Oppositional defiant disorder

COMORBIDITY IN ADHD

The present research investigated paired-associate learning (PAL) deficits in attention-deficit/hyperactivity disorder (ADHD) as a function of hyperactivity - impulsivity and oppositionality - aggression. Hyperactivity - impulsivity forms the basis for the three subtypes of ADHD in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association, 1994): Inattentive (ADHD/IT), hyperactive-impulsive (ADHD/HT), and combined (ADHD/CT). In comparison with ADHD/IT children, hyperactive ADHD patients (ADHD/HT and ADHD/CT) are more aggressive, impulsive, and unpopular as well as less likely to exhibit comorbid internalizing disorders (Cantwell & Baker, 1992; Epstein, Shaywitz, Shaywitz, & Woolsten, 1992). In contrast to the distinctive patterns of psychopathology for ADHD children varying in hyperactivity-impulsivity, evidence for cognitive differentiation of ADHD subtypes has been less consistent (Ackerman, Anhalt, Dykman, & Holcomb, 1986; Lahey & Carlson, 1991). However, a series of studies by Barkley and colleagues (Barkley, 1998; Barkley, DuPaul, & McMurray, 1990, 1991; Barkley, Grodzinsky, & DuPaul, 1992) reported several differences in cognitive deficits among ADHD subtypes. Specifically, these investigators found relatively worse performance by ADHD/CT children on the Continuous Performance and Wisconsin Selective Reminding Tests and lower scores by ADHD/IT patients on the Coding subtest of the Wechsler scale. As emphasized by Barkley (1998), there is a need for research that examines whether hyperactive and nonhyperactive ADHD children represent subtypes of a single syndrome or whether they display qualitatively different cognitive and attentional disorders. The present work addressed this question with respect to PAL.

In addition to hyperactivity, we focused on oppositionality - aggression (Loney, 1987), a condition that overlaps significantly with ADHD. As reviewed by Barkley (1998), at least 35% of ADHD patients meet full criteria for oppositional defiant disorder (ODD). Most research has found similar cognitive functioning in ADHD children distinguished by degree of aggression - oppositionality (Barkley, McMurray, Edelbrock, & Robbins, 1989; Dykman & Ackerman, 1992; Klorman et al., 1988; Klorman, Brumaghim, Fitzpatrick, Borgstedt, & Strauss, 1994). However, increased or different types of cognitive deficits have been reported for ADHD children with markedly high levels of aggression in comparison with nonaggressive ADHD children. Halperin et al. (1990) found that physically aggressive ADHD children made impulsive errors, whereas nonaggressive ADHD patients made inattentive errors on a Continuous Performance Test. Analogously, Moffitt and Silva (1988) reported that ADHD delinquent adolescents scored lower than nondelinquent ADHD adolescents on verbal neuropsychological measures. In a later study, Moffitt and Henry (1989) found depressed performance by ADHD delinquent adolescents on several executive function tests (Trail Making Test, Rey-Osterreith Complex Figure Test, and Wechsler Intelligence Scale for Children - Revised [WISC-R] Mazes); in contrast, ADHD nondelinquent youngesters and delinquent-only youngsters did not differ from nondisordered controls. Notably, previous research has not evaluated PAL performance in ADHD as a function of oppositionality, and the present research aimed at supplying this information.

Another major condition that overlaps with ADHD is learning disability. Depending on the definition used, prevalence rates of learning disorder ranging from 26% to 41% have been found for ADHD samples (Holbrow & Berry, 1986). In the present work, we carefully screened our participants for learning disorders in order to rule out the possibility that any PAL deficits found for ADHD might be attributed to comorbidity with learning disability.

PAIRED-ASSOCIATE LEARNING

PAL is facilitated by mnemonic strategies (Kintsch, 1970). Encoding strategies used in this and other memory tasks include rehearsal, that is, simple repetition of the paired associations; organization by conceptual or semantic categories; or elaboration, that is, generation of arbitrary relations between items (Schneider & Bjorklund, 1998). Search and retrieval strategies must also be deployed to retrieve the information from long-term storage. Proficiency in mnemonic strategies increases with age and intelligence (Beuhring & Kee, 1987; Schneider & Bjorklund, 1998). A model by Borkowski and Burke (1996) proposed that as children develop, they gradually acquire the capacity to match appropriate strategies with tasks and to monitor their performance as they master particular strategies. This development leads to the emergence of self-regulation and executive processes, weaknesses in which have been related to cognitive deficits in ADHD (Barkley, 1997a, 1997b; Pennington & Ozonoff, 1996). Although PAL may not involve all of these processes, it requires the deployment of mnemonic strategies and has attracted much attention in research on cognitive development and ADHD in particular.

There are several reasons for focusing on PAL as opposed to other memory tests. There is extensive evidence that PAL performance is correlated with children's verbal and nonverbal IQ as well as academic achievement (Gathercole, Hitch, Service, & Martin, 1997; Stevenson, Hale, Klein, & Miller, 1968; Zeaman & House, 1967). Importantly, these correlations were obtained for paired-associate learning of verbal as well as nonverbal stimuli, and performance on these two PAL formats was highly intercorrelated (rs = .60-.64; Stevenson et al., 1968). These findings attest to the reliability as well as ecological validity of this type of learning.

Another motivation for examining PAL is its long history in research on ADHD. Douglas (1988) proposed that ADHD children are particularly impaired in highly effortful tasks. Indeed, ADHD children performed normally on infraspan memory tasks but were deficient on supraspan tasks, particularly PAL (Benezra & Douglas, 1988; Douglas & Benezra, 1990). Douglas and Benezra (1990) found that ADHD children performed more poorly than psychiatrically normal controls on PAL; by the end of the task, both reading-disordered (RD) and ADHD youngsters performed worse than controls. Another aspect of this study related to reports that ADHD children used elaborative or efficient organizing strategies less often than RD or psychiatrically normal controls (August, 1987; Barber, Milich, & Welsh, 1996; O'Neill & Douglas, 1991, 1996; Weingartner et al., 1980). Specifically, on a recognition test of words from the PAL task, ADHD participants exceeded control and RD children in acoustically mediated errors, whereas RD children surpassed control and ADHD samples in semantically mediated errors (Douglas & Benezra, 1990). These findings imply that ADHD children use a lower level of encoding of verbal information (Craik & Lockhart, 1972).

Yet another reason for interest in PAL is that the cognitive benefits of stimulants for children with ADHD include improvement of PAL performance (e.g., Dalby, Kinsbourne, Swanson, & Sobol, 1977; Douglas, Barr, Amin, O'Neill, & Britton, 1988; Douglas, Barr, O'Neill, & Britton, 1986; Rapport, Stoner, DuPaul, Birmingham, & Tucker, 1985) and learning disorders (Conners, Rothschild, Eisenberg, Schwartz, & Robinson, 1969). These results, in combination with the report of deficient PAL by ADHD patients (Douglas & Benezra, 1990), might suggest that stimulants normalize these children's deficient PAL.

A difficulty with that conclusion is that despite its high quality, Douglas and Benezra's (1990) study represents the primary evidence for PAL deficits in ADHD. In fact, a recent investigation (Barber et al., 1996) found comparable PAL for ADHD/CT and control children; however, in this investigation, PAL may have been assessed before learning asymptoted. This possibility is likely because there were only three learning trials (repetitions of word pairs) for a rather long list (20 vs. 12 word pairs in Douglas & Benezra, 1990). In fact, Barber et al.'s (1996) control participants exhibited little learning (IQ-adjusted mean = 25% cumulative correct anticipations for semantically unrelated paired words in the no reinforcement condition vs. a mean of 47% in Douglas and Benezra, 1990). Thus, as a result of the limited number of trials allotted, Barber et al.'s (1996) task was probably too difficult for both their control and ADHD participants. Nevertheless, the discrepant findings of these two reports suggest the need for further evaluation of PAL deficits in ADHD.

Our study was aimed at evaluating the extent to which deficits and mnemonic strategies exhibited by ADHD children in PAL are affected or moderated by the presence of hyperactivity-impulsivity or comorbidity with oppositionality while ruling out learning disorders. To accomplish this goal, we recruited a large sample of children, including the combinations of presence or absence of inattention and hyperactivity-impulsivity as well as ODD. PAL competence was assessed by measures of the extent and speed of learning, and mnenomic strategies were evaluated by a qualitative analysis of overt errors.

METHOD

Participants

The present sample consisted of (a) 197 children referred to the Yale University Center for Learning and Attention for evaluation of attentional or learning problems by schools, parent groups, and professionals or enlisted by media announcements; and (b) 22 children without learning or disruptive disorders recruited by means of newspaper advertisements, letters to schools, and fliers in toy stores and libraries inviting participation by children without learning or attention problems. All participants met the following criteria: (a) 7 to 13.5 years-old, (b) WISC-R (Wechsler, 1974) Full Scale IQ of 80 or greater, (c) absence of reading or math disability (please see below), (d) English as the primary language, (e) absence of severe emotional problems or neurological disorders, (f) normal or corrected vision and hearing, (g) abstinence from all medications except over-the-counter analgesics or antibiotics for at least 7 days, and (h) no siblings in the sample.

DSM-IV diagnoses of ADHD subtypes and ODD were derived from a structured interview of parents (Diagnostic Interview Schedule for Children 2.3 [DISC]; Shaffer et al., 1996). Participants were classified into three ADHD categories (non-ADHD, ADHD/IT, or ADHD/CT) as well as by the presence or obsence of ODD. To simplify presentation of the findings, we combined the results for ADHD/CT (n = 102) and ADHD/HT (n = 24) participants; separate statistical analyses indicated that findings for these two samples were extremely similar.

Reading disability was defined as either (a) an age standard score on reading achievement (average of the Letter-Word Identification and Word Attack tests of the Woodcock-Johnson Psychoeducational Battery; Woodcock, 1978) at least 1.5 standard errors below that predicted from the participant's WISC-R Full Scale IQ by a regression equation derived from the Connecticut Longitudinal Study normative population (Shaywitz, Escobar, Shaywitz, Fletcher, & Makuch, 1992); or (b) placement in the lowest quartile of reading scores for this standardization population. Math disability was defined analogously to reading disability using the Woodcock-Johnson Calculation test (Woodcock, 1978). The rationale for utilizing both regression-based discrepancy criteria and low-achievement definitions is that, as demonstrated for reading and math disability, children defined by the two sets of criteria exhibit similar cognitive profiles (Fletcher et al., 1994; Fletcher et al., 1998).

Demographic and Psychoeducational Comparisons

Table I presents demographic and psychoeducational characteristics of each of the major samples. The only demographic differences involved a greater proportion of boys, [[Chi].sup.2] (1,N = 219) = 5.77, p [less than] .02, in the ADHD samples combined (ADHD/IT and ADHD/CT) compared with non-ADHD children. To address this imbalance, gender was included as a factor in statistical analyses.

Analyses of variance (ANOVAs) indicated that ADHD classifications did not differ significantly on age, Full Scale WISC-R IQ, or Woodcock-Johnson Reading and Calculation scores. The only finding for ODD was that this diagnosis was associated with slightly lower reading standard scores, F(1,213) = 4.38, p [less than] .05. There were no interactions between the ADHD and ODD classifications.

[TABULAR DATA FOR TABLE I OMITTED]

Clinical Comparisons

Table II presents clinical measures for each sample. Differences in DISC 2.3 symptom counts derive from selection criteria, so statistical analyses were not performed. Specifically, children in both ADHD subtypes received elevated inattentiveness scores, whereas only ADHD/CT youngsters had high impulsivity-hyperactivity counts. Control participants, as expected, were low on both dimensions.

Children were rated by one to three teachers, as applicable, on the Multigrade Inventory for Teachers (MIT; Agronin, Holahan, Shaywitz, & Shaywitz, 1992). Ratings for each child were averaged over his or her teachers and standardized for the appropriate grade and sex norms from the Connecticut Longitudinal Study (Shaywitz et al., 1992). As shown in Table II, these results supported the DISC 2.3 classifications. The three pairwise contrasts involving ADHD subtypes were evaluated by multivariate tests followed by univariate contrasts for each MIT scale with Bonferroni adjustment for probability levels ([[Alpha].sub.pc] = .008). Because the ADHD x ODD interaction was not significant, [F.sub.mult](12,416) = 1.23, ns, results are presented separately for ADHD and ODD.

[TABULAR DATA FOR TABLE II OMITTED]

Overall, non-ADHD children obtained scores indicative of overall lower magnitude of behavioral and academic problems than ADHD/CT children, [F.sub.mult](6,208) = 8.13, p [less than] .0001, and univariate comparisons indicated that the ADHD/CT sample exceeded the non-ADHD group for academic, activity, attention, behavior, and language problems. Similarly, non-ADHD children were rated more favorably by teachers than ADHD/IT children, [F.sub.mult](6, 208) = 5.06, p [less than] .0001, and univariate comparisons indicated that ADHD/IT children were rated as significantly higher in academic, attention, dexterity, and language problems. Interestingly, comparisons between ADHD subtypes revealed that, consistent with information provided by parents, teachers rated ADHD/CT children higher than ADHD/IT children only for activity and behavior problems, In turn, comparisons involving ODD revealed higher ratings of behavior problems for children with this diagnosis.

In summary, both subtypes of ADHD children, especially ADHD/CT, were viewed by their teachers as exhibiting a wide range of academic and behavioral problems. Not surprisingly, these elevated ratings included the language and dexterity scales, whose content reflects, respectively, expressive problems and difficulties in coordination such as poor penmanship. Most importantly, teachers confirmed the differentiation of the DISC-based ADHD subtypes in that children in both ADHD subtypes were described as inattentive but only ADHD/CT youngsters were rated as excessively hyperactive. Finally, the diagnoses of ODD were also supported by teachers' ratings of general behavioral difficulties but without significant elevation of inattentiveness, hyperactivity, or academic difficulties.

Procedure

The PAL task utilized a practice list and a test list developed by Douglas et al. (1988). All words were one-syllable, high-frequency (Thorndike & Lorge, 1944) concrete nouns selected from elementary school reading and spelling books, and paired words were semantically unrelated.

As is common in PAL research, the length of the PAL list was adjusted in an effort to compensate for estimated individual differences in memorization ability (Douglas et al., 1988; Swanson, Kinsbourne, Roberts, & Zucker, 1978). To this end, we used test lists of two different lengths (12 or 15 word pairs; the shorter list was a subset of the longer one). Because of a clerical error discovered after the completion of the research, the shorter list was actually made up of 13 pairs of words, which were presented in sets of 12 pairs per trial (5 pairs were presented four times each, and the other 8 pairs appeared five times). Assignment of test length was based on an age placement above or below 10.5 years, with the longer test being administered to older children. Because samples were matched on age, list length did not differ as a function of either ADHD, [[Chi].sup.2] (2, N = 219) = 2.89, ns, or ODD diagnosis, [[Chi].sup.2](1, N = 219) = 1.52, ns.

Participants first practiced for five trials with a list comprising eight word pairs; subsequently, the appropriate test list was administered in five thais. Each trial started with a reading of the list in a novel random order with 2 s between pairs. Next, the examiner read the first word of each pair, asked the child to recall the associated word and recorded the child's verbatim response. Participants did not receive immediate feedback on the accuracy of their responses, but had the opportunity to obtain this information from the reading of the list preceding the ensuing trial.

Scoring

Two measures of learning were scored: (a) Percentage of paired associates learned, based on a criterion of correct recall on two consecutive trials; and (b) rate of learning, defined as the linear slope of the percentage of correct responses over trials.

For a qualitative analysis of errors, we examined the content of substitution errors, that is, responses involving words that were not part of another pair. We excluded failures to respond (e.g., "don't know" and "forgot"). Because of their low frequency, we also omitted intrusions, defined as words that were identical to or very close semantic variations (e.g., "telephone" for "phone") of other words on the list.

All unique substitution errors made by participants in response to any word pair were assembled in alphabetical order and presented as an ensemble to four judges, who were blind to respondents' diagnoses. Judges independently rated each error for both acoustic and semantic similarity (0 = not similar, 4 = highly similar) to the second (response) word of each pair. The acoustic scale was further defined as follows: 0 = not acoustically similar, 1 = same consonant ending sound (e.g., fit-rat); 2 = same beginning consonant sound (e.g., box-bench), 3 = same beginning consonant and vowel sound (e.g., bug-bum) or same beginning consonant and consonant ending sound (e.g., bug-big), and 4 = rhymes (e.g., pill-hill). For instances of identity of only the vowel sound (e.g., bug-mud), a rating of 3 was recommended but not required. Acoustic and semantic similarity ratings yielded intraclass correlations of .98 and .87, respectively. For every participant (n = 173) who made at least one substitution error, ratings of each type were averaged over all of his or her responses and judges.

RESULTS

Dependent variables were submitted to weighted multivariate and univariate ANOVAs (BMDP4V; Myers & Well, 1995, p. 561) with two between-subjects factors: ADHD (non-ADHD, ADHD/IT, and ADHD/CT) and ODD. Two planned orthogonal comparisons of ADHD groups were conducted: (a) ADHD/IT versus ADHD/CT and (b) non-ADHD versus the combined ADHD samples. The proportion of variance explained for selected univariate effects is reported as [[Omega].sup.2].

Because ADHD samples differed in gender composition, statistical analyses were repeated substituting sex for ODD in the design. None of the main or interactive effects of sex approached significance, so this factor is not mentioned further.

Acquisition

Figure 1 illustrates the steady increment in learning over trials apparent for all groups combined. As shown in Figure 2, the substantial percentage of pairs learned by the sample as a whole considerably exceeded the expected value of zero, F(1, 213) = 627.38, p [less than] .0001, and the sample's rate of learning reflected a linear increment over trials, F(1, 213) = 594.36, p [less than] .0001.

There were no differences between the two ADHD subtypes for either percentage of pairs learned or rate of learning, both Fs(1, 213) [less than] 1. In contrast, as illustrated in Figures 1-3, pooling over ODD categories, non-ADHD children exceeded ADHD children on the two measures of learning, [F.sub.mult](2, 212) = 3.94, p [less than] .03. Univariate tests indicated that non-ADHD children had superior PAL to ADHD children for both acquisition, F(1, 213) = 5.90, p [less than] .02, [[Omega].sup.2] = .02; and learning rate, F(1,213) = 7.78,p [less than] .01, [[Omega].sup.2] = .03. Notably, none of the comparisons involving ODD or the interaction of ODD with ADHD classifications were significant (ps = . 12-.48).

Qualitative Analyses of Substitution Errors

Table III presents ratings of substitution errors on similarity to correct response words for the 173 participants who made at least one substitution error. These results are tabulated as a function of ADHD diagnosis, collapsing over ODD. As shown in Table III, substitution errors were rated as relatively low on the 0-4 scale of similarity to response words. Yet despite the relatively low magnitude of these ratings, substitutions were judged as more acoustically than semantically similar to response words, F(1, 167) = 105.34, p [less than] .0001. As found for measures of acquisition, children in the ADHD/IT and ADHD/CT subtypes did not differ in this respect, F(1, 167) = 2.24, ns. In contrast, the two ADHD samples combined exceeded their non-ADHD peers in the extent that their errors exhibited more acoustic than semantic similarity to correct responses, non-ADHD versus ADHD x acoustic versus semantic F(1, 167) = 8.19, p [less than] .005, [[Omega].sup.2] = .02. A multivariate comparison of non-ADHD versus. ADHD samples for the two similarity ratings disclosed significant differences, [F.sub.mult] (2, 166) = 4.64, p [less than] .02. Specifically, the substitution errors of ADHD children were rated higher on acoustic similarity, F(1, 167) = 4.90, p [less than] .03, [[Omega].sup.2] = .02, and lower on semantic similarity to the response word, F(1, 167) = 5.71, p [less than] .02, [[Omega].sup.2] = .03. Once again, there were no significant findings for ODD (ps = .57-.72).

DISCUSSION

Learning Deficits in ADHD

The sample as a whole demonstrated clear-cut paired-associate learning, as evidenced by the high degree of acquisition of paired associations and the concomitant monotonic increase of accuracy over trials. However, participants in both ADHD subtypes displayed more limited learning as well as a slower rate of learning than did children without ADHD. These effects were statistically significant, and their magnitude placed them in the range characterized by Cohen (1988) as small yet meaningful (1% to 5% of the variance).

In view of our strenuous attempts to closely reproduce the procedures used by Douglas and her colleagues (Douglas & Benezra, 1990; Douglas, Barr, O'Neill, & Britton, 1986; Douglas et al., 1988), it is particularly significant that we replicated the finding of deficient PAL in ADHD children. The repetition of this result with similar procedures by a different laboratory increases its credibility. This replication is also notable in view of Douglas and Benezra's (1990) and our use of an aural presentation of word pairs. In contrast, most other investigators (e.g., Kinsbourne, 1977) of ADHD children's PAL have used visual stimuli (e.g., pictures of animals paired with numerals). Importantly, participants are penalized more heavily for momentary inattentiveness during an aural than a visual presentation of PAL items. This discrepancy results from the fact that a visual format provides more opportunities for reviewing cues and paired responses. Thus, an auditory presentation requires immediate apprehension of the paired association as well as the prompt deployment of some mnemonic strategy such as rehearsal or an elaborative technique. In fact, Conte, Kinsbourne, Swanson, Zirk, and Samuels (1986) reported that ADHD children made less use than psychiatric controls of additional study time afforded by extended visual presentations of PAL items; this deficit was remedied by stimulant therapy (Dalby et al., 1977). In summary, our replication of this deficit with an aural format points to a core weakness by ADHD children in rote learning, possibly independent of deficits in utilization of study time.

Another aspect of our findings was the absence of differences in PAL accuracy or speed of acquisition among ADHD children as a function of hyperactivity-impulsivity, a result in agreement with most studies comparing cognitive deficits among ADHD subtypes (e.g., Dykman & Ackerman, 1992). PAL involves such aspects of executive functions as working memory and the deployment of mnemonic strategies, so our results suggest comparable deficits in these respects for ADHD children with and without hyperactivity-impulsivity. It is possible that tests tapping other aspects of executive functioning, such as inhibition or planning (Klorman, Hazel-Fernandez, et al., in press), may reflect differences in cognitive deficits between ADHD subtypes (Barkley, 1997b).

The additional finding that ODD was not associated with deficient PAL is consistent with reports of comparable cognitive processing for ADHD youngsters varying in aggression (Barkley et al., 1989; Dykman & Ackerman, 1992; Klorman et al., 1988; Klorman et al., 1994). Importantly, the present investigation extends this literature to ODD, a DSM-IV diagnosis that need not involve aggression.

Qualitative Analysis of Errors

Importantly, substitution errors were rated as low in both acoustic and semantic similarity to response words. It is likely that our open-ended format was less sensitive than Douglas and Benezra's (1990) forced-choice recognition method for assessing processes mediating PAL errors. However, Beuhring and Kee (1987) reported that 5th-, as opposed to 12th-graders, relied primarily on rehearsal rather than elaborate strategies to learn paired nouns. Although Barber et al. (1996) found much higher use of organization and elaboration techniques by children, half of the word pairs they used were semantically related, a condition promoting the use of strategies other than rehearsal. Although our study did not directly assess mnemonic techniques, our finding of minimal use of semantic and acoustic strategies is consistent with a developmental trend for underuse of elaborate mnemonic techniques. Significantly, within the limits of the present approach, ADHD children demonstrated moderate, yet statistically significant, biases for more acoustic and less semantic processing than did non-ADHD children. In view of the different methodologies across studies, the relative overreliance on acoustic strategies by our ADHD children replicates a similar report by Douglas and Benezra (1990). In both studies, ADHD children made greater use than did controls of a less advanced level of encoding of verbal information (Craik & Lockhart, 1972). In addition, our finding of lesser use of semantic strategies by our ADHD children may provide a partial explanation for their inferior PAL.

General Conclusions

The results provide continuing evidence of impaired PAL among ADHD children without learning disability and suggest that underuse of a semantic mnemonic strategy may contribute to this deficit. Future research should be designed so as to directly assess participants' mnemonic strategies (e.g., Barber et al., 1996; Douglas & Benezra, 1990). A second important finding was that inattention, regardless of the extent of hyperactivity-impulsivity, is associated with the cognitive deficits reflected in PAL. Future work needs to further delineate the degree of cognitive differences and similarities between ADHD subtypes, particularly with respect to executive functions.

ACKNOWLEDGMENTS

This research was supported by Grants HD25802 and MH47333. We are grateful to Virginia Douglas for graciously providing her PAL word lists and to Michael Tanenhaus for suggestions for rating similarity. We thank Susanne Isteero and Jeannetta Crabbe for help in data Processing.

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