Index (Block Design, Matrix Reasoning, and Visual Puzzles). From the WMS-III, the total scores on the subtest Spatial Span Forward and Backward were analyzed. Data Analyses The goodness-of-fit for five different factor models was tested in two separate sets of analyses, where total scores were entered in the first set. In the second set, the relevant span length was entered for the WAIS-IV data, because the WAIS provides an assessment of these measures as well. For Spatial Span, total scores for the forward and backward conditions were entered in both sets of analyses. The models read as follows: 1. One general WM factor underlying all six measures. 2. a, 2b, and 2c. Level of processing distinguishing between a simple storage factor and a manipulation factor. To be comprehensive, we tested three variants of the division in levels: In 2a, Digit Span Forward and Spatial Span Forward were considered simple storage tasks, while Digit Span Backward, Sequential Span, and Spatial Span Backward and the LetterNumber Sequencing subtest were considered manipulation tasks tapping the central executive. Model 2b tests the hypothesis that the two backward conditions are not complex enough to sufficiently tax executive functions and will therefore group together with the forward conditions of the same task. The Sequential task and the Letter-Number Sequencing Span will then comprise the more complex manipulation factor. Model 2c is based on the findings by Wilde, Strauss, and Tulsky (2004) and Kessels, van den Berg, Ruis, and Brands (2008) that Spatial Span Backward differs from Digit Span Backward by not being more demanding than the forward condition. Replicating this finding in healthy controls, Bacon, Parmentier, and Barr (2013) nevertheless found that in dyslexia,

Spatial Span Backward is differentially impaired. Thus, in this model, Digit Span Forward and both Spatial Span conditions are considered to measure simple storage, while Digit Span Backward, the Sequential task, and the Letter-Number Sequencing Span measure manipulation. 3. The modality model. In this model all auditory tests are grouped together (all three subtasks of Digit Span þLetter-Number Sequencing) in one factor hypothesized to differ from a visual factor composed of Spatial Span Forward and Backward. CFA was conducted on raw scores for each of the four models, applying the LISREL 8.3 program (Jöreskog & Sörbom, 1993). The goodness-of-fit measures should be interpreted as follows (Keith, 2005). Chi-square (v2). When applying chi-square in comparing the hypothesized and observed models, a low value means a good fit. v2/df. If the chi-squares for two models are the same, the more constrained or more parsimonious model is preferred. Parsimony is reflected in CFA models by df. Thus, the smallest v2 in comparison to df represents the best fit of the data and should be no more than 2 for a good model fit (Jöreskog & Sörbom, 1993). Goodness-of-fit index and adjusted goodness-of-fit index. These measures show how much better the model fits as compared with no model at all. The adjusted goodness-of-fit index is adjusted for degrees of freedom. Values can vary from 0 to 1. Values greater .90 indicate a good model fit. Comparative fit index. Values of .95 or greater suggest a good fit of the model to the data, and values greater than .90 suggest an adequate fit. Root mean square error of approximation. The root mean square error of approximation (RMSEA) is a measure of approximate fit. Smaller values suggest a better fit, with values of .06 or less suggesting a good fit and those of approximately .08 suggesting an adequate fit.

RESULTS Table 2 shows the results of the CFA based on total scores from each of the WM subtests. Only the modality factor model depicted in Figure 1 shows adequate or good fit between the model and the observed results for all fit indexes. The high v2/df and RMSEA values clearly show that the other factor models are not valid. Table 3 shows the equivalent fit statistics based on the best or longest span.

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WAIS WORKING MEMORY TABLE 2 CFA of Unitary, Level, and Modality Models of WM-Span Subtest Scores From WAIS-IV and WMS-III (N ¼ 226) Model 1. Unitary WM 2a. Level model: DF þ SF vs. DB, DS, SB, LN 2b. Alternative level model: DF, DB, SF, SB vs. DS, LN 2c. Alternative level model: DF, SF, SB vs. DB, DS, LN 3. Modality model

χ2 (df)

χ2/df

GFI

AGFI

CFI

RMSEA

32.43 (9) 27.13 (8) 30.83 (8) 32.52 (8) 6.55 (8)

3.60 3.39 3.85 4.06 0.82

.95 .96 .96 .95 .99

.90 .90 .88 .88 .97

.94 .94 .94 .93 1.0

.109 .104 .114 .118 .0

Note. Numbers in bold satisfy criteria for adequate fit between model and data. CFA ¼ confirmatory factor analysis; WM ¼ working memory; WAIS-IV ¼ Wechsler Adult Intelligence Scale-Fourth Edition; WMS-III ¼ Wechsler Memory Scale-Third Edition; GFI ¼ Goodness-of-Fit Index; AGFI ¼ Adjusted Goodness-of-Fit Index; CFI ¼ Comparative Fit Index; RMSEA ¼ root mean square error of approximation; DF ¼ Digit Span Forward; DB ¼ Digit Span Backward; DS ¼ Digit Span Sequence; LN ¼ Letter-Number Span; SF ¼ Spatial Span Forward; SB ¼ Spatial Span Backward.


DISCUSSION The study is based on clinical data. The research literature convincingly shows that WM can be subdivided according to both modality and level of processing (Martinussen et al., 2005) and that complex WM tasks better detect ecologically valid variability. The first issue addressed here is whether the difference in complexity or manipulation load between the simple storage test (i.e., the forward span condition on the one side) and the manipulation tasks (i.e., the backward, sequencing conditions, and the Letter-Number Sequencing subtest) are large enough to stand out as separate factors in a CFA. The second issue addressed is whether modality differences can confound interpretations of WM impairment when only the auditory modality is assessed as is the case in the WAIS-IV. Both issues are highly relevant for clinical practice because the WAIS-IV is so frequently in use in the clinic. The results show that the differences in level of processing were not large enough to separate a manipulation or complex WM element from the simple storage element. However, the CFA showed that a unitary WM model did not provide a good fit for the data either. The modality model was the only model that had satisfactory fit to the data when analyzing total scores, and it was superior to the other models when analyzing span length as well. The findings are consistent with the findings of Kessels et al. (2008) of a verbal and spatial factor in a principal

FIGURE 1 Two-factor modality-based model for Digit Span, Spatial Span, and Letter-Number Sequencing. DF ¼ Digit Span Forward; DB ¼ Digit Span Backward; DS ¼ Digit Span Sequence; LN ¼ Letter-Number Span; SF ¼ Spatial Span Forward; SB ¼ Spatial Span Backward.

The fit measures were best for the modality model as well, although the v2/df was not less than 2, as is considered the cutoff for a good model fit. Thus, the modality model based on total scores was superior to all other models.

TABLE 3 CFA of Unitary, Level, and Modality Models of WM-Span Best Performance Scores From the WAIS-IV and WMS-III (N ¼ 226) Model 1. Unitary WM 2a. Level model: DF þ SF vs. DB, DS, SB, LN 2b. Alternative level model: DF, DB, SF, SB vs. DS, LN 2c. Alternative level model: DF, SF, SB vs. DB, DS, LN 3. Modality model

χ2 (df) 54.48 52.07 41.38 55.20 18.09

(9) (8) (8) (8) (8)

χ2/df

GFI

AGFI

CFI

RMSEA

6.05 6.51 5.17 6.90 2.26

.92 .93 .94 .92 .97

.82 .81 .85 .80 .93

.88 .88 .91 .87 .97

.152 .158 .138 .164 .076

Note. Numbers in bold satisfy criteria for adequate fit between model and data. CFA ¼ confirmatory factor analysis; WM ¼ working memory; WAISIV ¼ Wechsler Adult Intelligence Scale-Fourth Edition; WMS-III ¼ Wechsler Memory Scale-Third Edition; GFI ¼ Goodness-of-Fit Index; AGFI ¼ Adjusted Goodness-of-Fit Index; CFI ¼ Comparative Fit Index; RMSEA ¼ root mean square error of approximation; DF ¼ Digit Span Forward; DB ¼ Digit Span Backward; DS ¼ Digit Span Sequence; LN ¼ Letter-Number Span; SF ¼ Spatial Span Forward; SB ¼ Spatial Span Backward.


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component analysis of Digit Span and Corsi Block Span in elderly individuals. Bowden, Petrauskas, Bardenhagen, Meade, and Simpson (2013) performed a CFA on all single Digit Span Forward and Backward items and found no fit for the forward/backward dichotomy. They concluded that forward and backward span reflect the same cognitive ability. Neither Perry et al. (2001) nor Twamley, Palmer, Jeste, Taylor, and Heaton (2006) found differences between forward and backward span in patients with schizophrenia, otherwise expected to have a specific deficit in the manipulation element of WM. This was taken to mean that the backward span was not sensitive enough to capture differential impairments. Instead one has to use more complex tests, as suggested by Engle (2010) and Miyake (2001). The fact that the differences in manipulation load were lower than the size of the modality effect even when including the Sequence task and the extended Letter-Number Sequencing subtest indicates that the addition and extension of the presumptive WM tasks in the WAIS-IV are not sufficient to test complex WM considered to be most clinically relevant. The fit between the modality model and the observed scores was not only better than the different complexity models, but it represented a good fit. The supremacy of the modality model was therefore not due to the lack of sensitivity to complexity differences. There could be several reasons for this. One is related to the sample: Could it be an overrepresentation of participants with specific learning deficits either related to language or visuospatial processing? Twenty percent were referred with a suspected learning deficit. Thirteen of these participants (5% of the sample) were suspected to have a language-related learning disorder, and five (2% of sample) were suspected to have a possible nonverbal learning deficit, which is as expected in a mixed clinical sample. Another explanation seems more plausible: As claimed by Engle (2010), simple tasks are more automatic and do not capture deficits in controlled attention, shifting, or inhibition that constitute the variability seen in more complex tests. The closer one gets to testing sensory capacity with little demand for executive monitoring, the more dominant the interindividual differences in preferred or dominant processing mode will be. If we then consider the span tests of the WAIS as more simple storage capacity tests and not tests of complex WM, it is important not to conclude that participants have a normal WM based on such tests alone. Conversely, impaired span test performance could not safely be interpreted as anything more than a deficit in auditory processing capacity unless it is supplemented by a corresponding test of visuospatial span, such as the one applied here from the WMS. Detecting a modality supremacy or deficit in individual cases, however, may be important both for the diagnosis of learning deficits and reading disorders and for guidance as to how patients will learn most effectively. The latter is particularly important if the modality

difference in simple span is also corroborated in memory tests or by a difference between the Perceptual Reasoning and Verbal Comprehension Indexes in the WAIS-IV. Lasonen, Leppämäki, and Hokkanen (2009) found that only participants with dyslexia and not those with ADHD performed poorer than controls on the Digit Span subtest of the WAIS-III. Goldstein, Beers, Siegel, and Minshew (2001) found that only those with a reading disorder or global deficits were impaired in the Digit Span in a comparison of children with deficits in arithmetic and reading, global learning disorders, and autism. These results are to be expected given the present findings, but to the author’s knowledge, they have not been replicated with the WAISIV extended span testing. The Digit Span and Letter-Number Sequencing of the WAIS are only two of the three tests comprising the WAIS-IV WM Index score. However, the problem of generalizing an auditory test result to WM in general might be accentuated when including the third subtest, Arithmetic. For the third version of the WAIS, the Arithmetic subtest was found to measure verbal learning more than WM (Gregoire, 2004), and it fit better into a verbal comprehension factor than a WM factor (Egeland, Bosnes, & Johansen, 2008). The CFA based on the normative sample of the WAIS-IV showed the best fit between model and observed scores when the Arithmetic subtest was allowed to load on both Verbal Comprehension and Working Memory (Wechsler, 2008). This finding had nevertheless no consequences for the computation of index scores in the WAIS-IV. There are some limitations to the study. The sample is not very well described in terms of diagnoses, only by initial cause of referral, which in some cases included a good diagnostic process. The purpose of the neuropsychological testing involving the WAIS-IV was to give a functional description, rather than a diagnosis. In the cases where a definite diagnosis was set, the test results were part of the diagnostic decision, thus confounding independent and dependent variables. Nevertheless, the list of causes of referral shows that a wide array of clinical problems was examined and contributed to variance in the examined measures. If only participants with specific learning deficits were examined, the modality effects would probably be even more prominent. The study is based on clinical file data. If the study had been designed as a WM study from the start, we would have included WM tests that involved a higher cognitive load, and thus, we would have expected that complexity level would emerge as a factor. However, the purpose of the study was not to test the dimensions of WM as such, but to check the validity of the presumed WM tests in the WAIS-IV. The finding of the prominence of modality advises the clinician on the one hand to also apply tests of complex span and to include a spatial or visual measure before drawing firm conclusions about a WM deficit.

WAIS WORKING MEMORY


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