Responsivity to familiar versus unfamiliar social reward in children with autism

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Responsivity to Familiar versus Unfamiliar Social Reward in Children with Autism

Azarakhsh Pankert1, Kilian Pankert1, Beate Herpertz-Dahlmann2, Kerstin Konrad1, and Gregor Kohls1§

1Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and

Psychotherapy, RWTH Aachen University, Neuenhofer Weg 21, D – 52074 Aachen, Germany

2Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy

RWTH Aachen University, Neuenhofer Weg 21, D – 52074 Aachen, Germany

§Corresponding author

Email: gkohls@ukaachen.de

Phone: +49 (0)241-80-88573

Fax: +49 (0)241-80-82544

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Abstract

In autism spectrum disorders (ASD), social motivation theories suggest that the core social communication

problems seen in children with ASD arise from diminished responsiveness to social reward. Although clinical

and experimental data support these theories, the extent to which the reward deficit in ASD is unique for social

rewards remains unclear. With the present investigation, we aimed to provide insight into the degree to which

sociality as well as familiarity of reward incentives impact motivated goal-directed behavior in children with

ASD. To do so, we directly compared the influence of familiar versus unfamiliar social reward relative to

nonsocial, monetary reward in children with ASD relative to age- and IQ-matched typically developing controls

(TDC) using a visual and auditory incentive go/nogo task with reward contingencies for successful response

inhibitions. We found that children with ASD responded stronger to visual familiar and unfamiliar social reward

as well as to nonsocial, monetary reward than TDC. While the present data are at odds with predictions made by

social motivation theories, individual variations beyond clinical diagnosis, such as reward exposure across

various social settings, help explain the pattern of results. The findings of this study stress the necessity for

additional research on intra-individual as well as environmental factors that contribute to social reward

responsiveness in individuals with ASD versus other neuropsychiatric disorders such as ADHD or conduct

disorder.

Keywords: Autism Spectrum Disorders · Social Reward · Nonsocial Reward · Familiarity · Cognitive Control

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Introduction

According to social motivation theories of autism spectrum disorders (ASD) dysfunction in social

reward processing may contribute significantly to the emergence of the core social communication impairments

seen in individuals with ASD (Chevallier et al., 2012; Dawson et al., 2005; Mundy, 1995; Schultz, 2005). More

specifically, different authors suggest that diminished responsiveness to social incentives (e.g., faces and facial

expressions, spoken language, gestures, biological motion, social interactions etc.), accompanied by intense

interests within the nonsocial realm (e.g., mechanical systems, actions and objects), may bias attention and

exploration away from the social surrounding towards the nonsocial environment (Kohls et al., 2012). This most

likely deprives affected individuals of crucial social perceptual and social cognitive learning opportunities

resulting in aberrant social skill acquisition and failed specialization of brain hubs subserving social information

processing (Schultz, 2005). While social reward responsiveness appears to be a pivotal factor in the

development of ASD, still relatively little is known about social reward function in this population (Dichter et

al., 2012a; Kohls et al., 2012).

Evidence from behavioral treatment programs suggests that children with ASD profit less from the use

of social rewards than from nonsocial rewards, such as candy, tokens, and stickers (Koegel et al., 2001; Matson

et al., 1996). Diminished reactivity to social reward is thought to be caused, at least partially, by problems

forming proper reward representations of social stimuli. In fact, stimulus–reward association learning has been

repeatedly highlighted as an area of difficulty for many young children with ASD, particularly when the reward

is a face versus an object (Dawson et al., 1998, 2001; Jones et al., 2013; Lin et al., 2012). Interestingly, the

variability in reward learning abilities has been identified as an important predictor of social communication

skills in this patient group (Munson et al., 2008). Moreover, several experimental studies have confirmed that

the performance of children with ASD is only minimally affected by socially rewarding stimuli and situations.

For instance, Garretson et al. found that low-functioning children with ASD performed significantly worse

under social reinforcement (i.e., praise) versus nonsocial, tangible reinforcement (i.e., pretzels or pennies) when

compared to a mental-age control group in a sustained attention task (Garretson et al., 1990). In addition, Geurts

et al. showed that performance on an interference control task increased when children thought they were

competing with peers; however, this modulating effect of social motivation was diminished in children with

ASD (Geurts et al., 2008). Moreover, Demurie et al. reported that children with ASD (as well as children with

ADHD) had slower response times than typical children when anticipating social versus monetary rewards in an

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incentive delay task (Demurie et al., 2011); however, this finding was not replicated in a later study (Demurie et

al., 2013a).

Although social reward dysfunction appears to be most consistent with the ASD social phenotype

(Grossman et al., 1997), the specificity of this reward deficit has recently been questioned given behavioral and

neural evidences in support of a more general reward dysfunction comprising social as well as nonsocial types

of reward, including money, food items, and typical autism-specific objects of circumscribed interest such as

machines, mechanical systems, vehicles, computers and so forth (Cascio et al., 2012, 2013; Dichter et al.,

2012b; Sasson et al., 2008). However, regarding social reward, prior investigations have exclusively utilized

rewards derived from unfamiliar people (i.e., strangers). It has long been known that children with ASD display

minimal exploration and responsiveness to unfamiliar environments and individuals, and often show signs of

discomfort and distress by contact with strangers (Kanner, 1943). In contrast, children with ASD exhibit better

social skills, increased rates of physical and eye contact, as well as are physiologically more responsive when

interacting with a familiar as opposed to an unfamiliar person (Van Hecke et al., 2009; Hudry and Slaughter,

2009; Kasari et al., 1993; Knott et al., 1995; Kylliäinen et al., 2012; Lord and Hopkins, 1986; Oberman et al.,

2008). Thus, it appears essential not only to study behavioral responsiveness to unfamiliar social reward, but

also to include social incentives, like praise or smiles from parents or other primary caregivers, that are

personally more meaningful and, thus, might hold more reward value for individuals with ASD. In fact, brain

imaging research in neurotypicals shows stronger reward circuitry responses to familiar individuals one is

emotionally attached to, such as family members or romantic partners, compared to unfamiliar or less

acquainted individuals (Acevedo et al., 2012; Aron et al., 2005; Bartels and Zeki, 2004; Ortigue et al., 2007). In

ASD, this also has been demonstrated in some studies (Pierce and Redcay, 2008; Pierce et al., 2001, 2004), but

not in others (Dalton et al., 2005). To date, experimental data comparing behavioral reward responsiveness to

familiar versus unfamiliar social reward are not yet available for children with ASD.

Furthermore, while the majority of studies on social reward responsiveness in ASD have focused on

visual incentives (e.g., smiling faces), the effects of auditory incentives (e.g., compliments) have mainly been

neglected. This is somewhat surprising given anecdotal, retrospective as well as experimental evidence that

children with ASD are particularly unresponsive to human as opposed to nonhuman sounds (Ceponiene et al.,

2003; Dawson et al., 2004; Kanner, 1943; Klin, 1991; Kuhl et al., 2005). Interestingly, using functional near-

infrared spectroscopy it has recently been demonstrated that already infants aged 4-6 months who are at risk for

ASD (and may go on to a diagnosis of ASD) showed less neural responsiveness to salient auditory (e.g.,

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laughing) as well as to salient visual social stimuli (e.g., female performing hand games) than low-risk control

infants (Lloyd-Fox et al., 2013). Thus, the incentive salience of social stimuli from both sensory modalities

seems to be diminished from very early on in the developmental course of ASD (Jones and Klin, 2013).

Relevant neuroimaging investigations indicate that the brain’s reward and salience network is

insufficiently activated when children with ASD process visual social rewards (Assaf et al., 2013; Delmonte et

al., 2012; Kohls et al., 2011, 2013a; Scott-Van Zeeland et al., 2010). This has most recently been extended to the

auditory domain: Abrams et al. found functional underconnectivity between human voice-selective brain regions

and the reward circuitry in children with ASD, which was correlated with symptom severity for social

communication deficits (Abrams et al., 2013). Taken together, these results suggest that developmental

abnormalities in social reward circuitry may impair the ability of children with ASD to experience visual and

auditory social stimuli as rewarding, with detrimental effects on goal-directed social behavior.

In fact, the ability to properly coordinate goal-directed behavior in social contexts has been suggested

as deficient in children with ASD (Ruble, 2001). Response inhibition, i.e., withholding an inappropriate action

in the service of higher-ranking goals such as reward attainment, constitute one crucial component of goal-

directed behavior (Casey et al., 2002). In order to investigate how response inhibition is modulated by reward,

we previously introduced a visual incentive a go/nogo task with social (positive facial expressions) and

monetary reward contingencies for successful motor inhibition. While both reward types improved ‘nogo’

response inhibition accuracy in typical children with substantially stronger effects for monetary reward (Kohls

et al., 2009a), children with attention deficit hyperactivity disorder (ADHD) exhibited a particularly greater

benefit from social reward (Kohls et al., 2009b). We adopted this approach for the present study and aimed to

directly compare the influence of familiar vs. unfamiliar social reward relative to nonsocial, monetary reward on

response inhibition processes in children with ASD across the visual and auditory modality using a modified

version of the incentive go/nogo task (see Methods section for details). This investigation may therefore provide

insight into the degree to which familiarity as well as sociality of reward incentives may impact motivated goal-

directed behavior in children with ASD.

Based on previous findings reviewed above (Kohls et al., 2011, 2013a), we expected that social reward

(i.e., smiling faces) and monetary rewards would enhance response inhibition accuracy in children (i.e.,

reducing nogo false alarm rates), but children with ASD would profit less than typical control children,

particularly when social reward was at stake, with most pronounced deficits observed under unfamiliar social

reward (i.e., face of strangers) versus familiar social reward (i.e., face of mother) conditions in both visual and

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auditory modality. We further tested the extent to which reward responsiveness in the incentive go/nogo task

(i.e., false alarm rates in response to different types of reinforcement) would be related to ASD severity of social

dysfunction (as assessed with ASD questionnaires) as well as to reward practices by caregivers (Fabes et al.,

1989).

Methods

Participants

A total of 34 children between the ages of 9 and 14 were included in the final data analysis of this

study, comprising 17 participants with an autism spectrum disorder (ASD) and 17 age- and IQ-matched

typically developing controls (TDC). All participants were required to have a full-scale IQ ≥ 80 (estimated

based on a short version of the WISC-III (Gleissner et al., 2003)). All subjects had normal or corrected-to-

normal vision, and none had a hearing impairment. Originally, 20 TDC and 19 children with ASD were

enrolled, but due to our strict matching procedure, three TDC were excluded. In addition, two children with

ASD were excluded, because they did not meet the cut-offs on the three ASD questionnaires (see below).

Children with ASD were recruited through flyers and word-of-mouth advertising in local Autism

treatment centers. The TDC group was recruited from local schools. Participants with ASD were included when

they had an established clinical diagnosis of ASD according to DSM-IV-TR criteria and/or when they fulfilled

the ASD algorithm cut-offs on at least three questionnaires that assess the core social communication

impairments of children with ASD: Social Communication Questionnaire (SCQ) (Rutter et al., 2003), Social

Responsiveness Scale (SRS) (Constantino and Gruber, 2005), and Marburg Rating Scale for Asperger's

Syndrome (MBAS) (Kamp-Becker et al., 2005). In the ASD group, diagnosis was confirmed in eight

participants using the Autism Diagnostic Observation Schedule-Generic (Lord et al., 2000) and the Autism

Diagnostic Interview-Revised (Lord et al., 1994). Please note that children with ASD whose diagnosis was

confirmed with ADOS-G/ADI-R did not differ on the ASD questionnaires from those children of whom no

ADOS-G/ADI-R measures were available (ps > 0.1). In addition, all parents were asked to evaluate their

children’s general mental health using the Child Behavior Checklist (Achenbach, 1991). Comorbid ADHD

symptomatology was assessed across all participants using the German parental report on ADHD symptoms

according to ICD-10 and DSM-IV (FBBHKS) (Döpfner and Lehmkuhl, 1998). Although DSM-IV and ICD-10

preclude comorbid diagnoses of ASD and ADHD, eight subjects in the ASD group fulfilled diagnostic criteria

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of ADHD (3 hyperactive-impulsive, 4 inattentive and 1 combined); this is consistent with common estimates of

co-occurrence (Leyfer et al., 2006). Two participants with ASD were on short-acting methylphenidate, but

discontinued medication at least 24 hours before testing. None of the other children used any kind of

psychotropic medication. None of the TDC had a history or presence of psychiatric or neurological disorders.

A summary of demographic data can be found in Table 1. The groups did not differ with respect to age,

estimated IQ and gender. As expected, however, children with ASD had significantly higher ratings on all three

ASD questionnaires (i.e., SCQ, SRS, and MBAS) than TDC. Also, participants with ASD were rated as having

significantly higher inattention, externalizing and internalizing scores as assessed with the CBCL, and stronger

ADHD symptom severity on the FBBHKS, including inattention, hyperactivity and impulsivity ratings.

Because the participant’s mother was utilized as familiar social reward in the current study, we were

also interested in the extent to which everyday maternal reward practices affected the child’s reward

responsiveness in the incentive go/nogo task. We focused on mothers given that they predominantly are the

primary caregiver in households of children with and without ASD, and they usually spent the most time with

their children than, for instance, fathers do (Konstantareas and Homatidis, 1992). Mothers were asked to

complete the Reward Scale, a short survey that was developed by Fabes and colleagues (Fabes et al., 1989) to

assess attitudes (e.g., "The use of rewards to motivate children can help produce desired behavior.”) and

practices (e.g., "To what extent do you provide your child with a reward for behaving properly?") regarding the

use of tangible instrumental rewards to motivate children for activities that they do not find attractive. Each

mother denoted on a scale from 1 (very low) to 5 (very high) the degree to which she agreed with or acted in the

way identified in the statement. In Fabes et al., the alpha reliability coefficient for the Reward Scale was

reported to be 0.85. Reward Scale ratings did not differ between groups (ASD: 31.7 ± 4.8, TDC: 31.5 ± 4.7; t

(32) = -0.14, p = 0.89).

Participants were compensated for their participation in the study. Informed consent was obtained from

all subjects and their parents prior to testing. The study was approved by the local Ethics Committee.

[insert Table 1 about here]

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Incentive go/nogo task

We used a modified version of the incentive go/nogo task by Kohls et al. (Kohls et al., 2009a, 2009b)

with social and monetary reward contingencies for successful nogo inhibitions presented either in the visual

modality or in the auditory modality. The order of the two task versions was counterbalanced among

participants and groups. Both tasks consisted of four experimental blocks, i.e., one non-reward baseline block

and three reward blocks. Each block contained 71 trials with 66% go signals and 33% nogo signals. Participants

were instructed to respond with their dominant hand as quickly as possible for all go signals, but to inhibit a

response for all nogo signals. To guarantee that all children understood the task instructions, each task version

was preceded by 20 practice trials, with the opportunity to repeat the practice trials if needed.

At the beginning and in the middle of each experimental block, subjects were reminded to react quickly

while maintaining a high level of accuracy. This instruction was included to avoid speed–accuracy trade-offs,

particularly in the reward conditions (slowing down the reaction time for go signals to improve accuracy for

nogo stimuli). Block duration was about four minutes, and a ten minute break was scheduled between the visual

and the auditory tasks. For each participant, the whole experimental procedure lasted about 60 minutes.

Visual modality. In the visual modality, the letter “X” served as the nogo stimulus, while the stimuli

for the go trials were the letters “A” through “E”. The stimuli were pseudorandomly presented in the center of

the computer screen for 350 msec with a fixed intertrial interval (ISI) of 1050 msec. Nogo trials were preceded

and followed by one to four go trials. Informative feedback (see below) was given after nogo trials and was

shown 1200 msec after the offset of the nogo signal for 700 msec.

For the first experimental block, i.e., the non-reward baseline condition, children were told that after

every nogo “X”, an abstract mosaic picture with a green frame would be shown after successful nogo inhibitions

and a red framed mosaic picture after failed inhibitions (Figure 1). In the following three counterbalanced

experimental blocks, i.e., the reward conditions, children were reinforced by either unfamiliar social reward,

familiar social reward or monetary reward presented group-wise (one grouping = eight rewards of the same

type). In the unfamiliar social reward condition, happy and exuberant facial expressions depicted by unknown

female adults served as positive social reinforcers, while neutral facial expressions were shown after false

alarms. In the familiar social reward condition, different pictures of the child’s mother smiling was used as

social reward for correct inhibitions and different neutral expressions were presented after failed inhibitions. In

the monetary reward condition, different colored wallets each filled with a 50 Eurocent coin served as nonsocial

reinforcers. Empty wallets were shown after false alarms. Children were told that a lower error rate would result

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in a higher amount of money paid after the experimental session. All children won an additional three Euros,

irrespective of their performance.

Auditory modality. In the auditory task version, a 600 Hz sinus tone served as the nogo stimulus,

while the stimuli for the go trials were the sinus tones 400 Hz, 410 Hz, 420 Hz, 430 Hz, and 440 Hz. The timing

of stimulus presentation was the same as in the visual modality. In the non-reward baseline condition, nonsense

words spoken by a foreign female voice were presented after correct nogo inhibitions and nonsense words

spoken by a foreign male voice were played after failed inhibitions. We chose a foreign female voice vs. a

foreign male voice to make both feedbacks as distinct as possible. Also, we chose a female voice to be presented

after correct inhibitions in order to keep it comparable to the social reward conditions, where either the mother

or an unfamiliar female voice was used. In the three counterbalanced reward blocks, verbal praise in form of

compliments (e.g., “Well done!”) spoken either by the child’s mother (familiar condition) or by an unknown

female adult (unfamiliar condition) served as positive social reinforcers, while neutral verbal comments (e.g.,

“Incorrect!”) from the child’s mother (familiar condition) or from an unknown female adult (unfamiliar

condition) were played after false alarms. Monetary reward was symbolized by the sound of coins dropping on a

table, and white noise was played after false alarms in the monetary condition.

[insert Figure 1 about here]

Subjective rating questionnaire

Following the non-reward baseline block and the reward blocks, children were interviewed with a

rating questionnaire to assess self-reports on subjective experiences associated with performing the different

experimental manipulations (Kohls et al., 2009b). The children rated how motivating they found the different

task conditions (i.e., baseline vs. reward blocks). A 5-point Likert-type scale was applied, ranging from 0 (not at

all) to 4 (very much).

Statistical analyses

False alarm rates (FA rates) were analyzed in a repeated measures ANOVA model with group as the

between-subjects factor (two levels: TDC, ASD) and reward type as a within-subjects repeated factor (four

levels: non-reward, familiar social reward, unfamiliar social reward, monetary reward), followed by planned

contrasts. Reaction times for hits (RT for hits) and reaction times for false alarms (RT for false alarms) were

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analyzed using a multivariate ANOVA model, followed by univariate ANOVAs. As age and IQ did not differ

significantly between the groups and were not correlated with the dependent measures, these variables were not

included as covariates in analysis of performance data. The alpha level was set at 0.05. In addition, effect sizes

were calculated using partial eta square (η2p). Since omission errors were very infrequent (below 2%), they were

not included in the analysis. Pearson product-moment correlations were computed to check for possible speed-

accuracy trade-off effects. For all correlational analyses, Bonferroni corrections were applied to adjust the alpha

level for multiple comparisons (i.e., critical p-value = 0.05/hypotheses tested). In order to explore the extent to

which reward responsiveness in the incentive go/nogo task (i.e., false alarm rates in response to different types

of reinforcement) would be predicted by ASD severity of social dysfunction (as assessed with ASD

questionnaires), ADHD symptom severity, or reward practices by caregivers, we computed stepwise multiple

regression analyses. All data were analyzed separately for the two task modalities.

To analyze the effects of performance feedback on subjective rating scores, the Wilcoxon signed-rank

test for related samples was employed. Mann-Whitney U-tests were applied to assess differences between

groups. Concerning subjective motivation ratings, we specifically analyzed the differential changes in the two

groups from the baseline to the reinforcement conditions using the non-parametric Pair Differences-U-test for

two independent samples. All statistical analyses were performed using SPSS version 21.0 (Armonk, NY: IBM

Corp.).

Results

Subjective motivation ratings

Visual modality. After the non-reward baseline condition, self-ratings revealed that both groups did

not differ in their subjective motivation to perform the task (MdnASD = 2, MdnTDC = 2; Mann-Whitney U = 133,

p = 0.67). Following the reward blocks, the TDC group, but not the ASD group, rated the task as more

motivating than the baseline condition (MdnTDC-Baseline = 2, MdnTDC-Rewards = 3; Z = -2.3, p = 0.023; MdnASD-Baseline

= 2, MdnASD-Rewards = 2; Z = -0.3, p = 0.71). However, self-rated motivation did not differ significantly between

both groups under reward conditions (Mann-Whitney U = 109.0, p(one-tailed) = 0.095). Regarding possible

differential changes of subjectively perceived motivation in the two groups from baseline to the reward

conditions, no significant differences were found (Pair Differences-U = 127.0, p > 0.05).

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Auditory modality. All effects were found to be non-significant (MdnTDC-Baseline = 2, MdnTDC-Rewards =

2; MdnASD-Baseline = 2, MdnASD-Rewards = 2; all ps > 0.05).

The impact of reward on false alarm rates

Visual modality. In the non-reward baseline condition, the ASD group showed a higher false alarm

rate than the TDC group (F (1, 32) = 4.62, p = 0.039, η2p = 0.13; Table 2). The 2 x 4 (group x reward) repeated

measures ANOVA revealed a significant main effect of reward (F (3, 96) = 5.52, p = 0.002, η2p = 0.15) and a

significant group x reward interaction effect (F (3, 96) = 3.12, p = 0.03, η2p = 0.09), while the main effect of

group was found to be non-significant (F (1, 32) = 0.59, p = 0.45, η2p = 0.02). These data suggest that false

alarm rates in all children changed under conditions of reinforcement, but that rewards differentially affected

inhibitory performance in the two groups. Planned contrasts showed that the significant reward effect was

related to all three types of reward vs. non-reward baseline (familiar social reward: p = 0.005, η2p = 0.23;

unfamiliar social reward: p = 0.001, η2p = 0.29; monetary reward: p = 0.004, η2

p = 0.23). False alarm rates did

not differ between the three incentive conditions (all ps ≥ 0.64). The significant group x reward interaction

effect emerged from a higher responsiveness of ASD participants to all three reward types versus non-reward

(unfamiliar social reward: p = 0.004, η2p = 0.26; familiar social reward: p = 0.048, η2

p = 0.12; monetary reward:

p = 0.036, η2p = 0.13; Figure 2).

Auditory modality. Only the main effect of reward was significant (F (3, 96) = 6.52, p < 0.001, η2p =

0.17) with both groups improving their false alarm rates under all three auditory reward conditions relative to

non-reward (all ps ≤ 0.033, all η2ps ≥ 0.13). All other effects were found to be non-significant.

[insert Table 2 and Figure 2 about here]

Cross-modality correlations. Next, we calculated cross-modality correlations across all participants

for false alarm rates separately for the four incentive conditions. We found positive associations for all three

reward conditions (familiar social reward: r = 0.57, p < 0.001; unfamiliar social reward: r = 0.57, p < 0.001;

monetary reward: r = 0.54, p = 0.001; non-reward baseline: r = 0.23, p = 0.2), indicating that inhibitory control

performance among participants was moderately comparable across the visual and auditory modality.

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Reaction times for hits and false alarms

Visual modality. The 2 x 4 (group x reward) repeated measures MANOVA with RT for hits and RT

for false alarms as dependent variables did not reveal any significant effects (all ps > 0.05). There was only a

tendency towards overall slower RTs in the ASD group relative to TDC (main effect of group: (F (2, 30) = 2.95,

p = 0.068, η2p = 0.16; Table 2).

Auditory modality. A significant main effect of reward emerged (F (6, 23) = 4.98, p = 0.002, η2p =

0.57), which was mainly related to faster RT for hits and false alarms when unfamiliar social reward was at

stake compared to the other reward conditions (significant ps ≤ 0.05). All other effects were found to be non-

significant.

Possible changes in performance strategies

Visual modality. Possible speed-accuracy trade-off effects were inspected by calculating correlations

between FA rates and RT for hits within the two groups and the four experimental conditions tested. All

correlation coefficients were found to be non-significant (ps > 0.05), suggesting that children did not slow down

reaction times for go signals to improve inhibition accuracy in each of the four visual incentive conditions. We

also did not find significant associations between FA rates and RT for false alarms (ps > 0.1).

Further, given that in the current task version only successful nogo inhibitions were rewarded, but not

fast and accurate go responses, we also checked for possible changes in performance strategy among participant

groups, such as slower responding under incentive conditions versus baseline to increase inhibitory control and,

thus, reward outcome. To do so, we calculated correlation coefficients between ∆RT (RT differences between

baseline and reward conditions) and the individual change index for false alarm rates (for details of these

measures, see (Kohls et al., 2009a)) within each group and separately for the incentive conditions. Correlation

coefficients in the TDC group were found to be non-significant (rs < 0.2, ps > 0.5). However, ASD participants

showed a change in performance strategy particularly from baseline to unfamiliar social reward (r = 0.68, p =

0.003 < Bonferroni-corrected p = 0.017; familiar social reward: r = -0.08, p = 0.8; monetary reward: r = 0.17, p

= 0.5), suggesting that they slowed down for go signals in favor of more successful nogo inhibitions, which

eventually resulted in greater unfamiliar social reward gain. In fact, the number of participants who changed

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their response strategy from baseline to unfamiliar social reward was significantly higher in the ASD group

relative to TDC (n = 9, 53% vs. n = 2, 12%; χ2 (1) = 6.6, p = 0.01).

Auditory modality. Neither speed-accuracy trade-off effects nor effects of changes in performance

strategy were detected in the auditory modality (ps > 0.05).

Stepwise multiple regression analyses for predicting reward responsiveness

Stepwise multiple regression analyses were conducted separately for groups and reward conditions in

order to explore the extent to which age, IQ, ADHD symptomatology, ASD severity or caregiver reward

practices would predict reward responsiveness in the incentive go/nogo task (i.e., false alarm rates in response to

different types of reinforcement). As shown in Table 3, caregiver reward practices was the only single best

predictor for both social and monetary reward responsiveness in children with ASD. The multiple correlation

coefficients indicate that between 31% and 36% of the variance of false alarm rates under monetary and social

reward conditions could be accounted for by their caregivers’ reward practices. The positive Beta values suggest

that those children with ASD whose mothers reported to value and use more tangible instrumental rewards on a

regular basis showed higher false alarm rates when social or nonsocial rewards were at stake.

[insert Table 3 about here]

Discussion

In this study, we applied a visual and auditory incentive go/nogo task with unfamiliar and familiar

social reward versus nonsocial, monetary reward contingencies for successful response inhibition to explore the

degree to which familiarity as well as sociality of reward incentives impact motivated goal-directed behavior in

children with ASD relative to TDC. We found that each type of reward improved response inhibition accuracy

(i.e., reducing false alarms) across all participants, confirming earlier findings (Demurie et al., 2013a; Kohls et

al., 2009a, 2009b; Vloet et al., 2011). This effect was virtually identical for both task modalities. However, the

ASD group in the current study showed greater responsiveness to visual reward contingencies compared to

healthy controls: While the children with ASD exhibited higher false alarm rates in the visual non-reward

baseline condition than TDC, they improved to the same level as TDC when reinforced with social and

monetary rewards. Such pattern of response inhibition enhancement under conditions of reinforcement has

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previously been ascribed to the core clinical symptomatology of ADHD (Luman et al., 2005). Intriguingly,

reward responsiveness in ASD was strongly predicted by caregivers’ reward attitude and practices, such that

those children whose mothers reported to value and use more tangible instrumental rewards on a regular basis

showed higher false alarm rates in response to social and nonsocial rewards.

Surprisingly, the present data do not support previous behavioral studies of diminished responsiveness

to social reward in children with ASD (Demurie et al., 2011; Freitag, 1970; Garretson et al., 1990; Geurts et al.,

2008). Thus, our results are at odds with predictions made by social motivation theories of ASD (Chevallier et

al., 2012; Dawson et al., 2005; Kohls et al., 2012; Schultz, 2005). However, several recent studies also failed to

reveal social reward dysfunction in children with ASD (Demurie et al., 2013a, 2013b; Ewing et al., 2013) (see

also discussion in (O’Connor and Kirk, 2008)). While the findings of this small-sample investigation prevent

any firm conclusions about the ASD population at large, the multiple regression data do suggest that specific

intra-individual factors beyond clinical diagnosis likely contributed to the pattern of reward responsiveness in

the ASD group tested.

Interestingly, the data at hand greatly resemble prior findings in children with ADHD that response

inhibition deficits can be improved through reward contingencies (Luman et al., 2005), with particularly strong

responsiveness to social reinforcement (Geurts et al., 2008; Kohls et al., 2009b; Matthys et al., 1998; Power,

1992). Considering that the majority of subjects with ASD scored high on clinical ADHD measures, the

existence of ADHD symptoms – and the cognitive and motivational mechanisms they are rooted in (Luman et

al., 2010) – might have impacted on reward responsiveness that is unique to ASD. Please note that we did not

exclude patients with pronounced ADHD phenotype, because core ADHD symptoms are common among

children with ASD (van der Meer et al., 2012), making our ASD sample representative for this clinical disorder.

However, we did not find that ADHD symptomatology contributed significantly to task performance in the

current ASD sample. Also, the subgroups of ASD children with and without comorbid ADHD were too small to

reliably detect any potential differences in reward responsiveness. Clearly, more systematic research is needed

that, for instance, compare children with and without ASD and with and without ADHD using a 2 by 2 between-

subjects factorial design in order to determine differences as well as similarities in reward processing for both

disorders.

Moreover, reward responsiveness in the sample of children with ASD was primarily predicted by their

caregivers’ usage of instrumental rewards to motivate them on an everyday basis. Because motivational drive

conditions (e.g., satiation vs. deprivation of social reinforcement) substantially affect a child’s reactivity to

J Neural Transm 2014 Apr 12. Epub

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reward (Gewirtz and Baer, 1958), differences in reward exposure most likely influenced reinforcer

responsiveness in a controlled laboratory setting as realized with the incentive go/nogo task. It is also plausible

that ASD children with greater reward-experience through their caregivers’ reward practices and attitudes put

particularly more effort into performing the task to attain the social and nonsocial reward goals, however, with

detrimental effects on response inhibition accuracy (“choking under pressure” phenomenon,- which we were not

able to capture properly with the current study design). In fact, psychological research has shown that reward

contingencies may often result, paradoxically, in less-than-optimal performance on tasks involving motor

execution skills (Mobbs et al., 2009), such as the go/nogo task applied in the current study. Also more than 50%

of the participants with ASD (n=9) were recruited from local autism therapy centers where they received

behavioral treatment. Considering that behavioral modification programs usually attempt to create first and

foremost responsiveness to social stimuli through operant conditioning techniques using reward (Dawson and

Zanolli, 2003), one can speculate that this type of intervention strategy might have biased (e.g., up- or down-

regulated) reward responsiveness in numerous children with ASD and could also explain why we did not find

the expected differential responsiveness to familiar versus unfamiliar social reward in the ASD group (Bray and

O’Doherty, 2007). It should be noted, though, that we did not systematically assess behavioral and social skill

treatment history in the current ASD sample to substantiate this hypothesis. Nevertheless, our study highlights

the necessity to evaluate variations in reward experiences across various social settings when exploring social

reward responsiveness among individuals with neuropsychiatric disorders including ASD.

Our study has some limitations that should to be considered. One main shortcoming previously

mentioned relates to our sample group and size, which consisted solely of 17 typical children and a group of 17

children with ASD. Moreover, we did not include a clinical comparison group, which limits the specificity of

the present findings. Thus, overall our conclusions are limited and need replication with larger samples,

including other neuropsychiatric disorders as point of reference (e.g., ADHD). In addition, the diagnosis of ASD

was confirmed in only half of the children using common observational schedules (ADOS-G) and interviews

(ADI-R). However, the presence of ASD was assured by three valid and reliable ASD parent questionnaires

(SCQ, SRS, and MBAS). At least, children with ASD whose diagnosis was confirmed with ADOS-G/ADI-R

did not differ on the ASD questionnaires from those children of whom no ADOS-G/ADI-R measures were

available (see Participant section). Also, in the current study design, the non-reward condition was always

presented first, followed by the three different reward conditions. Thus, practice effects might have contributed,

at least partly, to the performance improvements under reward (but see discussion in (Kohls et al., 2009a)). Most

J Neural Transm 2014 Apr 12. Epub

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importantly, though, it is unlikely that this potential bias fully accounts for the differential reward benefit found

in ASD versus TDC. Further, we relied on static face images to serve as social rewards which are relatively

artificial compared to real-life social encounters (Gossen et al., 2013; Kohls et al., 2013b). Thus, more

ecologically valid stimulus sets and experimental paradigms are necessary to provide a clearer picture about

social reward dysfunction in ASD which would be most consistent with the core clinical phenotype.

In sum, the present study explored the prediction made by social motivation theories that children with

ASD exhibit diminished responsiveness to social reward. The data at hand do not confirm this prediction.

Individual variations beyond clinical diagnosis, such as reward exposure across various social settings, help

explain the pattern of results. The findings of this study stress the necessity for additional research on intra-

individual as well as environmental factors that contribute to social reward responsiveness in individuals with

ASD versus other neuropsychiatric disorders such as ADHD or conduct disorder. We also suggest exploring

responsiveness to typical autism-specific rewards within the nonsocial realm (Sasson et al., 2008) and their

interference with social reward function to further test the value of social motivation theories of ASD (Kohls et

al., 2012). Ultimately, enhanced characterization of these processes will contribute to our understanding of the

biobehavioral heterogeneity of ASD (e.g., social subtypes (Wing and Gould, 1979)) and allow for the

development of new, more personalized and efficacious treatments (Dawson, 2008).

Competing interests

The authors declare that they have no competing interests.

Acknowledgements

We would like to thank all young volunteers and their families who participated in this study. We also

thank Astrid Pütz-Ebert for her help with data collection. This study was supported by the German Research

Foundation (Deutsche Forschungsgemeinschaft/DFG, IRTG 1328). We are also very grateful to two anonymous

reviewers for their helpful comments on earlier versions of the manuscript.

J Neural Transm 2014 Apr 12. Epub

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References

Abrams, D.A., Lynch, C.J., Cheng, K.M., Phillips, J., Supekar, K., Ryali, S., Uddin, L.Q., and Menon, V. (2013). Underconnectivity between voice-selective cortex and reward circuitry in children with autism. Proc. Natl. Acad. Sci. U. S. A.

Acevedo, B.P., Aron, A., Fisher, H.E., and Brown, L.L. (2012). Neural correlates of long-term intense romantic love. Soc. Cogn. Affect. Neurosci. 7, 145–159.

Achenbach, T.M. (1991). Manual for the Child Behavior Checklist/4–18 and 1991 Profile. (Burlington, VT: University of Vermont, Department of Psychiatry).

Aron, A., Fisher, H., Mashek, D.J., Strong, G., Li, H., and Brown, L.L. (2005). Reward, motivation, and emotion systems associated with early-stage intense romantic love. J. Neurophysiol. 94, 327–337.

Assaf, M., Hyatt, C.J., Wong, C.G., Johnson, M.R., Schultz, R.T., Hendler, T., and Pearlson, G.D. (2013). Mentalizing and motivation neural function during social interactions in autism spectrum disorders. NeuroImage Clin. 3, 321–331.

Bartels, A., and Zeki, S. (2004). The neural correlates of maternal and romantic love. NeuroImage 21, 1155–1166.

Bray, S., and O’Doherty, J. (2007). Neural coding of reward-prediction error signals during classical conditioning with attractive faces. J. Neurophysiol. 97, 3036–3045.

Cascio, C.J., Foss-Feig, J.H., Heacock, J.L., Newsom, C.R., Cowan, R.L., Benningfield, M.M., Rogers, B.P., and Cao, A. (2012). Response of neural reward regions to food cues in autism spectrum disorders. J. Neurodev. Disord. 4, 9.

Cascio, C.J., Foss-Feig, J.H., Heacock, J., Schauder, K.B., Loring, W.A., Rogers, B.P., Pryweller, J.R., Newsom, C.R., Cockhren, J., Cao, A., et al. (2013). Affective neural response to restricted interests in autism spectrum disorders. J. Child Psychol. Psychiatry.

Casey, B.J., Tottenham, N., and Fossella, J. (2002). Clinical, imaging, lesion, and genetic approaches toward a model of cognitive control. Dev. Psychobiol. 40, 237–254.

Ceponiene, R., Lepistö, T., Shestakova, A., Vanhala, R., Alku, P., Näätänen, R., and Yaguchi, K. (2003). Speech-sound-selective auditory impairment in children with autism: they can perceive but do not attend. Proc. Natl. Acad. Sci. U. S. A. 100, 5567–5572.

Chevallier, C., Kohls, G., Troiani, V., Brodkin, E.S., and Schultz, R.T. (2012). The social motivation theory of autism. Trends Cogn. Sci. 16, 231–239.

Cohen, J. (1992). A power primer. Psychol. Bull. 112, 155–159.

Constantino, J., and Gruber, C. (2005). The Social Responsiveness Scale(SRS) Manual (Los Angeles, CA: Western Psychological Services).

Dalton, K.M., Nacewicz, B.M., Johnstone, T., Schaefer, H.S., Gernsbacher, M.A., Goldsmith, H.H., Alexander, A.L., and Davidson, R.J. (2005). Gaze fixation and the neural circuitry of face processing in autism. Nat. Neurosci. 8, 519–526.

Dawson, G. (2008). Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Dev. Psychopathol. 20, 775–803.

Dawson, G., and Zanolli, K. (2003). Early intervention and brain plasticity in autism. In Autism: Neural Bases and Treatment Possibilities., G. Bock, and J. Goode, eds. (Chichester: Wiley), pp. 266–280.

J Neural Transm 2014 Apr 12. Epub

- 18 -

Dawson, G., Meltzoff, A.N., Osterling, J., and Rinaldi, J. (1998). Neuropsychological Correlates of Early Symptoms of Autism. Child Dev. 69, 1276–1285.

Dawson, G., Osterling, J., Rinaldi, J., Carver, L., and McPartland, J. (2001). Brief Report: Recognition Memory and Stimulus-Reward Associations: Indirect Support for the Role of Ventromedial Prefrontal Dysfunction in Autism. J. Autism Dev. Disord. 31, 337–341.

Dawson, G., Toth, K., Abbott, R., Osterling, J., Munson, J., Estes, A., and Liaw, J. (2004). Early social attention impairments in autism: social orienting, joint attention, and attention to distress. Dev. Psychol. 40, 271–283.

Dawson, G., Webb, S.J., and McPartland, J. (2005). Understanding the nature of face processing impairment in autism: insights from behavioral and electrophysiological studies. Dev. Neuropsychol. 27, 403–424.

Delmonte, S., Balsters, J.H., McGrath, J., Fitzgerald, J., Brennan, S., Fagan, A.J., and Gallagher, L. (2012). Social and monetary reward processing in autism spectrum disorders. Mol. Autism 3, 7.

Demurie, E., Roeyers, H., Baeyens, D., and Sonuga-Barke, E. (2011). Common alterations in sensitivity to type but not amount of reward in ADHD and autism spectrum disorders. J. Child Psychol. Psychiatry 52, 1164–1173.

Demurie, E., Roeyers, H., Wiersema, J.R., and Sonuga-Barke, E. (2013a). No Evidence for Inhibitory Deficits or Altered Reward Processing in ADHD: Data From a New Integrated Monetary Incentive Delay Go/No-Go Task. J. Atten. Disord.

Demurie, E., Roeyers, H., Baeyens, D., and Sonuga-Barke, E. (2013b). Domain-general and domain-specific aspects of temporal discounting in children with ADHD and autism spectrum disorders (ASD): A proof of concept study. Res. Dev. Disabil. 34, 1870–1880.

Dichter, G.S., Damiano, C.A., and Allen, J.A. (2012a). Reward circuitry dysfunction in psychiatric and neurodevelopmental disorders and genetic syndromes: animal models and clinical findings. J. Neurodev. Disord. 4, 19.

Dichter, G.S., Felder, J.N., Green, S.R., Rittenberg, A.M., Sasson, N.J., and Bodfish, J.W. (2012b). Reward circuitry function in autism spectrum disorders. Soc. Cogn. Affect. Neurosci. 7, 160–172.

Döpfner, M., and Lehmkuhl, G. (1998). Diagnostik-System für psychische Störungen im Kindes- und Jugendalter nach ICD-10 und DSM-IV. (Bern: Huber Verlag).

Ewing, L., Pellicano, E., and Rhodes, G. (2013). Using effort to measure reward value of faces in children with autism. PloS One 8, e79493.

Fabes, R.A., Fultz, J., Eisenberg, N., May-Plumlee, T., and Scott, F. (1989). Effects of rewards on children’s prosocial motivation: A socialization study. Dev. Psychol. 25, 509–515.

Freitag, G. (1970). An Experimental Study of the Social Responsiveness of Children with Autistic Behaviors. J. Exp. Child Psychol. 9, 436–453.

Garretson, H.B., Fein, D., and Waterhouse, L. (1990). Sustained attention in children with autism. J. Autism Dev. Disord. 20, 101–114.

Geurts, H.M., Luman, M., and van Meel, C.S. (2008). What’s in a game: the effect of social motivation on interference control in boys with ADHD and autism spectrum disorders. J. Child Psychol. Psychiatry 49, 848–857.

Gewirtz, J.L., and Baer, D.M. (1958). Deprivation and satiation of social reinforcers as drive conditions. J. Abnorm. Soc. Psychol. 57, 165–172.

Gleissner, U., von Ondarza, G., Freitag, H., and Karlmeier, A. (2003). Auswahl einer HAWIK-III-Kurzform für Kinder und Jugendliche mit Epilepsie. Z. Für Neuropsychol. 14, 3–11.

J Neural Transm 2014 Apr 12. Epub

- 19 -

Gossen, A., Groppe, S.E., Winkler, L., Kohls, G., Herrington, J., Schultz, R.T., Gründer, G., and Spreckelmeyer, K.N. (2013). Neural evidence for an association between social proficiency and sensitivity to social reward. Soc. Cogn. Affect. Neurosci.

Grossman, J.B., Carter, A., and Volkmar, F.R. (1997). Social behavior in autism. Ann. N. Y. Acad. Sci. 807, 440–454.

Van Hecke, A.V., Lebow, J., Bal, E., Lamb, D., Harden, E., Kramer, A., Denver, J., Bazhenova, O., and Porges, S.W. (2009). Electroencephalogram and Heart Rate Regulation to Familiar and Unfamiliar People in Children With Autism Spectrum Disorders. Child Dev. 80, 1118–1133.

Hudry, K., and Slaughter, V. (2009). Agent familiarity and emotional context influence the everyday empathic responding of young children with autism. Res. Autism Spectr. Disord. 3, 74–85.

Jones, W., and Klin, A. (2013). Attention to eyes is present but in decline in 2-6-month-old infants later diagnosed with autism. Nature 504, 427–431.

Jones, E.J.H., Webb, S.J., Estes, A., and Dawson, G. (2013). Rule learning in autism: the role of reward type and social context. Dev. Neuropsychol. 38, 58–77.

Kamp-Becker, I., Mattejat, F., Wolf-Ostermann, K., and Remschmidt, H. (2005). [The Marburg Rating Scale for Asperger’s Syndrome (MBAS)--a screening instrument for high-functioning autistic disorders]. Z. Für Kinder- Jugendpsychiatrie Psychother. 33, 15–26.

Kanner, L. (1943). Autistic disturbances of affective contact. Nerv. Child 2, 217–250.

Kasari, C., Sigman, M., and Yirmiya, N. (1993). Focused and social attention of autistic children in interactions with familiar and unfamiliar adults: A comparison of autistic, mentally retarded, and normal children. Dev. Psychopathol. 5, 403–414.

Klin, A. (1991). Young autistic children’s listening preferences in regard to speech: a possible characterization of the symptom of social withdrawal. J. Autism Dev. Disord. 21, 29–42.

Knott, F., Lewis, C., and Williams, T. (1995). Sibling interaction of children with learning disabilities: a comparison of autism and Down’s syndrome. J. Child Psychol. Psychiatry 36, 965–976.

Koegel, R.L., Koegel, L.K., and McNerney, E.K. (2001). Pivotal areas in intervention for autism. J. Clin. Child Psychol. 30, 19–32.

Kohls, G., Peltzer, J., Herpertz-Dahlmann, B., and Konrad, K. (2009a). Differential effects of social and non-social reward on response inhibition in children and adolescents. Dev. Sci. 12, 614–625.

Kohls, G., Herpertz-Dahlmann, B., and Konrad, K. (2009b). Hyperresponsiveness to social rewards in children and adolescents with attention-deficit/hyperactivity disorder (ADHD). Behav. Brain Funct. 5, 20.

Kohls, G., Peltzer, J., Schulte-Rüther, M., Kamp-Becker, I., Remschmidt, H., Herpertz-Dahlmann, B., and Konrad, K. (2011). Atypical brain responses to reward cues in autism as revealed by event-related potentials. J. Autism Dev. Disord. 41, 1523–1533.

Kohls, G., Chevallier, C., Troiani, V., and Schultz, R.T. (2012). Social “wanting” dysfunction in autism: neurobiological underpinnings and treatment implications. J. Neurodev. Disord. 4, 10.

Kohls, G., Schulte-Rüther, M., Nehrkorn, B., Müller, K., Fink, G.R., Kamp-Becker, I., Herpertz-Dahlmann, B., Schultz, R.T., and Konrad, K. (2013a). Reward system dysfunction in autism spectrum disorders. Soc. Cogn. Affect. Neurosci. 8, 565–572.

Kohls, G., Perino, M.T., Taylor, J.M., Madva, E.N., Cayless, S.J., Troiani, V., Price, E., Faja, S., Herrington, J.D., and Schultz, R.T. (2013b). The nucleus accumbens is involved in both the pursuit of social reward and the avoidance of social punishment. Neuropsychologia 51, 2062–2069.

J Neural Transm 2014 Apr 12. Epub

- 20 -

Konstantareas, M.M., and Homatidis, S. (1992). Mothers’ and Fathers’ Self-Report of Involvement with Autistic, Mentally Delayed, and Normal Children. J. Marriage Fam. 54, 153.

Kuhl, P.K., Coffey-Corina, S., Padden, D., and Dawson, G. (2005). Links between social and linguistic processing of speech in preschool children with autism: behavioral and electrophysiological measures. Dev. Sci. 8, F1–F12.

Kylliäinen, A., Wallace, S., Coutanche, M.N., Leppänen, J.M., Cusack, J., Bailey, A.J., and Hietanen, J.K. (2012). Affective-motivational brain responses to direct gaze in children with autism spectrum disorder. J. Child Psychol. Psychiatry 53, 790–797.

Leyfer, O.T., Folstein, S.E., Bacalman, S., Davis, N.O., Dinh, E., Morgan, J., Tager-Flusberg, H., and Lainhart, J.E. (2006). Comorbid Psychiatric Disorders in Children with Autism: Interview Development and Rates of Disorders. J. Autism Dev. Disord. 36, 849–861.

Lin, A., Rangel, A., and Adolphs, R. (2012). Impaired learning of social compared to monetary rewards in autism. Front. Neurosci. 6, 143.

Lloyd-Fox, S., Blasi, A., Elwell, C.E., Charman, T., Murphy, D., and Johnson, M.H. (2013). Reduced neural sensitivity to social stimuli in infants at risk for autism. Proc. Biol. Sci. 280, 20123026.

Lord, C., and Hopkins, J.M. (1986). The social behavior of autistic children with younger and same-age nonhandicapped peers. J. Autism Dev. Disord. 16, 249–262.

Lord, C., Rutter, M., and Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J. Autism Dev. Disord. 24, 659–685.

Lord, C., Risi, S., Lambrecht, L., Cook, E.H., Jr, Leventhal, B.L., DiLavore, P.C., Pickles, A., and Rutter, M. (2000). The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J. Autism Dev. Disord. 30, 205–223.

Luman, M., Oosterlaan, J., and Sergeant, J.A. (2005). The impact of reinforcement contingencies on AD/HD: a review and theoretical appraisal. Clin. Psychol. Rev. 25, 183–213.

Luman, M., Tripp, G., and Scheres, A. (2010). Identifying the Neurobiology of Altered Reinforcement Sensitivity in ADHD: A Review and Research Agenda. Neurosci. Biobehav. Rev. 34, 744–754.

Matson, J.L., Benavidez, D.A., Compton, L.S., Paclawskyj, T., and Baglio, C. (1996). Behavioral treatment of autistic persons: a review of research from 1980 to the present. Res. Dev. Disabil. 17, 433–465.

Matthys, W., van Goozen, S.H., de Vries, H., Cohen-Kettenis, P.T., and van Engeland, H. (1998). The dominance of behavioural activation over behavioural inhibition in conduct disordered boys with or without attention deficit hyperactivity disorder. J. Child Psychol. Psychiatry 39, 643–651.

Mobbs, D., Hassabis, D., Seymour, B., Marchant, J.L., Weiskopf, N., Dolan, R.J., and Frith, C.D. (2009). Choking on the money: reward-based performance decrements are associated with midbrain activity. Psychol. Sci. 20, 955–962.

Mundy, P. (1995). Joint Attention and Social-Emotional Approach Behavior in Children with Autism. Dev. Psychopathol. 7, 63–82.

Munson, J., Faja, S., Meltzoff, A., Abbott, R., and Dawson, G. (2008). Neurocognitive predictors of social and communicative developmental trajectories in preschoolers with autism spectrum disorders. J. Int. Neuropsychol. Soc. 14, 956–966.

O’Connor, K., and Kirk, I. (2008). Brief Report: Atypical Social Cognition and Social Behaviours in Autism Spectrum Disorder--A Different Way of Processing Rather Than an Impairment. J. Autism Dev. Disord. 38, 1989–1997.

J Neural Transm 2014 Apr 12. Epub

- 21 -

Oberman, L.M., Ramachandran, V.S., and Pineda, J.A. (2008). Modulation of mu suppression in children with autism spectrum disorders in response to familiar or unfamiliar stimuli: the mirror neuron hypothesis. Neuropsychologia 46, 1558–1565.

Ortigue, S., Bianchi-Demicheli, F., Hamilton, A.F. de C., and Grafton, S.T. (2007). The neural basis of love as a subliminal prime: an event-related functional magnetic resonance imaging study. J. Cogn. Neurosci. 19, 1218–1230.

Pierce, K., and Redcay, E. (2008). Fusiform function in children with an autism spectrum disorder is a matter of “who.”Biol. Psychiatry 64, 552–560.

Pierce, K., Müller, R.A., Ambrose, J., Allen, G., and Courchesne, E. (2001). Face processing occurs outside the fusiform “face area” in autism: evidence from functional MRI. Brain J. Neurol. 124, 2059–2073.

Pierce, K., Haist, F., Sedaghat, F., and Courchesne, E. (2004). The brain response to personally familiar faces in autism: findings of fusiform activity and beyond. Brain J. Neurol. 127, 2703–2716.

Power, T.J. (1992). Contextual factors in vigilance testing of children with ADHD. J. Abnorm. Child Psychol. 20, 579–593.

Ruble, L.A. (2001). Analysis of social interactions as goal-directed behaviors in children with autism. J. Autism Dev. Disord. 31, 471–482.

Rutter, M., Bailey, A., and Lord, C. (2003). The Social Communication Questionnaire (Los Angeles, CA: Western Psychological Services).

Sasson, N.J., Turner-Brown, L.M., Holtzclaw, T.N., Lam, K.S.L., and Bodfish, J.W. (2008). Children with Autism Demonstrate Circumscribed Attention During Passive Viewing of Complex Social and Nonsocial Picture Arrays. Autism 1, 31–42.

Schultz, R.T. (2005). Developmental deficits in social perception in autism: the role of the amygdala and fusiform face area. Int. J. Dev. Neurosci. 23, 125–141.

Scott-Van Zeeland, A.A., Dapretto, M., Ghahremani, D.G., Poldrack, R.A., and Bookheimer, S.Y. (2010). Reward processing in autism. Autism Res. 3, 53–67.

Vloet, T.D., Konrad, K., Herpertz-Dahlmann, B., and Kohls, G. (2011). [The effect of social and monetary reward on inhibitory control in boys with hyperkinetic conduct disorder]. Z. Für Kinder- Jugendpsychiatrie Psychother. 39, 341–349.

Wing, L., and Gould, J. (1979). Severe impairments of social interaction and associated abnormalities in children: Epidemiology and classification. J. Autism Dev. Disord. 9, 11–29.

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Table 1. Summary and Comparisons of Demographic and Clinical Characteristics for the Study Sample

ASD

(n=17)

TDC

(n=17)

p-values

M (SD) M (SD) 95% CI

Gender (male/female)

Age (years)

16/1

11.6 (1.5)

12/5

11.7 (1.2)

0.07

0.81

NA

-0.86 – 1.10

IQ (WISC-III) 109.3 (17.5) 109.2 (9.9) 0.98 -10.05 – 9.81

SRS (total)

SCQ (total)

MBAS (total)

CBCL Internalizing (T-score)

CBCL Externalizing (T-score)

CBCL Attention Problems (T-score)

FBBHKS (total)

108.0 (32.2)

19.5 (9.6)

118.7 (30.9)

68.2 (5.3)

61.2 (8.4)

67.9 (9.1)

27.4 (12.4)

17.4 (15.9)

4.8 (3.6)

37.2 (19.7)

50.9 (8.1)

49.1 (7.1)

50.7 (2.2)

5.8 (4.6)

< 0.001

< 0.001

< 0.001

< 0.001

< 0.001

< 0.001

< 0.001

-108.31 – -72.86

-19.94 – -9.59

-99.59 – -63.36

-22.23 – -12.51

-17.72 – -6.67

-21.97 – -12.44

-28.19 – -14.87

Note: ASD = Autism Spectrum Disorders; TDC = Typically Developing Children; WISC-III = Wechsler

Intelligence Scale for Children; SRS = Social Responsiveness Scale; SCQ = Social Communication

Questionnaire; MBAS = Marburg Rating Scale for Asperger's Syndrome; CBCL = Child Behavior Checklist 4-

18; FBBHKS = German Parental Report on ADHD symptoms according to ICD-10 and DSM-IV; CI =

Confidence Interval of the Difference

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Table 2. Main Performance Variables of the Incentive Go/Nogo Task

ASD TDC

p-values

M (SD) M (SD) 95% CI

Visual Modality:

FA rate NR 33.5 (15.0) 23.5 (11.8) 0.039 - 19.42 – -0.52

FA rate F-SR 23.0 (13.0) 21.5 (15.1) 0.75 -11.38 – 8.31

FA rate UF-SR 22.5 (9.3) 22.7 (13.3) 0.95 -7.73 – 8.26

FA rate MR 21.8 (11.8) 21.6 (12.9) 0.95 -8.91 – 8.42

RT hits NR 403.4 (27.4) 388.5 (28.5) 0.13 -34.46 – 4.58

RT hits F-SR 409.4 (35.5) 381.3 (35.7) 0.028 -52.96 – -3.24

RT hits UF-SR 410.2 (30.5) 377.0 (37.3) 0.008 -57.00 – -9.38

RT hits MR 411.3 (32.2) 387.7 (28.2) 0.03 -44.72 – -2.42

RT FA NR 358.1 (41.1) 338.5 (49.8) 0.22 -51.54 – 12.25

RT FA F-SR 358.9 (41.8) 326.0 (62.1) 0.083 -70.26 – 4.49

RT FA UF-SR 359.7 (47.0) 333.5 (34.7) 0.81 -55.62 – 3.37

RT FA MR 355.0 (44.1) 320.8 (75.1) 0.12 -77.27 – 8.76

Auditory Modality:

FA rate NR 35.0 (23.2) 25.6 (20.6) 0.27 -24.77 – 5.85

FA rate F-SR 22.5 (15.2) 19.7 (9.6) 0.52 -11.71 – 6.09

FA rate UF-SR 19.3 (18.3) 19.3 (12.8) 1.0 -11.03 – 11.0

FA rate MR 18.9 (12.3) 16.7 (9.5) 0.56 -9.91 – 5.50

RT hits NR 543.5 (92.5) 496.3 (97.7) 0.16 -113.68 – 19.23

RT hits F-SR 535.2 (87.8) 498.2 (88.7) 0.23 -98.61 – 24.73

RT hits UF-SR 496.0 (92.3) 481.8 (95.0) 0.66 -79.62 – 51.26

RT hits MR 525.4 (76.7) 492.7 (85.9) 0.25 -89.65 – 24.12

RT FA NR 474.2 (128.3) 424.6 (112.0) 0.25 -135.34 – 36.12

RT FA F-SR 496.6 (130.0) 468.4 (138.4) 0.55 -123.40 – 67.10

RT FA UF-SR 429.1 (109.7) 425.8 (158.0) 0.95 -103.05 – 97.18

RT FA MR 542.1 (138.7) 452.9 (128.1) 0.07 -187.48 – 9.09

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Note: ASD = Autism Spectrum Disorders; TDC = Typically Developing Children; FA rate = False Alarm rate

(in %); RT hits = Reaction time for hits (in msec); RT FA = Reaction time for false alarms (in msec); NR = non-

reward baseline; F-SR = familiar social reward; UF-SR = unfamiliar social reward; MR = monetary reward; CI

= Confidence Interval of the Difference

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Table 3. Summary of stepwise multiple regression analyses for predicting false alarm rates under different incentive conditions in children with ASD

Dependent variable Independent variable (predictor) R2 F (1,15) p-value b SE-b Beta 95% CI Beta Effect size (f2)**

FA Rate Non-reward

} Caregiver Reward Practice*

0.09 1.13 0.45 1.60 0.96 0.52 -0.68 – 3.89 0.09 (small effect)

FA Rate Social reward 0.36 8.30 0.011 1.28 0.44 0.60 0.33 – 2.22 0.56 (large effect)

FA Rate Monetary reward 0.31 6.84 0.020 1.37 0.52 0.56 0.25 – 2.48 0.45 (large effect)

Excluded non-significant model predictors (all ps > 0.25): SCQ, SRS, MBAS, FBBHKS, CBCL, IQ, and Age

Note: ASD = Autism Spectrum Disorders; SRS = Social Responsiveness Scale; SCQ = Social Communication Questionnaire; MBAS = Marburg Rating

Scale for Asperger's Syndrome; FBBHKS = German Parental Report on ADHD symptoms according to ICD-10 and DSM-IV; CBCL = Child Behavior

Checklist 4-18; IQ = Intelligence Quotient (WISC-III); FA rate = Nogo false alarm rate (in %); CI = Confidence interval.

*(Fabes et al., 1989). **(Cohen, 1992)

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Figure captions

Figure 1. Illustration of the incentive go/nogo task in the visual modality with the letters ‘A’ through ‘E’ as go

signals and the letter ‘X’ as the nogo stimulus. Correct inhibitions were either rewarded with unfamiliar social

incentives (i.e., happy facial expressions of an unknown person), with familiar social incentives (i.e., happy

facial expression of the child’s mother), with nonsocial incentives (i.e., wallet filled with money), or remained

unrewarded in the baseline condition. Details about the auditory task conditions can be found in the methods

section.

Figure 2. Changes in false alarm rate (i.e., nogo commission errors in %) from the non-reward baseline (NR) to

the three reward conditions (F-SR = familiar social reward; UF-SR = unfamiliar social reward; MR = monetary

reward) for both groups separately for the two sensory modalities.

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Figure 1.

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Figure 2.