Listeners prefer the laughs of children with autism to those of typically developing children

Abstract

The purpose of this study was to investigate the impact of laugh sounds produced by 8- to 10-year-old children with and without autism on naïve listeners, and to evaluate if listeners could distinguish between the laughs of the two groups. Results showed that listeners rated the laughs of children with autism more positively than the laughs of typically developing children, and that they were slightly above chance levels at judging which group produced the laugh. A subset of participants who reported listening for “uncontrolled” or “longer” laughs were significantly better at discriminating between the laughs of the two groups. Our results suggest that the laughs of children with autism have the potential to promote the formation of relationships.

Keywords

affect autism emotion laughs laughter perception

It is widely believed that emotional differences of children with autism spectrum disorders (ASD) contribute to their difficulty with forming social relationships (e.g. Dawson and Lewy, 1989; Hobson, 1989; Mundy and Sigman, 1989). This supposition has driven years of research on the emotional expressions of children with ASD. These studies have generated a wealth of knowledge about the emotional functioning of children with ASD; however, few studies examine the impact that these expressions have on others. We began to address this gap by exploring the impact of one emotional expression of children with autism on others: the production of laughter.

To date, most research on the emotional expressions of children with ASD examines how they produce facial expressions of emotion (e.g.Bauminger, 2004; Bieberich and Morgan, 2004; Capps et al., 1993; Dawson et al., 1990). Though researchers originally predicted substantial differences between children with ASD and their peers, studies revealed fewer overall differences between the groups than originally hypothesized (Capps et al., 1993; Dawson et al., 1990; Kasari et al., 1990; Snow et al., 1987; Yirmiya et al., 1989). In fact, one study indicated that children with ASD showed a greater variety of affective expressions than nonautistic participants (Yirmiya et al., 1989). These expressions consisted primarily of affective blends, which are defined as a combination of one or more facial expressions (Izard, 1979). One interpretation of this result is that children with ASD do not employ a greater variety of affective expressions per se, but rather produce facial expressions that are often more difficult for others to understand. Overall, however, the one notable difference between children with ASD and controls appears to be that children with ASD exhibit less positive affect during social interactions (Joseph and Tager-Flusberg, 1997; Snow et al., 1987; Yirmiya et al., 1989), and that they are less likely to reciprocate smiles with their mothers (Dawson et al., 1990).

The available literature indicates that there are also various differences between children with ASD and nonautistic children in the way they both imitate the emotions of others and describe their own emotions. Both children and adults with ASD show impairments in their ability to imitate emotional expressions relative to comparison participants (Hertzig et al., 1989; Macdonald et al., 1989). More recent work, however, suggests that children with ASD express differences in spontaneous but not voluntary mimicry of facial expressions (McIntosh et al., 2006; Oberman et al., 2009). Children with ASD have more difficulty describing “complex” or socially derived emotions (Oberman et al., 2009; Dennis et al., 2000), have difficulty labeling their own emotional state after watching affect-inducing videotapes (Yirmiya et al., 1992), and exhibit distinct differences in the way they describe causes and experiences of emotion relative to nonautistic participants (Jaedicke et al., 1994). Participants with ASD tend to emphasize material causes for emotion such as food, toys, and activities, whereas they deemphasize social interactions. The causes they cite for emotion are frequently idiosyncratic and referred to repeatedly (Jaedicke et al., 1994).

Some differences have also been noted between the vocal expressions of emotion of individuals with ASD and comparison participants. Reddy et al. (2002) examined humor and laughter in 3- to 5-year-old children with either ASD or Down syndrome. In their study, parents of children with ASD reported that their children’s laughter was rare in response to events such as funny faces or socially inappropriate acts, but was common in strange or inexplicable situations. They also found that participants with ASD were significantly more likely to produce unshared laughter than those with Down syndrome. Neither Reddy et al. nor others (Sheinkopf et al., 2000; St. James and Tager-Flusberg, 1994) reported significant differences in the mean rate of laugh production between children with ASD and children with Down syndrome. Despite similar rates in the expression of positive affect, more recently it has been shown that children with ASD exhibit only one type of laughter compared to typically developing participants (Hudenko et al., 2009).

Relative to our knowledge about the emotional expressions of children with ASD, comparatively little is known about how these expressions impact others. Of those studies that have systematically examined the impact of emotional expressions of children with ASD on others, Dawson et al. (1990) revealed that mothers of children with ASD were less likely to smile in response to their children’s smiles relative to mothers with typically developing children. These data suggest that mothers of children with ASD may respond less favorably to the expressions of their children than do mothers with typically developing children. Similarly, an investigation into the crying of children with ASD revealed that mothers were more likely to respond with a negative or uneasy state when hearing cries of children with ASD as compared with cries of typically developing or intellectually impaired controls (Esposito and Venuti, 2008). It has also been revealed that parents of children with ASD may be more attuned to the sounds of their own children than are naïve listeners (Ricks, 1975).

Understanding how the emotional expressions of children with ASD impact others is of critical importance. It is commonly understood that raising a child with ASD presents a host of difficulties for families. Numerous studies reveal that families of children with ASD exhibit higher levels of stress (Baker-Ericzen et al., 2005; Baker-Ericzen et al., 2005; Korn et al., 1978; Reddon et al., 1992), depression and anxiety (Baker-Ericzen et al., 2005; Hoppes and Harris, 1990; Wolf et al., 1989), and lower levels of marital happiness and family adaptability (Higgins et al., 2005; Rodrigue et al., 1990) than families with typically developing children. In addition, families containing a child with ASD exhibit higher levels of stress than families that have children with other developmental disabilities such as Down syndrome (Noh et al., 1989). Whereas higher levels of stress in families that have children with ASD may be largely due to the antisocial and disruptive behaviors of ASD that preclude a “normal” family life (Gray and Holden, 1992), it has also been suggested that the emotional expressions of children with ASD may negatively impact the behavior of family members (Dawson et al., 1990).

In the current study, we hoped to examine responses to laughs produced by children with autistic disorder. As mentioned previously, few studies have examined laugh production in children with ASD. Most of these studies do not find mean differences in the rate of laugh production between children with ASD and controls (Reddy et al., 2002; Sheinkopf et al., 2000; Snow et al., 1987; St. James and Tager-Flusberg, 1994). It has been shown, however, that children with ASD express qualitative differences in their laugh production relative to comparison participants (Hudenko et al., 2009). Specifically, children with ASD produce almost solely “voiced” laughter. Among other characteristics, laugh sounds are shown to vary dramatically in acoustic properties such as fundamental frequency (F₀, which is highly correlated with the perception of pitch) and duration (Bachorowski et al., 2001). One particularly salient distinction among laugh sounds is whether or not there is evident periodicity in vocal-fold vibrations (vocal folds are often referred to as “vocal chords” in common parlance). Laughs with periodicity in vocal fold vibration are referred to as “voiced”; these laughs have a measurable F₀ and perceptually have a tonal, song-like quality (sample laughs can be heard at http://www.ithaca.edu/faculty/whudenko/laughs/). In contrast, unvoiced laughs are largely atonal and noisier, and are produced with turbulence arising in the supralaryngeal (above the larynx) cavities. This distinction between voiced and unvoiced laughs is important because mounting evidence suggests that these laughs are functionally distinct. Most importantly, research shows that only voiced laughter is strongly associated with positive affect in typically developing young adults (Bachorowski and Owren, 2001; Grammer and Eibl-Eibesfeldt, 1990).

Given the available literature on laugh production in children with autism, we had three primary hypotheses. Previous research has revealed that children with ASD produce vocal sounds that are distinctive relative to typically developing children (Sheinkopf et al., 2000), and that listeners are attentive to subtle differences that distinguish the identity of children with autism (Ricks, 1975). Consequently, our first hypothesis was that listeners would be able to detect a difference between the laughs of children with autism and those of typically developing children. Given Ricks’ results, our second hypothesis was that individuals who had more experience with ASD would be better at discriminating between the laughs of the two groups. Third, in Hudenko et al.’s (2009) original study, it was suggested that higher rates of voicing produced by children with ASD may have been a marker of more genuine affect in these children. If these laughs are indeed more genuine, and presumably more engaging because of their voiced qualities, we hypothesized that in an experimental paradigm naïve listeners would exhibit a preference for laughs of children with ASD relative to laughs of typically developing children.

Method

Participants

Participants consisted of 135 college-aged students from a liberal arts college in central New York. Five participants were excluded from analyses due to a prior diagnosis of a hearing impairment and three participants were excluded due to missing 25 per cent or more of their data. Therefore, the final sample consisted of 127 participants (see Table 1).

Table 1

Participant characteristics

Age	Gender	Class	Ethnicity^a
19.2 (M)	42 Male	69 Freshman	114 Caucasian
	84 Female	33 Sophomore	8 Asian
	1 No response	13 Junior	4 Hispani
		12 Senior	2 Native American
			2 African American
			2 Other

Ethnic categories are not mutually exclusive.

Apparatus

All participants were tested on Dell Optiplex 760 (Dell, Inc., Round Rock, TX) computers. Stimuli were presented using custom-written PHP web pages presented on a Mozilla Firefox web browser (Mozilla Foundation, v. 20.12). Data were stored via a secure mySQL database application. Participants listened to audio samples using Senheiser model HD 202 circumaural headphones (Senheiser Electronic Corporation, Old Lyme, CT; frequency response range 18-18,000 Hz). Headphones were calibrated to ensure a common maximum amplitude using a Quest 2700 Type II sound level meter (Quest Technologies, Oconomowoc, WI). Each headphone earpiece was calibrated to 75 dB (± .05 dB) with a 460 Hz sinusoidal tone and then recalibrated to ± .05 dB with broadband white noise.

Laugh stimuli

Laugh samples were obtained from a prior study of laugh acoustics (Hudenko et al., 2009). Laughs from this investigation were collected during a 10-minute semi-structured laugh elicitation sequence designed to maximize laugh production in 8- to 10-year-olds. Laughs were obtained from 15 typically developing children and 15 children with a confirmed diagnosis of autistic disorder. Examples of laugh stimuli included building a tall tower with blocks, hitting a balloon back and forth, and asking the child to pop bubbles. All children were exposed to the same laugh eliciting stimuli, with the provision that the task was terminated if it did not result in laughter. Laughter was recorded using a wireless lavalier microphone and then extracted from the audio stream. Laughter was defined as being “any perceptibly audible sound that an ordinary person would characterize as a laugh if heard under everyday circumstances” (Bachorowski et al., 2001). Individual instances of laughter were typically produced during one exhalation and separated by more than 1 second of silence or speech sounds. Laughs were randomly selected using Microsoft Excel’s (Microsoft Corporation, Redmond, Washington) random number generator from a corpus of 390 laughs produced by 15 children with autistic disorder and 373 laughs produced by 15 typically developing children who were individually matched on chronological age. Following the selection procedure, laughs were excluded if the sound file contained overlapping speech, background noise, or if the file was corrupted. Laughs were excluded sequentially until a total of 58 laughs were selected from each group (see Table2 for laugh characteristics). A total of 265 laughs were excluded to obtain 116 clean samples. Through the process of random selection, laughs were selected from 12 of the original 15 typically developing children in the sample, and 9 of the original 15 children with autism. After selecting a random sample of laugh sounds from each group, laugh samples were normalized using a UNIX “normalize” algorithm (http://normalize.nongnu.org) to a common maximum amplitude value of -12dBFS.

Table 2

Laugh characteristics

	Laugh duration (s)	F0(Hz)	Child age (yrs)	CARS score
M
Autism	1.55*	606.10**	9–5	30.9
TD	.95	400.10	9–0	N/A
SD
Autism	1.00	298.13	9 mo.	5.9
TD	.90	137.32	6.8 mo.	N/A
Min
Autism	.32	263.42	8–7	22
TD	.14	213.13	8–2	N/A
Max
Autism	5.45	1388.47	10–8	41
TD	5.26	819.89	10–2	N/A

Note: CARS = Child Autism Rating Scale; TD = typically developing.

p < .05; **p < .01.

Given the repetitive nature of the laugh perception tasks in this study, pilot testing was conducted to determine the maximum number of laughs that participants would listen to without a decrement in their performance. Consequently, data were collected on the number of times a participant played each laugh, and how long it took for a participant to select a rating after playing each laugh. These measures were collected with the assumption that participants would play the laughs fewer times and spend less time judging laughs as their attention to task demands decreased. Data analysis revealed that there was no substantial decrease in attention after participants listened to 116 laugh samples. Therefore, 40 unique laughs were selected for the affective rating task, and 76 unique laughs were selected for the discrimination task.

Procedure

Participants were tested in groups of up to five individuals. Participants were seated in front of a computer and were instructed to put on a pair of headphones. Written informed consent was obtained from each participant and then demographics were acquired. Prior to rating laugh sounds, participants were asked to rate their current emotional state on a five-point Likert scale ranging from strongly negative to strongly positive. Next, participants were asked to rate a series of 40 laughs. A child with autism produced 20 of the laughs, and 20 were produced by an age-matched typically developing child. Laughs were presented in random order. Participants were instructed to “listen to the laugh below and then select the appropriate rating. You may play the laugh as many times as you need to make a rating.” Participants were asked to rate how each laugh made them feel on a five-point Likert scale ranging from strongly negative to strongly positive. Participants were not informed that children with autism produced any of the laughs. In addition to recording Likert-scale responses, custom-written Javascript code collected information about the number of times a participant played each laugh, and how long it took for a participant to select a rating after playing the laugh.

The second half of the study required participants to make judgments about laugh sounds. Participants were informed prior to the task that either a child with autism or a typically developing child produced the subsequent laughs. Next, participants listened to 76 randomly presented laughs that were different from the 40 in the first part of the study. A child with autism produced 38 of these laughs, and 38 were produced by a typically developing child. Participants were instructed: “Please listen to the laugh below and then decide if the individual does or does not have autism. You may play the laugh as many times as you need to make a judgment”. Participants were able to choose dichotomously between “has autism” and “does NOT have autism” for each laugh. Once again, information was collected about the number of times a participant played each laugh, and how long it took for a participant to select a rating after he or she played the laugh. At the conclusion of the study, participants were asked if they thought they could tell the difference between the laughs of children with autism and those of typically developing children. They were also asked to define autism, to rate their amount of experience interacting with children who have autism on a four-point Likert scale ranging from “no experience” to “a lot of experience”, and to explain qualitatively what they listened for to distinguish the laughs. Lastly, we asked participants to rate their current emotional state on the same five-point Likert scale utilized at the beginning of the study.

Data analysis

Paired-samples t tests were used to examine participants’ summed affective ratings between laughs produced by children with and without autism. When comparing affective ratings of only voiced laughs, ratings were averaged for each participant to control for differences in the number of laughs presented from each group. Paired-samples t tests were also used to examine changes in an individual’s affective state from the beginning to the end of the study. An independent-samples t test was used to examine differences in preference for laughs based on gender. One-way ANOVAs were used to test for differences in discrimination ability based on demographic characteristics. Between-group standard deviations were utilized for all effect size calculations as recommended by Dunlop et al. (1996). All quantitative statistics were run using SPSS v. 17.0.

Signal detection analysis was used to evaluate participants’ ability to discriminate between laughs of children with and without autism. D prime (d’) is a signal detection parameter (Green and Swets, 1966) that measures a participant’s ability to differentiate target stimuli from distracter stimuli. It is calculated by assessing the distance between the means of signal and noise distributions in units of their common standard deviation.

d^{'} = z (H) - z (F)

Higher d’values indicate greater sensitivity, with the highest possible value equaling 6.93 (Macmillan and Creelman, 2005). To obtain d’ from our data, the number of laughs produced by children with autism that were correctly identified (H, the hit rate), and the number of laughs produced by typically developing children that were incorrectly identified (F, the false positive rate) were obtained. In order to examine response bias, the criterion placement parameter was calculated (Macmillan and Creelman, 2005). Criterion placement (c) reflects bias through a calculation of the “zero-bias point” where the valid and invalid distributions intersect. Conservative biases (minimizing false alarms at the cost of a reduced hit rate) produce positive c values whereas liberal biases (maximizing hits at the cost of increasing false alarms) produce negative c values.

c - . 5 [z (H) + z (F)]

Lastly, a Receiver Operating Characteristic (ROC) curve was obtained to evaluate if participants were above chance levels at discriminating between the laughs of children with and without autism. ROC results can be analyzed in various ways. Typically, an ROC is either converted to z-space and standard regression analyses are used to assess the slope and intercept of the functions (Yonelinas and Parks, 2007), or the total Area Under the Curve (AUC) is calculated and compared against a value of .5 (at .5 H = F, and thus discrimination is no better than chance; Green and Swets, 1966). In the present study, the UAC was obtained because it was unnecessary to specify discrimination cutoff points that might be obtained from a zROC analysis.

Given that individuals who have more knowledge about autism may be more accurate at identifying laughs produced by each group, we created a custom scale to evaluate each participant’s knowledge about the disorder by coding responses to the open-ended question “How do you define autism?” We created a four-point scale based on DSM-IV-TR (American Psychiatric Association, 2000) criteria, whereby participants received one point for each diagnostic category identified. Two PhD-level clinical/counseling psychologists who were not previously involved with the study coded responses independently. An initial comparison between raters revealed a moderate inter-rater reliability of r = .7. All discrepancies between the two raters were resolved with discussion to result in a single score for each participant.

Participants were asked to identify the characteristics they used to distinguish between the laughs of children with and without autism, and grounded theory (Strauss and Corbin, 1990, 1997) was used to analyze responses. The second author coded keywords that summarized one or more elements of each participant’s response. One hundred and thirty three keywords were coded from the 127 student responses. Forty of these keywords occurred more than twice between participants. These keywords were reduced over three iterations into 10 final descriptive categories consisting of: volume, pitch, nonverbal sounds, uncontrolled, length, emotions/expressions, abnormal, tone, intensity of laugh, or miscellaneous cues. During each iteration, categories increased in breadth to incorporate words or phrases that described similar ideas. For example, the terms “unusual” and “different” were grouped together under the new category of “abnormal laughs.” Categories were not mutually exclusive, thus a single participant’s response could contain multiple codes. A univariate ANOVA was used to examine the relation between codes and discrimination ability. ANOVAs were also used to evaluate the number of categories a participant used to make a judgment, as well as which combinations of categories were reported.

Results

Paired-samples t tests showed that listeners rated laughs of children with autism more positively than laughs of typically developing children t(126) = 11.1, p < .001, d = 1.33 (see Figure 1). This was still the case when only voiced laughs were compared t(126) = 11.73, p < .001, d = 1.38. Independent-samples t tests revealed that females rated the laughs of both typically developing children t(124) = 2.31, p = .024, d = .45 and children with autism t(124) = 2.13, p = .035, d = .41 more positively than males. Both males and females exhibited less positive affect from the beginning to the end of the study t(126) = 3.39, p = .001, d = .28.

Figure 1.

Mean affective ratings of laughs produced by children with autism vs. those produced by chronological age-matched typically developing children (scale range 20-100).

Signal detection analysis revealed that participants had poor sensitivity when identifying laughs of children with autism d’= .18, and that they were more likely to indicate that laughs were produced by typically developing participants c= .31 t(126) = -8.34, p < .001, d = 1.48. Sensitivity and specificity for the judgments were .45 and .62, respectively. Despite a bias toward indicating that laughs were produced by typically developing children, ROC analysis revealed that participants were significantly above chance level at judging if the laugh was produced by a child with autism or a typically developing child AUC = .598, p< .01. Though participants were above chance at discriminating between the laughs of children with autism and typically developing children, only 19 per cent of individuals thought that they could tell the difference between the two groups. There were no significant differences in accuracy of judgments between males and females, year in college, amount of experience with autism, knowledge about the disorder, time spent considering the laugh, or difference in GPA. There were not enough participants of differing ethnicities to accurately examine the relation between ethnicity and accuracy of judgment. It was found that participants played the laughs of typically developing children significantly more times than they did the laughs of children with autism t(126) = 9.06, p < .001, d = .48, but that participants spent significantly more time before making a judgment about the laughs of children with autism t(126) = 7.11, p < .001, d = .40.

A univariate ANOVA revealed that participants who reported listening for “length” F(1) = 7.47, p = .007, h_p² = .07 and “uncontrolled” laughs F(1) = 16.65, p < .001, h_p² = .13 were significantly better at distinguishing between the laughs of children with and without autism. There were no significant differences between the discrimination ability of participants who reported listening for volume, pitch, nonverbal sounds, emotions/expressions, abnormal, tone, intensity of laugh, or miscellaneous cues. Additionally, we tested the number of cues that were used to make judgments and did not find significant differences between participants’ ability to discriminate. Though laugh samples were selected randomly, we found that the characteristics of laugh samples did differ between groups. Laughs of children with autism were significantly longer, t(114) = 3.40, p = .001, d = .63, and higher frequency, t(113) = 4.75, p < .001, d = .89, than those of typically developing children. There was no correlation, however, between either laugh duration or fundamental frequency and mean affective ratings for each laugh (r = .29, p = .067; r = .19, p = .24, respectively).

Discussion

Prior research has revealed that children with autism exhibit distinct laugh acoustics relative to typically developing participants (Hudenko et al., 2009). Most notably, children with autism produce primarily “voiced” laughter. This type of laugh is most often associated with feelings of positive affect in listeners (Bachorowski and Owren, 2001). Given this association, we tested the hypothesis that naïve listeners would prefer the laughs of children with autism to those of typically developing children. Consistent with our hypothesis, we revealed that listeners rate the laughs of children with autism more positively than they rate the laughs of typically developing children. This effect was more pronounced for female than male listeners.

Given the relatively rare occurrence of unvoiced laughter produced by children with autism, it is logical to conclude that the voicing in their laughter drives listener preference for their laughs. Much to our surprise, however, we revealed that preference for their laughs was not due to voicing alone. When we compared ratings of only voiced laughs in each group we found that there was still a significant preference for the laughs of children with autism. Consequently, additional factors may help to explain why listeners had a strong preference for these laughs. First, analyses revealed that the laughs of the two groups differed in their acoustic properties. The laughs of children with autism were significantly longer and had significantly higher fundamental frequencies than those produced by typically developing children. This result was not anticipated because laughs were randomly selected from a larger sample of data where they did not differ statistically between these two groups on either duration or fundamental frequency (see Hudenko et al., 2009). To examine the possibility that duration and fundamental frequency drive laugh preference, we correlated mean affective ratings of each laugh with both duration and fundamental frequency values. There was no significant correlation between affective ratings and either acoustic parameter, suggesting that neither variable alone can explain the preference for laughs of children with autism.

Instead of a single acoustic parameter that activates positive affect in listeners, we hypothesize that a blend of supra-segmental acoustic cues has a positive impact on the perceptual systems of listeners. Research on the perception of laugh sounds produced by adults suggests that a number of cues such as change in fundamental frequency, rhythm, and duration of laugh sounds have an impact on listener ratings (Kipper and Todt, 2003a, 2003b). In particular, it appears that dynamic-acoustic variation may be the most salient determinant of positive responses to laugh production (Kipper and Todt, 2001). For example, rapid reductions in fundamental frequency and laugh episodes with segments of varying duration appear to be most engaging to adults (Kipper and Todt, 2003b). Interestingly, naturally occurring laugh sounds are highly variable in their acoustic features (Bachorowski et al., 2001), and thus it appears that listeners may be most positively influenced by the dynamic variation that is inherent to more spontaneous expressions of laughter. This explanation is consistent with recent evolutionary accounts of vocal signaling, which suggest that hominids have evolved to be maximally responsive to the acoustic features of their environment that promote survival (Owren and Bachorowski, 2003). For example, infant cries are particularly distressing to human ears because they must reliably elicit caregiving. Similarly, more genuine expressions of affect, such as Duchenne smiles (Ekman et al., 1990) or spontaneous laughs, are most likely to influence us positively because of their capacity to accurately signal important social activities such as a willingness to cooperate in the signal producer.

In the current study, we believe that listeners preferred the laughs of children with autism because they were highly attuned to supra-segmental cues that indicate a more genuine expression of positive affect. During the original recording session, children with autism produced almost solely voiced laughter (Hudenko et al., 2009). Given the link between voicing and positive affect, it is our hypothesis that children with autism were more likely to express only voiced laughter because they were less socially motivated to influence others and more likely to display a veridical expression of their internal state. In contrast, typically developing participants may have produced more lower-arousal, less genuine sounds in response to social cues. Though there was no direct measure of internal state in the initial study to confirm this hypothesis, it is reasonable to suppose that listeners are sensitive to many of the acoustic cues that mark the internal state of the laugher. To directly test our hypothesis, it will be important for future studies to examine the link between internal affect and the production of laugh sounds.

Though we hypothesize that listeners were attentive to acoustic cues that signaled more genuine expressions of positive affect, there are other possible explanations of our results. For example, it may be that children with autism possess an underlying biological difference in the neurological system that drives their vocal production. If dynamic-acoustic variation is the most important variable that drives listener preference for laughs (Kipper and Todt, 2001), it could be that subtle differences in the neural organization of children with autism results in more dynamic laugh production that is consequently perceived as pleasurable by listeners. There are some data to support abnormalities in the vocal acoustics of children with autism. Sheinkopf et al. (2000) showed that children with autism produce nonspeech sounds that are comprised of atypical vocal qualities. These atypical qualities include significantly higher rates of sounds defined as squeals, growls, and non-distress yells. In addition, increasing evidence suggests that expressive prosody differs in the speech of children with ASD relative to controls (Peppé et al., 2011). These observed differences in prosody may be independent of language skill, and therefore suggest an underlying difference in the vocal production system of children with autism.

Regardless of the specific causal mechanism that drives listener preference for the laughs of children with autism over those of typically developing children, we found that listeners were slightly above chance at distinguishing between the laughs of the two groups. This ability appears to be largely nonconscious, because only 19 percent of participants reported thinking that they could identify the difference. Our results showed that participants were biased toward suspecting that typically developing children produced each laugh. We hypothesized that individuals with more ASD experience would exhibit higher discrimination scores; however, our data indicated that listeners were equally able to distinguish between groups regardless of their level of experience with ASD. Neither experience with ASD, knowledge about the disorder, nor demographic characteristics related to discrimination ability. An analysis of the listening strategies used to distinguish the two groups revealed that those who described listening for “length” or “uncontrolled” laughs were more accurate at distinguishing between the groups. Since the laughs of children with autism in our sample were significantly longer than the laughs of typically developing children, it is likely that some listeners perceived this difference. It is currently unclear, however, if this finding was influenced by the random subset of laughs that were selected for this study given that there were no significant differences in length or fundamental frequency in the larger corpus of laughs. It is also unclear why a subset of listeners associated longer laughs with children who have autism. In contrast, we suspect that listeners who associated a more “uncontrolled” expression of positive affect may intuitively suspect that typically developing children are more apt to use socially mediated laughs. This is consistent with our hypothesis that children with autism may be less likely to produce purely social laughs that are not associated with their internal state. Overall, these data suggest that most listeners possess the aural sensitivities necessary to perceive the subtle acoustic cues that identify characteristics of the laugh producer. Nevertheless, these data should be interpreted with caution because listeners were only slightly above chance at discriminating between the two groups.

Data from this study have important implications for assisting children with autism. In contrast with a number of studies that detail the emotional deficits of children with autism, our results suggest that laughter may be one area of considerable strength for this population. Children with autism appear to produce primarily voiced laughs that are highly engaging to listeners. Though it is currently unclear how these laugh sounds may be used in a clinical setting, we believe that laughter presents one promising avenue for intervention. Numerous researchers suggest that expressions of positive affect such as laughter are powerful tools that are an important part of building relationships with others (Baumeister and Leary, 1995; Fogel, 1982; Fredrickson, 2001). Consequently, if children with autism are producing laughs that are enjoyable to listeners, they may be encouraged to use these sounds to build positive social bonds with peers or caregivers. Much additional research is needed, however, to understand if laughter can be elicited in appropriate contexts. Even if laughter has the potential to assist with relationship formation, if used excessively or in an inappropriate context it is unlikely to assist individuals on the spectrum. For example, listeners in our study exhibited less overall positive affect by the end of the study, presumably because they heard numerous laugh samples without any context for their occurrence.

Additional studies are needed to broaden our knowledge about the impact of laughs produced by children with autism on others. Prior research has shown that mothers of children with autism are less likely to smile in response to their children’s smiles relative to mothers with typically developing children (Dawson et al., 1990). We revealed that naïve listeners respond more favorably to the laughs of children with autism; however, similar to Dawson et al.’s findings, mothers may not respond as favorably to the laughs of their own children. It may be that mothers have developed a negative association with certain affective signals, even if these signals are typically paired with positive responses. It may also be that caregivers of children with autism are more adept at discriminating between the laughs of children with autism and typically developing children. Unfortunately, in our study there was a restriction of range in the experience of our participants with autism spectrum disorders, which may have resulted in Type II error.

A great deal of further research is also needed to understand the acoustic parameters that influence listener preference for the laughs of children with autism. In the present study, we were unable to isolate a variety of laugh acoustics because we hoped to examine responses to laughs that were produced in a more naturalistic setting. Future inquiry into the acoustic qualities of the laugh sounds that drive listener preference will require controlling for a number of variables such as duration, change in fundamental frequency, voicing, and number of laugh sounds per episode. Additionally, a detailed analysis of the variation inherent to laugh production may provide useful insights.

Footnotes

Acknowledgements

We would like to acknowledge Olivera Stojsin, Sean Sunderland, Lauren Ashley, Karen Yoshida, Joseph A. Fraioli, Matthew Worhach, Julia Lyon, and Matti Hautala for their assistance with this research. We would also like to thank Hugh Stephenson, PhD, and Jeffery Holmes, PhD, for their assistance with scale reliability.

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