Deficient attention is hard to find: applying the perceptual load model of selective attention to attention deficit hyperactivity disorder subtypes

Deficient attention is hard to find: applying theperceptual load model of selective attention to

attention deficit hyperactivity disordersubtypes

Cynthia L. Huang-Pollock,1 Joel T. Nigg2 and Thomas H. Carr21Department of Psychology, Pennsylvania State University, State College PA, USA; 2Department of Psychology,

Michigan State University, East Lansing MI, USA

Background: Whether selective attention is a primary deficit in childhood Attention Deficit Hyperac-tivity Disorder (ADHD) remains in active debate. Methods: We used the perceptual load paradigm toexamine both early and late selective attention in children with the Primarily Inattentive (ADHD-I) andCombined subtypes (ADHD-C) of ADHD. Results: No evidence emerged for selective attention deficitsin either of the subtypes, but sluggish cognitive tempo was associated with abnormal early selec-tion. Conclusions: At least some, and possibly most, children with DSM-IV ADHD have normalselective attention. Results support the move away from theories of attention dysfunction as primary inADHD-C. In ADHD-I, this was one of the first formal tests of posterior attention network dysfunction,and results did not support that theory. However, ADHD children with sluggish cognitive tempo (SCT)warrant more study for possible early selective attention deficits. Keywords: ADHD, interferencecontrol, selective attention.

Despite the syndrome name, the role of ‘attention’ asa core dysfunction in childhood Attention DeficitHyperactivity Disorder (ADHD) subtypes has beenunclear at best. For the combined subtype (ADHD-C), the target of most neuropsychological theorizing,attention deficit per se fell out of favor in the pastdecade due to negative findings in studies of selectiveattention. Subsequently, emphasis on causalmechanisms in ADHD-C shifted to impulsivity, andhence to executive control (Barkley, 1997), as well asother regulatory processes (Sergeant, Oosterlaan, &van der Meere, 1999). Nonetheless, debate overwhether selective attention may be a core dysfunc-tion in ADHD-C has continued in the literature(Brown, 1999; Douglas, 1999; Sergeant et al., 1999).

In the meantime, concerns have grown that theprimarily inattentive subtype (ADHD-I) may be anetiologically distinct group, perhaps characterized bytrue attentional impairment (Milich, Balentine, &Lynam, 2001) associated with the posterior attentionnetwork of the brain (Barkley, 1997). However, de-spite emergence of neuropsychological studies com-paring ADHD-I and ADHD-C, studies that examineselective attention in ADHD-I have been scarce(Milich et al., 2001). Furthermore, ADHD-I may notbe optimally defined in DSM-IV, with recent interestin ‘sluggish cognitive tempo,’ which is characterizedby drowsiness, lethargy, and hypoactivity, as asubgroup within that syndrome (Carlson & Mann,2002; McBurnett, Pfiffner, & Frick, 2001). Onehypothesis is that a true attention deficit may occuronly in this subgroup (Milich et al., 2001).

To best pursue these questions, we argue that it isessential to draw upon the most recent empirically

supported cognitive process models to conceptualizeand measure selective attention. It is not important,from this perspective, that a task be an analogue ofreal world situations. It is important, however, thatthe task measure the hypothesized core function. If aperformance deficit is observed, then the cognitivemodel can guide hypotheses about neural networksthat may be involved, and predictions about lifeactivities. Cognitively, selective attention can bebroadly defined as the ability to facilitate the pro-cessing of one source of environmental informationwhile attenuating the processing of others. Models ofselective attention are many, yet all arise from theidea that people have a limited capacity system toprocess information. Therefore, an act of selectionmust occur at some point, after which only some ofthe available information is processed further.

Whether selection occurs early (after relatively lit-tle processing, based on perceptible properties suchas size, shape, and color) or late (after deeper pro-cessing based on conceptual properties such asidentity, meaning, and afforded actions), has longbeen debated, with strong arguments on both sides.The debate is relevant to interpreting the literatureon ADHD. A recent ‘perceptual load’ model integratesthese ‘early’ versus ‘late’ selection perspectives andserves the field’s need for an updated approach toselective attention in ADHD subtypes.

Supporting this model are recent data indicatingthat the locus of selection (early versus late in thestream of information processing) is a function ofperceptual load in both adults (Lavie, 1995; Lavie &Tsal, 1994) and children (Huang-Pollock, Carr, &Nigg, 2002). Perceptual load is measured by the

Journal of Child Psychology and Psychiatry 46:11 (2005), pp 1211–1218 doi: 10.1111/j.1469-7610.2005.00410.x

� Association for Child Psychology and Psychiatry, 2005.Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

amount of potentially relevant information within adisplay, or by the amount of effort required to pro-cess that display. Figure 1 illustrates examples oflow and high loads in the visual domain. Supposethat a display contains task-relevant information aswell as distractors. If the perceptual load imposed bythe task-relevant information is too small to con-sume the perceiver’s available attentional capacity,then all stimuli are processed automatically, task-relevant and distractors alike, until that capacity isexhausted (Lavie, 1995). This allows most or all ofthe stimuli available to the senses to be identified,and to influence decision making and responseselection. Thus, under these ‘low-load’ circum-stances, selection occurs in the later stages ofinformation processing. When such late selection isin effect, distractors can cause marked costs toperformance if they compete, or are incompatiblewith, the required response. The Stroop Effect is oneexample of this phenomenon. Late selection requireswhat is often referred to as interference control, andlikely relies on an anterior neural network includingthe anterior cingulate gyrus and regions of the pre-frontal cortex (Barch, Braver, Sabb, & Noll, 2000;Ziegler, Besson, Jacobs, Nazir, & Carr, 1997). It isone function of the larger network involved in ex-ecutive control (Berger & Posner, 2000) and hy-pothesized to be compromised in ADHD-C (Barkley,1997).

As perceptual load increases and begins to ex-haust attentional capacity, selection shifts from lateto early, and comes under the control of processesthat respond to perceptual features. Thus, earlyselection reduces the number of stimuli that are

processed deeply (Lavie, 1995). In this situation,incompatible distractors create little or no cost toperformance, because their processing will be cut offbefore their meaning and response potential havebeen computed. The mechanisms that implementearly selection likely involve a posterior neural net-work including structures in the parietal and tem-poroparietal cortices (Arrington, Meyer, Carr, & Rao,2000; Posner & Petersen, 1990), as well as sub-cortical structures including the thalamus and su-perior colliculus. This is the network hypothesized tobe involved in ADHD-I (Barkley, 1997).

According to the perceptual load framework, the‘selection’ in ‘selective attention’ occurs along acontinuum from early to late during informationprocessing. This framework contrasts with earlierconceptualizations of selective attention in which‘early’ and ‘late’ selection were seen as dichotomousprocesses. Instead, from the perceptual load per-spective, the degree of interference experienced inthe presence of an incompatible distractor provides asingle direct measure of where along the early-to-latecontinuum selection is occurring at that point intime. The recent emergence of a model of a tradingrelation between early versus late selection, and theassociated method of measuring these effects, offeran opportunity to more definitively evaluate thestatus of both early and late selective attention inADHD-C and ADHD-I than before.

From the perspective of this model, most priorstudies did not include an incompatible distractor toprobe late selective attention (Sharma, Halperin,Newcorn, & Wolf, 1991; Sergeant & Scholten, 1983,1985; Tarnowski, Prinz, & Nay, 1986; van der Meere& Sergeant, 1987). Without incompatible dis-tractors, there is little demand placed upon the in-terference control system, making it difficult tomeasure selection failures. Other studies failed toprovide sufficient or variable perceptual load condi-tions to assess early selective attention (van derMeere & Sergeant, 1988; de Sonneville, Njiokiktjien,& Hilhorst, 1991; Barkley, Anastopoulos, Guevre-mont, & Fletcher, 1991). In this case, neither groupwould employ early selection because the perceptualload is insufficient to induce it.

Summary and hypotheses

Recently improved understanding of themechanismsof selective attention has created an opportunity tobetter evaluate early versus late selective attention inADHD. This measurement advance arrives at a timeof controversy over the relative role of attention inADHD-C and ADHD-I in DSM-IV. Consistent withtheories of executive dysfunction in ADHD-C,Hypothesis 1 was that children with ADHD-C wouldhave developmentally weaker late selective attentionprocesses than their same-aged peers. Hypothesis 2

was that ADHD-I would have developmentallyweaker early selective attention processes than their

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Figure 1 Example stimulus displays. Participantsindicate whether an ‘X’ or an ‘N’ (the target) is presentanywhere in the circle of small letters. The larger letterlocated outside the circle in the periphery representsthe competing distractor. (a–b): Manipulations of loadby changing display sizes, (c–d): Manipulations of loadby changing processing demands

1212 Cynthia L. Huang-Pollock, Joel T. Nigg, and Thomas H. Carr

same-aged peers. As a secondary analysis, weexamined performance of a subset of children with‘sluggish cognitive tempo,’ perhaps the most likelygroup to show deficient selective attention.

Method

Participants

Participants were 79 children aged 8–12 diagnosed asADHD-I (n ¼ 16), ADHD-C (n ¼ 28), and control (n ¼35). Ethnicity was similar to the geographic region: 71%Caucasian, 13% African American, 5% Hispanic, 9%Other/Mixed, with 2% Undisclosed. In a standardmultistage screening process, children were recruitedby advertisements to all parents in the school district.At initial screening they were required to exceed the81st percentile on both parent and teacher versions ofat least one of the following scales: BASC attention,hyperactive, or conduct problem scales (Reynolds &Kamphaus, 1992), the ADHD rating scale (DuPaul,Power, Anastopoulos, & Reid, 1998), or the Conners’(1997) ADHD index. Cutoffs were chosen based onpredictive validity data in the cited studies. A struc-tured diagnostic interview, the DISC-IV (Shaffer, Fisher,& Lucas, 1997), was then completed with the primarycaregiver. An ‘or’ algorithm was employed, based onDSM-IV field trial data (Lahey et al., 1994), to integrateparent and teacher data in assigning diagnosis. Thus, ifchildren met age of onset, duration, impairment, andcross-situational criteria on the DISC-IV, a symptomwas counted as present if either the parent or theteacher rated it as present. Children with 5 symptomsof hyperimpulsivity or inattention were excluded be-cause subtypes cannot be accurately determined inthose cases (Lahey et al., 1994).

In addition, parents and teachers rated children on a0–3 scale for three ‘sluggish cognitive tempo’ (SCT) itemstaken from reports in the literature (McBurnett et al.,2001; Carlson & Mann, 2002): (1) ‘stares into space/daydreams,’ (2) ‘low in energy, sluggish, or drowsy,’ and(3) ‘apathetic or unmotivated to engage in goal-directedactivities.’ ‘SCT’ was present in 44% of ADHD-I (n ¼ 7),18% of ADHD-C (n ¼ 5), and no controls, defined asparent and teacher ratings at least one standard devi-ation above the total sample’s mean. This cutoff waschosen to identify those children with relatively seriousbehavioral symptoms of SCT, while still identifyingenough children to enable statistical analyses.

Comorbid Oppositional Defiant, Conduct, Anxiety,and mood disorders were defined by the DISC-IV.Reading ability was assessed with the WIAT Screener(Wechsler, 1992). IQ-achievement discrepancy deter-mined through predicted-achievement regressionmethods, combined with an absolute reading standardscore <85, defined reading disability. Removal of chil-dren with reading disability, ODD, or CD did not affectresults, so all children are included in the results re-ported. Results also did not vary by sex; boys and girlsare included in all analyses.

Children included in the control group were negativefor all subtypes of ADHD on the DISC-IV, had four orfewer symptoms of either inattention or hyperactivity/impulsivity by the ‘or’ algorithm, were below cutoffs

(£80th percentile) on all parent and teacher ratingscales, had never been previously diagnosed with ortreated for ADHD, and were free of conduct or readingdisorder.

Children were excluded if they had a primary senso-rimotor handicap, frank neurological disorder, PDD,psychosis, Tourette’s, Full Scale IQ below 75 by a WISC-III short form, or were prescribed non-stimulant long-acting psychoactive medications (anti-depressants). Toensure valid interpretation of data, children wereexcluded if they were unable to maintain a 60% accu-racy rate on the primary task (Huang-Pollock et al.,2002), which required that we retain slightly olderchildren in the sample because the task was difficult foryounger children.1 Children with ADHD who wereexcluded versus retained on the basis of <60% accuracyon the task did not differ in IQ (p ¼ .9), level of inatten-tion or hyperactivity on normative scales, or number ofinattentive or hyperactive symptoms (all p > .21). Thus,retained children are representative of the larger ADHDsamples. Children taking psychostimulant medicationcompleted the experimental task after a washout periodaveraging 99 hours for the shorter-acting preparations(13 children, range ¼ 28–350 hours) and 100 hours forthe longer-acting preparations (16 children, range ¼24–350 hours). The short half-life of stimulants(Pelham, 1993) suggests that any medication effects onperformance thus were likely to be minimal.

Selective attention paradigm

Methods were based on Maylor and Lavie (1998) andidentical to Huang-Pollock et al. (2002). The 20-minutecomputerized experiment presented light gray upper-case letters on a black background arranged as shownin Figure 1a and 1c. During each trial, a fixation pointappeared for 1000 ms, after which the letter displaywas presented for 200 ms (rapid enough in this agegroup to prevent eye movements; Paus, Babenko, &Radil, 1990). A target letter (‘X’ or ‘N’) randomly andequiprobably appeared at one of six positions in thedisplay. Children were instructed to identify the targetas quickly as possible with a key-press.

The target letter appeared alone, or grouped with one,three, or five non-target letters (Z, K, Y, V, or H) thatrandomly appeared in different positions around thecircle. A larger distractor letter (T, L, X, or N) appearedeither to the left or to the right of the letter display. Thelarger size of the distractor was to compensate for itsgreater distance from the fixation point. Distractors hadan equal probability of being either incompatible orneutral. An incompatible distractor was X or N in theperiphery. Because this distractor was never the sameas the target letter, it would activate an incorrect re-sponse if identified. A neutral distractor was T or L inthe periphery, which would not activate a response evenif identified. Children were told that the target letterwould always appear in the central circle, and that theperipheral letter should always be ignored. A computertone provided feedback for incorrect responses (e.g.,the child responds ‘X’ when an ‘N’ is required, referred

1 Ten participants who met the 60% total accuracy cutoff per-

formed below chance on 1 or 2 blocks. Results did not change

when these 10 children were removed from analysis.

Selective attention in ADHD 1213

to as ‘errors’). There was no tone on correct trials. Aone-second inter-trial interval followed every response,after which the next trial was automatically initiated.If there was no response after 3000 ms, the trial wascoded as an ‘omission,’ and the next trial began auto-matically following the inter-trial interval.

Task-relevant stimuli were presented to foveal vision.The target and non-target letters subtended a visualangle of .6� vertically and .4� horizontally. The imagi-nary circle on which the target and non-target letterswere placed had a radius of 2.1� from a central fixationpoint. The peripheral distractor subtended a visualangle of .9� vertically and .5� horizontally, and appearedto the right or left of the circle of letters at a distance of4.3� from fixation.

Children completed 3 blocks of 6 practice trials (notanalyzed) prior to the experimental trials, with theinstructions repeated or re-explained as necessary.Once the task began, experimenters refrained fromconversation with the child. Five blocks of 96 trials werepresented (with optional rest periods in between). Eachblock presented a new randomization of trials.

Power. Meta-analytic ADHD vs. control effect sizesrange from f � .33 or larger when an ADHD task deficitis observed (Pennington & Ozonoff, 1996). Huang-Pol-lock et al. (2002) found distractor effects of f ¼ 1.02, setsize effects of f ¼ .80, and distractor · set size interac-tion effects of f ¼ .45. Power for simple effects as well asall two- and three-way interactions exceeded .90 atthese effect sizes, and exceeded .80 for the key within-group interference effects (i.e., distractor type and setsize). Power for between-groups analyses was .80 todetect effect sizes of f ¼ .35 for between-group simpleand main effects for two (ADHD-C or ADHD-I vs. con-trol) as well as three groups.

Data reduction and analysis. Dependent variableswere total errors and the reaction time (RT) to key-pressfor correct trials. Responses faster than 100 ms wereremoved from analysis (‘anticipations’). Anticipationsand omissions were present on <2% of trials. Resultsdid not change when all RTs were included in the ana-lysis, regardless of how fast the children responded. Theonly significant sex difference was a two-way interac-tion of sex · set size, in which boys showed greater in-creases in RT with set size than did girls. Neither thisnor any other effect of sex was significantly related tothe manipulations of group or distractor type that weretheoretically important to the present investigation.Therefore, analyses were collapsed across sex for thepurposes of drawing conclusions about similarities anddifferences between the two ADHD groups and thecontrol group. All results were unchanged with age, IQ,reading ability, or a composite comorbid oppositional-conduct symptom score covaried. We therefore reportsimple model results with no covariates.

Results

Overview

Table 1 describes the diagnostic groups. Groups didnot differ in age or reading level (all p > .10), but

controls had higher IQ than either ADHD subtype(p ¼ .05). The pattern of behavioral ratings wasconsistent with diagnostic assignments, and indic-ated substantial symptom levels in the ADHDgroups.

Reassuringly, there were no significant correla-tions between number of errors and RT at any setsize (all p > .12, r’s ¼ ).18 to .07). We also calculatedthe 95%CI for mean RT of both errors and hits todetermine whether RTs to errors and hits werecomparable. For all eight conditions, there was highdegree of overlap between ranges, with RTs to hitscompletely within the range of RTs to errors. Thus,we concluded that any strategy or speed–accuracytradeoff effects were fairly small (Cohen, 1988), andwere unlikely to substantially affect the data.

RT and error rate data are presented in Table 2.Figure 2 illustrates the interference effects for RTacross set sizes for all groups, but we discuss resultsin relation to pair-wise two-group comparisonsrelated to our specific hypotheses. Interference ef-fects were calculated as the RT difference betweenincompatible and neutral distractors at each set size.Groups did not differ in overall mean RT, RT vari-ability, or total number of errors/omissions (allp > .46). There were also no group differences acrosssuccessive blocks of trials for RT, RT variability, ornumber of errors/omissions (all p > .46). Thus,children exhibited similar levels of effort and taskengagement across groups.

Control group performance

For the control group, task effects replicated previ-ous findings (Huang-Pollock et al., 2002). As expec-ted, performance was worse for incompatible thanfor neutral distractors in terms of slower RT,F(1,34) ¼ 27.05, g2 ¼ .44, p < .001, and more errors,F(1,34) ¼ 29.70, g2 ¼ .47, p < .001. Also as ex-pected, as set size increased, RT slowed, F(3,102) ¼50.78, g2 ¼ .60, p < .001, and errors increased,F(3,102) ¼ 134.53, g2 ¼ .80, p < .001, due to thelarger number of items to be scanned.

Consistent with the perceptual load model, signi-ficant linear trends of decreasing interference withincrease set size were found for RT, F(1,34) ¼ 33.18,g2 ¼ .49, p < .001, and error rate, F(1,34) ¼ 8.48,g2 ¼ .20, p ¼ .006. The decrease in interference firstreached significance between set sizes 1 and 2 forRT, F(1,34) ¼ 4.38, p ¼ .04, and between set sizes 2and 4 for errors, F(1,34) ¼ 6.73, p ¼ .01. Overall,control children demonstrated the expected effects ofthe selective attention task.

Hypothesis 1: Late Selection in ADHD-C

In the ADHD-C group, main effects replicated thoseof controls. RTs were slower, F(1,27) ¼ 12.97, g2 ¼.32, p ¼ .001, and error rates were higher, F(1,27) ¼23.17, g2 ¼ .46, p < .001, for incompatible than for


neutral distractors. Likewise, as set size increased,RT slowed, F(3,81) ¼ 42.24, g2 ¼ .62, p < .001, anderrors increased, F(3,81) ¼ 61.21, g2 ¼ .69,p < .001.

Consistent with normal early selective attentionprocesses, linear trends of decreasing interferenceemerged with increasing set size for RT, F(1,27) ¼7.39, g2 ¼ .21, p ¼ .01, and error rate, F(1,27) ¼11.98, g2 ¼ .31, p ¼ .002. The decrease in interfer-ence with respect to RT was observed between set

sizes 2 and 6 for RT, F(1,27) ¼ 8.72, p ¼ .006, andbetween set sizes 1 and 4 for errors, F(1,27) ¼ 5.85,p ¼ .02. Thus, the onset of early selection, as mani-fested by a reduction in interference as set size in-creased, was normal in ADHD-C, as expected.Neither quantitative nor qualitative properties of theADHD-C group’s shift to early selection withincreasing set size distinguished them in any wayfrom the control group.

With ADHD-C, late selective attention deficits werehypothesized (greater interference at small set sizes).However, no group differences in performance acrossset size between ADHD-C and controls occurred forRT, F(3,183) ¼ .28, g2 ¼ .005, p ¼ .84, or errors,F(3,183) ¼ 1.57, g2 ¼ .02, p ¼ .20. Likewise, simplegroup effects at set sizes 1 and 2 were non-signific-ant for RT and errors (all p > .05). In all, there was noevidence of an early or late selection deficit forADHD-C.

Hypothesis 2. Early Selection in ADHD-I

In the ADHD-I group, main effects of the taskmanipulations were as expected. Following incom-

Table 1 Description of groups after removal of children with high errors

Control (n ¼ 35) ADHD-I (n ¼ 16) ADHD-C (n ¼ 28)

# attention symptoms .94 (1.31) 7.94 (1.00) 8.36 (.87)# hyper/impulsive symptoms .83 (1.12) 2.47 (2.07) 7.89 (1.16)Parent ratings (T-scores)BASC hyperactivity 41.46 (6.85) 49.19 (11.29) 70.61 (15.48)BASC attention 43.66 (7.22) 71.69 (6.84) 67.64 (7.14)Conners’ ADHD 46.59 (5.11) 71.25 (7.90) 71.56 (9.09)Teacher ratings (T-scores)BASC hyperactivity 43.89 (4.00) 54.93 (11.15) 62.24 (9.50)BASC attention 43.26 (6.31) 65.38 (5.82) 61.88 (7.82)Conners’ ADHD 44.51 (3.42) 66.86 (9.12) 66.17 (8.71)Average parent and teacher SCT score .75 (1.01) 4.19 (1.82) 2.75 (1.64)Full scale IQ 114.00 (16.16) 103.80 (17.26) 104.82 (16.34)WIAT reading scaled score 108.88 (12.37) 101.50 (12.51) 102.30 (15.74)Age in months 118.67 (13.89) 121.37 (15.87) 118.27 (9.77)Boys:Girls 20:15 8:8 23:5ComorbiditiesODD/CD 3 4 16RD 0 0 1

Note. Standard deviations in parentheses. BASC ¼ Behavior Assessment Scale for Children, WIAT ¼ Wechsler Intelligence Scale forChildren, SCT ¼ sluggish cognitive tempo, ODD/CD ¼ Oppositional Defiant Disorder/Conduct Disorder, RD ¼ Reading Disability.

Table 2 Mean reaction time in ms (SD) to correct target identification and percentage errors (SD)

Distractor Set Size

Control (n ¼ 35) ADHD-I (n ¼ 16) ADHD-C (n ¼ 28)

Mean RT (SD) % Errors (SD) Mean RT (SD) % Errors (SD) Mean RT (SD) % Errors (SD)

Incompatible 1 826.95 (221.52) 15.71 (10.25) 852.03 (278.38) 22.02 (10.24) 794.43 (215.20) 18.13 (9.41)Neutral 1 742.62 (185.30) 10.19 (6.96) 768.84 (263.26) 10.44 (7.23) 731.97 (170.38) 11.03 (6.27)Incompatible 2 902.53 (228.62) 22.52 (9.70) 961.14 (321.53) 22.86 (8.68) 875.02 (213.22) 19.92 (9.29)Neutral 2 852.09 (225.37) 13.33 (7.90) 880.32 (307.97) 13.26 (7.38) 819.94 (194.24) 14.79 (8.90)Incompatible 4 949.53 (253.40) 28.76 (11.48) 954.58 (288.25) 32.46 (9.62) 943.23 (188.45) 26.24 (9.32)Neutral 4 923.31 (247.20) 24.00 (10.99) 920.45 (308.23) 24.95 (9.80) 923.03 (208.86) 23.56 (9.72)Incompatible 6 954.36 (263.51) 34.52 (10.48) 963.80 (311.76) 36.53 (9.04) 942.61 (187.83) 32.21 (10.71)Neutral 6 956.48 (244.78) 34.33 (8.53) 928.20 (291.23) 37.37 (6.27) 951.30 (199.04) 32.33 (8.85)

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patible versus neutral distractors, RT slowed,F(1,15) ¼ 31.48, g2 ¼ .68, p < .001, and errors in-creased, F(1,15) ¼ 29.35, g2 ¼ .66, p < .001. Also asexpected, as set size increased, RT slowed, F(3,45) ¼13.73, g2 ¼ .48, p < .001, and errors increased,F(3,45) ¼ 56.87, g2 ¼ .79, p < .001.

With ADHD-I, deficits in the early selection pro-cess were hypothesized (greater interference at largeset sizes). However, as with the other groups andcontrary to the hypothesis, a linear trends ofdecreasing interference with increasing set size wasagain observed for RT, F(1,15) ¼ 14.74, g2 ¼ .49,p ¼ .002 and error rate, F(1,15) ¼ 16.98, g2 ¼ .53,p ¼ .001. The decrease in interference occurredbetween set sizes 2 and 6 for RT, F(1,15) ¼ 3.31, p ¼.09, which was just shy of significant, and betweenset sizes 2 and 6 for errors, F(1,15) ¼ 14.36, p ¼.002.

Also failing to support the hypothesis of an earlyselection deficit, when ADHD-I children were com-pared to controls in a two-group analysis, the relevantthree-way interactions were not significant for RT(p ¼ .63) or errors (p ¼ .15), implying no groupdifferences in performance between ADHD-I andcontrols. Simple group effects at set sizes 4 and6werealso not significant for RT or errors (all p > .17). In all,there was no compelling body of evidence for either anearly or late selective attention deficit in ADHD-I.

SCT analyses

High and low SCT groups did not differ in age, IQ,reading achievement, or the proportion of boys (allp > .35). On the perceptual load task there were nogroup differences among the children with ADHD-Iwith (n ¼ 7) or without (n ¼ 9) SCT in the three-waygroup · set size · distractor type interaction for RT(p ¼ .20) or error rate (p ¼ .42). The performance ofthe seven ADHD-I children with SCT was also com-pared against seven age- and sex-matched controlchildren, which would theoretically be the mostpowerful test of the SCT hypothesis. There againwere no group differences for RT interference (p ¼.39) or error rate interference (p ¼ .44). However,power was low for these three-way interactions.

To gain more power, children with (n ¼ 12) vs.without SCT (n ¼ 66) were compared (regardless ofdiagnosis). Children without SCT demonstratednormal attention, with a linear trend of decreasinginterference with increasing set size for both RT,F(1,65) ¼ 36.64, g2 ¼ .36, p < .001 and errors,F(1,65) ¼ 22.94, g2 ¼ .26, p < .001. However,whereas children with SCT demonstrated adecreasing linear trend for errors, F(1,11) ¼ 14.00,g2 ¼ .56, p ¼ .003, they did not show this sametrend for RT, F(1,11) ¼ .36, g2 ¼ .03, p ¼ .56. Thissuggests that at least for RT, the children with SCTdid not have normal early selective attention pro-cesses. With respect to simple group effects, at a setsize of 6, children with SCT exhibited more RT

interference than children without SCT (p ¼ .03).The three-way SCT by distractor type by set sizeinteraction was just shy of significant (p ¼ .07). SCTscores were not significantly correlated with RT orerror interference at either set size 1 or 6 (all p > .19).Figure 3 illustrates the reaction time interferenceeffects over set size for the high and low SCT groups.

Discussion

Despite movement away from selective attention intheories of ADHD, questions have lingered forADHD-C (Brown, 1999; Douglas, 1999) and theor-ists have posited posterior attention system mal-function in ADHD-I (Hynd et al., 1991; Milich et al.,2001). The present study was the first to apply aload-dependent attention paradigm to formallyisolate early (posterior, perceptual) versus late(anterior, interference control) selection processes inDSM-IV ADHD subtypes.

Overall, the results were consistent with normalearly and late selective attention in ADHD-C. Ourfinding for normal early selective attention, using anewer and more analytic task paradigm, supportsthe field’s movement away from theories that includesuch deficits in ADHD-C. This is important, becauseprevious studies generally did not control for per-ceptual load, which is necessary to induce earlyselection. In contrast to the fairly strong evidence ofADHD-C deficits in suppressing or canceling a pre-potent motor response (e.g., go–no-go or stoppingtasks; Oosterlaan, Logan, & Sergeant, 1998; Nigg,2001), evidence to support late selective attentiondeficits in children with ADHD-C, resting primarilyon data from the Stroop task, has been weak (Nigg,2001; van Mourik, Oosterlaan, & Sergeant, 2004).Our results are likewise consistent with normal lateselection in ADHD-C. However, recent data examin-ing ADHD-C on a low-load version of the Eriksenflanker task did find ADHD-C deficits in performance(Jonkman et al., 1999). Comparing performance inthe perceptual load paradigm to performance in theEriksen flanker task may be beneficial in clarifyingthe remaining discrepancies in findings.

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RT

Int

erfe

renc

e (m

s)

Low SCT

High SCT

Figure 3 Reaction time interference effects for high andlow SCT. Standard error bars displayed


In the case of ADHD-I, not only were late selectiveattention processes normal, but there was also littlesupport for a posterior-system attentional dysfunc-tion. The most convincing evidence for this deficitwould have been a significant three-way interactionin the two-group comparison of ADHD-I with normalcontrols, but this was not observed despite con-siderable power to find it. Admittedly, the absolutemagnitude of change in interference from set sizes4–6 (1 ms) was only a small fraction of the changeseen in the control children and the children withADHD-C (28–29 ms). Yet, children with ADHD-Idid demonstrate a significant linear trend of areduction in interference with increasing set size forRT and error rates. Overall, the weight of evidencewas against an early selective attention deficit inADHD-I.

These negative results may not surprise someobservers, due to concern that the DSM-IV ADHD-Isubtype is flawed by omission of SCT symptoms, andmay too often simply capture subthreshold ADHD-C.SCT appears to identify a meaningful subgroup ofchildren with ADHD-I (McBurnett et al., 2001), andat least behaviorally, such children may be the bestcandidates for an attentional deficit. However, iden-tifying the cognitive processes underlying SCT hasbeen elusive. When compared against other ADHD-Ichildren, children with ADHD-I and SCT do not differin the degree of teacher-reported academic problems(Carlson & Mann, 2002), nor do they show qualita-tively different patterns of performance on a batteryof neuropsychological tests (Hinshaw, Carte, Sami,Treuting, & Zupan, 2002). In the current study,power was low for checking on an SCT hypothesis.Post-hoc simple group comparisons found possibleproblems in early selective attention, and althoughintriguing, these small-sample secondary analysesmust be viewed as exploratory. Future work on thisparadigm would do well to examine a larger SCTsubgroup rather than ADHD-I as defined in DSM-IV.

Before concluding, we make one methodologicalnote. Although our effort to evaluate task compliancewas a definitive strength of the study, one limitationon conclusions, especially for null findings, is thatyounger children with ADHD were unable to appro-priately engage the high-speed task and complete itsuccessfully. It is possible that younger childrenwith ADHD do demonstrate cognitively-defined se-lective attention deficits that remediate over time,such that by age 9–10, this process functions withinnormal limits. However, it seems safe to concludethat at least by age 9–10, there exist a substantialproportion of children who meet DSM-IV criteria forADHD who have normal processes of selectiveattention. Future work includes adjusting the task toallow for a longer stimulus presentation rate to ren-der the task more accessible to younger children.However, doing so would necessitate monitoring ofeye movements that could occur at slower presen-tation rates.

In summary, the load-dependent model of select-ive attention was able to clarify the status of early aswell as late selective attention processes in childrenwith ADHD-C and ADHD-I. The results failed tosupport the theory that ADHD subtypes could bedifferentiated from each other or from non-ADHDcontrols on the basis of deficits in selective attention,or that ADHD-I may be represented by neuropsy-chological deficits in the posterior attention system,despite the added power of the perceptual loadparadigm to find and document such effects.

Acknowledgements

This work was supported by NIMH fellowshipMH12333 to Huang-Pollock, and NIMH grantMH59105 to Nigg. We thank the Lansing SchoolDistrict Office of Evaluation Services.

Correspondence to

Cynthia L. Huang-Pollock, 545 Moore Building,Pennsylvania State University, University Park, PA16802, USA; Tel: 814-865-8498; Fax: 814-863-7002; Email: [email protected]

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Manuscript accepted 23 August 2004


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Deficient attention is hard to find: applying the perceptual load model of selective attention to attention deficit hyperactivity disorder subtypes