Introduction
Autism spectrum disorders (ASDs), including autism, Asperger syndrome and pervasive developmental disorder not otherwise specified (PDD-NOS), are neurobiological developmental disorders in which restricted, repetitive behaviors and interests, and social and communicational problems predominate (American Psychiatric Association [APA]
2000). Children with ASD have deficits in executive functioning (Russell
1997), especially cognitive flexibility. Cognitive flexibility is the ability to switch rapidly between multiple tasks (Monsell
2003). Individuals with ASD have trouble adapting to variable demands of the environment (Kenworthy et al.
2010), show rigid behavior, hold on to previous behavior patterns, and show difficulty in adapting to changing plans or alterations of their routine in daily life. This restricted and repetitive behavior seems to be closely related to, or even an expression of impairment of, cognitive flexibility (Yerys et al.
2009). Although cognitive flexibility deficits in everyday life seem evident, the empirical laboratory data is not convincing (Geurts et al.
2009b). It seems that individuals with ASD perform better on computerized than on face to face administered tasks (Kenworthy et al.
2008), but the data is still inconclusive on both types of tasks (Van Eylen et al.
2011). In a recent review it was reported that the findings regarding cognitive flexibility in ASD are not merely inconsistent, but sometimes even contradictory (Geurts et al.
2009b). This inconsistency might be a result of the heterogeneity of the cognitive profiles in ASD, the high levels of comorbidity, and the overlap of different executive functions (Kenworthy et al.
2008). It is hard to find an accurate way to measure the construct of cognitive flexibility in the laboratory setting, and most tasks seem to lack ecological validity (Kenworthy et al.
2008). In the current study we try to bridge the gap between cognitive flexibility deficits as seen in everyday life and as measured in the laboratory setting.
In short, we used three kinds of tasks to measure cognitive flexibility in the research-setting; (1) traditional clinical neuropsychological measures; such as the Wisconsin Card Sorting Task (WCST), Trail Making Test (TMT), or Dellis-Kaplan executive function system (D-KEFS) color-word task; (2) hybrid neuropsychological/experimental measures, such as the intra-dimensional/extra-dimensional (ID/ED) set shift task (Cambridge Cognition
1996); and (3) experimental task-switch paradigms, for instance, switch tasks (Geurts et al.
2009b; Monsell
2003). Children with ASD show deficits on the WCST, and the D-KEFS color word task (Van Eylen et al.
2011). However, these are no a pure cognitive flexibility measurements (Geurts et al.
2009b, Ozonoff
1995), as they measure various cognitive functions, like working memory (WM), learning from feedback, and noticing changes in strategy. Switches in these tasks often occur in both an unpredictable and an unannounced manner. This makes it hard to disentangle what causes the failure on these tasks. Moreover, performance on these tasks is influenced by developmental level, which could also be accountable for the results (Happé et al.
2006). On the ID/ED shift task, results are also varying (Yerys et al.
2009). In this task it is necessary to shift within a single dimension (ID), from one dimension to another (ED), and to shift reversed (applying the same rule to an alternate exemplar; Cambridge Cognition
1996). Children with ASD show deficits in the ED reversal shifts, but do not in other shifts (Yerys et al.
2009, but see Happé et al.
2006). Apparently, when more stimulus-features have to be processed and more complex reasoning is necessary, children with ASD show cognitive flexibility deficits. Sustained attention also influences task performance (Geurts et al.
2009b), and because children with ASD experience attention deficits (Patten and Watson
2011), it might be that cognitive flexibility alone is not accountable for failure on these hybrid neuropsychological/experimental measures. In sum, it seems that traditional clinical neuropsychological measures, and hybrid neuropsychological/experimental measures, are not pure cognitive flexibility measurements; the findings on these measures seem to be influenced by many other variables.
The experimental task switch paradigm, for instance, switch task, is a relatively pure measurement of cognitive flexibility. Stimuli have to be sorted on two (or more) simple rules, for instance, sorting on color or sorting on form. After a number of consecutive trials performing one task (repeat trials), the other task has to be performed (switch trial). Performance is known to be slower and less accurate on switch trials than on repeat trials. The measures of performance usually are error rate (percentage of trials answered incorrectly), switch cost in reaction time (reaction time on switch trials minus reaction time on repeat trials) and switch cost in number of errors (error rate on switch trials minus error rate on repeat trials; Monsell
2003). Several studies that used switch tasks to compare children with ASD with typically developing (TD) children, revealed inconsistent results (Geurts et al.
2009b).
A possible explanation is that children with ASD simply do not have cognitive flexibility deficits, but given the prominent cognitive flexibility deficits exhibited in daily life, and the link between repetitive behavior and cognitive flexibility (Lopez et al.
2005; Yerys et al.
2009, but see Landa and Goldberg
2005), this conclusion seems too rigorous. The current study tries to overcome the conflicting findings by taking into account several task properties. Up to now the studied switch tasks roughly differ on three dimensions; WM-load, predictability, and the used stimuli. These differences influence both performance and ecological validity, and will be discussed in the next paragraphs.
Firstly, WM-load influences performance. In daily life, various tasks are influenced by both cognitive flexibility and WM, for instance, interacting with people in various situations requires both flexibility in interpreting the situation, and flexibility in remembering and processing information. Individuals with ASD show WM deficits (Alloway et al.
2009; Barnard et al.
2008, but see Happé et al.
2006). Both verbal, and spatial WM seems deficient (Kenworthy et al.
2008, Willcutt et al.
2008, but see Geurts et al.
2004), but some argue that the deficits in visual-spatial WM are the most prominent (Williams et al.
2005,
2006). Switch tasks rely on WM because arbitrary rules need to be memorized. The amount of WM-load varies (Dichter et al.
2010; Stoet and López
2010); the cue predicting a task switch can be available during each trial (Schmitz et al.
2006), at the beginning of a task run (Poljac et al.
2010; Shafritz et al.
2008), or at the beginning of the whole task (Maes et al.
2010). Hence, the poor WM in ASD is likely to influence switch task performance and might partly explain the inconsistent findings.
The joint influence of WM and cognitive flexibility as seen in everyday life is confirmed in the research setting (Stoet and López
2010). On switch tasks with minimal WM demand, children with ASD do not show difficulties (Schmitz et al.
2006; Stoet and López
2010), whereas difficulties are reported when the WM demand is higher (Maes et al.
2010; Shafritz et al.
2008; Stoet and López
2010, but see Poljac et al.
2010). In fact, performance on switch tasks seems to be more influenced by WM demand in children with ASD than in TD children (Dichter et al.
2010; Stoet and López
2010). Children with ASD are less accurate than TD children, and show larger switch costs when performing a switch task with high memory demand. However, on a similar task with low memory demand, both groups perform equal (Stoet and López
2010). Hence, WM capacity influences switch task performance (Alloway et al.
2009; Karbach and Kray
2009; Williams et al.
2005), especially in ASD (Stoet and López
2010). This partly explains the inconsistent findings, as the varying WM demand in switch tasks is often not controlled for. In the current study, a task cue is always present, so that WM capacity cannot influence performance.
Secondly, switches can occur in a predictable (Shafritz et al.
2008), or an unpredictable manner (Maes et al.
2010). Put differently, switches can occur after every other trial, and can be preceded by a switch cue (i.e., predictable switches) or occur after a varying inconsistent numbers of trials, and occur completely unannounced (i.e., unpredictable switches). Children with ASD do not show deficits on switch tasks with predictable switches (Stahl and Pry
2002; Whitehouse et al.
2006), but do show deficits when switches occur unpredictably (Maes et al.
2010; Stoet and López
2010; Yerys et al.
2009, but see Schmitz et al.
2006). In the current study a switch task with unpredictable switches is used to measure cognitive flexibility, especially since this increases ecological validity, as in everyday life the need for behavioral adaptation is normally not preceded by a warning.
Thirdly, most studies use simple geometrical figures as stimuli (Maes et al.
2010; Poljac et al.
2010; Schmitz et al.
2006; Shafritz et al.
2008; Stahl and Pry
2002; Stoet and López
2010), minimizing WM-load, and mental processing, but also reducing ecological validity. These tasks seem unable to discriminate ASD from TD individuals (Poljac et al.
2010; Schmitz et al.
2006; Stahl and Pry
2002; Stoet and López
2010). In everyday life, cognitive flexibility is needed while processing complex stimuli and more specifically, while participating in social interaction, as people tend to act differently in various situations. In the current study, ecological validity has been improved by administering a switch task with relatively complex and socially relevant stimuli in the form of male and female faces with different facial expressions. Children with ASD process emotions in a different way than TD individuals (Santos et al.
2008). An enhanced focus on irrelevant details leads to a reduced ability to recognize emotions (Begeer et al.
2008), and reacting to other people’s emotions appears to be difficult (Golan et al.
2008). Emotion processing also influences rigidity and deficits in social interactions in ASD (APA
2000), like adapting behavior and perspective, and inhibiting inappropriate behavior (Causton-Theoharis et al.
2009). This specific way of processing emotions—both in the laboratory setting (Santos et al.
2008), and in everyday life (Begeer et al.
2008)—is likely to influence switch task performance in the current task. To prevent that task performance would be influenced by emotion recognizing problems per se, we only used basic emotions. In the laboratory setting individuals with ASD do recognize basic emotions (Balconi et al.
2012; Boggs and Gross
2010), and are equally able as TD children to differentiate between happy and angry faces (Geurts et al.
2009a, Santos et al.
2008). Children with ASD also seem very well able to categorize faces by emotion or gender (Harms et al.
2010; Santos et al.
2008). In short, the distinct way children with ASD process emotional faces is thought to influence both everyday life behavior and performance on the current task. Hence, compared to most switch tasks, using simple geometric forms as stimuli, the ecological validity is improved. In daily life, interpreting emotions and gender is necessary in social interaction, but sorting on color or form is hardly ever needed.
In sum, in the current study we tried to bridge the gap between daily life cognitive flexibility deficits, and laboratory setting cognitive flexibility measurements in ASD. We compared the performance of a clinical group of children with ASD and an age- and IQ-matched TD group on a gender emotion switch task. Participants sorted pictures of happy or angry looking male or female faces, based on gender or emotion, randomly switching between the two sorting rules. A standardized switch task with a constant present task cue was used as a relatively pure measurement of cognitive flexibility with no WM influence. Switches occurred unannounced, and stimuli consisted of faces to improve ecological validity. The faces showed basic emotions, as these are recognized well by children with ASD (Boggs and Gross
2010; Geurts et al.
2009a), ensuring that emotion recognition deficits would not influence the findings. The difference between individuals with ASD and TD in processing faces (Santos et al.
2008) and emotions (Balconi et al.
2012) was expected to influence cognitive flexibility in everyday life and on this task similarly.
Firstly, performance on the gender emotion switch task was expected to be worse in children with ASD than in TD children, due to the unannounced switches (Maes et al.
2010; Stoet and López
2010; Yerys et al.
2009), and the increased ecological validity. Secondly, we expected that children with ASD that performed relatively poorly on the gender emotion switch task, would have higher scores on the ‘stereotyped behavior’, and ‘fear of changes’ scales of the Children’s Social Behavior Questionnaire (CSBQ; Luteijn et al.
1998), and the ‘repetitive behavior’ scale of the Autism Diagnostic Interview Schedule-Revised (ADI-R: Lord et al.
1994). Thirdly, children with ASD were expected to have trouble to disengage attention from an emotional task set, resulting in worse performance on emotion to gender switch trials than vice versa.
The last hypothesis is based on the assumption that various emotions are processed differently (Harms et al.
2010; Johnson
2009). For example, angry faces are perceived as more threatening and consequently remembered better than happy faces, and are more resistant to modification (Willis et al.
2010). Emotional stimuli influence task performance in general (Johnson
2009), and probably differently in individuals with and without ASD, because of the dissimilar emotion processing (Santos et al.
2008). Individuals that score high on a trait anxiety scale, and a worrisome thoughts scale of a stress state questionnaire have trouble disengaging their attention from an emotional task set, when switching to a neutral task set (Johnson
2009). These individuals have less effective emotional attention control, and poorly regulated attentional deployment; a strategy to reduce emotional reactivity by shifting attention away from emotion-eliciting stimuli (Johnson
2009). This dysregulation in attentional deployment, or attentional inflexibility, has been linked to anxiety, depression, and ASD (Maes et al.
2010), and was expected to influence the performance of the children with ASD in the current study.
Discussion
The goal of the current study was to bridge the gap between cognitive flexibility deficits in ASD as reported in everyday life and the inconsistencies in findings in studies trying to detect these deficits in the laboratory (Geurts et al.
2009b). To this end, performance of children with and without ASD was compared on a switch task with minimal WM-load, and increased ecological validity (as unpredictable switches, and more complex stimuli, i.e., emotional faces, were included). Switch costs on this so called gender emotion switch task, measuring cognitive flexibility, were comparable to switch costs reported in other switch task studies (Poljac et al.
2010; Schmitz et al.
2006; Shafritz et al.
2008; Stoet and López
2010; Yerys et al.
2009). In contrast with our expectation, children with ASD did not show cognitive flexibility deficits on the current task. Nonetheless, in line with the findings of Yerys et al. (
2009), an increase in repetitive behavior was related to an increase in switch costs within the ASD group. Apparently, children with ASD that perform poorly (i.e., less accurate) on a switch task also show relatively more repetitive behavior in everyday life. The null findings on the switch task in combination with the observed relation with repetitive behavior, is in correspondence with the finding that there were relatively large individual differences within the ASD group. It appears that only a subgroup of children with ASD show cognitive flexibility deficits, and in the current study, only a subgroup performed relatively slow and was less accurate. Also, children with ASD had higher switch costs in speed of responding, when switching from emotion to gender trials than the other way around.
Our findings are in line with other studies using switch tasks with low WM-load (Schmitz et al.
2006; Stoet and López
2010). However, because of the unpredictable switches (Maes et al.
2010; Stoet and López
2010; Yerys et al.
2009) and increased ecological validity, it was expected that children with ASD would perform worse than children without ASD. There are at least three possible explanations for the current findings.
Firstly, although switches occurred in an unpredictable manner, children did know that switches would occur at some point, so they were still somehow prepared for the switches. In everyday life, children with ASD seem especially rigid when a change of plans or a disruption in their routine happens entirely unexpected. When warned, guided, or prepared for a certain change, children with ASD seem better able to adjust to a new situation (Meadan et al.
2011). A switch task is quite predictable compared to everyday life, and consists of explicit rules. Possibly, children with ASD performed as well as children without ASD because the current task was still too predictable. Moreover, alongside cognitive flexibility, performing a switch task also relies on systemizing skills, as understanding the rules concerning causality, and predictability of outcome is necessary for a good performance on this type of tasks (Lawson et al.
2004). Systemizing skills are thought to be well developed in individuals with ASD (Lawson et al.
2004) and might compensate for the flexibility deficits in children with ASD when performing the current task.
Secondly, a switch task might be an overly pure cognitive flexibility measurement, while in everyday life, cognitive flexibility is never entirely isolated. Cognitive flexibility seems intact in ASD in an artificial isolated form, but it might exacerbate perseverative behavior (Dichter et al.
2010) when combined with other constructs (e.g., WM). Indeed, Schmitz et al. (
2006) reported no cognitive flexibility deficits in ASD on a switch task with unpredictable switches and low WM-load, while cognitive flexibility deficits are reported on switch tasks with both unpredictable switches and high WM-load (Maes et al.
2010; Stoet and López
2010). Hence, our choice to reduce the WM-load, to increase the purity of the measurement of cognitive flexibility, might have led to decreased ecological validity.
Thirdly, ecological validity could still be insufficient in the current task for other reasons. Using faces as stimuli probably improves ecological validity, because in everyday life, individuals have to deal with other people’s emotions. However, to prevent that instead of cognitive flexibility, emotion recognition abilities would influence task performance, the current task contained only the most basic emotions. In everyday life individuals with ASD specifically experience problems with recognizing more complex and subtle emotions (Begeer et al.
2008). These subtle emotions might, in particular, require more flexible behavior, as an appropriate response depends more on the context in which the emotion is displayed. It is relatively easier to recognize, and interpret, basic emotions like ‘angry’ and ‘happy’, to predict which behavior is most appropriate, and to act accordingly. Also, the faces are administered on a computer screen and no real social response is needed (Ozonoff
1995). Hence, in future studies the inclusion of more complex emotions might increase the ecological validity (see for other suggestions Kenworthy et al.
2008) to a higher extent.
Some might argue that the task in itself was not the reason for our null-findings, but that the pattern of findings was due to the validity of our ASD sample. We chose not to administer an Autism Diagnostic Observation Schedule (ADOS; Lord et al.
2000) to determine the present ASD characteristics. However, given the thorough diagnostic trajectory all the children in the current sample completed, including a parent report regarding the current ASD characteristics, this not seem to be a plausible explanation for lack of an overall group deficit in cognitive flexibility.
A potentially interesting finding was the trend in the ASD group that slow responding participants were less accurate than fast responding participants, compared to the TD group, where slow and fast responding participants were equally accurate. In both the ASD and TD group, the slower children made more omission errors, but in the ASD group, this contrast was larger, resulting in an interaction trend. The most simple explanation would be that slower children just responded too late more often (i.e., exceeding the time limit). However, this does not seem to be the case as the relatively slower responding children did not show such long reaction times. These relatively large individual differences within the ASD group, with only some participants performing relatively poorly overall (high reaction times as well as high error rates), might indicate that only a subgroup of the ASD population experience pure cognitive flexibility deficits and perform poorly on a switch task. Indeed, the ASD population is known for its variability in both behavior and cognition, and even in the basic features of ASD, i.e., social interaction, communication, and restricted and repetitive behaviors and interest (Happé and Ronald
2008). There are individual differences even in very young children with ASD, in theory of mind, executive functioning, and central coherence, and such individual differences seem also to be present at an slightly older age (Pellicano
2010). In that light, the ASD population cannot be seen as a completely homogeneous group. Probably, only some individuals with ASD perform poorly on switch tasks, and only some show repetitive behavior in everyday life. These large individual differences within the ASD population could explain the high variability in performance within the ASD group in the current study. Especially since seven children with ASD in the current sample did not meet the criteria for repetitive behavior on the ADI-R. It is questionable if the ASD population can be considered and studied as one homogenous group with respect to cognitive flexibility. The high variability within the ASD group makes it hard to find any group differences when comparing the whole ASD group to a TD group.
Additionally, we found that the ASD group showed higher switch costs in reaction time when switching from emotion to gender trials than the other way around. In the TD group switch costs in reaction time were equal in both directions. The current findings are still preliminary, but they do suggest that children with ASD indeed need more time to disengage from an emotional task set (Johnson
2009). Both groups processed emotion trials more slowly than gender trials, but as switch costs were compared, this relatively slow processing of the emotions cannot explain the findings. In the current task, participants had to react to the emotions in the emotion task, and in the gender task, the still visible emotions had to be ignored. A stronger distinction between the gender and emotion task might result in more robust differences between the two groups. Moreover, Johnson (
2009) found a similar effect in anxious and worrying individuals. Therefore, it will be important that in a future study frequently occurring comorbid anxiety within the ASD children (White et al.
2009) will be measured to determine whether our pattern of findings can indeed be related to ASD or can be explained by the presence of comorbid anxiety.
In conclusion, children with ASD do not show deficits on the gender emotion switch task, but switch performance is related to the amount of repetitive behavior. Moreover, a subgroup of children with ASD performs relatively poorly overall, and children with ASD seem to have more difficulty disengaging from an emotional task set. The high variability within the ASD group reflects individual differences, and heterogeneity within this population. This implies that instead of focusing on analyses on a group level, an individual differences approach might be more fruitful for future research.