- Email: [email protected]
or attention disorders
BRAIN
AND
COGNITION
5,
22-40 (1986)
Effortful Processing Deficits in Children with Reading and/or Attention Disorders PEGGY
T. ACKERMAN, JEAN M. ANHALT, ROSCOE A. DYKMAN, AND PHILLIP J. HOLCOMB University
of Arkansas for Medical
Sciences
Three groups of educationally troublesome boys were contrasted with adequate students on several tasks tapping effortful processing. The nonhyperactive reading disabled (RD) group differed both from controls and two attention deficit disorder (ADD) groups, one with and one without hyperactivity (H), on aspects of a memory task involving acoustic and semantic associations. All three clinical groups differed from controls in memory for low-imagery as opposed to highimagery words and in computational efficiency. A stepwise regression analysis to predict reading grade level showed age and WISC-R IQ to account for 38% of the variance with an additional 28% explained by the effortful task variables (multiple R = 83). It is theorized here that attentional disorder impedes automatization of number facts; and, inasmuch as RD children receive adverse attention ratings, even if not considered hyperactive, they, as well as ADD and H/ADD boys, exhibit this deficiency. o 1986 Academic PESS, IX
INTRODUCTION
Hasher and Zacks (1979) have proposed a useful framework for the conceptualization of a broad range of memory phenomena. In short, they postulated that some mental operations are innately automatic and that others become automatic via extensive practice. Whereas both innate and acquired operations require only limited allocation of attentional capacity, nonautomatic operations require considerable capacity. The latter type operations have been variously described as controlled, effortful, conscious, and intentional. Automatic and effortful processing also figured large in the earlier theorizing of Shiffrin and Schneider (1977), Kahneman (1973), Posner and Snyder (1975), and Brown (1975). These theorists, This research was supported by National Institute of Mental Health Grant MH35679 and by the Marie Wilson Howells Memorial Fund. Special thanks go to the staff of the UAMS Child Study Center and to Behavioral Laboratory team members Michael Oglesby, Yvonne Boudreau, and Betty Patterson. Reprint requests should be sent to Dr. Dykman, Slot 588, University of Arkansas for Medical Sciences, 4301 West Markham, Little Rock, AR 72205. 22 0278-2626186$3.OO Copyright All rights
0 1986 by Academic Press. of reproduction in any form
EFFORTFUL
PROCESSING
IN RD AND ADD SUBJECTS
23
like Hasher and Zacks, have been concerned with limits on the energy available for performing mental operations. Educators aim, of course, toward the automatization of basic skills in elementary school children, for only when these skills have become automatic can the children have sufficient free attentional capacity for comprehension and problem solving, which require conscious effort (Ackerman & Dykman, 1982; Sternberg & Wagner, 1982). The Hasher-Zacks model has special appeal to those who study attention dysfunctions and impaired skill acquisitions in normally intelligent children. Three groups of children described in DSM-III particularly invite study within the automatic-effortful framework, i.e., those with attention deficit disorder (ADD), with or without hyperactivity (H), and those with developmental reading disorders (RD). Since it is not uncommon to see both types of ADD children who have acquired basic skill competence despite their dysfunction, it cannot be claimed that attention dysfunction per se causes RD. Yet, RD children, as a rule, exhibit major attentional problems (Ackerman, Dykman, & Oglesby, 1983), and a sizable fraction are hyperactive. If one starts, on the other hand, with a heterogeneous group of hyperactives and employs stringent criteria for RD, then one recent study indicates only about 10 percent are doubly afflicted (Halperin, Gittelman, Klein, & Rudel, 1984). This is not to say, however, that the remainder of the hyperactives are across-the-board normal achievers or that their classwork is satisfactory (Cantwell & Satterfield, 1978).Moreover, there is some evidence that the older the hyperactive child, the more apt he is to display a basic skill deficit (Halperin et al., 1984). Thus, attention dysfunction appears to interact with other factors in affecting achievement levels. The Hasher-Zacks model suggests that children who present with specific learning disability may have more generalized problems in acquired automatization of subskills (see, e.g., Denckla & Rudel, 1976; Rudel, 1980), whereas many who present with attention disorders as the primary difficulty will exhibit normal progress in automatization of basic skills yet falter in tasks requiring sustained effortful processing. Learning disabled children would also be expected to perform poorly on effortful tasks, particularly if the task entails skills which have not become automatized. If the effortful task does not depend on prior learning, however, the RD child would not necessarily be expected to perform poorly, provided he does not have a concomitant attention disorder. For example, RD children generally perform above average on Block Design (Ackerman et al., 1983). It is, of course, simplistic to speak of a general attention disorder, for attention has many facets. Also, the dysfunction can be situation, modality, and material specific (Rosenthal & Allen, 1978). It would be virtually impossible in one experiment to contrast problem students on all aspects
24
ACKERMAN
ET AL.
of attention. The study reported below is but a beginning into what could prove a fertile area of research. It gains strength from the comparison of three clinical groups with each other as well as controls. Chapman and Chapman (1973) have warned of the shortcoming of studies that do not include clinical contrast groups. The study also includes two age bands (S-10 and IO-12), inasmuch as efficiency in effortful processing would be expected to be age related. Age x group interactions, then, could point to maturational lag. Elsewhere (Ackerman, Anhalt, Dykman, & Holcomb, 1985) we have presented results from tasks of innate and acquired automatic processing in the groups of children described below. While the focus here is on effortful processing, several of the tasks allowed appraisal of level of processing (e.g., sensitivity to levels of meaning and to imagery levels) as well as strategic approaches. Memory tasks are all the more demanding of attentional capacity if subjects do not employ strategic behaviors such as rehearsal, chunking, and clustering. From the work of Bauer (1979), Brown (1975), Torgeson and Goldman (1977) and Weingartner, Rapoport, and Buchsbaum, et al. (1980), among others, it appears that RD subjects and hyperactives do not as often employ these strategies as normal learners. Some evidence suggests that problem learners can effectively use certain strategies, once they are pointed out, but that these children are less likely to use strategies spontaneously (Brown, 1979). Two of the memory tasks below allow appraisal of spontaneous clustering and one also has an induced clustering condition. METHOD Twenty-four normal achieving elementary school boys were contrasted with three groups of educationally handicapped boys. All students were Caucasian, normally intelligent, without physical handicap, and from above poverty level homes. The control children were recommended to the investigators by their teachers and principals at two local parochial schools. The educationally handicapped students were referred either to the Child Study Center of the University of Arkansas or to our laboratory by school personnel; they were assigned to one of three diagnostic groups on the basis of teacher ratings, WISC-R IQs, and standard scores on the Wide Range Achievement Test (WRAT). Twentyone boys were classified as having attention deficit disorder (ADD) without hyperactivity and without reading disability; 24 were categorized as ADD with hyperactivity but without reading disability (H/ADD); 24 were diagnosed as having developmental reading disorders in the absence of hyperactivity (RD). Criteria set down in the new Diagnostic and Statistical Manual (DSM-III) of the American Psychiatric Association were employed in definitions of ADD, with and without hyperactivity, and reading disability. Additionally, H/ADD subjects had scores of 15 or higher on the Conners’ (Teacher Questionnaire) hyperkinesis index. Nonhyperactive ADD subjects and RD subjects had scores under 15 except in a few instances where the elevated score resulted from attention items rather than adverse ratings on core hyperkinesis items (i.e., restless in the squirmy sense; restless, always up and on the go; excitable, impulsive). All ratings were made when children were not on stimulant medications. Eleven items on the Teacher Questionnaire were scored to provide an ADD index (maximum score = 33); 3
EFFORTFUL
PROCESSING IN RD AND ADD SUBJECTS
25
of these items overlap with the hyperkinesis index (distractibility or attention span problem; fails to finish things that he starts; and easily frustrated in efforts). The 8 other items were fails to follow instructions fully, difficulty concentrating on assigned work, daydreams, often does not seem to listen, ditficulty sticking to a play activity, complains about assignments, dawdles, does not work up to capacity, task persistence is a problem. RD subjects had mean standard scores below 90 (the 25th percentile) on the WRAT reading and spelling subtests; additionally, these mean scores were at least 10 points below either the Full Scale or Verbal WISC-R IQ. Children with both Verbal and Performance IQs under 90 were excluded as were those who met criteria both for RD and H/ADD. However, as is discussed below, a significant fraction of RD subjects met ADD without hyperkinesis criteria, and some subjects in all three clinical groups had standard scores of less than 90 on the WRAT arithmetic subtest. Table 1 gives mean descriptive data for the four groups, divided into two age bands (8 years to 9 years, 11 months; 10 years to 11 years, 11 months). Note that the mean IQ of the RD subjects was almost 20 points above their mean reading-spelling standard scores. By design, the RD children read and spelled less well than other groups. Not by design, the control (CON) group read, spelled, and computed better than the two ADD groups. The RD children had lower WRAT arithmetic scores than control and H/ADD groups but not the ADD group. On the arithmetic test only, younger subjects had higher standard scores than older ones (for group, F(3, 85) = 40.56 on reading, 31.99 on spelling, 5.99 on arithmetic, p < ,001 for all; for age, F(1, 85) = 11.72 on arithmetic, p < .Ol). The CONS’ mean WISC IQ exceeded that of the other groups (F(3, 85) = 9.56, p < .OOl), but further analyses were indicated because of a group x factor interaction (F(6, 170) = 5.61, p < .OOl). The major differences were on the Verbal factor (Vocabulary, Comprehension, and Similarities) and the so-called ACID pattern (Arithmetic, Coding, Information, and Digit Span), but the Spatial factor (Picture Completion, Block Design, and Object Assembly) also showed a group effect (F(3, 85) = 9.91, 12.10, and 2.86, respectively; p < ,001 for the first two and p < .05 for the latter). On the Verbal factor, all groups scored higher than RDs, and CONS were higher than H/ADDS. On the ACID pattern, CONS were higher than all others and H/ADDS were higher than RDs. On the Spatial factor, CONS had an advantage over the H/ADD but not RD and ADD groups. The ANOVA for scores on the hyperkinesis index showed the H/ADD group mean to exceed all others, but the ADD and RD means were higher than for the CONS (F(3, 85) = 95.51, p < ,001). On the ADD index, the H/ADD and ADD groups both were higher than the RD group, and all three higher than CONS (F(3, 85) = 72.52, p < .OOl). There was an age effect (F(l) 85) = 4.79, p < .05) and a marginal group x age interaction on this index (F(3, 85) = 2.20, p < .lO) because in all groups except the ADDS the older subjects had lower scores than the younger ones.
Procedures Generally, the mother only, but in some instances both parents jointly, filled out Conners’ Parent Questionnaire and the Werry-Weiss-Peters Activity Scale questionnaire which provides more detailed information on hyperactive behaviors at home and in public places. Prior to his laboratory visit, each child was given visual and hearing screening tests, as well as a developmental neurological examination (all the above results are reported later). During a morning session in the behavioral laboratory, the child completed the tasks reported in this paper as well as several to tap automatic processing (Ackerman et al., 1985). In an afternoon session, he completed computerized tasks of attention while electroencephalographic recordings were made (also to be reported later). No child was taking psychotropic medication during any test sessions. All children were acquainted with the layout of the laboratory and with the staff before testing began. Frequent breaks were given between tasks.
Young (12) Old (12) Young (11) Old (13) Young (11) Old (10) Young (10) Old (14)
Control
RD
ADD
H-ADD
Age group 09
Groups
109.0 131.1 108.4 129.5 109.1 129.4 114.3 134.4
(6.3) (5.8) (6.5) (5.9) (7.1) (7.1) (4.7) (8.8)
CA (mos) 112.8 117.9 106.0 103.1 113.3 102.3 100.9 102.6
IQ
DATA:
( 5.9) ( 7.1) ( 9.4) ( 9.1) (15.7) ( 9.0) ( 7.8) ( 7.5)
WISC-R
DESCRIPTIVE
TABLE
1
119.7 118.7 106.1 109.5 109.6 107.9 84.2 83.9
(10.4) (12.8) (11.1) (11.3) (18.6) ( 9.8) ( 4.2) ( 7.4)
Reading
WRAT
DEVIATIONS
107.7 111.8 100.4 103.4 101.6 99.2 82.9 79.8
(10.6) (15.0) ( 7.1) (10.9) (15.7) ( 6.6) ( 5.1) ( 5.9)
Spelling
standard scores
MEANS AND STANDARD
101.8 98.0 97.3 94.3 98.7 92.5 94.6 90.3
(3.5) (5.5) (7.1) (6.3) (7.6) (5.4) (5.0) (6.9)
Math
3.5 1.3 19.9 20.5 10.6 11.2 11 .o 9.5
Per (2.8) (1.3) (3.2) (4.2) (3.4) (2.7) (3.4) (5.8)
Teacher
3.3 1.1 24.6 21.3 19.6 21.5 19.3 13.1
(3.0) (2.5) (4.9) (4.7) (7.3) (6.8) (5.4) (6.3)
ADD
ratings
F
2
c?
E
F
b
EFFORTFUL
PROCESSING IN RD AND ADD SUBJECTS
27
1. Semantic and acoustic encoding. This paradigm was developed by Weingartner et al. (1980) and used to contrast normal and hyperactive children under drug and placebo. The child was asked to listen to 20 sets of three words each. Before the word sets were presented, the children were told they should try to remember the words they would hear. Ten sets contained two words that were semantically related with one unrelated, and 10 sets had two words that rhymed and one that did not. The sets were given in alteration after two coaching trials (i.e., “Which word does not belong because of what it means: peas, spinach, house? Which word does not belong because of how it sounds: man, can, clock?“). When, after the training trials, a child made an error, he was corrected by the examiner (e.g., “No, the word that does not belong is car.“). After completion of the 20 sets, the child proceeded to a frequency of occurrence task, which served as a distractor. Then he was asked to think back to the sets of words he had heard and to freely recall as many as possible. One minute after recalling the last word (and being asked if he could remember any more), the child was read one of the two related words from each set and asked to recall the other word of that pair (cued recall). For example, he was asked, “Which word went with tree because of the way it sounds?” If the child said “I don’t know,” he was encouraged to try. Following Weingartner et al. (1980), the number of words of each type (semantic or acoustic) recalled under each condition (free or cued) was analyzed. Additionally, clusteringthe tendency to recall related words in pairs-was studied in the free recall condition. Also studied was the number of unrelated acoustic and semantic words (foils) correctly discriminated during the encoding phase. 2. List learning of high- and low-imagery words. Weingartner, Caine, and Ebert (1979) used a high-imagery-low-imagery paradigm to study memory deficits of patients with Huntington’s disease. Unlike normal controls, these patients did not exhibit relatively better recall of high-imagery than low-imagery words. By using the method of selected prompted recall, Weingartner et al. (1979) were also able to look at retrieval versus encoding impairment in these patients. We asked each child to learn a list of 12 common one-syllable words, half of which were highly imageable (e.g., king, house) and half less easily imageable (e.g., hope, fact). To control for serial position effects, the two lists employed were composed of alternating high-imagery (HI) and low-imagery (LI) words, with one list beginning with a HI word and the other a LI word. Lists were alternated within groups. On the first learning trial, all 12 words were read to the subject, 2 s apart. He was asked to recall as many words as he could and then was prompted on the words he did not remember. The experimenter said, for example, “That’s good. You remembered 5 words. Here are the 7 you did not remember. Try to recall all 12 words this time.” Eight trials were allowed to master the list: but in the event a child repeated the entire list correctly twice in a row, the task was terminated, with perfect scores given for remaining trials. Approximately 2 h later, after lunch and intervening tasks, the child was asked to try to recall as many of the 12 words as possible. Global scores (total HI and LI words recalled after each trial) were analyzed as well as measures of encoding and retrieval consistency during learning. Averaging the number of the trial at which each word was first recalled for each child provided his mean trial of encoding. Retrieval consistency was defined as the proportion of words on a given trial (2 through 8) recalled on the previous trial (therefore unprompted). This task assesses levels of effortful processing via the high-imagery-lowimagery contrast and the initial and delayed recall conditions. 3. Memory of 12 printed words. The words used for this task were first-grade level and should have been familiar to all subjects, even the most retarded readers. Each word was printed in lower case on a 3 x 5-in. card. Three decks of 12 cards (4 words from three categories) were randomly used in the three conditions. First the child was handed Deck 1 (randomly arranged) and told, “Spread out the cards so that you can see them all at the same time, then read them to me.” The examiner
28
ACKERMAN
ET AL.
corrected any errors or omissions by pronouncing the word. Then, the examiner said, “Look at the words carefully for a bit.” When the child looked up, the deck was removed and he was asked, “Now tell me as many of the words as you can remember. What words did you see?” His free recall was recorded. Next the examiner showed the child Deck II (also randomly arranged) and said, “Now spread out these cards and read them to me. (Correction was done as before.) Fine. Now I want to see how many words you can remember if you study them first. Do anything you wish to help you remember the words. Tell me when you think you can say all of them.” The examiner recorded the child’s answers. Then the examiner showed the child Deck I again and said, “When I want to remember lots of words, I try to see if some words go together. Let’s put these words in groups. See, four of them are animals (e.g., cow, pony, goat, pig), four are things to eat (cake, corn, egg, apple), and four are numbers (two, five, six, ten). If I study the words in groups, I can remember them better.” Then the examiner showed the child Deck III and said, “Spread out the cards and read them to me. Study them until you think you can say them all back to me.” The total number of words correctly recalled under each condition was recorded as was the number of clustered words (words from a category recalled as pairs, triplets, or quadruplets). The task was theorized to tap effortful processing as well as ability to use a strategy spontaneously or after modeling. 4. Rapid addition and subtraction. The child was seated in a test cell facing a display panel which is a unit of a Varian computer. He was told that he was going to play an arithmetic game with the computer-that the computer would present him with a series of problems, that he was to figure out the right answer in a limited amount of time, and then type his answer on a number console. He was given six easy practice problems (e.g., 6 + 5 = -) and reminded that the computer would erase the problem if he took too long. The first procedure consisted of 10 addition and 10 subtraction problems, randomly presented. The addition problems were a sample of all combinations involving the numbers 4 through 9 which yield a two-digit answer (e.g., 7 + 6) while the subtraction problems involved at least a 4-unit difference and also required a two-digit answer (e.g., 19 - 5). The 4-unit minimum was used to discourage (or reveal) a counting strategy. The child was allowed a maximum of 16 s to type in his answer. Latencies and answers were stored, and the child received visual feedback (good or bad at the lower left edge of the screen) during each 3-s interstimulus interval. After completion of this set of problems, the child was told, “Now you will have a chance to win money for answering the problems correctly. The computer will show you how much you are winning. We are going to give you 5Oc to start with and you will win 4$ for each problem you get right but lose 2$ for each you get wrong. Also, the computer will erase the problem and take away 2c if you take too long.” The 20 problems were of the same difficulty level as the fist 20, and all other conditions were the same. After completion of the second set, the child was congratulated on his earnings and told he would be asked to solve some slightly harder problems. These 20 problems required regrouping (i.e., borrowing or carrying) and, like the others, involved a difference of 4 or more units (29 + 5 or 62 - 6) and a two-digit answer. Reward conditions were the same and built on the child’s earnings from the previous set of problems. While even the youngest children in the sample had completed second grade and had had experience with regrouping, it was expected that processes would not be automatized in all children and that Iatencies, if not errors, would show age and group differences. The two conditions that did not involve borrowing and carrying were theorized to tap acquired automatization in both age bands (see Ackerman et al., 1985). The regrouping problems were considered effortful.
EFFORTFUL
PROCESSING IN RD AND ADD SUBJECTS
29
5. Paper and pencil arithmetic tests. These tests were analogs of the computer math tests. The child was, however, allowed up to 15 m to complete each set of 20 items (one involving regrouping, one not). Thus the child who had not committed number facts to memory could still score well if he used a counting strategy. Time to complete each set was noted along with number correct. No reward was offered. These tasks were included to assess automatization failure as evidenced principally by time to complete each set. The regrouping condition results are analyzed below; the simple computational task was included in the acquired automatization battery. Latencies were logged prior to all analyses. Per problem mean latencies are reported below.
RESULTS
Inasmuch as CONS had significantly higher IQs than the three diagnostic groups, and since IQ was signit?cantly correlated with most of the measures of effortful processing, a multivariate analysis of covariance (BMDP4V) was first performed on the 15 measures shown in Table 2. There being overall significant (p = .05) group and age differences, univariate contrasts on each measure are reported in Table 3. The results of the mixed design ANOVAs are also given in Table 3 for contrast with the ANCOVAs. Because there was not an overall group x age interaction in the multivariate analysis, the F’s for the univariate interactions are not shown. All significant pairwise contrasts have been given, even when the overall group effect was nonsignificant, because of the large number of studies in the literature that contrast only two groups, usually CONS versus a clinical diagnostic group. Also, since IQ did not significantly separate the three clinical groups, any pairwise contrasts of these groups significant by ANOVA merit consideration. In addition to the analyses of the separate measures, several withintask analyses were done to explore the automatic-effortful dimension via consideration of levels of processing, immediate and delayed recall, spontaneous and coached strategic rehearsal, and the effect of novelty and reward on computational efficiency. The four measures from the acoustic-semantic tasks were subjected to mixed design ANCOVA, with group and age as between-level factors and stimulus type and condition (immediate free recall versus cued recall) as within factors. This analysis, as expected, confirmed significant effects for stimulus type (F( 1, 85) = 64.71, p < .OOl) and recall condition (F( 1, 85) = 134.51, p < .OOl) as well as an age effect (F(1, 84) = 6.26, p < .02). The group effect was marginal (F(3, 84) = 2.34, p < .08), with RDs poorest. There was also a recall condition x stimulus type interaction (F(1, 85 = 8..52, p < .Ol) inasmuch as the children’s recall of semantic stimuli was enhanced more by cuing than their recall of the acoustic stimuli. Two measures, thought perhaps to underpin the poorer performance of RDs on the acoustic-semantic task, were then analyzed. First, the number of unrelated words (foils) correctly discriminated in the encoding
TABLE 2
Acoustic-semantic: Free, acoustic Free, semantic Cued, acoustic Cued, semantic Hi-lo imagery: Learning, Hi Learning, Lo Delayed, Hi Delayed, Lo Memory for words: Look Study Cluster Computation: Paper, correct Computer, correct Paper, latency Computer, latency 3.0 4.5 4.3 3.3 3.8 1.6 7.0 8.0 10.9
2.7 5.0 4.2 3.5 4.1 2.3 7.0 7.6 10.2 12.3 7.4 4.4 4.0
5.0 4.3 4.9 3.8
8.0 9.8 11.2
17.0 14.9 4.1 4.0 11.8 8.4 4.3 4.0
i.2 2.5
H/ADD (N = 24)
1.4 2.6
ADD (N = 21)
2.1 2.8 3.2 5.2
(N = 24)
CON
Groups
11.4 9.2 4.3 4.0
7.3 7.8 10.1
4.2 3.0 4.0 1.3
1.8 3.6
1.0 1.8
RD (N = 24)
10.5 6.8 4.3 4.1
6.8 7.6 10.3
4.3 3.3 4.0 2.0
2.3 4.3
1.0 2.5
Young (N = 46)
15.8 13.3 4.1 4.0
7.8 9.0 11.0
4.6 3.7 4.4 2.5
3.0 4.9
1.8 2.3
Old (N = 47)
Age bands
(0.8) (0.9) (1.5) (1.8)
13.2 (6.3) 10.1 (6.4) 4.2 (0.3) 4.0 (0.1)
7.3 (2.4) 8.3 (2.5) 10.6 (1.9)
4.4 3.5 4.1 2.3
2.6 (1.7) 4.6 (1.9)
1.4 (1.5) 2.4 (2.1)
Total sample (N = 93)
GROUP AND AGE BAND MEANS AND SAMPLE MEANS AND STANDARD DEVIATIONS FOR 15 MEASURES OF EFFFORTFUL MENTAL PROCESSING
F
G ii! % 3
&
* p < .05. ** p < .Ol.
Age
3.53 8.70** 3.90*
23.02** 41.60** 19.89** 21.21**
6.33** 11.41** 6.75’* 4.26**
3.75 6.84* 2.60 3.13
5.66** I I .33** 2.80’ 12.74**
I .02 5.29** 2.08
5.77* 0.10 6.29* 3.54*
1, 85df
2.96* 0.72 4.17** 4.17**
Group 3, 85df
ANOVA F values
a Indicates IQ is a significant covariate.
Memory for words Look Study Cluster Computation Paper, correct” Computer, correct” Paper, latency” Computer, latency”
Acoustic-semantic Free, acoustic” Free, semantic Cued, acoustic Cued, semantic” Hi-lo imagery Learning, Hi” Learning, Lo” Delayed, Hi Delayed, Lo”
-
TABLE 3
> > < i
all all all all
2.25 6.41** 3.77* 1.48
1.82 3.55* 0.82 4.89**
CON CON CON CON
all all H/ADD, RD all RD 0.32 3.91* 1.25
> > > > >
0.90 0.29 2.85* I .79
Group 3, 84df _____
ANCOVA F values
PROCESSING
CON > all CON > RD
CON CON CON CON ADD
H/ADD, RD < CON RD < all RD < ADD, CON
p < .05 89df
Between groups
GROUP AND AGE CONTRASTS ON EFFORTFUL
27.38** 49.50** 25.28** 24.36”*
4.41* 8.67** 4.38*
6.03* I1.60** 3.99* 5.21*
6.90** 0.04 6.84** s.oo*
1, 84df
Age
-
CON > all CON > all CON, RD < ADD -
CON > all
CON > ADD CON > all CON > all
RD < CON, H/ADD RD < ADD
-
p < .05 89df
Between groups
w
32
ACKERMAN
ET AL.
phase were subjected to ANCOVA. This maneuver revealed that the children were more accurate in discriminating semantic than acoustic foils (F(1, 85) = 4.36, p < .05), that older children discriminated better than younger ones (F(1, 84) = 22.19, p < .OOl), and that the IQ-adjusted discrimination scores of ADDS were significantly higher than those of RDs, with CONS’ adjusted scores marginally higher than RDs (F(3, 84) = 4.12, p < .Ol), Second, the number of words freely recalled in clusters (acoustic-semantic combined) was examined. This ANCOVA showed an age effect (F( 1, 84) = 5.17, p < .05) and a marginal group effect (F(3, 84) = 2.32, p = .08). In pairwise contrasts, RDs were significantly poorer than both the ADDS and CONS. Thus, RDs appear to have been less sensitive to associations between words than ADDS and CONS. The four measures from the high-imagery-low-imagery task were also subjected to a mixed design ANCOVA (group x age x stimulus type x recall condition). This analysis confirmed a group effect (F(1, 84) = 3.42, p < .05) and age effect (F(l) 84) = 9.65, p < .Ol), and also showed, as predicted, that immediate recall was better than delayed (F( 1, 85) = 57.19, p < .OOl) and that high-imagery words were recalled better than low-imagery words (F(1, 85) = 221.26, p < .OOl). There were several interactions of interest, including the overall finding that delayed recall of high-imagery words showed less decrement than delayed recall of lowimagery words (F(1, 85) = 48.05, p < .OOl). The interactions of stimulus type and condition with group are of greatest interest. Expressing decay as a percentage of the mean immediate recall scores and summing over stimulus type, CONS exhibited significantly less memory decrement than H/ADDS (9% versus 27%) with RDs (23%) and ADDS (17%) intermediate (F(3, 85) = 3.75, p < .05). Summing over recall conditions, CONS had significantly less difference between scores on the two types of stimuli than RDs, with the two ADD groups intermediate (F(3, 85) = 5.92, p < .OOl). There was a marginal stimulus type x recall condition x group interaction (F(3, 85) = 2.51, p < .06), reflecting the fact that only the H/ADDS showed a significant decay in high-imagery recall. Percentage decay for the low-imagery words was 49% for RDs, 48% for H/ADDS, 34% for ADDS, and 17% for CONS; corresponding figures for highimagery words were 5, 14, 4, and 3%. The trial effect on acquisition of the high- and low-imagery words was explored in another ANOVA to look for possible interactions with trial, but none reached an acceptable significance level. ANCOVAs were also performed to examine whether mean trial to initial encoding of all words discriminated groups; this showed only that older children encoded more efficiently than younger ones (F(1, 84) = 9.53, p < .Ol>.Another ANCOVA looked at a retrieval measure (mean percentage of words recalled on each trial also recalled on the next trial); this too showed only an age
EFFORTFUL
PROCESSING
IN RD AND ADD SUBJECTS
33
effect (F(1, 84) = 4.81, p < .05). Thus, the variable in this data set providing best discrimination of groups was low-imagery performance. The three measures from the memory for words task were next considered in a mixed design ANCOVA. There was a marginal group effect (F(3, 84) = 2.26, p < .lO), highly significant condition effect (F(2, 170) = 76.77, p < .OOl), but no significant interactions. The data from this task were also examined for spontaneous and modeled clustering (i.e., the number of words recalled in clusters of 2, 3, or 4 related words was tallied for each condition). This mixed design ANCOVA yielded significant age (F(1, 85) = 7.12, p < .Ol) and condition (F(2, 170) = 107.22, p < .OOl) effects and a marginal group x age interaction (F(3, 84) = 2.39, p < .lO) reflecting the fact that older H/ADDS employed clustering somewhat less than younger ones, whereas in all other groups old subjects clustered more than younger ones. However, the group x condition interaction was not significant. Two ANCOVAs were done on the latency and accuracy scores from the two computational tasks, again to explore interactions. The accuracy analysis showed main effects for group (F(1, 84) = 4.77, p < .Ol), age (F(1, 84) = 47.58, p < .OOl), and condition (F(1, 85) = 41.94, p < .OOl). Because of the time restrictions with the computer version of the task, the children solved fewer problems than with paper and pencil. The IQadjusted accuracy scores showed CONS superior to each of the other groups, but neither age nor group interacted with mode of presentation. Interactions were found, however, for the logged latencies. This ANCOVA revealed strong age (F(l) 84) = 31.OO,p < .OOl) and condition effects (F(1, 85)= 130. 38, p < .OOl) as well as a group effect (F(3, 84) = 3.41) and both group x condition (F(3, 85) = 4.96, p < .Ol) and age x condition (F(1, 85) = 9.23, p < .Ol) interactions. The younger subjects showed a greater difference between the two conditions than the older subjects, and CONS exhibited less difference between their paper-pencil and computer latencies than the three clinical groups. Also, the IQadjusted latencies showed both CONS and RDs to be significantly faster than ADDS in the paper-pencil condition with no group differences in the computer condition. In summary, the computer condition pushed the children to respond rapidly, which cost all groups in accuracy (see Table 2), particularly the ADDS (though the interaction was not significant). Given no time constraints, the ADDS took the longest times, suggesting they had least automatized simple borrowing and carrying and when hurried showed greatest cost in accuracy. A final set of multivariate analyses explored the intercorrelations and the factor structure of all the above measures and the efficacy of these measures in prediction of reading skill and ADD ratings. A principal components analysis yielded four factors (62% of the variance) which,
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when varimax rotated, provided easy interpretation. Factor I described the high-low imagery task; Factor II incorporated both the latency and accuracy scores from the computation tasks; Factor III had highest loadings from the acoustic-semantic measures; Factor IV tapped the memory for words tasks. ANCOVAs on these factor scores (IQ as the covariate) yielded a significant age effect on Factor II (F(1, 84) = 36.82, p < .OOl) and significant group effects on Factors II and III (F(3, 84) = 2.79 and 3.39, respectively, p < .05). The F’s for the covariate were significant for Factors I and II (15.47 and 5.02, respectively, p < .OOl and p < .05 for 1, 84 dfl. On Factor II, the adjusted factor scores of CONS were significantly different from each of the other groups, and on Factor III, the RDs’ adjusted factor scores were significantly different from the H/ADDS and ADDS with CONS intermediate (the unadjusted scores show the same ordering). Inasmuch as the clinical groups were not impressively separated by the effortful variables under study, two overall stepwise regression analyses were done, one to predict WRAT reading grade level and one to predict the investigatory ADD index. In both instances, age and IQ were forced first and then the computer (with F to enter set at 4.00) chose the best variables. These analyses (for the total sample of 93 children, diagnosis ignored) are summarized in Tables 4 and 5. Finally, the H/ADD and ADD groups were combined and contrasted with the RD group via stepwise discriminant analysis, with age and IQ forced first. With F to enter set at 4.00, the computer chose number of acoustic foils discriminated (Step 3) and number of acoustic-semantic clusters (Step 4) to achieve 77% correct classifications. When the combined ADD groups were contrasted with CONS under the same constraints, the computer chose computer math accuracy as Step 3, memory for visual words (study condition) as Step 4, and delayed memory for lowimagery words to achieve 88% correct classifications. When RDs and CONS were contrasted (same constraints), the discriminant function TABLE 4 WRAT READING
STEPWISE REGRESSION FOR
Variable
R
RZ(adjusted)
r
Stepwise partial r
Cued recall, acoustic Delayed recall, low imagery Accuracy, computer math Cued recall, semantic Discrimination, acoustic foils
.39 .63 .73 .78 .80 .82 .83
.14 .38 .52 59 .62 .64 .66
.39 .42 .53 .60 .48 .48 .58
.54 .47 .41 .29 .25 .22
Step
I” 2” 3 4 5 6 7
GRADE LEVEL
Age IQ
’ Indicates variable forced first.
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35
TABLE 5 STEPWISE REGRESSION OFADD INDEX SCORES Variable
R
R' (adjusted)
r
Stepwise partial r
Age”
.15 .39 .49 .54
.Ol .I3 .22 .26
.I5 - .33 - .46 - .36
- .36 - .33 - .21
IQ Computer arithmetic, accuracy Memory for words, study ’ Indicates variables forced first.
achieved 96% correct classifications; however, after age and IQ were forced, the computer took not the acoustic-semantic variables but paper math accuracy and memory for 12 written words (study condition of Task 3). DISCUSSION
Normal reading ADD children, with or without hyperactivity, were expected to perform less well on effortful mental tasks than normally attentive and adequately achieving age mates (CONS). RD children, because most are rated by their teachers to have attentional problems, even when not hyperactive, were also expected to be at disadvantage on effortful encoding and retrieval-but possibly as much because they have not automatized the skills and/or strategies needed for successful performance as because of an underlying attention disorder. At the risk of sounding judgmental, we earlier theorized that RD children intend (or try) to attend but cannot always store or retrieve information adequately, whereas ADD children, especially hyperactive ones, often do not appear motivated to attend (Dykman & Ackerman, 1976). Accuracy scores or response latencies might be equally impaired for these groups of children, then, but for different reasons. In designing the above study, we reasoned that one tip-off to the “attention” versus “intention” dimension could be in how the groups respond to reward and novelty. Another tip-off could be in the contrast of automatic versus effortful processing or in levels of effortful processing. For the exploration of these ideas a computational task and three memory tasks were employed. All three memory tasks allowed assessment of levels of effortful processing. The features were suggested from the levels of processing literature (see Hasher & Zacks, 1979). That is, in one task, half the to-be-remembered words were acoustically related (rhymed) and half semantically related (same category). The literature suggests the semantically related words will be recalled better than the acoustically related ones. Similarly, the high-imagery words of a second task were predicted to be recalled better than low-imagery words (Paivio,
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1971). Categorically related words (third task), even if not pointed out to subjects, should be recalled better than nonrelated words, provided the subject spontaneously employs clustering in rehearsal and retrieval (Brown, 1975). Thus, group x condition interactions in the memory task employed could have major implications for the attention-learning dimension. Similarly, group x condition interactions when reward and novelty are a feature could have interpretative significance foT the intention hypothesis. These manipulations were incorporated in the computation task. As expected, the three clinical groups performed less well than CONS overall, and, attesting to the effortful nature of the tasks, older children performed better than younger ones. WISC-R IQ scores, whatever the controversy about their ultimate worth, also correlated significantly with most of the performance measures, suggesting that the higher the IQ the less effortful a given task may be. Because CONS had significantly higher IQs than the clinical groups, ANCOVAs were performed on all data sets, which lowered F values obtained in ANOVAs. But, the inference would have to be that had we obtained a CON group with IQs not significantly higher than those of the clinical children, performance differences would still have been found, particularly the more difficult the task-memory for low-imagery words and computational efficiency with borrowing and carrying involved. As there was very little evidence that H/ADDS and ADDS differed on any measures under study, these two groups were combined and contrasted with the CON and RD groups. The computational task best separated ADDS and RDs from CONS, but memory variables also contributed. It is tempting to argue that attentional dysfunction per se impairs computational efficiency. Granted, the CONS of the present study were also much less advanced in arithmetic than reading and spelling as indicated by WRAT standard scores-despite normal attentiveness as rated by teachers. Moreover, with IQ as the covariate, the WRAT arithmetic scores of CONS did not differ from those of the clinical children (Ackerman & Dykman, 1985). But, for younger students the WRAT arithmetic test seems not to be sufficiently sensitive to automatization failure. Even the simpler arithmetic tasks included in the acquired automatization battery (Ackerman et al., 1985) revealed the clinical groups to be less adept than CONS (and with IQ as the covariate). We earlier discovered (Ackerman, Dykman, Peters, 1977) that boys diagnosed as learning disabled in grade school and studied again as adolescents had even lower WRAT arithmetic than reading scores despite equivalent group standardized test scores on the two skills in Grade 4. Since the WRAT arithmetic section has no reading problems, the deficiency cannot be attributed to reading disability per se. We now believe that many ADD and RD children fail to commit number facts, and later multiplication tables, to memory. This weakness
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in basic arithmetic appears to be related to low WISC-R ACID scores, apparent in all clinical groups. In the current sample, the multiple R of age and ACID with arithmetic grade level was .77; WISC verbal and spatial factor scores made no significant additional contribution (Ackerman & Dykman, 1985). The presentation of the arithmetic problems on the computer, with reward for accuracy, did not produce the type group x condition interactions we had theorized. That is, if the two groups of ADD boys could perform better than they do in the classroom or on standardized tests, reward might be expected to boost their performance relatively more than that of the presumably better intentioned RD and CON boys. Such was not the case either with the simpler problems included in the automatization battery (Ackerman et al., 1985) or with the more difficult regrouping problems used here. Rather, the computer condition was more successful at showing automatization failure. When the children were required by the computer to respond within limits, unlike in the paperpencil condition, accuracy suffered-expecially in the case of the solely ADD children on the regrouping problems. We have seen that reward can speed certain types of information processing (Ackerman et al., 1982), but the evidence from the present study suggests that children who do not compute efficiently probably cannot. It would be instructive to see the effect of methylphenidate on these children’s computational efficiency, however. One task used in another experiment with the four groups here studied (Ackerman, Holcomb, Dykman, & Anhalt, 198.5)gave some evidence that RD children are better intentioned than ADD and H/ADD children. The paradigm was a variant of the Sternberg (1969) task, wherein the children were presented with to-be-remembered item sets containing one, three, or five consonant letters. After viewing a given memory set, the child saw a probe letter and had to indicate with response keys whether the probe was or was not in the previously viewed set. When the memory set contained one or three letters, the RDs were significantly more accurate than the ADD groups; but with five letters, all clinical groups were equally impaired. We surmise then that RDs perform accurately when the memory load is not too taxing, but that ADDS have a less orderly interaction of accuracy with load. There is evidence, also, that RDs show a greater differential than other groups in remembering less taxing than more taxing verbal material. Their performance both in learning and recalling low-imagery words was more disparate from their performance on highly imageable words than true for the other groups. A tangential finding comes from a recent study by Bruskin and Blank (1984). These investigators found that third and fifth graders read and spelled nouns and verbs (content words) faster and more accurately than equal difficulty level noncontent words (such
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as “these, could, why, very”). More interesting, the less skilled readers showed significantly greater disparity between the two word types than the more skilled readers. Bruskin and Blank (1984) review several studies that suggest that noncontent words may be stored and accessed via a different mechanism than content words. Since noncontent words occupy a higher percentage of any page of text than content words, poor access to these words would obviously impede automaticity in reading and writing. It was the acoustic-semantic task which provided best discrimination (77% correct) of the combined ADDS and RDs. Also, when reading grade level was predicted for the full sample, the acoustic-semantic variables made a major contribution above the variance explained by age and IQ. The relative insensitivity to rhyme and alliteration of RD children has been of central interest to the Haskins group (e.g., Liberman, Liberman, Mattingly, & Shankweiler, 1980) and to Bradley and Bryant (1983) in England, among others. Obviously, if children cannot easily sense the phonological similarities between words, their mastery of new words becomes much more effortful. If their semantic elaboration is sluggish, then comprehension will suffer, as will paired associate memory (e.g., learning the states and their capitals). And numeric elaboration no doubt abets automatization of number combinations, including the multiplication tables. In short, the efficient learner actively fits new knowledge into existing schemata, and then he fine tunes it (Rumelhart & Norman, 1981). That is, by use of analogy, he goes “beyond the information given” (Bruner, 1966) to develop new or enlarged schemata. Elaboration is most successful with concrete material (viz. high-imagery or content words). The challenge with RD children, then, is to try to link some of their seemingly intact concrete schemata with the more abstract concepts of phonetics. The Bruskin and Blank study suggests that less skilled readers slowly master a core of nouns and verbsperhaps by pattern recognition. But, even if the only words known are concrete and recognized by visual patterns, these schemata can be elaborated to illustrate more abstract phonetic associations. For example, suppose the child “knows” cat and box. Can he not be taught that he also has the information available to know bat and cox? The Bradley and Bryant (1983) studies suggest that he can. Numerous devices and diagrams have been used to make number facts more concrete to grade school children. Most, given time, can solve number combinations via counting, a concrete approach. But, the only way to make number associations automatic is via repetition, which is a curse to children with ADD. However, a combination of stimulant medication and trial-by-trial reward might be effective for many of them (Douglas, 1984).
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REFERENCES Ackerman, P. T., Anhalt, J. M., Dykman, R. A., & Holcomb, P. J. 1985. Innate and acquired automatization in children with attention and/or reading disorders. Journal of Child Psychology and Psychiatry, in press. Ackerman, P. T., & Dykman, R. A. 1982. Automatic and effortful information-processing deficits in children with learning and attention disorders. Topics in Learning and Learning Disabilities, 2, 12-22. Ackerman, P. T., & Dykman, R. A. 1985. Arithmetic automatization failure in children with ADD. Journal of Learning Disabilities, in press. Ackerman, P. T., Dykman, R. A., Holcomb, P. J., & McCray, D. S. 1982.Methylphenidate effects on cognitive style and reaction time in four groups of children. Psychiatq Research, 7, 199-213. Ackerman, P. T., Dykman, R. A., & Oglesby, D. M. 1983. Sex and group differences in reading and attention disordered children, with and without hyperkinesis. Journal of Lenrning Diabilities, 16, 407-415. Ackerman, P. T., Dykman, R. A., & Peters, J. E. 1977.Learning disabled boys as adolescents: Cognitive factors and achievement. Journal of fhe American Academy of Chi/d Psychiarr?. 16, 296-313. Ackerman, P. T., Holcomb, P. J., Dykman. R. A., & Anhalt, J. M. 1985. Information processing in educationally troublesome children. Manuscript submitted for publication. Bauer, R.H. 1979.Memory, acquisition, and category clustering in learning disabled children. Journal
of Experimental
Child Psychology,
27, 365-383.
Bradley, L. & Bryant, P. E. 1983. Categorizing sounds and learning to read-a causal connection. Nature (London), 301, 419-421. Brown, A. L. 197.5.The development of memory: Knowing, knowing about knowing, and knowing how to know. In H. W. Reese (Ed.), Advnnces in child development and behavior, (Vol. 10). New York: Academic Press. Brown, A. L., 1979. Theories of memory and the problems of development: Activity, growth, and knowledge. In L. S. Cermak & F. I. M. Craik (Eds.). Le\,els of processing in human memory (pp. 255-258). Hillsdale, NJ: Erlbaum. Bruskin, C. & Blank, M. 1984.The effects of word class on children’s reading and spelling. Brain and Language, 21, 219-232. Cantwell, D. P. & Sattertield, J. H. 1978. The prevalence of academic underachievement in hyperactive children. Journul of Pediatric Psychology, 3, 168-171. Chapman, L. J., & Chapman, J. P. 1973. Disordered thought in schizophrenia. Englewood Cliffs, NJ: Prentice-Hall. Denckla, M. B., & Rude], R. G. 1976. Naming of object-drawings by dyslexic and other learning disabled children. Bruin and Language, 3, l-15. Douglas, V. I. 1984. [Symposium on hyperactivity.] American Psychological Association Meeting, Toronto. Dykman, R. A., & Ackerman, P. T. 1976. The MBD problem: Attention, intention, and information processing. In R. P. Anderson & C. G. Holcomb (Eds.), Learning disability/minimal brain dysfunction syndrome. (pp. 27-43). Springfield, IL: Thomas. Halperin, J. M., Gittelman, R., Klein, D. F., & Rudel, R. G. 1984. Reading-disabled hyperactive children: A distinct subgroup of attention deficit disorder with hyperactivity? Journal of Abnormal Child Psychology, 12, l-14. Hasher, L., & Zacks, R. T. 1979. Automatic and effortful processes in memory. Journal of Experimental
Psychology:
General.
108(3), 356-388.
Kahneman, D. 1973. Attention and effort. Englewood Cliffs, NJ: Prentice-Hall. Liberman, I. Y., Liberman, A. M., Mattingly, I., & Shankweiler, D. 1980. Orthography and the beginning reader. In J. F. Kavanaugh & R. L. Venezky (Eds.), Orthography, reading, and dyslexia, (pp. 137-154). Baltimore: Univ. Park Press.
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Paivio, A. 1971. Imagery and verbal processes. New York: Holt, Rinehart & Winston. Posner, M. I., & Snyder, C. R. R. 1975. Attention and cognitive control. In R. L. Solso (Ed.), Information processing and cognition: The Loyola Symposium. Hillsdale, NJ: Erlbaum. Rosenthal, R. H., & Allen, T. W. 1978. An examination of attention, arousal, and learning dysfunctions of hyperkinetic children. Psychological Bulletin, 85, 689-715. Rudel, R. G., 1980. Learning disability: Diagnosis by exclusion and discrepancy. Journal of the American
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of Child Psychiatry,
19, 547-569.
Rumelhart, D. E., & Norman, D. A. 1981. Analogical processes in learning. In J. R. Anderson (Ed.), Cognitive skills and their acquisition, (pp. 355-360). Hillsdale, NJ: Erlbaum. Shiffrin, R. M., & Schneider, W. 1977. Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127-190. Stemberg, S. 1969. Memory scanning: Mental processes revealed by reaction time experiments. American Scientist, 4, 421-457. Sternberg, R. J., & Wagner, R. K. 1982. Automatization failure in learning disabilities. Topics in Learning and Learning Disabilities, 2, l-12. Torgeson, J., & Goldman, T. 1977. Verbal rehearsal and short-term memory in reading disabled children. Child Development, 48, 56-60. Weingartner, H., Caine, E. D., & Ebert, M. H. 1979. Imagery, encoding, and retrieval of information from memory: Some specific encoding-retrieval changes in Huntington’s disease. Journal of Abnormal Psychology, 88, 52-58. Weingartner, H., Rapoport, J. L., Buchsbaum, M. S., et al. 1980. Cognitive processes in normal and hyperactive children and their response to amphetamine treatment. Journal of Abnormal
Psychology,
89, 25-37.
AND
COGNITION
5,
22-40 (1986)
Effortful Processing Deficits in Children with Reading and/or Attention Disorders PEGGY
T. ACKERMAN, JEAN M. ANHALT, ROSCOE A. DYKMAN, AND PHILLIP J. HOLCOMB University
of Arkansas for Medical
Sciences
Three groups of educationally troublesome boys were contrasted with adequate students on several tasks tapping effortful processing. The nonhyperactive reading disabled (RD) group differed both from controls and two attention deficit disorder (ADD) groups, one with and one without hyperactivity (H), on aspects of a memory task involving acoustic and semantic associations. All three clinical groups differed from controls in memory for low-imagery as opposed to highimagery words and in computational efficiency. A stepwise regression analysis to predict reading grade level showed age and WISC-R IQ to account for 38% of the variance with an additional 28% explained by the effortful task variables (multiple R = 83). It is theorized here that attentional disorder impedes automatization of number facts; and, inasmuch as RD children receive adverse attention ratings, even if not considered hyperactive, they, as well as ADD and H/ADD boys, exhibit this deficiency. o 1986 Academic PESS, IX
INTRODUCTION
Hasher and Zacks (1979) have proposed a useful framework for the conceptualization of a broad range of memory phenomena. In short, they postulated that some mental operations are innately automatic and that others become automatic via extensive practice. Whereas both innate and acquired operations require only limited allocation of attentional capacity, nonautomatic operations require considerable capacity. The latter type operations have been variously described as controlled, effortful, conscious, and intentional. Automatic and effortful processing also figured large in the earlier theorizing of Shiffrin and Schneider (1977), Kahneman (1973), Posner and Snyder (1975), and Brown (1975). These theorists, This research was supported by National Institute of Mental Health Grant MH35679 and by the Marie Wilson Howells Memorial Fund. Special thanks go to the staff of the UAMS Child Study Center and to Behavioral Laboratory team members Michael Oglesby, Yvonne Boudreau, and Betty Patterson. Reprint requests should be sent to Dr. Dykman, Slot 588, University of Arkansas for Medical Sciences, 4301 West Markham, Little Rock, AR 72205. 22 0278-2626186$3.OO Copyright All rights
0 1986 by Academic Press. of reproduction in any form
EFFORTFUL
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23
like Hasher and Zacks, have been concerned with limits on the energy available for performing mental operations. Educators aim, of course, toward the automatization of basic skills in elementary school children, for only when these skills have become automatic can the children have sufficient free attentional capacity for comprehension and problem solving, which require conscious effort (Ackerman & Dykman, 1982; Sternberg & Wagner, 1982). The Hasher-Zacks model has special appeal to those who study attention dysfunctions and impaired skill acquisitions in normally intelligent children. Three groups of children described in DSM-III particularly invite study within the automatic-effortful framework, i.e., those with attention deficit disorder (ADD), with or without hyperactivity (H), and those with developmental reading disorders (RD). Since it is not uncommon to see both types of ADD children who have acquired basic skill competence despite their dysfunction, it cannot be claimed that attention dysfunction per se causes RD. Yet, RD children, as a rule, exhibit major attentional problems (Ackerman, Dykman, & Oglesby, 1983), and a sizable fraction are hyperactive. If one starts, on the other hand, with a heterogeneous group of hyperactives and employs stringent criteria for RD, then one recent study indicates only about 10 percent are doubly afflicted (Halperin, Gittelman, Klein, & Rudel, 1984). This is not to say, however, that the remainder of the hyperactives are across-the-board normal achievers or that their classwork is satisfactory (Cantwell & Satterfield, 1978).Moreover, there is some evidence that the older the hyperactive child, the more apt he is to display a basic skill deficit (Halperin et al., 1984). Thus, attention dysfunction appears to interact with other factors in affecting achievement levels. The Hasher-Zacks model suggests that children who present with specific learning disability may have more generalized problems in acquired automatization of subskills (see, e.g., Denckla & Rudel, 1976; Rudel, 1980), whereas many who present with attention disorders as the primary difficulty will exhibit normal progress in automatization of basic skills yet falter in tasks requiring sustained effortful processing. Learning disabled children would also be expected to perform poorly on effortful tasks, particularly if the task entails skills which have not become automatized. If the effortful task does not depend on prior learning, however, the RD child would not necessarily be expected to perform poorly, provided he does not have a concomitant attention disorder. For example, RD children generally perform above average on Block Design (Ackerman et al., 1983). It is, of course, simplistic to speak of a general attention disorder, for attention has many facets. Also, the dysfunction can be situation, modality, and material specific (Rosenthal & Allen, 1978). It would be virtually impossible in one experiment to contrast problem students on all aspects
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of attention. The study reported below is but a beginning into what could prove a fertile area of research. It gains strength from the comparison of three clinical groups with each other as well as controls. Chapman and Chapman (1973) have warned of the shortcoming of studies that do not include clinical contrast groups. The study also includes two age bands (S-10 and IO-12), inasmuch as efficiency in effortful processing would be expected to be age related. Age x group interactions, then, could point to maturational lag. Elsewhere (Ackerman, Anhalt, Dykman, & Holcomb, 1985) we have presented results from tasks of innate and acquired automatic processing in the groups of children described below. While the focus here is on effortful processing, several of the tasks allowed appraisal of level of processing (e.g., sensitivity to levels of meaning and to imagery levels) as well as strategic approaches. Memory tasks are all the more demanding of attentional capacity if subjects do not employ strategic behaviors such as rehearsal, chunking, and clustering. From the work of Bauer (1979), Brown (1975), Torgeson and Goldman (1977) and Weingartner, Rapoport, and Buchsbaum, et al. (1980), among others, it appears that RD subjects and hyperactives do not as often employ these strategies as normal learners. Some evidence suggests that problem learners can effectively use certain strategies, once they are pointed out, but that these children are less likely to use strategies spontaneously (Brown, 1979). Two of the memory tasks below allow appraisal of spontaneous clustering and one also has an induced clustering condition. METHOD Twenty-four normal achieving elementary school boys were contrasted with three groups of educationally handicapped boys. All students were Caucasian, normally intelligent, without physical handicap, and from above poverty level homes. The control children were recommended to the investigators by their teachers and principals at two local parochial schools. The educationally handicapped students were referred either to the Child Study Center of the University of Arkansas or to our laboratory by school personnel; they were assigned to one of three diagnostic groups on the basis of teacher ratings, WISC-R IQs, and standard scores on the Wide Range Achievement Test (WRAT). Twentyone boys were classified as having attention deficit disorder (ADD) without hyperactivity and without reading disability; 24 were categorized as ADD with hyperactivity but without reading disability (H/ADD); 24 were diagnosed as having developmental reading disorders in the absence of hyperactivity (RD). Criteria set down in the new Diagnostic and Statistical Manual (DSM-III) of the American Psychiatric Association were employed in definitions of ADD, with and without hyperactivity, and reading disability. Additionally, H/ADD subjects had scores of 15 or higher on the Conners’ (Teacher Questionnaire) hyperkinesis index. Nonhyperactive ADD subjects and RD subjects had scores under 15 except in a few instances where the elevated score resulted from attention items rather than adverse ratings on core hyperkinesis items (i.e., restless in the squirmy sense; restless, always up and on the go; excitable, impulsive). All ratings were made when children were not on stimulant medications. Eleven items on the Teacher Questionnaire were scored to provide an ADD index (maximum score = 33); 3
EFFORTFUL
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25
of these items overlap with the hyperkinesis index (distractibility or attention span problem; fails to finish things that he starts; and easily frustrated in efforts). The 8 other items were fails to follow instructions fully, difficulty concentrating on assigned work, daydreams, often does not seem to listen, ditficulty sticking to a play activity, complains about assignments, dawdles, does not work up to capacity, task persistence is a problem. RD subjects had mean standard scores below 90 (the 25th percentile) on the WRAT reading and spelling subtests; additionally, these mean scores were at least 10 points below either the Full Scale or Verbal WISC-R IQ. Children with both Verbal and Performance IQs under 90 were excluded as were those who met criteria both for RD and H/ADD. However, as is discussed below, a significant fraction of RD subjects met ADD without hyperkinesis criteria, and some subjects in all three clinical groups had standard scores of less than 90 on the WRAT arithmetic subtest. Table 1 gives mean descriptive data for the four groups, divided into two age bands (8 years to 9 years, 11 months; 10 years to 11 years, 11 months). Note that the mean IQ of the RD subjects was almost 20 points above their mean reading-spelling standard scores. By design, the RD children read and spelled less well than other groups. Not by design, the control (CON) group read, spelled, and computed better than the two ADD groups. The RD children had lower WRAT arithmetic scores than control and H/ADD groups but not the ADD group. On the arithmetic test only, younger subjects had higher standard scores than older ones (for group, F(3, 85) = 40.56 on reading, 31.99 on spelling, 5.99 on arithmetic, p < ,001 for all; for age, F(1, 85) = 11.72 on arithmetic, p < .Ol). The CONS’ mean WISC IQ exceeded that of the other groups (F(3, 85) = 9.56, p < .OOl), but further analyses were indicated because of a group x factor interaction (F(6, 170) = 5.61, p < .OOl). The major differences were on the Verbal factor (Vocabulary, Comprehension, and Similarities) and the so-called ACID pattern (Arithmetic, Coding, Information, and Digit Span), but the Spatial factor (Picture Completion, Block Design, and Object Assembly) also showed a group effect (F(3, 85) = 9.91, 12.10, and 2.86, respectively; p < ,001 for the first two and p < .05 for the latter). On the Verbal factor, all groups scored higher than RDs, and CONS were higher than H/ADDS. On the ACID pattern, CONS were higher than all others and H/ADDS were higher than RDs. On the Spatial factor, CONS had an advantage over the H/ADD but not RD and ADD groups. The ANOVA for scores on the hyperkinesis index showed the H/ADD group mean to exceed all others, but the ADD and RD means were higher than for the CONS (F(3, 85) = 95.51, p < ,001). On the ADD index, the H/ADD and ADD groups both were higher than the RD group, and all three higher than CONS (F(3, 85) = 72.52, p < .OOl). There was an age effect (F(l) 85) = 4.79, p < .05) and a marginal group x age interaction on this index (F(3, 85) = 2.20, p < .lO) because in all groups except the ADDS the older subjects had lower scores than the younger ones.
Procedures Generally, the mother only, but in some instances both parents jointly, filled out Conners’ Parent Questionnaire and the Werry-Weiss-Peters Activity Scale questionnaire which provides more detailed information on hyperactive behaviors at home and in public places. Prior to his laboratory visit, each child was given visual and hearing screening tests, as well as a developmental neurological examination (all the above results are reported later). During a morning session in the behavioral laboratory, the child completed the tasks reported in this paper as well as several to tap automatic processing (Ackerman et al., 1985). In an afternoon session, he completed computerized tasks of attention while electroencephalographic recordings were made (also to be reported later). No child was taking psychotropic medication during any test sessions. All children were acquainted with the layout of the laboratory and with the staff before testing began. Frequent breaks were given between tasks.
Young (12) Old (12) Young (11) Old (13) Young (11) Old (10) Young (10) Old (14)
Control
RD
ADD
H-ADD
Age group 09
Groups
109.0 131.1 108.4 129.5 109.1 129.4 114.3 134.4
(6.3) (5.8) (6.5) (5.9) (7.1) (7.1) (4.7) (8.8)
CA (mos) 112.8 117.9 106.0 103.1 113.3 102.3 100.9 102.6
IQ
DATA:
( 5.9) ( 7.1) ( 9.4) ( 9.1) (15.7) ( 9.0) ( 7.8) ( 7.5)
WISC-R
DESCRIPTIVE
TABLE
1
119.7 118.7 106.1 109.5 109.6 107.9 84.2 83.9
(10.4) (12.8) (11.1) (11.3) (18.6) ( 9.8) ( 4.2) ( 7.4)
Reading
WRAT
DEVIATIONS
107.7 111.8 100.4 103.4 101.6 99.2 82.9 79.8
(10.6) (15.0) ( 7.1) (10.9) (15.7) ( 6.6) ( 5.1) ( 5.9)
Spelling
standard scores
MEANS AND STANDARD
101.8 98.0 97.3 94.3 98.7 92.5 94.6 90.3
(3.5) (5.5) (7.1) (6.3) (7.6) (5.4) (5.0) (6.9)
Math
3.5 1.3 19.9 20.5 10.6 11.2 11 .o 9.5
Per (2.8) (1.3) (3.2) (4.2) (3.4) (2.7) (3.4) (5.8)
Teacher
3.3 1.1 24.6 21.3 19.6 21.5 19.3 13.1
(3.0) (2.5) (4.9) (4.7) (7.3) (6.8) (5.4) (6.3)
ADD
ratings
F
2
c?
E
F
b
EFFORTFUL
PROCESSING IN RD AND ADD SUBJECTS
27
1. Semantic and acoustic encoding. This paradigm was developed by Weingartner et al. (1980) and used to contrast normal and hyperactive children under drug and placebo. The child was asked to listen to 20 sets of three words each. Before the word sets were presented, the children were told they should try to remember the words they would hear. Ten sets contained two words that were semantically related with one unrelated, and 10 sets had two words that rhymed and one that did not. The sets were given in alteration after two coaching trials (i.e., “Which word does not belong because of what it means: peas, spinach, house? Which word does not belong because of how it sounds: man, can, clock?“). When, after the training trials, a child made an error, he was corrected by the examiner (e.g., “No, the word that does not belong is car.“). After completion of the 20 sets, the child proceeded to a frequency of occurrence task, which served as a distractor. Then he was asked to think back to the sets of words he had heard and to freely recall as many as possible. One minute after recalling the last word (and being asked if he could remember any more), the child was read one of the two related words from each set and asked to recall the other word of that pair (cued recall). For example, he was asked, “Which word went with tree because of the way it sounds?” If the child said “I don’t know,” he was encouraged to try. Following Weingartner et al. (1980), the number of words of each type (semantic or acoustic) recalled under each condition (free or cued) was analyzed. Additionally, clusteringthe tendency to recall related words in pairs-was studied in the free recall condition. Also studied was the number of unrelated acoustic and semantic words (foils) correctly discriminated during the encoding phase. 2. List learning of high- and low-imagery words. Weingartner, Caine, and Ebert (1979) used a high-imagery-low-imagery paradigm to study memory deficits of patients with Huntington’s disease. Unlike normal controls, these patients did not exhibit relatively better recall of high-imagery than low-imagery words. By using the method of selected prompted recall, Weingartner et al. (1979) were also able to look at retrieval versus encoding impairment in these patients. We asked each child to learn a list of 12 common one-syllable words, half of which were highly imageable (e.g., king, house) and half less easily imageable (e.g., hope, fact). To control for serial position effects, the two lists employed were composed of alternating high-imagery (HI) and low-imagery (LI) words, with one list beginning with a HI word and the other a LI word. Lists were alternated within groups. On the first learning trial, all 12 words were read to the subject, 2 s apart. He was asked to recall as many words as he could and then was prompted on the words he did not remember. The experimenter said, for example, “That’s good. You remembered 5 words. Here are the 7 you did not remember. Try to recall all 12 words this time.” Eight trials were allowed to master the list: but in the event a child repeated the entire list correctly twice in a row, the task was terminated, with perfect scores given for remaining trials. Approximately 2 h later, after lunch and intervening tasks, the child was asked to try to recall as many of the 12 words as possible. Global scores (total HI and LI words recalled after each trial) were analyzed as well as measures of encoding and retrieval consistency during learning. Averaging the number of the trial at which each word was first recalled for each child provided his mean trial of encoding. Retrieval consistency was defined as the proportion of words on a given trial (2 through 8) recalled on the previous trial (therefore unprompted). This task assesses levels of effortful processing via the high-imagery-lowimagery contrast and the initial and delayed recall conditions. 3. Memory of 12 printed words. The words used for this task were first-grade level and should have been familiar to all subjects, even the most retarded readers. Each word was printed in lower case on a 3 x 5-in. card. Three decks of 12 cards (4 words from three categories) were randomly used in the three conditions. First the child was handed Deck 1 (randomly arranged) and told, “Spread out the cards so that you can see them all at the same time, then read them to me.” The examiner
28
ACKERMAN
ET AL.
corrected any errors or omissions by pronouncing the word. Then, the examiner said, “Look at the words carefully for a bit.” When the child looked up, the deck was removed and he was asked, “Now tell me as many of the words as you can remember. What words did you see?” His free recall was recorded. Next the examiner showed the child Deck II (also randomly arranged) and said, “Now spread out these cards and read them to me. (Correction was done as before.) Fine. Now I want to see how many words you can remember if you study them first. Do anything you wish to help you remember the words. Tell me when you think you can say all of them.” The examiner recorded the child’s answers. Then the examiner showed the child Deck I again and said, “When I want to remember lots of words, I try to see if some words go together. Let’s put these words in groups. See, four of them are animals (e.g., cow, pony, goat, pig), four are things to eat (cake, corn, egg, apple), and four are numbers (two, five, six, ten). If I study the words in groups, I can remember them better.” Then the examiner showed the child Deck III and said, “Spread out the cards and read them to me. Study them until you think you can say them all back to me.” The total number of words correctly recalled under each condition was recorded as was the number of clustered words (words from a category recalled as pairs, triplets, or quadruplets). The task was theorized to tap effortful processing as well as ability to use a strategy spontaneously or after modeling. 4. Rapid addition and subtraction. The child was seated in a test cell facing a display panel which is a unit of a Varian computer. He was told that he was going to play an arithmetic game with the computer-that the computer would present him with a series of problems, that he was to figure out the right answer in a limited amount of time, and then type his answer on a number console. He was given six easy practice problems (e.g., 6 + 5 = -) and reminded that the computer would erase the problem if he took too long. The first procedure consisted of 10 addition and 10 subtraction problems, randomly presented. The addition problems were a sample of all combinations involving the numbers 4 through 9 which yield a two-digit answer (e.g., 7 + 6) while the subtraction problems involved at least a 4-unit difference and also required a two-digit answer (e.g., 19 - 5). The 4-unit minimum was used to discourage (or reveal) a counting strategy. The child was allowed a maximum of 16 s to type in his answer. Latencies and answers were stored, and the child received visual feedback (good or bad at the lower left edge of the screen) during each 3-s interstimulus interval. After completion of this set of problems, the child was told, “Now you will have a chance to win money for answering the problems correctly. The computer will show you how much you are winning. We are going to give you 5Oc to start with and you will win 4$ for each problem you get right but lose 2$ for each you get wrong. Also, the computer will erase the problem and take away 2c if you take too long.” The 20 problems were of the same difficulty level as the fist 20, and all other conditions were the same. After completion of the second set, the child was congratulated on his earnings and told he would be asked to solve some slightly harder problems. These 20 problems required regrouping (i.e., borrowing or carrying) and, like the others, involved a difference of 4 or more units (29 + 5 or 62 - 6) and a two-digit answer. Reward conditions were the same and built on the child’s earnings from the previous set of problems. While even the youngest children in the sample had completed second grade and had had experience with regrouping, it was expected that processes would not be automatized in all children and that Iatencies, if not errors, would show age and group differences. The two conditions that did not involve borrowing and carrying were theorized to tap acquired automatization in both age bands (see Ackerman et al., 1985). The regrouping problems were considered effortful.
EFFORTFUL
PROCESSING IN RD AND ADD SUBJECTS
29
5. Paper and pencil arithmetic tests. These tests were analogs of the computer math tests. The child was, however, allowed up to 15 m to complete each set of 20 items (one involving regrouping, one not). Thus the child who had not committed number facts to memory could still score well if he used a counting strategy. Time to complete each set was noted along with number correct. No reward was offered. These tasks were included to assess automatization failure as evidenced principally by time to complete each set. The regrouping condition results are analyzed below; the simple computational task was included in the acquired automatization battery. Latencies were logged prior to all analyses. Per problem mean latencies are reported below.
RESULTS
Inasmuch as CONS had significantly higher IQs than the three diagnostic groups, and since IQ was signit?cantly correlated with most of the measures of effortful processing, a multivariate analysis of covariance (BMDP4V) was first performed on the 15 measures shown in Table 2. There being overall significant (p = .05) group and age differences, univariate contrasts on each measure are reported in Table 3. The results of the mixed design ANOVAs are also given in Table 3 for contrast with the ANCOVAs. Because there was not an overall group x age interaction in the multivariate analysis, the F’s for the univariate interactions are not shown. All significant pairwise contrasts have been given, even when the overall group effect was nonsignificant, because of the large number of studies in the literature that contrast only two groups, usually CONS versus a clinical diagnostic group. Also, since IQ did not significantly separate the three clinical groups, any pairwise contrasts of these groups significant by ANOVA merit consideration. In addition to the analyses of the separate measures, several withintask analyses were done to explore the automatic-effortful dimension via consideration of levels of processing, immediate and delayed recall, spontaneous and coached strategic rehearsal, and the effect of novelty and reward on computational efficiency. The four measures from the acoustic-semantic tasks were subjected to mixed design ANCOVA, with group and age as between-level factors and stimulus type and condition (immediate free recall versus cued recall) as within factors. This analysis, as expected, confirmed significant effects for stimulus type (F( 1, 85) = 64.71, p < .OOl) and recall condition (F( 1, 85) = 134.51, p < .OOl) as well as an age effect (F(1, 84) = 6.26, p < .02). The group effect was marginal (F(3, 84) = 2.34, p < .08), with RDs poorest. There was also a recall condition x stimulus type interaction (F(1, 85 = 8..52, p < .Ol) inasmuch as the children’s recall of semantic stimuli was enhanced more by cuing than their recall of the acoustic stimuli. Two measures, thought perhaps to underpin the poorer performance of RDs on the acoustic-semantic task, were then analyzed. First, the number of unrelated words (foils) correctly discriminated in the encoding
TABLE 2
Acoustic-semantic: Free, acoustic Free, semantic Cued, acoustic Cued, semantic Hi-lo imagery: Learning, Hi Learning, Lo Delayed, Hi Delayed, Lo Memory for words: Look Study Cluster Computation: Paper, correct Computer, correct Paper, latency Computer, latency 3.0 4.5 4.3 3.3 3.8 1.6 7.0 8.0 10.9
2.7 5.0 4.2 3.5 4.1 2.3 7.0 7.6 10.2 12.3 7.4 4.4 4.0
5.0 4.3 4.9 3.8
8.0 9.8 11.2
17.0 14.9 4.1 4.0 11.8 8.4 4.3 4.0
i.2 2.5
H/ADD (N = 24)
1.4 2.6
ADD (N = 21)
2.1 2.8 3.2 5.2
(N = 24)
CON
Groups
11.4 9.2 4.3 4.0
7.3 7.8 10.1
4.2 3.0 4.0 1.3
1.8 3.6
1.0 1.8
RD (N = 24)
10.5 6.8 4.3 4.1
6.8 7.6 10.3
4.3 3.3 4.0 2.0
2.3 4.3
1.0 2.5
Young (N = 46)
15.8 13.3 4.1 4.0
7.8 9.0 11.0
4.6 3.7 4.4 2.5
3.0 4.9
1.8 2.3
Old (N = 47)
Age bands
(0.8) (0.9) (1.5) (1.8)
13.2 (6.3) 10.1 (6.4) 4.2 (0.3) 4.0 (0.1)
7.3 (2.4) 8.3 (2.5) 10.6 (1.9)
4.4 3.5 4.1 2.3
2.6 (1.7) 4.6 (1.9)
1.4 (1.5) 2.4 (2.1)
Total sample (N = 93)
GROUP AND AGE BAND MEANS AND SAMPLE MEANS AND STANDARD DEVIATIONS FOR 15 MEASURES OF EFFFORTFUL MENTAL PROCESSING
F
G ii! % 3
&
* p < .05. ** p < .Ol.
Age
3.53 8.70** 3.90*
23.02** 41.60** 19.89** 21.21**
6.33** 11.41** 6.75’* 4.26**
3.75 6.84* 2.60 3.13
5.66** I I .33** 2.80’ 12.74**
I .02 5.29** 2.08
5.77* 0.10 6.29* 3.54*
1, 85df
2.96* 0.72 4.17** 4.17**
Group 3, 85df
ANOVA F values
a Indicates IQ is a significant covariate.
Memory for words Look Study Cluster Computation Paper, correct” Computer, correct” Paper, latency” Computer, latency”
Acoustic-semantic Free, acoustic” Free, semantic Cued, acoustic Cued, semantic” Hi-lo imagery Learning, Hi” Learning, Lo” Delayed, Hi Delayed, Lo”
-
TABLE 3
> > < i
all all all all
2.25 6.41** 3.77* 1.48
1.82 3.55* 0.82 4.89**
CON CON CON CON
all all H/ADD, RD all RD 0.32 3.91* 1.25
> > > > >
0.90 0.29 2.85* I .79
Group 3, 84df _____
ANCOVA F values
PROCESSING
CON > all CON > RD
CON CON CON CON ADD
H/ADD, RD < CON RD < all RD < ADD, CON
p < .05 89df
Between groups
GROUP AND AGE CONTRASTS ON EFFORTFUL
27.38** 49.50** 25.28** 24.36”*
4.41* 8.67** 4.38*
6.03* I1.60** 3.99* 5.21*
6.90** 0.04 6.84** s.oo*
1, 84df
Age
-
CON > all CON > all CON, RD < ADD -
CON > all
CON > ADD CON > all CON > all
RD < CON, H/ADD RD < ADD
-
p < .05 89df
Between groups
w
32
ACKERMAN
ET AL.
phase were subjected to ANCOVA. This maneuver revealed that the children were more accurate in discriminating semantic than acoustic foils (F(1, 85) = 4.36, p < .05), that older children discriminated better than younger ones (F(1, 84) = 22.19, p < .OOl), and that the IQ-adjusted discrimination scores of ADDS were significantly higher than those of RDs, with CONS’ adjusted scores marginally higher than RDs (F(3, 84) = 4.12, p < .Ol), Second, the number of words freely recalled in clusters (acoustic-semantic combined) was examined. This ANCOVA showed an age effect (F( 1, 84) = 5.17, p < .05) and a marginal group effect (F(3, 84) = 2.32, p = .08). In pairwise contrasts, RDs were significantly poorer than both the ADDS and CONS. Thus, RDs appear to have been less sensitive to associations between words than ADDS and CONS. The four measures from the high-imagery-low-imagery task were also subjected to a mixed design ANCOVA (group x age x stimulus type x recall condition). This analysis confirmed a group effect (F(1, 84) = 3.42, p < .05) and age effect (F(l) 84) = 9.65, p < .Ol), and also showed, as predicted, that immediate recall was better than delayed (F( 1, 85) = 57.19, p < .OOl) and that high-imagery words were recalled better than low-imagery words (F(1, 85) = 221.26, p < .OOl). There were several interactions of interest, including the overall finding that delayed recall of high-imagery words showed less decrement than delayed recall of lowimagery words (F(1, 85) = 48.05, p < .OOl). The interactions of stimulus type and condition with group are of greatest interest. Expressing decay as a percentage of the mean immediate recall scores and summing over stimulus type, CONS exhibited significantly less memory decrement than H/ADDS (9% versus 27%) with RDs (23%) and ADDS (17%) intermediate (F(3, 85) = 3.75, p < .05). Summing over recall conditions, CONS had significantly less difference between scores on the two types of stimuli than RDs, with the two ADD groups intermediate (F(3, 85) = 5.92, p < .OOl). There was a marginal stimulus type x recall condition x group interaction (F(3, 85) = 2.51, p < .06), reflecting the fact that only the H/ADDS showed a significant decay in high-imagery recall. Percentage decay for the low-imagery words was 49% for RDs, 48% for H/ADDS, 34% for ADDS, and 17% for CONS; corresponding figures for highimagery words were 5, 14, 4, and 3%. The trial effect on acquisition of the high- and low-imagery words was explored in another ANOVA to look for possible interactions with trial, but none reached an acceptable significance level. ANCOVAs were also performed to examine whether mean trial to initial encoding of all words discriminated groups; this showed only that older children encoded more efficiently than younger ones (F(1, 84) = 9.53, p < .Ol>.Another ANCOVA looked at a retrieval measure (mean percentage of words recalled on each trial also recalled on the next trial); this too showed only an age
EFFORTFUL
PROCESSING
IN RD AND ADD SUBJECTS
33
effect (F(1, 84) = 4.81, p < .05). Thus, the variable in this data set providing best discrimination of groups was low-imagery performance. The three measures from the memory for words task were next considered in a mixed design ANCOVA. There was a marginal group effect (F(3, 84) = 2.26, p < .lO), highly significant condition effect (F(2, 170) = 76.77, p < .OOl), but no significant interactions. The data from this task were also examined for spontaneous and modeled clustering (i.e., the number of words recalled in clusters of 2, 3, or 4 related words was tallied for each condition). This mixed design ANCOVA yielded significant age (F(1, 85) = 7.12, p < .Ol) and condition (F(2, 170) = 107.22, p < .OOl) effects and a marginal group x age interaction (F(3, 84) = 2.39, p < .lO) reflecting the fact that older H/ADDS employed clustering somewhat less than younger ones, whereas in all other groups old subjects clustered more than younger ones. However, the group x condition interaction was not significant. Two ANCOVAs were done on the latency and accuracy scores from the two computational tasks, again to explore interactions. The accuracy analysis showed main effects for group (F(1, 84) = 4.77, p < .Ol), age (F(1, 84) = 47.58, p < .OOl), and condition (F(1, 85) = 41.94, p < .OOl). Because of the time restrictions with the computer version of the task, the children solved fewer problems than with paper and pencil. The IQadjusted accuracy scores showed CONS superior to each of the other groups, but neither age nor group interacted with mode of presentation. Interactions were found, however, for the logged latencies. This ANCOVA revealed strong age (F(l) 84) = 31.OO,p < .OOl) and condition effects (F(1, 85)= 130. 38, p < .OOl) as well as a group effect (F(3, 84) = 3.41) and both group x condition (F(3, 85) = 4.96, p < .Ol) and age x condition (F(1, 85) = 9.23, p < .Ol) interactions. The younger subjects showed a greater difference between the two conditions than the older subjects, and CONS exhibited less difference between their paper-pencil and computer latencies than the three clinical groups. Also, the IQadjusted latencies showed both CONS and RDs to be significantly faster than ADDS in the paper-pencil condition with no group differences in the computer condition. In summary, the computer condition pushed the children to respond rapidly, which cost all groups in accuracy (see Table 2), particularly the ADDS (though the interaction was not significant). Given no time constraints, the ADDS took the longest times, suggesting they had least automatized simple borrowing and carrying and when hurried showed greatest cost in accuracy. A final set of multivariate analyses explored the intercorrelations and the factor structure of all the above measures and the efficacy of these measures in prediction of reading skill and ADD ratings. A principal components analysis yielded four factors (62% of the variance) which,
34
ACKERMAN
ET AL.
when varimax rotated, provided easy interpretation. Factor I described the high-low imagery task; Factor II incorporated both the latency and accuracy scores from the computation tasks; Factor III had highest loadings from the acoustic-semantic measures; Factor IV tapped the memory for words tasks. ANCOVAs on these factor scores (IQ as the covariate) yielded a significant age effect on Factor II (F(1, 84) = 36.82, p < .OOl) and significant group effects on Factors II and III (F(3, 84) = 2.79 and 3.39, respectively, p < .05). The F’s for the covariate were significant for Factors I and II (15.47 and 5.02, respectively, p < .OOl and p < .05 for 1, 84 dfl. On Factor II, the adjusted factor scores of CONS were significantly different from each of the other groups, and on Factor III, the RDs’ adjusted factor scores were significantly different from the H/ADDS and ADDS with CONS intermediate (the unadjusted scores show the same ordering). Inasmuch as the clinical groups were not impressively separated by the effortful variables under study, two overall stepwise regression analyses were done, one to predict WRAT reading grade level and one to predict the investigatory ADD index. In both instances, age and IQ were forced first and then the computer (with F to enter set at 4.00) chose the best variables. These analyses (for the total sample of 93 children, diagnosis ignored) are summarized in Tables 4 and 5. Finally, the H/ADD and ADD groups were combined and contrasted with the RD group via stepwise discriminant analysis, with age and IQ forced first. With F to enter set at 4.00, the computer chose number of acoustic foils discriminated (Step 3) and number of acoustic-semantic clusters (Step 4) to achieve 77% correct classifications. When the combined ADD groups were contrasted with CONS under the same constraints, the computer chose computer math accuracy as Step 3, memory for visual words (study condition) as Step 4, and delayed memory for lowimagery words to achieve 88% correct classifications. When RDs and CONS were contrasted (same constraints), the discriminant function TABLE 4 WRAT READING
STEPWISE REGRESSION FOR
Variable
R
RZ(adjusted)
r
Stepwise partial r
Cued recall, acoustic Delayed recall, low imagery Accuracy, computer math Cued recall, semantic Discrimination, acoustic foils
.39 .63 .73 .78 .80 .82 .83
.14 .38 .52 59 .62 .64 .66
.39 .42 .53 .60 .48 .48 .58
.54 .47 .41 .29 .25 .22
Step
I” 2” 3 4 5 6 7
GRADE LEVEL
Age IQ
’ Indicates variable forced first.
EFFORTFUL
PROCESSING IN RD AND ADD SUBJECTS
35
TABLE 5 STEPWISE REGRESSION OFADD INDEX SCORES Variable
R
R' (adjusted)
r
Stepwise partial r
Age”
.15 .39 .49 .54
.Ol .I3 .22 .26
.I5 - .33 - .46 - .36
- .36 - .33 - .21
IQ Computer arithmetic, accuracy Memory for words, study ’ Indicates variables forced first.
achieved 96% correct classifications; however, after age and IQ were forced, the computer took not the acoustic-semantic variables but paper math accuracy and memory for 12 written words (study condition of Task 3). DISCUSSION
Normal reading ADD children, with or without hyperactivity, were expected to perform less well on effortful mental tasks than normally attentive and adequately achieving age mates (CONS). RD children, because most are rated by their teachers to have attentional problems, even when not hyperactive, were also expected to be at disadvantage on effortful encoding and retrieval-but possibly as much because they have not automatized the skills and/or strategies needed for successful performance as because of an underlying attention disorder. At the risk of sounding judgmental, we earlier theorized that RD children intend (or try) to attend but cannot always store or retrieve information adequately, whereas ADD children, especially hyperactive ones, often do not appear motivated to attend (Dykman & Ackerman, 1976). Accuracy scores or response latencies might be equally impaired for these groups of children, then, but for different reasons. In designing the above study, we reasoned that one tip-off to the “attention” versus “intention” dimension could be in how the groups respond to reward and novelty. Another tip-off could be in the contrast of automatic versus effortful processing or in levels of effortful processing. For the exploration of these ideas a computational task and three memory tasks were employed. All three memory tasks allowed assessment of levels of effortful processing. The features were suggested from the levels of processing literature (see Hasher & Zacks, 1979). That is, in one task, half the to-be-remembered words were acoustically related (rhymed) and half semantically related (same category). The literature suggests the semantically related words will be recalled better than the acoustically related ones. Similarly, the high-imagery words of a second task were predicted to be recalled better than low-imagery words (Paivio,
36
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1971). Categorically related words (third task), even if not pointed out to subjects, should be recalled better than nonrelated words, provided the subject spontaneously employs clustering in rehearsal and retrieval (Brown, 1975). Thus, group x condition interactions in the memory task employed could have major implications for the attention-learning dimension. Similarly, group x condition interactions when reward and novelty are a feature could have interpretative significance foT the intention hypothesis. These manipulations were incorporated in the computation task. As expected, the three clinical groups performed less well than CONS overall, and, attesting to the effortful nature of the tasks, older children performed better than younger ones. WISC-R IQ scores, whatever the controversy about their ultimate worth, also correlated significantly with most of the performance measures, suggesting that the higher the IQ the less effortful a given task may be. Because CONS had significantly higher IQs than the clinical groups, ANCOVAs were performed on all data sets, which lowered F values obtained in ANOVAs. But, the inference would have to be that had we obtained a CON group with IQs not significantly higher than those of the clinical children, performance differences would still have been found, particularly the more difficult the task-memory for low-imagery words and computational efficiency with borrowing and carrying involved. As there was very little evidence that H/ADDS and ADDS differed on any measures under study, these two groups were combined and contrasted with the CON and RD groups. The computational task best separated ADDS and RDs from CONS, but memory variables also contributed. It is tempting to argue that attentional dysfunction per se impairs computational efficiency. Granted, the CONS of the present study were also much less advanced in arithmetic than reading and spelling as indicated by WRAT standard scores-despite normal attentiveness as rated by teachers. Moreover, with IQ as the covariate, the WRAT arithmetic scores of CONS did not differ from those of the clinical children (Ackerman & Dykman, 1985). But, for younger students the WRAT arithmetic test seems not to be sufficiently sensitive to automatization failure. Even the simpler arithmetic tasks included in the acquired automatization battery (Ackerman et al., 1985) revealed the clinical groups to be less adept than CONS (and with IQ as the covariate). We earlier discovered (Ackerman, Dykman, Peters, 1977) that boys diagnosed as learning disabled in grade school and studied again as adolescents had even lower WRAT arithmetic than reading scores despite equivalent group standardized test scores on the two skills in Grade 4. Since the WRAT arithmetic section has no reading problems, the deficiency cannot be attributed to reading disability per se. We now believe that many ADD and RD children fail to commit number facts, and later multiplication tables, to memory. This weakness
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in basic arithmetic appears to be related to low WISC-R ACID scores, apparent in all clinical groups. In the current sample, the multiple R of age and ACID with arithmetic grade level was .77; WISC verbal and spatial factor scores made no significant additional contribution (Ackerman & Dykman, 1985). The presentation of the arithmetic problems on the computer, with reward for accuracy, did not produce the type group x condition interactions we had theorized. That is, if the two groups of ADD boys could perform better than they do in the classroom or on standardized tests, reward might be expected to boost their performance relatively more than that of the presumably better intentioned RD and CON boys. Such was not the case either with the simpler problems included in the automatization battery (Ackerman et al., 1985) or with the more difficult regrouping problems used here. Rather, the computer condition was more successful at showing automatization failure. When the children were required by the computer to respond within limits, unlike in the paperpencil condition, accuracy suffered-expecially in the case of the solely ADD children on the regrouping problems. We have seen that reward can speed certain types of information processing (Ackerman et al., 1982), but the evidence from the present study suggests that children who do not compute efficiently probably cannot. It would be instructive to see the effect of methylphenidate on these children’s computational efficiency, however. One task used in another experiment with the four groups here studied (Ackerman, Holcomb, Dykman, & Anhalt, 198.5)gave some evidence that RD children are better intentioned than ADD and H/ADD children. The paradigm was a variant of the Sternberg (1969) task, wherein the children were presented with to-be-remembered item sets containing one, three, or five consonant letters. After viewing a given memory set, the child saw a probe letter and had to indicate with response keys whether the probe was or was not in the previously viewed set. When the memory set contained one or three letters, the RDs were significantly more accurate than the ADD groups; but with five letters, all clinical groups were equally impaired. We surmise then that RDs perform accurately when the memory load is not too taxing, but that ADDS have a less orderly interaction of accuracy with load. There is evidence, also, that RDs show a greater differential than other groups in remembering less taxing than more taxing verbal material. Their performance both in learning and recalling low-imagery words was more disparate from their performance on highly imageable words than true for the other groups. A tangential finding comes from a recent study by Bruskin and Blank (1984). These investigators found that third and fifth graders read and spelled nouns and verbs (content words) faster and more accurately than equal difficulty level noncontent words (such
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as “these, could, why, very”). More interesting, the less skilled readers showed significantly greater disparity between the two word types than the more skilled readers. Bruskin and Blank (1984) review several studies that suggest that noncontent words may be stored and accessed via a different mechanism than content words. Since noncontent words occupy a higher percentage of any page of text than content words, poor access to these words would obviously impede automaticity in reading and writing. It was the acoustic-semantic task which provided best discrimination (77% correct) of the combined ADDS and RDs. Also, when reading grade level was predicted for the full sample, the acoustic-semantic variables made a major contribution above the variance explained by age and IQ. The relative insensitivity to rhyme and alliteration of RD children has been of central interest to the Haskins group (e.g., Liberman, Liberman, Mattingly, & Shankweiler, 1980) and to Bradley and Bryant (1983) in England, among others. Obviously, if children cannot easily sense the phonological similarities between words, their mastery of new words becomes much more effortful. If their semantic elaboration is sluggish, then comprehension will suffer, as will paired associate memory (e.g., learning the states and their capitals). And numeric elaboration no doubt abets automatization of number combinations, including the multiplication tables. In short, the efficient learner actively fits new knowledge into existing schemata, and then he fine tunes it (Rumelhart & Norman, 1981). That is, by use of analogy, he goes “beyond the information given” (Bruner, 1966) to develop new or enlarged schemata. Elaboration is most successful with concrete material (viz. high-imagery or content words). The challenge with RD children, then, is to try to link some of their seemingly intact concrete schemata with the more abstract concepts of phonetics. The Bruskin and Blank study suggests that less skilled readers slowly master a core of nouns and verbsperhaps by pattern recognition. But, even if the only words known are concrete and recognized by visual patterns, these schemata can be elaborated to illustrate more abstract phonetic associations. For example, suppose the child “knows” cat and box. Can he not be taught that he also has the information available to know bat and cox? The Bradley and Bryant (1983) studies suggest that he can. Numerous devices and diagrams have been used to make number facts more concrete to grade school children. Most, given time, can solve number combinations via counting, a concrete approach. But, the only way to make number associations automatic is via repetition, which is a curse to children with ADD. However, a combination of stimulant medication and trial-by-trial reward might be effective for many of them (Douglas, 1984).
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