**4. Executive functioning**

110 Current Topics in Children's Learning and Cognition

**3. Learning of patterns** 

research.

could be changed under ideal task contexts.

processing, weak central coherence is now seen as a tendency, a preference of some sort that

As mentioned above, ASD is characterized by delays in language learning, including the learning of new words, their use, the pragmatics of language, or the fluidity of use (see e.g., Lord, Risi, & Pickles, 2004, for a review). These delays, as well as other symptoms of autism, have been attributed to differences in how patterns of information are learned (e.g., L. G. Klinger, Klinger, & Pohlig, 2007). More specifically, individuals with autism might have difficulty learning underlying patterns of events when hypothesis-testing strategies cannot be applied. This kind of learning is commonly studied under the umbrella of implicit learning (see Perruchet, 2008; Shanks, 2005, for reviews), artificial-grammars learning (e.g., Reber, 1967), or pattern detection in category formation (e.g., Ashby & Maddox, 2005; Keri, 2003). Here, we use the term "implicit learning", consistent with the term used in ASD

Studies of pattern learning have led to interesting findings in ASD. On the one hand, there are several findings that suggest impaired implicit learning in ASD (e.g., Romero-Munguía, 2008). Consider, for example, findings obtained with the so-called serial reaction time (SRT) task: Participants are asked to press a key to indicate a particular stimulus in a sequence. Learning is reflected in a decrease in reaction time for sequences that contain subtle repeated patterns, compared to random sequences (cf., Nissen & Bullemer, 1987). While typically developing children demonstrated such learning, participants with ASD did not (Mostofsky, Goldberg, Landa, & Denckla, 2000). Further support for compromised implicit learning comes from findings on prototype learning (Klinger & Dawson, 2001). The task was to categorize fictitious animals that differed in features like ear length, leg length, and neck length (cf., Younger, 1993). Children with ASD performed more poorly than control participants match in verbal age (see also Klinger et al., 2007). In fact, performance on implicit learning tasks was highly correlated with ASD symptomatology, including

communication skills, social skills, and the occurrence of repetitive behaviors.

However, the difference in implicit-learning abilities between ASD and control participants is not stable across task context, even when tested in the same lab (cf., Klinger & Dawson 2001; Klinger et al., 2001). Consider the SRT task again: when the inter-stimulus interval was reduced to 120ms (Barnes, Howard, Howard, Gilotty, Kenworthy, Gaillard 2008) or omitted altogether (Travers, Klinger, Mussey, & Klinger 2010), there was no difference between ASD and control participants. Both groups of children could learn to anticipate the rule-based sequence, compared to a random sequence (see also Muller, Cauich, Rubio, Mizuno, & Couchesne, 2004). Similarly, there was evidence for sequence learning when the rule was greatly simplified and the training extended to multiple sessions (Gordon & Stark, 2007). Furthermore, children with ASD demonstrate repetition priming effects comparable to those of controls (i.e., they could identify studies items better than non-studied items; Renner, Klinger, & Renner, 2000). And they were found to have intact semantic priming for simple common words (Toichi & Kamio, 2002) – further evidence for implicit-learning abilities in Executive functioning (EF) is an umbrella term to describe various cognitive abilities assumed to be involved in conscious problem-solving. They pertain, for example, to inhibiting incorrect but dominant actions, planning a future action, and flexibly switching attention when instructed to do so (e.g., Zelazo & Mϋeller, 2002). EF plays an important role in cognitive development, as it leads to an improved ability to override automatic responses (Garon, Bryson, & Smith, 2008). A classical EF task – but by far not the only one – is the Stroop task, a task in which participants are asked to name the color of the ink used for a printed word, the word spelling a particular color (Stroop, 1935). The central finding is a slowing in reaction time when the ink color differs from the spelled-out color (compared to trials in which the ink color matches the spelled-out color), demonstrating the difficulty of inhibiting the automatic tendency to read the word.

EF is thought to be associated with typical ASD attributes, including the need for sameness, difficulty with switching attention, a tendency to perseverate, and a lack of impulse control. Indeed, there are tasks in which participants with ASD show difficulty with inhibition (for a review see Rajendran & Mitchell, 2007). Consider, for example, an inhibition task in which participants have to point to an empty window in order to receive the reward shown in a non-empty window (Hala, & Russel 2001). Unlike control participants, a majority of participants with ASD have difficulty inhibiting their natural response of pointing to the reward they desire, compared to controls matched on mental age. Other examples of EF difficulties consist of difficulties with planning (e.g., (Ozonoff & Jensen, 1999, Ozonoff, Pennington, & Rogers, 1991), mental flexibility (e.g., Hughes, Russell, & Robbins, 1994;

Ozonoff, 1997), the generation of novel ideas (Turner, 1999), and self-monitoring (e.g., Hughes, 1996; Phillips, Baron-Cohen, & Rutter 1998; Russell & Jarrold, 1998, 1999).

Beyond the Black-and-White of Autism: How Cognitive Performance Varies with Context 113

For example, a child needs to know what a person is looking at to understand what a new label might refer to. Indeed, joint attention has been studied extensively in relation to children's word learning (e.g., Baldwin, 1995; Mundy & Newell, 2007; Carpenter, Nagell, & Tomasello, 1998; Tomasello, 1995; see also Flom, Lee, & Muir, 2007). A common task involves presenting children with a set of objects, and an adult visibly looking at the one that is being named. Both the amount of time the participant follows the eye-gaze of the adult and the degree of labelling are thought to reflect the amount of joint attention that

Children with ASD have demonstrated difficulty following the gaze of an adult in joint attention tasks (for a review, see Meindl & Cannella-Malone, 2011). And this deficit is observed alongside difficulties with learning new object names (Baron-Cohen, Baldwin, & Crowson, 1997; McDuffie, Yoder, & Stone, 2006; Parish-Morris, Hennon, Hirsh-Pasek, Golinkoff, & Tager-Flusberg, 2007; Preissler & Carey, 2005). For example, there is a pronounced learning difference between children with ASD and typically developing children when the labeled object was held by the experimenter, versus by the child (Preissler & Carey, 2005). This difference cannot be attributed to general word-learning deficits because word learning did not differ between diagnostic groups when the labeled object was in the child's hand. Similarly, learning did not differ between diagnostic groups when

Yet, despite strong evidence in favor of ASD impairments in joint attention, findings from other research complicate the picture: participants with ASD appear perfectly capable of joint attention in some contexts, if not even more skilled than their typically developing counterparts (Chawarska, Klin, Volkmar 2003; Kylliainen & Hietanen, 2004; Vlamings, Strauder, van Son, Mottron 2005). Consider, for example, a task in which participants have to press a corresponding button as soon as they see a target appear either at the top left or the bottom right of a monitor. A face was also shown in the center of the monitor. The gaze of the face was straight ahead, averted to the top left, or averted to the bottom right, 200ms before the target appeared. Findings show faster reaction time on trials in which the target appeared on the same side of the screen as the face's gaze, with no difference between

An argument could be made that different joint-attention tasks are not equally suited to capture the construct of joint attention. Maybe the reaction time task is a better reflection of joint-attention processes than a word-learning task. Such argument about what task might best reflect a stable factor is a common argument in the larger literature of cognition and cognitive development. However, it gets quickly overwhelmed as more context effects accumulate.

Another aspect of social reasoning is the ability to understand someone else's mental state, including their desires, motivation or beliefs. This kind of understanding is coined as theory of mind, with numerous studies investigating it and its development (Perner, 1991; Wellman, 1990). In a traditional theory of mind task, children are presented with two hiding

occurs between them.

the labeled object was the only novel object.

diagnostic groups (Kylliainen & Hietanen, 2004).

**5.2. Theory of mind** 

However, there are findings that undermine a straightforward ASD theory surrounding EF differences. For example, participants with ASD do not have more difficulty with the Stroop task than control participants (e.g., Ozonoff & Jensen, 1999; Eskes, Bryson, & McCormick, 1990): participants with ASD were found to show a typical slowing in reaction time when naming the ink of a word that spells a different color. Similarly, context effects were found with planning task that involves keeping in mind a certain set of rules to produce an outcome (e.g., Tower of Hanoi task, Stockings of Cambridge task). ASD performance was equivalent to typically developing performance on trials with only a small number of required steps for completion. Performance only differentiated between groups on longer, more complex trials. Further, performance appeared modulated by each individual child's nonverbal IQ, rather than symptomology (Hughes, Russell, & Robbins, 1994).

It appears that a claim about EF differences between typical development and ASD is not supported in all instances (for further review, see Hill, 2004). Performance seems instead dependent on specifics of the tasks and individual differences among children. Of course, it is always possible to interpret discrepant results consistent with a reductionist viewpoint. For example, one could argue that EF differences between typical and atypical development are most pronounced in so-called "hot" EF task, those that involve an emotional component (cf., Hongwanishkul, Happaney, Lee, & Zelazo, 2005). The differences might disappear in "cool" EF tasks, those that lack immediate rewards. These claims, though plausibly incorporating currently existing data, might not be able to capture context effects likely to accumulate as more data is being collected.
