Cognitive Processes in ADHD and Asperger’s Disorder: Overlaps and Differences in PASS Profiles

Abstract

Objective: Many studies report on the usefulness of the evaluation of Executive Functions (EF) in the assessment of participants with ADHD, while others underline how deficits of EF in these participants are not consistent and that the same executive deficits are present in many other disorders, particularly in Asperger’s disorder. Using the Planning Attention Simultaneous Successive (PASS) theory, the present study explores the cognitive profiles of participants with ADHD or Asperger’s disorder and compares the cognitive functioning of these two diagnostic groups. Method: Forty-four children, 24 with a diagnosis of ADHD and 20 with a diagnosis of Asperger’s disorder, participated and their cognitive processes were evaluated with the Cognitive Assessment System. Results: Results underline specific cognitive profiles in ADHD and Asperger’s disorder characterized by weaknesses in planning and attention, but with a diverse level of severity. Conclusion: Implications of the different cognitive profiles of these diagnostic groups are discussed.

Keywords

ADHD cognitive functioning Asperger’s

Introduction

ADHD is a neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity, and impulsivity (American Psychiatric Association [APA], 2000). Children with ADHD show unpredictable and unsettled behavior both at school and at home and, frequently during adulthood, develop psychiatric, emotional, and social problems (Faraone, Sergeant, Gillberg, & Biederman, 2003).

From a neuropsychological perspective, ADHD is characterized by executive dysfunction that particularly involves inhibition, vigilance, cognitive flexibility, and planning (Wilcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Particularly, the symptoms of ADHD involve inhibition (Nigg, 2001), planning and sustained attention (Solanto et al., 2007), cognitive flexibility (Hill, 2004), response monitoring (Happè & Frith, 2006), and task shifting (Hill & Bird, 2006). The involvement of Executive Functions (EF) in ADHD is explained by two different points of view (Holmes et al., 2010): theories that identify a core inhibitory problem (Barkley, 1997) or theories that highlight the interactive and additive effects of multiple factors as inhibition and working memory (Castellanos & Tannock, 2002; Wilcutt et al., 2005). Many studies report the usefulness of the evaluation of EF in the assessment of participants with ADHD (Holmes et al., 2010), while others underline how the deficits in EF in these participants are not consistent and that the same executive deficits are presented in many other disorders (Stern & Morris, 2012). Particularly, executive dysfunctions are typical of aging (Kennedy & Raz, 2009), schizophrenia (Groom et al., 2008), bipolar disorder (Torralva et al., 2011), reading disabilities (Marzocchi et al., 2008), mathematical disabilities (DeWeerdt, Desoete, & Roeyers, 2013), and autism spectrum disorders (ASD; Johnston, Madden, Bramham, & Russell, 2011). EF involved in reading and mathematical disabilities concern working memory and seems to be different from those involved in ADHD and autism, which concern, most of all, planning and inhibition.

Regarding EF involved in autism, particularly in Asperger’s syndrome or High Functioning Autism (HFA), some authors underline the role of error monitoring (Goldberg et al., 2011), cognitive flexibility, planning, and inhibition (Hill, 2004; Kaland, Smith, & Mortensen, 2008; Sanders, Johnson, Garavan, Gill, & Gallagher, 2008). Keehn, Müller, and Townsend (2012) suggest the presence of atypical attentional networks in participants with autism and others attempt to verify the presence of neurological correlates for these executive dysfunctions (Solomon et al., 2009). Nevertheless, some authors suggest a high overlapping of these executive dysfunctions between autism and ADHD (Goldberg et al., 2005), disorders that overlap even for early language delay and a similar early cognitive development (Hagberg, Miniscalco, & Gillberg, 2010).

For these reasons, several studies compare EF in participants with ADHD and those with ASD, emphasizing not only their similarities but also their differences. Examining these differences, Happè and Frith (2006) point out the lower severity of impairment in EF of participants with ASD than those with ADHD. Bramham et al. (2009) underline difficulties in knowing when to respond in participants with ADHD and how to respond in participants with ASD. The authors explain these differences as a significant impairment for participants with ADHD in withholding the response and in those with ASD in planning. Other studies suggest, instead, that children with ASD have more impairment in EF than those with ADHD (Corbett, Constantine, Hendren, Rocke, & Ozonoff, 2009; Salcedo-Marin, Moreno-Granados, Ruiz-Veguilla, & Ferrin, 2013).

A factor that could further increase the complexity of these results is the absence of correlation between the measures of attention and impulsivity obtained with traditional instruments (Gualtieri & Johnson, 2005). Therefore, the assessment of EF in ADHD becomes so difficult that some authors suggest a comprehensive evaluation that considers the measurement of specific intellectual processes and explains the neuropsychological impairment of children with ADHD (Frazier et al., 2004) and Inagaki (2011) underlines the necessity to use behavioral, neuropsychological, and neuroimaging studies to understand ADHD. From this point of view, Naglieri and Goldstein (2011) suggest the importance of measuring the cognitive processes of participants with ADHD because a better understanding of cognitive impairments causes a better evaluation and, most of all, a better capability of interventions (Naglieri, 2008; Naglieri, Pickering, Otero, & Moreno, 2010). In this context, the comprehension of neuropsychological processes involved in ADHD is possible through the clinical application of Planning Attention Simultaneous Successive (PASS) theory.

This theory has its origins in Luria’s (1966, 1973) work, which identifies three functional units in the human brain responsible for cognitive functioning. Starting from these functional units, Das, Naglieri, and Kirby (1994) and more recently Naglieri, Das, and Goldstein (2012) propose a new way to look at intelligence, identifying four cognitive processes which, interacting with the base of knowledge, determine the cognitive functioning of the participant. Planning is the participant’s ability to make a plan to solve problems; attention is the ability to focus on specific stimuli, inhibiting responses to competitive stimuli; simultaneous refers to the ability to understand relationships between things; and successive is the ability to work with information in a specific order. The operationalization of PASS theory is the Cognitive Assessment System (CAS; Naglieri & Das, 1997), an instrument that allows measuring these cognitive processes and obtaining a cognitive profile of the participant. This instrument highlights the presence of strengths and weaknesses in the four processes within the participant’s profile (relative weakness or relative strength) and between the participant and the standardized sample, as well. When a relative weakness or strength is less than 90 or exceeds a score of 110, respectively, it is possible to underline a cognitive weakness or a cognitive strength. The CAS seems to be culture-free (Kroesbergen, Van Luit, Naglieri, Taddei, & Franchi 2010; Naglieri, Otero, DeLauder, & Matto, 2007; Naglieri, Taddei, & Williams, 2012) and is reputed to be an instrument useful for measuring EF in children and adolescents (Chan, Shum, Toulopoulou, & Chen, 2008).

The applications of CAS in the evaluation of children with ADHD have provided interesting results where some authors propose the evaluation of cognitive processes as a diagnostic criterion for ADHD (Goldstein & DeVries, 2011; Naglieri & Das, 2005). In fact, it seems possible to associate a specific cognitive profile with ADHD, characterized by a failure in planning (Dehn, 2000; Naglieri, Goldstein, Iseman, & Schwebach, 2003; Naglieri, Salter, & Edwards, 2004; Paolitto, 1999; Van Luit, Kroesbergen, & Naglieri, 2005). The presence of a specific cognitive profile allows elaborating remediation programs (Iseman & Naglieri, 2011) and differentiating children with ADHD (Naglieri & Goldstein, 2011) from those with reading disabilities who show difficulties in successive (Naglieri et al., 2004, 2007; Taddei, Venditti, & Cartocci, 2009), or those with autism who have a weakness in Attention (Goldstein & Naglieri, 2009). Comparing participants with ADHD to children with learning disabilities, Taddei, Contena, Caria, Venturini, and Venditti (2011) have underlined a weakness in the successive process in the profile of children with learning disabilities and weaknesses in planning and attention in the profile of children with ADHD. In these studies, the typical cognitive profile of participants with ADHD reveals failures in planning and attention. The shape of these ADHD cognitive profiles is partially congruent with the previous results. In fact, on one hand, it is possible to underline the failure in planning, according to Naglieri and colleagues (2003), but, on the other hand, even attention seems to be a weak process. Probably, the neuropsychiatric context of the collection of data influences the severity of clinical conditions, as underlined in other previous research (Taddei et al., 2009; Taddei & Venditti, 2010). However, these weaknesses in planning and in attention recall the profile of participants with autism highlighted by different studies (Goldstein & Naglieri, 2009; Taddei & Contena, 2013), and require specific attention to the possibility that the PASS evaluation of these children involves an overlapping of profiles, such as the evaluation of EF, particularly in severe clinical conditions.

If the EF involved in ADHD and Asperger’s disorder overlap, it is possible to think that even the analysis of cognitive profiles could not differentiate between these clinical conditions. However, the first application of PASS theory in the comprehension of these disorders seems to give promising results and it could be interesting to investigate the presence of different PASS cognitive profiles in these clinical situations. From this point of view, it would be interesting to analyze the cognitive processes in children with a diagnosis of ADHD and of Asperger’s disorder to explore the contribution of PASS theory to a major understanding of cognitive functioning of these participants. Particularly, it is possible to hypothesize that the PASS profiles of the two diagnostic groups could show weaknesses in Planning and Attention but that these weaknesses could be different in terms of severity. Moreover, the overall PASS profiles of these two diagnostic groups could present differences in the other cognitive processes, even as cognitive strengths.

Method

Participants

We enrolled 44 children from 6 to 18 years, 24 with a diagnosis of ADHD and 20 with a diagnosis of Asperger’s disorder; both groups were without comorbidities. As shown in Table 1, the two groups were homogeneous by gender (V = .06; p = .71), but not by age (T = −5.87; p = .000). All children were tested in the neuropsychiatric units of National Health Service in two Italian regions: Lombardy and Tuscany. They received a diagnosis in accordance with the diagnostic criteria for ADHD and for Asperger’s disorder of Diagnostic and statistical manual of mental disorders (4th ed., text rev.; DSM-IV-TR; APA, 2000). All participants showed an IQ score higher than 70, measured with The Wechsler Intelligence Scale for Children–Third Edition (WISC-III; Wechsler, 1991) in its Italian adaptation (Orsini & Picone, 2006).

Table 1.

Participants: Gender and Age in Diagnostic Groups.

Diagnostic groups	Gender		n	Age
Diagnostic groups	Male	Female	n	M (SD)
ADHD	18	6	24	8.25 (2.27)
Asperger’s disorder	14	6	20	12.95 (3.03)
n	32	12	44	10.41 (3.57)

Instruments

The CAS (Naglieri & Das, 1997), in its Italian adaptation by Naglieri and Das (2005), was administered to all participants. The CAS is composed of four scales that measure the four cognitive processes: Planning (P), Attention (A), Simultaneous (Si), and Successive (Su). The score for every scale is determined by three subtests (Table 2) and the CAS offers the possibility to obtain an overall measure of cognitive functioning (Full Scale; FS). The evaluation with CAS reveals relative and cognitive weaknesses and strengths too. A relative weakness (or strength) is a significant weakness (or strength) relative to a specific cognitive profile. A cognitive weakness is a relative weakness that falls below the average range (<90), while a cognitive strength is a relative strength that falls above the average range (>110). The Italian standardization sample has a mean PASS score of 100 with a standard deviation of 10 (Naglieri & Das, 2005).

Table 2.

Cognitive Assessment System: Subtests and Scales.

Planning	Simultaneous	Attention	Successive
Matching numbers	Nonverbal matrices	Expressive attention	Words series
Planned codes	Verbal-spatial relations	Numbers detection	Sentence repetition
Planned connections	Figure memory	Receptive attention	Speech rate or sentence questions

Statistical Procedures

The data collected were transferred on informatics support and they were analyzed by the Statistical Package for the Social Sciences (SPSS.20; IBM Corp, 2011).

Descriptive statistics were calculated to describe the performance of participants on the CAS and to obtain their cognitive profiles. A one-way MANOVA was calculated to verify the presence of differences in the mean PASS scores between participants with ADHD and those with Asperger’s disorder. The effect size was calculated to evaluate the size of differences. The differences between the mean standard scores of these two diagnostic groups and the normative sample were evaluated by computing a d ratio with this formula:

d = \frac{(\bar{M} 1 - \bar{M} 2)}{\sqrt{[n 1 {(SD 1)}^{2} n 2 {(SD 2)}^{2}] / n 1 + n 2}} .

Results

The mean PASS scores and standard deviations of the two groups are provided in Table 3. The mean PASS scores of participants with ADHD ranged from 70 (Attention) to 95 (Simultaneous) and those of participants with Asperger’s disorder from 56 (Attention) to 96 (Successive).

Table 3.

Mean PASS Standard Scores and Standard Deviations of the Two Diagnostic Groups.

CAS	ADHD		Asperger’s disorder
CAS	M	SD	M	SD
Planning	79.25	15.35	64.85	17.95
Simultaneous	95.00	14.74	90.25	19.71
Attention	70.96	10.15	56.20	13.03
Successive	93.96	14.27	96.10	22.10
Full Scale	80.33	12.08	69.40	19.05

Note. PASS = Planning Attention Simultaneous Successive; CAS = Cognitive Assessment System.

Both cognitive profiles had a relative weakness in Attention but also two relative strengths in Simultaneous and Successive. A cognitive weakness in Attention emerged in both profiles (Figure 1).

Figure 1.

PASS Cognitive profiles of Asperger’s disorder group, ADHD group, and Italian normative sample.

A one-way MANOVA was conducted to compare the mean PASS scores of participants with ADHD and those with Asperger’s disorder. The overall effect between the two groups was significant (Wilks’s λ = .66; F = 3.92; p < .01). The two groups differ significantly for Planning (F = 8.23; p < .01) and Attention (F = 17.83; p < .01). The effect size (Table 4) was large for Planning (d = .87) and huge for Attention (d = 1.28). The differences between the two groups in Simultaneous and Successive were not significant, but the effect size was small for Simultaneous (d = .28).

Table 4.

Effect Size of the Differences of the PASS Mean Standard Scores Between Group With Asperger’s Disorder, Group With ADHD and Normative Sample.

CAS	Asperger’s disorder vs. ADHD	Asperger’s disorder vs. Italian normative sample	ADHD vs. Italian normative sample
Planning	0.87	2.33	1.38
Simultaneous	0.28	0.64	0.33
Attention	1.28	2.93	1.95
Successive	−0.11	0.26	0.40
Full Scale	0.70	2.03	1.32

Note. PASS = Planning Attention Simultaneous Successive; CAS = Cognitive Assessment System.

Evaluating the differences between the ADHD group and the Italian standardization sample, a huge effect size for Planning (d = 1.38) and for Attention (d = 1.95), and small effect size for Simultaneous (d = .33) and Successive (d = .40) were seen. The comparison between the Asperger’s disorder group and the normative sample showed huge effect sizes for Planning (d = 2.33) and Attention (d = 2.93), medium effect size for Simultaneous (d = .64) and small effect size for Successive (d = .26).

Discussion

The cognitive profiles of the two groups underline the difficulties in planning and attention processes of these participants. Particularly, participants with a diagnosis of Asperger’s disorder seem to have a major difficulty in both attention and planning. According to the studies that underline a major impairment in participants with a diagnosis of ASD than those with ADHD (Corbett et al., 2009; Salcedo-Marin et al., 2013), our data suggest a greater difficulty in processing information using planning and attention for participants with Asperger’s disorder. Contrary to Goldstein and DeVries (2011), our data suggest a similar shape of cognitive profiles for these two groups, which seem to be differentiated depending on severity. A possible explanation for the difference between these data could be the fact that our participants were collected in a clinical context and not in an educational one. The processes with higher impairment seem to be planning and attention for both diagnostic groups but in the group with Asperger’s disorder, the impairment is more severe than in the group with ADHD.

The cognitive impairments may perhaps explain executive dysfunctioning in terms of difficulties in cognitive flexibility, in planning, and in inhibition (Hill, 2004; Kaland et al., 2008). It is possible to hypothesize that the analysis of cognitive processes can overcome the problem of overlapping present in the studies regarding EF. In fact, the PASS cognitive processes depend on specific functional units, interactively linked, that constitute a neuroanatomical base able to influence the EF (Chan et al., 2008). Therefore, the study of PASS cognitive functioning could improve the comprehension of the basis of executive dysfunctioning.

Results allow us to underline strengths in participants’ profiles, particularly in simultaneous and successive processes, which could be resources for intervention and remediation programs. Particularly, the capability to elaborate information in order seems to be a strength in children with ADHD and Asperger’s disorder too. The analysis of cognitive profiles allows us to differentiate between these clinical conditions, suggesting a different involvement of the specific functional units, which could be important elements not only for assessment and for diagnosis, but even to design remedial programs (Naglieri, 2008; Naglieri et al., 2010).

Footnotes

Acknowledgements

The authors thank Doctor Carlo Benvenuti and Doctor Marco Armellini for their contribution to data collection.

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) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Stefano Taddei is a Psychologist and Assistant Professor at School of Psychology, Department of Health Sciences, University of Florence.

Bastianina Contena is a Psychologist, PhD student and research fellow at Department of Health Sciences, University of Florence.

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