A Componential Analysis of Visual Attention in Children With ADHD

Abstract

Objective: Inattentive behaviour is a defining characteristic of ADHD. Researchers have wondered about the nature of the attentional deficit underlying these symptoms. The primary purpose of the current study was to examine this attentional deficit using a novel paradigm based upon the Theory of Visual Attention (TVA). Method: The TVA paradigm enabled a componential analysis of visual attention through the use of a mathematical model to estimate parameters relating to attentional selectivity and capacity. Children’s ability to sustain attention was also assessed using the Sustained Attention to Response Task. The sample included a comparison between 25 children with ADHD and 25 control children aged 9-13. Results: Children with ADHD had significantly impaired sustained attention and visual processing speed but intact attentional selectivity, perceptual threshold and visual short-term memory capacity. Conclusion:The results of this study lend support to the notion of differential impairment of attentional functions in children with ADHD.

Keywords

children with ADHD attentional selectivity and capacity sustained attention theory of visual attention (TVA)

Children with ADHD display inattentive behavior in their daily lives. They tend to be highly distractible, disorganized, and have difficulty sustaining attention to task (American Psychiatric Association, 2000). Researchers have wondered about the nature of the attentional deficit underlying these inattentive symptoms (Bellgrove, Robertson, & Gill, 2007; Douglas, 1972; Nigg, 2005a; Swanson et al., 2000; Wilding, 2005). The attention system of the human brain has been described as a multidimensional system involving functionally independent but interacting subsystems, each of which relies on discrete neural circuits (Posner & Fan, 2008; Posner & Petersen, 1990). Several typologies of attentional ability have been proposed (Cohen, 1993; Cornish & Wilding, 2010; Parasuraman, 2000; Posner & Boies, 1971; Posner & Dehaene, 1994; Posner & Fan, 2008; Posner & Petersen, 1990; Robertson, Ward, Ridgeway, & Nimmo-Smith, 1996; Sturm, 1996; Sturm, Willmes, Orgass, & Hartje, 1997; vanZomeren & Brouwer, 1994). While these taxonomies differ in detail, three elements have been commonly espoused. These are sustained attention, referring to the ability to self-sustain mindful, conscious processing of stimuli whose repetitive, nonarousing qualities would otherwise lead to habituation and distraction to other stimuli (Robertson, Manly, Andrade, Baddeley, & Yiend, 1997); selective attention, referring to the ability to focus processing on relevant features and objects while ignoring irrelevant features and objects (Bundesen, 1990; Bundesen & Habekost, 2008); and divided attention, referring to the ability to share or divide attention among multiple sources of information (Sturm, 1996).

Previous research has revealed a pattern of differential impairment of attentional functions in children with ADHD. Studies investigating sustained attention in children with ADHD have rather consistently shown that these children have frequent lapses of attention during continuous performance tasks (Johnson, Kelly, et al., 2007; Losier, McGrath, & Klein, 1996). In contrast, studies investigating selective attention have shown children with ADHD to have intact selective attention on a variety of tasks, including visual search (Hazell et al., 1999; Mason, Humphreys, & Kent, 2003, 2004), visuospatial orienting (Adolfsdottir, Sorensen, & Lundervold, 2008; Huang-Pollock & Nigg, 2003), perceptual load (Huang-Pollock, Nigg, & Carr, 2005), and perceptual discrimination-distractibility tasks (Friedman-Hill et al., 2010). Studies investigating both sustained and selective attention in children with ADHD have also suggested that the former ability is impaired with the latter remaining intact in these children (Heaton et al., 2001; Hooks, Milich, & Pugzles Lorch, 1994; Huang-Pollock, Nigg, & Halperin, 2006; Manly et al., 2001). Findings relating to divided attention are more equivocal. For example, children with ADHD were found to show greater switch and dual task costs during task switching and dual task paradigms in two studies (Cepeda, Cepeda, & Kramer, 2000; Fuggetta, 2006) but not in a more recent study (Inasaridze & Bzhalava, 2010).

The primary purpose of the current study was to examine the attentional deficit experienced by children with ADHD using a novel paradigm, not previously applied to an ADHD population. This paradigm was based on the theory of visual attention (TVA; Bundesen, 1990), which is a computational theory that enables a componential analysis of attentional ability.

The TVA was first proposed by Bundesen (1990) as a computational theory that provided quantitative accounts for a wide range of attentional effects reported in the psychological literature. Two equations lay at the heart of TVA. In 2005, Bundesen, Habekost, and Kyllingsbæk (2005) presented the neural TVA (NTVA), which provided a neurophysiological interpretation of the original equations. In NTVA, one of the two basic mechanisms of selective attention, filtering (selection of objects), changes the number of cortical neurons in which an object is represented so that this number increases with the behavioral importance of the object. Another mechanism of selection, pigeonholing (selection of features), scales the level of activation in neurons coding for a particular feature. By these mechanisms, behaviorally important objects and features are likely to win the biased competition (Desimone & Duncan, 1995) to become encoded into visual short-term memory (VSTM). The VSTM system is conceived as a feedback mechanism that sustains activity in the neurons that have won the attentional competition. NTVA accounts both for a wide range of attentional effects in human performance (error rates and reaction times) and a wide range of effects observed in single cells (firing rates) in the primate visual system (for an overview, see Bundesen & Habekost, 2008; Kyllingsbæk, 2006).

When it comes to the assessment of attentional ability, the TVA paradigm enables a componential analysis of attentional ability. Participants are asked to perform a verbal report task, which is a mixture of a whole and partial report task (involving the presentation of target and distractor letters). Computational models based on the TVA are applied to each participant’s raw data to estimate the components of visual attention that are thought to underlie performance on the task. These are attentional selectivity, the ability to focus on target letters and ignore distractors, and aspects of attentional capacity, namely, span of VSTM, visual processing speed, and perceptual threshold. A recent development in TVA modeling (Dyrholm, Kyllingsbæk, Espeseth, & Bundesen, 2011) has enabled the estimation of these attentional parameters while accounting for “lapses” or trials during which the child was off-task. A detailed explication of these computational procedures is beyond the scope of this article, but these procedures have been well documented in the following sources (Bundesen, 1990; Bundesen & Habekost, 2008; Dyrholm et al., 2011; Kyllingsbæk, 2006). TVA-based paradigms have been used to assess attentional ability in a wide array of clinical populations, including patients with visual neglect (Bublak et al., 2005; Duncan et al., 1999; Finke et al., 2005), simultanagnosia (Duncan et al., 2003), integrative agnosia (Gerlach, Marstrand, Habekost, & Gade, 2005), developmental dyslexia (Dubois et al., 2010), alexia (Habekost & Starrfelt, 2006; Starrfelt, Habekost, & Leff, 2009; Starrfelt, Habekost, & Gerlach, 2010), Huntington’s disease (Finke et al., 2007), Alzheimer’s disease (Bublak, Redel, & Finke, 2006; Bublak et al., 2011; Redel et al., 2012), and stroke (Habekost & Bundesen, 2003; Habekost & Rostrup, 2006, 2007; Peers et al., 2005; see Habekost & Starrfelt, 2009, for a review). The TVA paradigm has not thus far been applied in an ADHD population.

While the TVA paradigm provides estimates of attentional selectivity and aspects of attentional capacity, it does not provide an estimate of sustained attention (see McAvinue et al., 2012), which is a core component of attentional ability and deficits of which are considered to be a hallmark feature of ADHD. Children’s ability to sustain attention was, therefore, also assessed using a continuous performance test known as the Sustained Attention to Response Task (SART; Robertson et al., 1997). As a simple repetitive, go/no-go task, the SART is designed to be sensitive to transitory lapses of attention during a 5-min period of sustained attention. Activation of the posited right hemisphere frontoparietal sustained attention system has been observed during performance of the SART (Manly et al., 2003). It has also been found to be predictive of real-life attentional failures (Smilek, Carriere, & Cheyne, 2010), and sensitive to sleep deprivation (Fronczek, Middelkoop, van Dijk, & Lammers, 2006), normal circadian rhythms (Manly, Lewis, Robertson, Watson, & Datta, 2002), and sustained attention deficits in clinical conditions such as traumatic brain injury (McAvinue, O’Keeffe, McMackin, & Robertson, 2005). A number of studies have already found that children with ADHD display sustained attention deficits on this task (Johnson, Kelly, et al, 2007; Johnson, Robertson, et al., 2007; Johnson et al., 2008; O’Connell, Bellgrove, Dockree, & Robertson, 2004). Finally, in an attempt to investigate children’s self-perception of alertness, children were asked to indicate their level of alertness on a Visual Analogue Scale several times during the testing session.

Method

Participants

A total of 25 children with ADHD and 25 children without ADHD, aged from 9 to 13 years, M = 10.86, SD = 1.28, participated in this study. Children with ADHD were recruited through referral from psychiatrists and advertisements in local newspapers, child and adolescent clinics, and ADHD support groups. Children without ADHD were recruited through local schools. This study was granted ethical approval by the Department of Psychology Ethics Committee and three Hospital Ethics Committees. Parents of children provided informed consent and children gave informed assent prior to participation. In all, 21 children were diagnosed with ADHD–Combined Type and four with ADHD–Predominantly Inattentive Type. Children’s diagnoses were confirmed using the Parental Accounts of Childhood Symptoms Interview (Chen & Taylor, 2006). Parents of three of the children were unavailable to participate in this interview. A total of 15 children had additional diagnoses, which included dyslexia, dyspraxia, dysgraphia, specific reading disorder, mild and borderline general learning disability (GLD), oppositional defiant disorder (ODD), and pervasive developmental disorder–not otherwise specified (PDDNOS). Only one child in the control group had been given a diagnosis, which was of dyspraxia. In all, 21 children were currently on medication for ADHD. Children in each group were matched in terms of gender and age, resulting in an equal number of girls (n = 3) and boys (n = 22) in each group and no significant difference between the two groups in terms of age, t(48) = 0.33, p = .74. Children with ADHD had significantly higher ratings on the following Conners 3 subscales: ADHD Index (AI), t(48) = 11.15, p < .001; Inattentive (AN), t(32.4) = 9.71, p < .001; Hyperactive-Impulsive (AH), t(38.96) = 12.23, p < .001.

IQ and Academic Achievement

Children with ADHD had significantly lower scores on all subtests of IQ and academic achievement. (Please see the “Materials” section for a description of IQ and achievement subtests used.) A mixed ANOVA with between-participants factor, group (two levels: ADHD and control), and within-participants factor, IQ (four levels: standard scores on Block Design, Similarities, Digit Span, and Coding), revealed a significant main effect of group, F(1, 47) = 19.91, p < .001. Post hoc independent samples t tests revealed significant differences between the ADHD and control groups on all IQ subtests. A similar analysis examining group differences on the Wide Range Achievement Test–Fourth Edition (WRAT4) subtests (Reading, Comprehension, Spelling, Maths) also revealed a significant main effect of group, F(1, 48) = 40.11, p < .001, and significant group differences on post hoc independent samples t tests for each subtest. Tables 1 and 2 present descriptive statistics and the results of independent samples t tests for each IQ and academic achievement subtest.

Table 1.

Comparison of ADHD and Control Groups on WISC-IV Subtests

	Block Design	Similarities	Digit Span	Coding
ADHD
Mean (SD)	8.68 (2.67)	8.6 (2.29)	9.12 (2.46)	8 (2.52)
Control
Mean (SD)	10.52 (2.66)	10.92 (2.02)	11.28 (2.95)	9.58 (2.36)
t test	t(48) = −2.44, p = .019	t(48) = −3.8, p < .001	t(48) = −2.81, p = .007	t(47) = −2.27, p = .028
Effect size	0.69	1.01	0.88	0.63

Note. WISC-IV = Wechsler Intelligence Scale for Children–Fourth edition.

Table 2.

Comparison of ADHD and Control Groups on WRAT4 Subtests

	Reading		Comprehension		Spelling		Maths
	SS	PR	SS	PR	SS	PR	SS	PR
ADHD
Mean (SD)	95.76 (11.17)	39.12 (24.5)	96.72 (12.4)	41.16 (25.62)	96.24 (11.84)	40.56 (25.91)	91.56 (9.55)	30.6 (20.77)
Control
Mean (SD)	103.4 (6.39)	58.24 (15.5)	110.4 (9.76)	71.76 (19.67)	113.56 (14.46)	73.83 (22.67)	115.6 (10.39)	80.28 (16.59)
t test	t(38.2)^a = −2.97*	t(40.6)^a = −3.3*	t(48) = −4.34**	t(48) = −4.74**	t(48)= −4.63**	t(48) = −4.83**	t(48) = −8.52**	t(48) = −9.35**
Effect size	0.68	19^b	1.1	31^b	1.46	33^b	2.52	50^b

Note. WRAT4 = Wide Range Achievement Test–Fourth Edition; SS = standard score; PR = percentile rank.

Correction of dfs for unequal variances.

Effect size for PR reflects the difference between the mean PR for the ADHD and control groups.

p < .01. **p < .001.

Materials

Conners 3 Parent Questionnaire

The full-length Conners 3 Parent Questionnaire (Conners, 2008) provides an assessment of ADHD symptoms and related problems. The scales referred to above are the ADHD Index, Diagnostic and Statistical Manual of Mental Disorders (4th ed.; text rev.; DSM-IV-TR; American Psychiatric Association, 2000) ADHD Inattentive, and Hyperactive-Impulsive subscales.

Parental Accounts of Children’s Symptoms (PACS2)

The PACS2 (Chen & Taylor, 2006) is a semistructured interview that aims to obtain detailed information about the symptoms of inattention and hyperactivity-impulsivity displayed by each child. The parent is asked to describe the child’s behavior in a number of everyday life situations, such as watching the television, eating meals, or going shopping, and the researcher rates the frequency and severity of inattention and hyperactivity-impulsivity based on the parental report. An algorithm is used to determine the number of DSM-IV symptoms that the child is displaying.

Near and color vision assessment

Children’s near and color vision were assessed using the Revised Sheridan Gardiner Test (Sheridan, 1970) and the Ishihara Test for Color Blindness (Ishihara, 1960) to ensure that the children’s near and color vision were intact.

Wechsler Intelligence Scale for Children–Fourth edition (WISC-IV)

Four subtests representing each of the WISC-IV (Wechsler, 2003) indices were administered in the following order: Block Design representing the Perceptual Reasoning Index, Similarities representing the Verbal Comprehension Index, Digit Span representing the Working Memory Index, and Coding representing the Processing Speed Index.

WRAT4

The Word Reading, Sentence Comprehension, Spelling and Math Computation subtests of the WRAT4 (Wilkinson & Robertson, 2006) were administered in that order.

Self-Report of Alertness

Children were asked to indicate their subjective level of alertness along a Vertical Visual Analogue Scale (Wewers & Lowe, 1990), which was a 10-cm-long line, with “Wide Awake” (and a picture of a bucking donkey) printed at the top and “Very Sleepy” (and a picture of the dwarf, Sleepy) printed at the bottom. The self-reported alertness was scored by measuring the distance in centimeters from the bottom anchor point (0) to the line drawn by the child.

CombiTVA paradigm

The CombiTVA paradigm (Vangkilde, Bundesen, & Coull, 2011; see Figure 1) involved a brief flash of red and blue letters on the computer screen with the participant’s task being to report all red letters (targets) but to ignore blue (distractors). The test took approximately 40 min for each participant and included 324 trials arranged into 9 blocks. Each trial consisted of the presentation of a red fixation cross (1,000 ms), the stimulus display, and a postdisplay mask (500 ms). The participant was instructed to fixate on the red cross and to make an unspeeded verbal report of the red letters he or she had seen when the screen went blank following the mask presentation. The researcher typed the letters as the participant reported them. Figure 1 shows the trial outline and the three possible stimulus display types: whole report of six targets, whole report of two targets, partial report of two targets and four distractors. When estimating the perceptual threshold, t ₀, VSTM capacity, K, and processing speed (C; see below), the development of performance with increasing stimulus exposure times must be sampled. Therefore, the six-target whole-report trials were presented for 10, 20, 50, 80, 140 and 200 ms, with 27 trials being presented at each exposure duration. The estimation of these three parameters is illustrated in Figure 2, which displays a TVA-based fit to the data of a representative participant. In contrast, selectivity is probed by introducing distractors to the stimulus display, but the estimation does not require variations of the display durations. Thus, the two-target whole-report trials and the partial report trials were presented at a fixed exposure duration of 80 ms, with 81 trials being presented for each of the two conditions. In summary, for each participant, the following parameters were estimated through the use of TVA-based modeling procedures (Dyrholm, et al., 2011; Kyllingsbæk, 2006).

Figure 1.

Illustration of the CombiTVA paradigm

Figure 2.

Whole-report performance of a representative ADHD participant showing the mean number of correctly reported letters as a function of exposure duration (squares)

t₀: Perceptual threshold—The minimum exposure duration (ms) at which letter identification is better than chance.

K: VSTM capacity—The maximum number of letters the participant can store in VSTM.

C: Visual Processing Speed—The speed of encoding items into VSTM, measured in letters per second.

α (alpha): Efficiency of top-down selection—A value reflecting the participant’s ability to focus on the target letters and ignore distractors. A value of 0 indicates perfect selectivity with higher values indicating poorer selectivity. A value of 1 or higher indicates complete nonselectivity on the part of the participant.

The TVA-based modeling procedure afforded a method of estimating the number of times a child was off-task or “lapsed” during the CombiTVA paradigm (Dyrholm et al., 2011). A Plapse index representing an estimate of the proportion of trials on which children were off-task was calculated. The estimates of the TVA parameters (described above) were made while controlling for lapses.

Sustained attention tasks

The SART (Robertson et al., 1997) measured fluctuations in sustained attention by measuring the ability to inhibit a response to an infrequent target stimulus in the context of maintaining an ongoing, monotonous action. Numbers between 1 and 9 were presented on screen and participants were required to press a button for every number that appeared but to withhold their response to number 3. In the Fixed version of the task, numbers appeared in the fixed sequence, 1 to 9, and in the Random version, numbers appeared in a pseudorandom order. The numbers appeared in white against a black background and remained for 313 ms. Each digit was followed by a mask, a cross within a circle, which lasted for 1,126 ms. Embedded within the mask period was a response cue (63 ms), a thicker cross within a circle. Participants were asked to respond on appearance of the response cue to minimize differences in inter- and intra-individual response speeds. There were 225 digits in all, including 25 no-go targets (number 3) and 200 go-trials (all other numbers). Each SART lasted 5.4 min. The three measures of sustained attention were errors of commission (i.e., number of times a participant pressed for number 3), errors of omission (i.e., number of times a participant did not press for a go-trial number), and variability of reaction time expressed as the coefficient of variation (i.e., SD/mean reaction time).

Procedure

All children, accompanied by a parent, were tested by the first author during a single session in the Institute of Neuroscience, which lasted a maximum of 2.5 hr, including multiple breaks throughout. If the children with ADHD were currently taking medication for ADHD, parents were asked not to give the children the medication for 48 hr prior to the testing session. They completed the paper and pencil and computer tests in the following order:

Near and color vision assessment;

CombiTVA paradigm (preceded by one practice block of 24 trials);

Fixed and Random SARTs;

WISC-IV subtests; and

WRAT 4 subtests.

Children were asked to indicate their subjective sense of alertness at five time points during the first hour. Parents were asked to complete a background questionnaire and the Conners 3 Parent Questionnaire. On a separate occasion, parents of children with ADHD met with the first author to participate in the PACS2 interview. Parents were given €20 to cover any traveling expenses they incurred due to participation.

Results

TVA Parameters

Table 3 presents the mean scores, standard deviations, effect sizes, and the results of independent samples t tests relating to comparisons between ADHD and control groups on each TVA parameter. Where Levene’s test for equality of variances was statistically significant, the t value corresponding to an analysis in which equal variances are not assumed was adopted. Effect sizes were calculated in the following manner: (M_control − M_ADHD)/SD_ADHD (Cohen’s d; Howell, 2008). The differences between the groups are illustrated in Figure 3. There was no significant difference between ADHD and control groups in terms of perceptual threshold, t₀: t(47) = 1.07, p = .29; VSTM capacity, K: t(48) = 1.62, p = .11; or efficiency of top-down attentional selection, α: t(48) = 1.4, p = .17. Children with ADHD displayed a significantly lower visual processing speed (C) than control children, 32.06 letters/s (SD = 14.47) versus 40.35 letters/s (SD = 12.54), t(48) = −2.17, p = .035.

Table 3.

Comparison Between ADHD and Control Group on TVA Parameters

	ADHD mean (SD)	Control mean (SD)	t test	Effect size
t ₀ ^a	19.74 (18.1)	15.46 (8.3)	t(47) = 1.07, p = .29	0.24
K	3.2 (0.62)	2.85 (0.88)	t(48) = 1.62, p = .11	0.56
C	32.06 (14.47)	40.35 (12.54)	t(48) = −2.17, p = .035	0.57
Alpha	0.87 (0.5)	0.69 (0.37)	t(48) = 1.4, p = .17	0.36
Plapse	0.036 (0.04)	0.0003 (0.001)	t(24)^b = 4.32, p < .001	0.89

Note. TVA = theory of visual attention.

Note that one participant in the ADHD group was excluded from the analysis relating to t₀ due to an abnormally low estimate (i.e., −181 ms).

Correction of dfs for unequal variances.

Figure 3.

Mean values for ADHD and control groups on TVA parameters

Probability of lapses (Plapse)

The Plapse variable presented in Table 3 represents an estimate of the proportion of trials on which children were off-task. The estimates suggest that on average, children with ADHD lapsed on 3.6% (SD = 4) of the 324 trials in the CombiTVA paradigm while control children lapsed on 0.03% (SD = 0.1) of trials. This difference was statistically significant, t(24) = 4.32, p < .001, suggesting that children with ADHD had a more inconsistent response style than control children.

Sustained Attention

Table 4 presents the mean scores, standard deviations, effect sizes, and the results of independent samples t tests relating to comparisons between ADHD and control groups on errors of commission, errors of omission, and reaction time variability on the Fixed and Random SARTs. Children with ADHD made significantly more errors of commission and errors of omission and had significantly higher reaction time variability on the Fixed and Random SARTs.

Table 4.

Comparison Between ADHD and Control Group on Sustained Attention Variables

	ADHD	Control
	Mean (SD)	Mean (SD)	t test	Effect size
Fixed ERC	6.28 (4.08)	3.04 (3.2)	t(48) = 3.12*	0.79
Fixed ERO	16.52 (12.44)	5.36 (5.7)	t(33.7)^a = 4.1**	0.9
Fixed RTCov	0.67 (0.19)	0.4 (0.17)	t(48) = 5.17**	1.42
Random ERC	16.36 (5.3)	10.68 (5.5)	t(48) = 3.72**	1.07
Random ERO	22.8 (23.16)	4.08 (6.8)	t(28.1)^a = 3.88**	0.81
Random RTCov	0.63 (0.22)	0.34 (0.15)	t(42.9)^a = 5.44**	1.32

Note. ERC = errors of commission; ERO = errors of omission; RTCov = reaction time coefficient of variation.

Correction of dfs for unequal variances.

p < .01. **p ≤ .001.

Self-Reported Alertness

A mixed ANOVA, including the between-participants variable, group (two levels: ADHD and control), and within-participants variable, alertness over time (five levels: Time Points 1, 2, 3, 4, 5) was conducted to examine whether there was any difference between ADHD and control children’s subjective sense of alertness across time. The ANOVA revealed no significant main effect of alertness over time, F(4, 192) < 1; no main effect of group, F(1, 48) < 1; and no significant interaction between group and alertness, F (4, 192) < 1. It was clear that children with ADHD and control children did not differ in terms of subjective sense of alertness.

Discussion

The primary purpose of the current study was to examine the attentional deficit experienced by children with ADHD using the TVA paradigm. The results showed that children with ADHD did not differ significantly from control children in terms of estimates for perceptual threshold (t₀), VSTM (K), or efficiency of top-down attentional selection (α). Children with ADHD did, however, show a significantly lower visual processing speed (C) and had a significantly higher number of lapses during the CombiTVA task. In comparison to control children, children with ADHD also displayed statistically significant impairment, with large effect size (Cohen, 1992), on all measures of sustained attention (i.e., errors of commission, errors of omission, reaction time variability) on two versions of the SART. Children with ADHD did not, however, differ significantly from control children in terms of self-reported alertness.

Children with ADHD are highly distractible and show a lack of attention to detail in their daily tasks. Researchers have asked whether these inattentive symptoms are due to a true deficit in selective attention. In this study, TVA-based assessment provided a method of isolating and estimating the efficiency of top-down attentional selection, independently of attentional capacity. Children with ADHD did not differ significantly from control children in terms of efficiency of top-down attentional selection. This finding adds to a body of work using different paradigms, such as visuospatial orienting (Huang-Pollock & Nigg, 2003), visual search (Mason et al., 2003, 2004), and perceptual load (Huang-Pollock et al., 2005), which suggests that selective attention is intact in children with ADHD. It is likely that failures of selective attention displayed by ADHD children in their daily lives are due to a failure to deploy selective attention rather than a deficit in selective attention per se. The results of Friedman-Hill et al.’s (2010) study are of particular interest within this context. They showed that a deficit in selective attention only really emerged for children with ADHD during easy, nonchallenging trials.

The use of the TVA paradigm in this study enabled the componential analysis of attentional capacity and subsequent identification of an impaired processing speed, alongside intact VSTM and perceptual threshold. Processing speed deficits have been identified in children with ADHD on a range of tasks requiring quick and accurate processing of information (Shanahan et al., 2006). One difficulty in this line of research is determining which level of processing is actually slowed. For example, slow speed on tasks like the WISC Coding subtest or visual matching tasks could be due to slowing at the perceptual, cognitive, or output level (Shanahan et al., 2006). An advantage of the TVA paradigm is that it is an accuracy-based measure, in which children make an unspeeded verbal report. The estimate of visual processing speed is therefore uncontaminated by speed of motor response. Furthermore, visual processing speed is estimated independently of perceptual threshold. Here, we found that, in comparison to control children, children with ADHD displayed a slow visual processing speed despite an equivalent perceptual threshold. This slow processing speed may be related to the sustained attention deficit displayed by the children.

An inability to sustain attention is one of the hallmark symptoms of ADHD. In this study, the children with ADHD showed an impaired ability to sustain attention during the SART, as evidenced by a significantly higher number of errors and greater reaction time variability, and during the CombiTVA paradigm, as evidenced by a significantly higher number of lapses during this task. This finding is in keeping with previous research that has documented sustained attention deficits on the SART and other continuous performance tests (Johnson, Kelly, et al., 2007; Johnson, Robertson, et al., 2007; Johnson et al., 2008; Losier et al., 1996; O’Connell et al., 2004). Sustained attention, the ability to endogenously maintain alertness and focus over time, is mediated by a right frontal-parietal cortical network, which interacts closely with a subcortical arousal system (Robertson & Garavan, 2004). At any given moment, a person’s ability to sustain attention will be determined by a dynamic interplay between cognitive factors, motivational factors, and degree of physiological arousal. An impaired ability to sustain attention could arise from a deficit at any of these levels, and it is interesting that theories of ADHD have targeted each of these levels in the attempt to identify the cause of the inattentive and hyperactive symptoms characteristic of ADHD.

At the cognitive level, impaired sustained attention could result from a failure to monitor and maintain optimal arousal levels to match current task demands or a failure to maintain goal representations throughout the task (Robertson & Garavan, 2004). A deficit of this nature could be considered as part of an overall impairment in executive functioning, which has been one of the most prominent accounts put forward to explain ADHD (Barkley, 1997; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Impaired sustained attention could also result from inadequate levels of physiological arousal (Sergeant, 2000; Zentall & Zentall, 1983). The slow visual processing speed observed in this and many other studies is compatible with this notion of deficient physiological arousal in ADHD. An interesting inconsistency is the finding that children with ADHD did not rate themselves as feeling any less alert than their peers. If one considers that most of the children in the ADHD group were currently taking medication for ADHD and had abstained from the medication for a short period to attend the testing session, this is a rather surprising finding. Finally, impaired sustained attention could also arise from a lack of intrinsic motivation to sustain attention to task. Motivational accounts of ADHD have been proposed in various guises (Luman, Oosterlaan, & Sergeant, 2005), and children with ADHD have reported lower levels of intrinsic motivation (Carlson, Booth, Shin, & Canu, 2002).

The examination of neurocognitive deficits in ADHD populations is complicated by a number of factors. First is the high level of comorbidity evident within ADHD populations. In this study, 15 out of 25 children in the ADHD group, compared with one child in the control group, had additional diagnoses. It is unclear what effect these additional conditions may have had on children’s performance. Of particular relevance to this study is the concern that some children, due for example to an additional diagnosis of dyslexia, may have had difficulty processing the letter or number stimuli used in the tasks. While one might be tempted to control for comorbidity by excluding all children with comorbid disorders, one runs the risk of using an atypical ADHD sample, given that comorbidity is such a common adjunct to the condition (Sharkey & Fitzgerald, 2007). However, a lack of control for comorbidity may lead to a situation in which samples used in different studies differ so much as to be incomparable. One remedy for this predicament may be to use as comprehensive a battery as possible when conducting a study. This study had the advantage of using measures of sustained attention, attentional selectivity and capacity, thereby enabling a comparison between these attentional functions. However, other attentional functions of interest, such as divided attention, attentional control, and phasic and tonic alertness, were not tested. Future studies should use an assessment battery that is as comprehensive as possible to enable comparisons between attentional abilities and the isolation of attentional deficits within a single sample.

A second factor that complicates the examination of the neurocognitive deficit is the difficulty in distinguishing between a deficit in a specific cognitive function and a deficit in general cognitive ability. Many studies have found children with ADHD to obtain lower scores on IQ and academic achievement tests (Frazier, Demaree, & Youngstrom, 2004). Similarly, in this study, children with ADHD obtained significantly lower scores on all subtests of IQ and academic achievement administered. Again, one might be tempted to control for IQ differences by matching ADHD and control groups on IQ or by controlling for IQ during statistical analysis. However, it has been argued that to do so would be methodologically tenuous as lower IQ scores are deemed to be a feature of the disorder and partialling out IQ would amount to partialling out some of the effects of the condition (Dennis et al., 2009; Frazier et al., 2004).

A third issue that complicates the examination of neurocognitive abilities in children with ADHD is the test-taking behavior of the children, who characteristically display an inconsistent level of task engagement during a testing session (Jepsen, Fagerlund, & Mortensen, 2009). In this study, the erratic task engagement of the children was not only witnessed by the first author but was also captured in the inconsistent performance on the SART (evidenced by increased errors and reaction time variability) and the CombiTVA (evidenced by increased lapses). A child who does not have the ability or motivation to sustain attention or maintain task engagement throughout a testing session may perform at a lower level than his or her ability might otherwise dictate (Glutting, Youngstrom, Oakland, & Watkins, 1996). It is very difficult to differentiate between poor performance on a test caused by a genuinely impaired ability on the relevant cognitive function and poor performance due to inconsistent task engagement. Indeed, the literature is replete with evidence of widespread cognitive deficits in children with ADHD, including sustained attention (Losier et al., 1996), response inhibition (Oosterlaan, Logan, & Sergeant, 1998), working memory (Martinussen, Hayden, Hogg-Johnson, & Tannock, 2005), set shifting (Romine et al., 2004), processing speed (Shanahan et al., 2006), response to reward (Luman et al., 2005), and general IQ (Frazier et al., 2004; see also Castellanos, Sonuga-Barke, Milham, & Tannock, 2006; Nigg, 2005b). It is difficult to say whether a lack of ability or motivation to sustain attention during test sessions might explain these findings of widespread neurocognitive deficits.

One method of dealing with this issue is to use a sophisticated paradigm that enables measurement of the cognitive function of interest without relying on overall task performance. The TVA is one such paradigm. Rather than using an overall score, such as total number of letters reported, it applies a mathematical model to estimate from a participant’s performance the various parameters of visual attention. Furthermore, it provides a method of estimating the number of times a child lapsed or was off-task during the test and estimating the aforementioned parameters while controlling for these lapses. The field of research into attention is in fact renowned for the use of sophisticated paradigms, such as visuospatial orienting (Huang-Pollock & Nigg, 2003), visual search (Mason et al., 2003), perceptual load (Huang-Pollock et al., 2005), and the attention network task (Adolfsdottir et al., 2008). It could be that the facility within these paradigms to measure cognitive processes despite inconsistent performance on the part of the children has enabled the identification of intact selective attention in children with ADHD. In this study, the use of the TVA paradigm enabled the identification of intact attentional selectivity, perceptual threshold, and visual STM capacity in children with ADHD. These findings are quite remarkable within the context of significant impairments in sustained attention, perceptual processing speed, and significantly poorer performance on IQ and academic achievement tests.

Inattentive behavior is a defining characteristic of the condition known as ADHD, and research has attempted to delineate the attentional deficit underlying these inattentive behaviors. The attention system of the human brain is thought to be a multidimensional system, and several typologies of attentional ability, emphasizing various attentional functions, have been proposed. The findings of the current study lend support to the notion of differential impairment of attentional functions in children with ADHD. The use of comprehensive batteries of attentional measures in future studies would enable further delineation of the attentional deficit experienced by these children.

Footnotes

Acknowledgements

Thank you to the Irish Research Council for the Humanities and Social Sciences and to the European Science Foundation for supporting this research. Thanks are also due to Professor Michael Fitzgerald who helped with recruitment of participants with ADHD and Mr. T. P. Parker, who helped with recruitment of control children.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The project was funded by the Irish Research Council for the Humanities and Social Sciences and the European Science Foundation.

Author Biographies

Laura P. McAvinue is a Research Fellow at the Trinity College Institute of Neuroscience. Her interests include the measurement and enhancement of cognition, particularly in children with developmental disorders. She is currently pursuing a Masters in Educational Psychology.

Signe Vangkilde is a Postdoctoral Research Fellow at the Center for Visual Cognition, Department of Psychology, University of Copenhagen. Her research interests include an examination of visual attention in terms of its neurochemistry and modulation.

Katherine A. Johnson is a Senior Lecturer at the Melbourne School of Psychological Sciences. Her research interests include the cognitive and physiological deficits associated with developmental disorders, especially ADHD and autism.

Thomas Habekost is Associate Professor at the Center for Visual Cognition, Department of Psychology, University of Copenhagen. His research interests include the neuropsychology of visual attention, with an emphasis on populations with brain injury or neuropsychological disorders.

Søren Kyllingsbæk is Associate Professor at the Center for Visual Cognition, Department of Psychology, University of Copenhagen. His research interests include computational cognitive modelling, intentional action, attention to objects and working memory.

Claus Bundesen is Professor of Cognitive Psychology and Director of the Center for Visual Cognition at the University of Copenhagen. Author of the Theory of Visual Attention (TVA), his research objective is to develop an account of visual attention that integrates the psychological and neurobiological perspectives.

Ian. H. Robertson is Professor of Psychology at Trinity College Dublin and was the founding director of the Trinity College Institute of Neuroscience. His research interests include the neuropsychology of brain rehabilitation with an emphasis on attention, arousal and memory.

References

Adolfsdottir

Sorensen

Lundervold

A. J.

(2008). The attention network test: A characteristic pattern of deficits in children with ADHD. Behavioral and Brain Functions, 4, 1-9.

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.

Barkley

R. A.

(1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin, 121, 65-94.

Bellgrove

M. A.

Robertson

I. H.

Gill

(2007). Genes, cognition and brain activity: The endophenotype approach to ADHD. In Fitzgerald

Bellgrove

Gill

(Eds.), Handbook of attention deficit hyperactivity disorder (pp. 237-267). West Sussex, UK: John Wiley.

Bublak

Finke

Krummenacher

Preger

Kyllingsbæk

Müller

H. J

Schneider

W. X.

(2005). Usability of a theory of visual attention (TVA) for parameter-based measurement of attention II: Evidence from two patients with frontal or parietal damage. Journal of the International Neuropsychological Society, 11, 843-854.

Bublak

Redel

Finke

(2006). Spatial and non-spatial attention deficits in neurodegenerative diseases: Assessment based on Bundesen’s theory of visual attention (TVA). Restorative Neurology and Neuroscience, 24, 287-301.

Bublak

Redel

Sorg

Kurz

Förstl

Müller

H. J.

Finke

(2011). Staged decline of visual processing capacity in mild cognitive impairment and Alzheimer’s disease. Neurobiology of Aging, 32, 1219-1230.

Bundesen

(1990). A theory of visual attention. Psychological Review, 97, 523-547.

Bundesen

Habekost

(2008). Principles of visual attention: Linking mind and brain. Oxford, UK: Oxford University Press.

10.

Bundesen

Habekost

Kyllingsbæk

(2005). A neural theory of visual attention: Bridging cognition and neurophysiology. Psychological Review, 112, 291-328.

11.

Carlson

C. L.

Booth

J. E.

Shin

Canu

W. H.

(2002). Parent-, teacher-, and self-rated motivational styles in ADHD subtypes. Journal of Learning Disabilities, 35, 104-113.

12.

Castellanos

F. X.

Sonuga-Barke

E. J. S.

Milham

M. P.

Tannock

(2006). Characterizing cognition in ADHD: Beyond executive dysfunction. Trends in Cognitive Sciences, 10, 117-123.

13.

Cepeda

N. J.

Cepeda

M. L.

Kramer

A. F.

(2000). Task switching and attention deficit hyperactivity disorder. Journal of Abnormal Child Psychology, 28, 213-226.

14.

Chen

Taylor

(2006). Parental account of children’s symptoms (PACS), ADHD phenotypes and its application to molecular genetic studies. In Oades

R. D.

(Ed.), Attention-deficit/hyperactivity disorder and the hyperkinetic syndrome (pp. 3-20). New York, NY: Nova Science Publishers.

15.

Cohen

(1992). A power primer. Psychological Bulletin, 112, 155-159.

16.

Cohen

R. A.

(1993). The neuropsychology of attention. London, England: Plenum.

17.

Conners

C. K.

(2008). Conners manual (3rd ed.). Toronto, Ontario, Canada: Multi-Health Systems.

18.

Cornish

Wilding

(2010). Attention, genes, and development disorders. New York, NY: Oxford University Press.

19.

Dennis

Francis

D. J.

Cirino

P. T.

Schachar

Barnes

M. A.

Fletcher

J. M.

(2009). Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders. Journal of the International Neuropsychological Society, 15, 331-343.

20.

Desimone

Duncan

(1995). Neural mechanisms of selective visual attention. Annual Reviews Neuroscience, 18, 193-222.

21.

Douglas

V. I.

(1972). Stop, look and listen: The problem of sustained attention and impulse control in hyperactive and normal children. Canadian Journal of Behavioural Science, 4, 259-282.

22.

Dubois

Kyllingsbæk

Prado

Peiffer

Lassus

Valdois

(2010). Fractionating the multi-character processing deficit in developmental dyslexia: Evidence from two case studies. Cortex, 46, 717-738.

23.

Duncan

Bundesen

Olson

Humphreys

Chavda

Shibuya

(1999). Systematic analysis of deficits in visual attention. Journal of Experimental Psychology: General, 128, 450-478.

24.

Duncan

Bundesen

Olson

Humphreys

Ward

Kyllingsbæk

Chavda

(2003). Dorsal and ventral simultanagnosia. Cognitive Neuropsychology, 20, 675-701.

25.

Dyrholm

Kyllingsbæk

Espeseth

Bundesen

(2011). Generalizing parametric models by introducing trial-by-trial parameter variability: The case of TVA. Journal of Mathematical Psychology, 55, 416-429.

26.

Finke

Bublak

Krummenacher

Kyllingsbæk

Müller

H. J.

Schneider

W. X.

(2005). Usability of a theory of visual attention (TVA) for parameter-based measurement of attention I: Evidence from normal subjects. Journal of the International Neuropsychological Society, 11, 832-842.

27.

Finke

Schneider

W. X.

Redel

Dose

Kerkhoff

Müller

H. J.

Bublak

(2007). The capacity of attention and simultaneous perception of objects: A group study of Huntington’s disease patients. Neuropsychologia, 45, 3272-3284.

28.

Frazier

T. W.

Demaree

H. A.

Youngstrom

E. A.

(2004). Meta-analysis of intellectual and neuropsychological test performance in attention-deficit/hyperactivity disorder. Neuropsychology, 18, 543-555.

29.

Friedman-Hill

S. R.

Wagman

M. R.

Gex

S. E.

Pine

D. S.

Leibenluft

Ungerleider

L. G.

(2010). What does distractibility in ADHD reveal about mechanisms for top-down attentional control? Cognition, 115, 93-103.

30.

Fronczek

Middelkoop

van Dijk

Lammers

(2006). Focusing on vigilance instead of sleepiness in the assessment of narcolepsy: High sensitivity of the Sustained Attention to Response Task (SART). Sleep, 29, 187-191.

31.

Fuggetta

G. P.

(2006). Impairment of executive functions in boys with attention deficit/hyperactivity disorder. Child Neuropsychology, 12, 1-21.

32.

Gerlach

Marstrand

Habekost

Gade

(2005). A case of impaired shape integration: Implications for models of visual object processing. Visual Cognition, 12, 1409-1443.

33.

Glutting

J. J.

Youngstrom

E. A.

Oakland

Watkins

M. W.

(1996). Situational specificity and generality of test behaviors for samples of normal and referred children. School Psychology Review, 25, 94-107.

34.

Habekost

Bundesen

(2003). Patient assessment based on a theory of visual attention (TVA): Subtle deficits after a right frontal-subcortical lesion. Neuropsychologia, 41, 1171-1188.

35.

Habekost

Rostrup

(2006). Persisting asymmetries of vision after right side lesions. Neuropsychologia, 44, 876-895.

36.

Habekost

Rostrup

(2007). Visual attention capacity after right hemisphere lesions. Neuropsychologia, 45, 1474-1488.

37.

Habekost

Starrfelt

(2006). Alexia and quadrant-amblyopia: Reading disability after a minor visual field deficit. Neuropsychologia, 44, 2465-2476.

38.

Habekost

Starrfelt

(2009). Visual attention capacity: A review of TVA-based patient studies. Scandinavian Journal of Psychology, 50, 23-32.

39.

Hazell

P. L.

Carr

V. J.

Lewin

T. J.

Dewis

S. A. M.

Heathcote

D. M.

Brucki

B. M.

(1999). Effortful and automatic information processing in boys with ADHD and specific learning disorders. Journal of Child Psychology and Psychiatry, 40, 275-286.

40.

Heaton

S. C.

Reader

S. K.

Preston

A. S.

Fennell

E. B.

Puyana

O. E.

Gill

Johnson

J. H.

(2001). The test of everyday attention for children (TEA-Ch): Patterns of performance in children with ADHD and clinical controls. Child Neuropsychology, 7, 251-264.

41.

Hooks

Milich

Pugzles Lorch

(1994). Sustained and selective attention in boys with attention deficit hyperactivity disorder. Journal of Clinical Child Psychology, 23, 69-77.

42.

Howell

D. C.

(2008). Fundamental statistics for the behavioral sciences. Toronto, Ontario, Canada: Thomson Wadsworth.

43.

Huang-Pollock

C. L.

Nigg

J. T.

(2003). Searching for the attention deficit in attention deficit hyperactivity disorder: The case of visuospatial orienting. Clinical Psychology Review, 23, 801-830.

44.

Huang-Pollock

C. L.

Nigg

J. T.

Carr

T. H.

(2005). Deficient attention is hard to find: Applying the perceptual load model of selective attention to attention deficit hyperactivity disorder subtypes. Journal of Child Psychology and Psychiatry, 46, 1211-1218.

45.

Huang-Pollock

C. L.

Nigg

J. T.

Halperin

J. M.

(2006). Single dissociation findings of ADHD deficits in vigilance but not anterior or posterior attention systems. Neuropsychology, 20, 420-429.

46.

Inasaridze

K. K.

Bzhalava

(2010). Dual task performance in children and adolescents with ADHD. Behavioural Neurology, 23, 193-194.

47.

Ishihara

(1960). Tests for colour-blindness. London, England: H.K. Lewis & Co.

48.

Jepsen

J. R. M.

Fagerlund

Mortensen

E. L.

(2009). Do attention deficits influence IQ assessment in children and adolescents with ADHD? Journal of Attention Disorders, 12, 551-562.

49.

Johnson

K. A.

Barry

Bellgrove

M. A.

Cox

Kelly

S. P.

Daibhis

Gill

(2008). Dissociation in response to methylphenidate on response variability in a group of medication naïve children with ADHD. Neuropsychologia, 46, 1532-1541.

50.

Johnson

K. A.

Kelly

S. P.

Bellgrove

M. A.

Barry

Cox

Gill

Robertson

I. H.

(2007). Response variability in attention deficit hyperactivity disorder: Evidence for neuropsychological heterogeneity. Neuropsychologia, 45, 630-638.

51.

Johnson

K. A.

Robertson

I. H.

Kelly

S. P.

Silk

T. J.

Barry

Daibhis

Bellgrove

M. A.

(2007). Dissociation in performance of children with ADHD and high-functioning autism on a task of sustained attention. Neuropsychologia, 45, 2234-2245.

52.

Kyllingsbæk

(2006). Modeling visual attention. Behavior Research Methods, 38, 123-133.

53.

Losier

B. J.

McGrath

P. J.

Klein

R. M.

(1996). Error patterns on the continuous performance test in non-medicated and medicated samples of children with and without ADHD: A meta-analytic review. Journal of Child Psychology and Psychiatry, 37, 971-987.

54.

Luman

Oosterlaan

Sergeant

J. A.

(2005). The impact of reinforcement contingencies on AD/HD: A review and theoretical appraisal. Clinical Psychology Review, 25, 183-213.

55.

Manly

Anderson

Nimmo-Smith

Turner

Watson

Robertson

I. H.

(2001). The differential assessment of children’s attention: The Test of Everyday Attention for Children (TEA-Ch), normative sample and ADHD performance. Journal of Child Psychology and Psychiatry, 42, 1065-1081.

56.

Manly

Lewis

G. H.

Robertson

I. H.

Watson

P. C.

Datta

A. K.

(2002). Coffee in the cornflakes: Time-of-day as a modulator of executive response control. Neuropsychologia, 40, 1-6.

57.

Manly

Owen

A. M.

McAvinue

L. P.

Datta

Lewis

G. H.

Scott

S. U.

Robertson

I. H.

(2003). Enhancing the sensitivity of a sustained attention task to frontal damage: Convergent clinical and functional imaging evidence. Neurocase, 9, 340-349.

58.

Martinussen

Hayden

Hogg-Johnson

Tannock

(2005). A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 44, 377-384.

59.

Mason

D. J.

Humphreys

G. W.

Kent

L. S.

(2003). Exploring selective attention in ADHD: Visual search through space and time. Journal of Child Psychology and Psychiatry, 44, 1-20.

60.

Mason

D. J.

Humphreys

G. W.

Kent

L. S.

(2004). Visual search, singleton capture, and the control of attentional set in ADHD. Cognitive Neuropsychology, 21, 661-687.

61.

McAvinue

L. P.

O’Keeffe

McMackin

Robertson

I. H.

(2005). Impaired sustained attention and error awareness in traumatic brain injury: Implications for insight. Neuropsychological Rehabilitation, 15, 569-587.

62.

McAvinue

L. P.

Vangkilde

Johnson

K. A.

Habekost

Kyllingsbaek

Robertson

I. H.

Bundesen

(2012). The relationship between sustained attention, attentional selectivity, and capacity. Journal of Cognitive Psychology, 24, 313-328.

63.

Nigg

J. T.

(2005a). Attention, task difficulty, and ADHD. British Journal of Developmental Psychology, 23, 513-516.

64.

Nigg

J. T.

(2005b). Neuropsychologic theory and findings in attention-deficit/hyperactivity disorder: The state of the field and salient challenges for the coming decade. Biological Psychiatry, 57, 1424-1435.

65.

O’Connell

R. G.

Bellgrove

M. A.

Dockree

P. M.

Robertson

I. H.

(2004). Reduced electrodermal response to errors predicts poor sustained attention performance in attention deficit hyperactivity disorder. NeuroReport, 15, 2535-2538.

66.

Oosterlaan

Logan

G. D.

Sergeant

J. A.

(1998). Response inhibition in AD/HD, CD, comorbid AD/HD + CD, anxious and control children: A meta-analysis of studies with the stop task. Journal of Child Psychology and Psychiatry, 39, 411-425.

67.

Parasuraman

(2000). The attentive brain: Issues and prospects. In Parasuraman

(Ed.), The attentive brain (pp. 3-15). London, England: MIT Press.

68.

Peers

P. V.

Ludwig

C. J.

Rorden

Cusack

Bonfiglioli

Bundesen

Duncan

(2005). Attentional functions of parietal and frontal cortex. Cerebral Cortex, 15, 1469-1484.

69.

Posner

M. I.

Boies

S. J.

(1971). Components of attention. Psychological Review, 78, 391-408.

70.

Posner

M. I.

Dehaene

(1994). Attentional networks. Trends in Neurosciences, 17, 75-79.

71.

Posner

M. I.

Fan

(2008). Attention as an organ system. In Pomerantz

J. R.

(Ed.), Topics in integrative neuroscience: From cells to cognition (pp. 31-61). New York, NY: Cambridge University Press.

72.

Posner

M. I.

Petersen

S. E.

(1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25-42.

73.

Redel

Bublak

Sorg

Kurz

Förstl

Müller

H. J.

Finke

(2012). Deficits of spatial and task-related attentional selection in mild cognitive impairment and Alzheimer’s disease. Neurobiology of Aging, 33, 195.e27-195.e42.

74.

Robertson

I. H.

Garavan

(2004). Vigilant attention. In Gazzaniga

M. S.

(Ed.), The cognitive neurosciences III (pp. 631-640). Cambridge: MIT Press.

75.

Robertson

I. H.

Manly

Andrade

Baddeley

B. T.

Yiend

(1997). “Oops!”: Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 35, 747-758.

76.

Robertson

I. H.

Ward

Ridgeway

Nimmo-Smith

(1996). The structure of normal human attention: The test of everyday attention. Journal of the International Neuropsychological Society, 2, 525-534.

77.

Romine

C. B.

Lee

Wolfe

M. E.

Homack

George

Riccio

C. A.

(2004). Wisconsin card sorting test with children: A meta-analytic study of sensitivity and specificity. Archives of Clinical Neuropsychology, 19, 1027-1041.

78.

Sergeant

(2000). The cognitive-energetic model: An empirical approach to attention-deficit hyperactivity disorder. Neuroscience & Biobehavioral Reviews, 24, 7-12.

79.

Shanahan

M. A.

Pennington

B. F.

Yerys

B. E.

Scott

Boada

Willcutt

E. G.

DeFries

J. C.

(2006). Processing speed deficits in attention deficit/hyperactivity disorder and reading disability. Journal of Abnormal Child Psychology, 34, 585-602.

80.

Sharkey

Fitzgerald

(2007). Diagnosis and classification of ADHD in childhood. In Fitzgerald

Bellgrove

Gill

(Eds.), Handbook of attention deficit hyperactivity disorder (pp. 13-35). West Sussex, UK: John Wiley.

81.

Sheridan

M. D.

(1970). New appliances–Sheridan Gardiner Test for visual-acuity. British Medical Journal, 1, 108-109.

82.

Smilek

Carriere

J. S. A.

Cheyne

J. A.

(2010). Failures of sustained attention in life, lab, and brain: Ecological validity of the SART. Neuropsychologia, 48, 2564-2570.

83.

Starrfelt

Habekost

Gerlach

(2010). Visual processing in pure alexia: A case study. Cortex, 46, 242-255.

84.

Starrfelt

Habekost

Leff

A. P.

(2009). Too little, too late: Reduced visual span and speed characterize pure alexia. Cerebral Cortex, 19, 2880-2890.

85.

Sturm

(1996). Evaluation in therapeutical contexts: Attentional and neglect disorders. European Review of Applied Psychology, 46, 207-215.

86.

Sturm

Willmes

Orgass

Hartje

(1997). Do specific attention deficits need specific training? Neuropsychological Rehabilitation, 7, 81-103.

87.

Swanson

Posner

M. I.

Cantwell

Wigal

Crinella

Filipek

Nalcioglu

(2000). Attention-deficit/hyperactivity disorder: Symptoms domains, cognitive processes, and neural networks. In Parasuraman

(Ed.), The attentive brain (pp. 445-460). Massachusetts: MIT Press.

88.

Vangkilde

Bundesen

Coull

J. T.

(2011). Prompt but inefficient: Nicotine differentially modulates discrete components of attention. Psychopharmacology, 218, 667-680.

89.

VanZomeren

A. H.

Brouwer

W. H.

(1994). Clinical neuropsychology of attention. New York, NY: Oxford University Press.

90.

Wechsler

(2003). Wechsler Intelligence Scale for Children–Fourth edition. San Antonio, TX: Harcourt Assessment.

91.

Wewers

M. E.

Lowe

N. K.

(1990). A critical review of visual analogue scales in the measurement of clinical phenomena. Research in Nursing & Health, 13, 227-236.

92.

Wilding

(2005). Is attention impaired in ADHD? British Journal of Developmental Psychology, 23, 487-505.

93.

Wilkinson

G. S.

Robertson

G. J.

(2006). Wide Range Achievement Test: Fourth edition. Wilmington, DE: Wide Range.

94.

Willcutt

E. G.

Doyle

A. E.

Nigg

J. T.

Faraone

S. V.

Pennington

B. F.

(2005). Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review. Biological Psychiatry, 57, 1336-1346.

95.

Zentall

S. S.

Zentall

T. R.

(1983). Optimal stimulation: A model of disordered activity and performance in normal and deviant children. Psychological Bulletin, 94, 446-471.