Individual Differences in the Real-Time Comprehension of Children with ASD

Individual Differences in the Real-Time Comprehension ofChildren with ASDCourtney E. Venker, Elizabeth R. Eernisse, Jenny R. Saffran, and Susan Ellis Weismer

Many children with autism spectrum disorders (ASD) demonstrate deficits in language comprehension, but little isknown about how they process spoken language as it unfolds. Real-time lexical comprehension is associated withlanguage and cognition in children without ASD, suggesting that this may also be the case for children with ASD. Thisstudy adopted an individual differences approach to characterizing real-time comprehension of familiar words in agroup of 34 three- to six-year-olds with ASD. The looking-while-listening paradigm was employed; it measures onlineaccuracy and latency through language-mediated eye movements and has limited task demands. On average, childrendemonstrated comprehension of the familiar words, but considerable variability emerged. Children with better accu-racy were faster to process the familiar words. In combination, processing speed and comprehension on a standardizedlanguage assessment explained 63% of the variance in online accuracy. Online accuracy was not correlated withautism severity or maternal education, and nonverbal cognition did not explain unique variance. Notably, onlineaccuracy at age 5½ was related to vocabulary comprehension 3 years earlier. The words typically learned earliest in lifewere processed most quickly. Consistent with a dimensional view of language abilities, these findings point to simi-larities in patterns of language acquisition in typically developing children and those with ASD. Overall, our resultsemphasize the value of examining individual differences in real-time language comprehension in this population. Wepropose that the looking-while-listening paradigm is a sensitive and valuable methodological tool that can be appliedacross many areas of autism research. Autism Res 2013, 6: 417–432. © 2013 International Society for AutismResearch, Wiley Periodicals, Inc.

Keywords: autism; comprehension; language processing; receptive vocabulary; eye-gaze methodology; individual differences

Introduction

Language comprehension is markedly delayed in manychildren with autism spectrum disorders (ASD), but thenature and extent of these deficits are not well understood.Notably, very little is known about how quickly and accu-rately children with ASD make sense of spoken language asit unfolds—knowledge that is central to a more completeunderstanding of comprehension processes in this popu-lation. This study adopted an individual differencesapproach to investigating real-time comprehension inchildren with ASD using a well-known methodology fromdevelopmental psychology—the looking-while-listening(LWL) paradigm [Fernald, Zangl, Portillo, & Marchman,2008].

The study objectives were to characterize online com-prehension of familiar nouns in a heterogeneous groupof children with ASD, and to identify relationshipsbetween online lexical processing, and child-leveland word-level factors. A final objective was to evaluate

the utility of the LWL method for use with childrenwith ASD.

Language Comprehension in Children with ASD

Spoken language deficits in children with ASD areclearly detrimental and immediately recognizable, butseveral recent studies have suggested that languagecomprehension may be even more severely delayedthan language production in children with ASD early indevelopment [Charman, Drew, Baird, & Baird, 2003;Ellis Weismer, Lord, & Esler, 2010; Hudry et al., 2010].These findings point to the importance of investigatingearly language comprehension in children with ASD asit appears to be the source of marked deficits in thispopulation. Although most children with ASD experi-ence early language delays, their language skills becomeincreasingly variable over the course of development[Charman et al., 2003, 2005; Kjelgaard & Tager-Flusberg,2001; Tager-Flusberg, Paul, & Lord, 2005]. Indeed, some

From Waisman Center, University of Wisconsin, Madison, Wisconsin (C.E.V., E.R.E., J.R.S., S.E.W.)Received October 12, 2012; accepted for publication April 17, 2013Address for correspondence and reprints: Courtney E. Venker, University of Wisconsin, Waisman Center, 1500 Highland Avenue Room 475, Madison,

WI, 53705. E-mail: [email protected] online 20 May 2013 in Wiley Online Library (wileyonlinelibrary.com)DOI: 10.1002/aur.1304© 2013 International Society for Autism Research, Wiley Periodicals, Inc.

RESEARCH ARTICLE

INSAR 417Autism Research 6: 417–432, 2013

children with ASD eventually attain age-appropriate oreven precocious language abilities, whereas others expe-rience lasting impairments.

One empirical approach to characterizing this variabil-ity in language skills has been to examine associationswith factors that are hypothesized to support successfullanguage learning—including nonverbal cognition, imi-tation, joint attention, gesture use, and play. For example,a large literature has demonstrated that joint attentionsupports language development in children with ASD[e.g. Charman, 2003; Mundy, Sigman, & Kasari, 1990].Nonverbal cognitive abilities have also emerged as arobust predictor of language comprehension in childrenwith ASD both concurrently [Ellis Weismer et al., 2010;Luyster, Kadlec, Carter, & Tager-Flusberg, 2008] and lon-gitudinally [Charman et al., 2005; Thurm, Lord, Lee, &Newschaffer, 2007], leading to our interest in examiningcognition in the current study.

Researchers have also begun to explore how the sever-ity of children’s autism symptoms relates to their lan-guage abilities—a remarkably complex issue, given thearray of skills that represent “language,” and the array ofbehaviors that represent “autism.” Prior to the develop-ment of a standardized metric, raw totals from the AutismDiagnostic Observation Schedule [ADOS; Lord, Rutter,DiLavore, & Risi, 2002] were sometimes used as a proxyfor autism severity. This practice was imperfect not onlybecause raw totals were not designed to measure severity,but also because they were often correlated with lang-uage [Gotham, Risi, Pickles, & Lord, 2007; Joseph,Tager-Flusberg, & Lord, 2002]. Calibrated ADOS severityscores [Gotham, Pickles, & Lord, 2009], which are used inthe current study, address both issues. Although somedegree of association remains, language as measured bystandardized assessments and parent report is moreweakly associated with ADOS calibrated severity scoresthan with raw totals [Gotham et al., 2009; Shumwayet al., 2012].

In sum, previous studies have investigated how lan-guage outcomes in children with ASD are related tofactors such as nonverbal cognition and autism severity;the current study extended this approach to the study oflexical processing. This allowed us to determine whetherthe skills that support broader language outcomes are alsoassociated with a specific aspect of language: comprehen-sion of familiar vocabulary words in real time.

Measuring Language Comprehension in Real Time

To date, most studies of language comprehension in chil-dren with ASD have utilized parent report or standardizedbehavioral assessments. Although these measures arevaluable methodological tools, they capture comprehen-sion offline, after it has occurred—meaning they provideno information about language processing in real time.

Additionally, these measures incorporate relatively hightask demands because they are socially mediated andrequire children to produce purposeful responses (e.g.pointing to pictures) as an indication of comprehension.For these reasons, we turned to an alternative method formeasuring language comprehension: eye-gaze paradigms.

In contrast to traditional assessments, eye-gaze para-digms measure language comprehension in real timethrough language-mediated eye movements. Eye-gazeparadigms also have relatively limited task demands—namely, sitting reasonably still and looking at visualstimuli on a screen—that are particularly advantageousfor children with ASD because their limited social reci-procity, attentional deficits, and other challenging behav-iors often interfere in the administration of standardizedlanguage assessments [Charman et al., 2003; Kjelgaard &Tager-Flusberg, 2001]. Nevertheless, it is important toacknowledge that some children with ASD have difficultyeven with the limited requirements of eye-gaze para-digms; for example, three children were excluded fromthe eye-gaze analyses in this study because of limitedattention to the screen (see Methods).

The LWL paradigm [Fernald et al., 2008] is a well-established methodology for measuring the accuracy andspeed of real-time lexical comprehension in young chil-dren. In a typical trial, children see two images on ascreen and hear speech describing one of the images. Thecritical assumption is that children’s eye movements willbe guided by the auditory signal—if they understand thetarget word, they will look at the named image [Fernaldet al., 2008]. Eye gaze is recorded and coded offline, andsubsequent analyses provide measures of real-time lexicalprocessing accuracy and latency. Following the proce-dures outlined by Fernald and colleagues, accuracy isdefined as the relative amount of time spent looking atthe target image as compared with the distracter image,and latency is defined as the speed with which childreninitiate a gaze shift toward the target image, calculatedfrom the onset of the target word (see Methods for fulldetails).

Studies using the LWL paradigm have revealed a greatdeal about the time course of vocabulary comprehensionin young children. Most relevant to the current study isevidence that individual differences in real-time lexicalprocessing relate to vocabulary levels in young typicallydeveloping children [Fernald, Perfors, & Marchman,2006; Fernald, Swingley, & Pinto, 2001; Marchman &Fernald, 2008], children with cochlear implants [Grieco-Calub, Saffran, & Litovsky, 2009], and late-talking tod-dlers [Eernisse, Ellis Weismer, & Saffran, 2011; Fernald& Marchman, 2012]. Interestingly, these relationshipsappear to be quite robust, as lexical and grammaticalskills in 12-month-old infants are associated with theirreal-time comprehension at 25 months of age [Fernaldet al., 2006]. Even more striking is the finding that lexical

INSAR418 Venker et al./Real-time comprehension in children with ASD

processing speed in toddlerhood is associated withworking memory and expressive language at 8 years ofage in typically developing children, over and above theeffects of early vocabulary [Marchman & Fernald, 2008].Studies have also identified associations between accu-racy and processing speed in both typically developingchildren [Fernald et al., 2006] and late talkers [Eernisseet al., 2011], meaning that the children with more accu-rate lexical processing were also more efficient.

Real-Time Language Comprehension in Children with ASD

Recent findings have suggested that eye-gaze paradigmshold promise as a more implicit measure of comprehen-sion in children with ASD [Edelson, Fine, & Tager-Flusberg, 2008; Swensen, Kelley, Fein, & Naigles, 2007].For example, eye-gaze paradigms have been used to inves-tigate aspects of language learning in children with ASD,including attention to social cues [Norbury, Griffiths, &Nation, 2010], noun-learning biases [Swensen et al., 2007;Tek, Jaffery, Fein, & Naigles, 2008], and syntactic boot-strapping [Naigles, Kelty, Jaffery, & Fein, 2011].

A recent eye-gaze study by Goodwin, Fein, and Naigles[2012] demonstrated that real-time comprehension ofwh- questions was associated with vocabulary and adap-tive behavior skills in young children with ASD, but notwith nonverbal cognition. This finding is relevant to thecurrent study because it confirms variability in real-timelanguage processing in children with ASD—variabilitythat is related to some, but not all, child-level abilities.Additionally, Naigles et al. [2011] identified a longitudi-nal effect of real-time language processing in ASD that ledto the retrospective investigation of early language abili-ties in the current study. Specifically, Naigles and col-leagues found that children with ASD who demonstratedhigher vocabulary skills and faster real-time processing ofsubject-verb-object word order early in life were moreaccurate on a processing measure of syntactic bootstrap-ping 8 months later.

What remains unknown is how children with ASDprocess familiar nouns in real time, and whether patternsof lexical processing in children with ASD are similar tothose identified in other groups of children—issues inves-tigated by the current study. We propose that determin-ing whether patterns of language processing in ASD arequalitatively similar to those in other populations invitesdiscussion of two contrasting theoretical viewpointsregarding language abilities in ASD: the dimensionalaccount and the distinct category account.

The dimensional account was originally proposed tocharacterize language abilities in children with languagedelay and/or language disorders relative to typicallydeveloping children [e.g. Dollaghan, 2004; Leonard,1991; Rescorla, 2009]. This view asserts that languageabilities fall on a continuum and that some children

simply show more language facility than others; empha-sis is placed on qualitative similarities in patterns of lan-guage development, while acknowledging the potentialfor quantitative differences in the level of ability. Appliedto individuals with ASD, the dimensional account sug-gests that language abilities in ASD may be quantitativelydelayed but qualitatively similar to those seen in otherpopulations [Ellis Weismer et al., 2011; Gernsbacher,Geye, & Ellis Weismer, 2005; Tager-Flusberg et al., 1990].A contrasting view is the distinct category account[Gernsbacher et al., 2005], which posits that childrenwith language disorders—or, in this case, children withASD—have language deficits that comprise a qualitativelydifferent category of phenomena than those seen in othergroups of children.

In support of the dimensional view, a study byTager-Flusberg et al. [1990] found that a small sampleof children with ASD showed patterns of lexical andgrammatical development in spoken language that werequalitatively similar to those that would be expectedin typical language development. Ellis Weismer et al.[2011] found that toddlers with ASD and non-spectrumtoddlers with language delay who were matched on pro-ductive vocabulary produced words that fell into similarsemantic categories, despite the more severe delay in theASD group; additionally, the groups were indistinguish-able on the basis of the grammatical complexity of theirspoken language. If the findings had indicated that tod-dlers with ASD produced different categories of wordsthan the late-talking toddlers, or displayed a uniqueprofile of association between vocabulary and grammati-cal abilities, the distinct category view would have beensupported. A study by Rescorla and Safyer [2013] pro-duced very similar findings—namely, that children withASD had spoken language delays, but the lexical com-position of their expressive vocabularies was similar tothat seen in typically developing children and late-talking toddlers.

Consistent with a dimensional account of languageabilities in ASD, we hypothesized that patterns of real-time lexical comprehension in the current study wouldbe similar to those identified in children without ASD.Specifically, we predicted that children with ASD whowere more accurate at processing language would also befaster; that real-time processing would be supported bylanguage ability and nonverbal cognition, both concur-rently and retrospectively; and that words typicallylearned earlier in life would be processed more accuratelyor efficiently than words acquired later in life. On theother hand, it was entirely possible that our results mightpoint to qualitatively different patterns than those seen intypical development, or might reveal that typical patternsof real-time language processing were simply absent inthis population—findings more consistent with a distinctcategory account of language processing in ASD.

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The Current Study

We propose that a comprehensive understanding of real-time lexical processing in children with ASD is critical forthree primary reasons. First, comprehension is oftenseverely delayed in children with ASD, and it is essentialto understand how these striking deficits manifest in realtime. Second, language skills in ASD are remarkably vari-able, and it is unlikely that real-time language processingis an exception. Third, early lexical processing relates tolanguage and cognitive abilities in other groups of chil-dren, suggesting that this may also be the case for chil-dren with ASD. Understanding individual differences inreal-time vocabulary comprehension has the potential toshed light on the underlying mechanisms of languageand cognitive development in autism, and to informtheories of language acquisition in this population.Importantly, the LWL paradigm may prove to be a valu-able methodology that can be applied to many areas ofautism research.

The current study investigated individual differences inthe time course of vocabulary comprehension in a hetero-geneous group of 3- to 6-year-old children with ASD usingthe LWL paradigm, a method for measuring subtle differ-ences in real-time accuracy and processing efficiencywithout requiring explicit behavioral responses. Wehypothesized that our results would point to patterns oflexical processing that are qualitatively similar to thoseseen in children without ASD, consistent with a dimen-sional view of language development. The first objectivewas to characterize real-time comprehension of familiarwords in young children with ASD. Specifically, we wereinterested in analyzing comprehension at the group level,examining individual differences in online processing,and determining whether faster and more accurate pro-cessing were associated. Although the target words werefamiliar and early emerging, we anticipated that somechildren would be quite adept at processing familiarwords, whereas others would do poorly. Based on previouswork, we hypothesized a negative association betweenonline accuracy and latency.

The second objective was to specify the relationshipbetween individual differences in lexical processing andchild-level characteristics to better understand the skillsthat support real-time comprehension in this population.In order to address this question both concurrently andretrospectively, we examined children’s abilities at thesame time they completed the LWL task, as well as 3 yearsearlier. Based on the findings of Fernald and colleagues,we hypothesized that children’s online processing wouldrelate to their language abilities both concurrently andretrospectively. Based on findings regarding predictorsof language outcomes in children with ASD, we alsoexpected to identify associations between real-time pro-cessing and cognition and autism severity—most likely

because both of these factors capture information aboutchildren’s more general attentional abilities, which couldrelate directly to their performance on the LWL task.

Our third objective was to determine whether differ-ences in real-time lexical processing were related to acharacteristic of the words themselves. Specifically, weexamined the relationship between real-time accuracyand latency, and an index of age-of-acquisition specific toeach target noun, which was based on the proportion oftypically developing 8-month-old infants reported tounderstand each word (see Methods). We expected thatwords typically learned earlier in life would be processedmore accurately and efficiently than words typicallylearned later in life.

Our final objective was to establish whether the LWLparadigm is a feasible method for measuring languagecomprehension in children with ASD, drawing on ourfindings from the previous three objectives.

MethodParticipants

Participants were 34 children with ASD (3–6 years of age)from a broader longitudinal investigation of early lan-guage development in children with ASD. In the broaderstudy, children were evaluated annually from age 2½to 5½. Evaluations involved two sessions, each lastingapproximately three hours. Parents provided writtenconsent, and procedures were approved by the institu-tional review board.

Initial DSM-IV best-estimate diagnoses were deter-mined by an experienced clinician through integration ofinformation from the ADOS [Lord et al., 2002] or theAutism Diagnostic Observation Schedule-Toddler Module[Luyster et al., 2009], the Autism Diagnostic Interview-Revised [Rutter, Le Couteur, & Lord, 2003], and clinicalexpertise. Children with known chromosomal abnor-malities, cerebral palsy, seizure disorder, uncorrectedvision or hearing impairments, or frank neurologicalinsult were excluded. All children were from monolin-gual English speaking homes.

The LWL task took place at children’s second, third, orfourth visit for the longitudinal study, when they were,on average, 3½, 4½, or 5½ years old. The timing of chil-dren’s participation in the LWL task was dependent onthe schedule dictated by the broader longitudinal study;any families coming into the lab during the time whenthe LWL study was taking place were offered the chanceto participate (see Table 1). The retrospective analysisfocused on children’s language and cognitive abilities attheir initial visit to the lab, when they were 2½ years old.To account for differences in timing, only children whoparticipated in the LWL task at their final visit (age 5½)were included in this retrospective analysis; childrenwho participated at age 3½ or 4½ were excluded because


relatively less time had elapsed since their initial visit.Thirty children were Caucasian, one child was African-American, and three identified with multiple races orethnicities. Thirty-one children were male.

Standardized Measures

The ADOS informed ASD diagnosis and provided ameasure of autism severity. Following Gotham et al.[2009], raw algorithm totals were converted to calibratedautism severity scores. The calibrated scores indicate clas-sifications of non-spectrum (1–3), ASD (4–5), and autism(6–10). The Visual Reception scale of the Mullen Scales ofEarly Learning [Mullen; Mullen, 1995] measured nonver-bal cognition. Items include sorting objects, remember-ing pictures, and matching shapes. The PreschoolLanguage Scale, 4th edition [PLS-4; Zimmerman, Steiner,& Pond, 2002], is an omnibus measure of lexical, gram-matical, and conceptual aspects of language. The PLS-4Auditory Comprehension and Expressive Communica-tion subscales assessed comprehension and production,respectively. The MacArthur-Bates Communicative Deve-lopment Inventory-Infant Form [CDI; Fenson et al.,1993] assessed parent report of vocabulary comprehen-sion (number of words understood) at the initial visit;this variable was used in the retrospective analysis.

Mullen, PLS-4, and CDI raw scores were used in theanalyses because we were interested in children’s absolutecognitive and language abilities, not their extent of delayrelative to age-matched peers. This decision was furthersupported because t-scores could not be derived for chil-dren who were older than the age range for which theMullen manual provides normative data. Additionally,PLS-4 standard scores were positively skewed and did notappear to capture variability at the lower end of the scale.(The lowest possible standard score on the PLS-4 is 50,and over one third of the sample achieved standardscores of 52 or lower on either subscale.) Sample charac-teristics are presented in Table 2.

LWL Task

Procedure. Children sat in a chair approximately 1.5 min front of a 58′ wall-mounted LCD screen in a sound-attenuated booth with draped black walls. A research

assistant sat on the floor behind the child’s chair. Childrenwere told that they were going to watch a “movie” andthat their job was to watch and listen; no other instruc-tions were given. Children viewed two familiar images onthe screen and heard speech describing one image. Audi-tory stimuli were presented at 65 dB sound pressure levelfrom a central speaker. A video camera below the screenrecorded children’s eye movements.

Stimuli. The eight target words (ball, baby, car, doggie,cup, spoon, shoe, and book) are among the earliest wordslearned by young children [Dale & Fenson, 1996].Because many participants had language deficits, weasked parents to complete a questionnaire indicatingwhether their child understood each target word. Due toa change in study protocol, the parent questionnaire wasunavailable for four participants; in these cases, assess-ments from previous years were examined at the itemlevel (e.g. the child had demonstrated understanding ofball on the PLS-4 at the first visit). Based on these criteria,four trials were eliminated for four participants becausethere was no evidence available that the words werefamiliar.

Each word was depicted by two prototypical images.Auditory stimuli were recorded in an infant-directed reg-ister by a female native English speaker. Target wordswere presented within a carrier phrase (Where’s the ___?),followed by a tag question (Do you see it? or Do you likeit?). Mean target word duration was 590 ms (range = 440–760 ms).

Table 1. Number of Children Participating in the Looking-While-Listening Task at Each Visit in the Broader Study

Number ofchildren

Percentage oftotal sample

Visit 2 (Age 31⁄2) 2 5.9Visit 3 (Age 41⁄2) 3 8.8Visit 4 (Age 51⁄2) 29 85.3

Table 2. Participant Characteristics

Characteristics n M (SD) Range

Chronological age 34 65.32 (7.56) 44–79Maternal education 34 14.15 (1.88) 12–18Calibrated autism severity score 34 7.15 (1.84) 3–10Mullen Visual Reception

Raw score 34 43.62 (7.82) 24–50Age equivalent 34 53.56 (15.31) 21–69

PLS-4 comprehensionRaw score 33 43.58 (16.34) 17–62Age equivalent 33 48.24 (25.50) 10–81Standard score 33 79.73 (29.27) 50–129

PLS-4 productionRaw score 33 45.36 (15.96) 18–67Age equivalent 30 42.87 (21.31) 12–81Standard score 33 76.15 (26.75) 50–133

Note. Maternal education is presented in years. The calibrated autismseverity scores were derived from the Autism Diagnostic Observation Sched-ule. Visual Reception scores were obtained from the Mullen Scales of EarlyLearning. Language scores were obtained from the Preschool LanguageScale, 4th edition (PLS-4) Auditory Comprehension (comprehension) andExpressive Communication (production) subscales. One child did not com-plete the PLS-4, and three children had production age equivalents of > 6years, 11 months, which are omitted.


Pictures of two familiar objects appeared at the start ofeach trial. Trials lasted 7 sec, and the target noun waspresented 4 sec into the trial. Each word was presentedtwice, for a total of 16 trials. The task lasted approxi-mately 2 min and 15 sec. Words were presented in yokedpairs (e.g. doggie with baby) to control for potentialsalience effects. Words served an equal number of timesas target and distracter, and order and side of presenta-tion were counterbalanced. Four short animated movieswere interspersed to maintain attention.

Two versions of the task with different trial orders werecreated; approximately half of the sample (n = 19 vs.n = 15) received each version. Data were collapsed acrossthe two versions because t-tests identified no signifi-cant differences in mean accuracy, t(d.f. = 29) = -1.34,P = 0.19, or latency, t(d.f. = 25) = -0.48, P = 0.64.

Eye-gaze coding. Eye movements were coded offlineat a rate of 30 frames per second. No auditory or visualinformation about trial content was available duringcoding. Looks were coded as left, right, shifting, or awayfrom the screen. Six randomly selected video files (18%of the total sample) were independently recoded by thefirst author. Inter-coder frame agreement (the propor-tion of all frames on which the two coders agreed) was98.0%. Inter-coder shift agreement (the proportion ofshift frames on which the two coders agreed) was98.1%.

Measuring online accuracy and latency. Two mea-sures of online language processing were obtained: accu-racy and latency. These measures were calculatedfollowing the standard procedures outlined by Fernaldet al. [2008]. Accuracy was defined as the proportion oflooking to the target image during the target window,300–2000 ms after noun onset. Specifically, accuracy wasthe amount of time a child looked at the target image,divided by the amount of time a child looked at the targetand distracter images, during the target window. Follow-ing Fernald and colleagues, shifts between images andlooks away from the screen did not directly affect chil-dren’s accuracy; in cases of extreme inattention, trialswere eliminated (see below). Eye movements earlier than300 ms after noun onset were considered too rapid to berelated to the auditory information, given the timerequired to plan and execute an eye movement [Fernald& Marchman, 2012; Fernald et al., 2008]. Eye movementslonger than 2000 ms after noun onset were no longerassumed to be related to the auditory signal [Fernaldet al., 2008; Grieco-Calub et al., 2009]. Trials wereexcluded if the child looked away from the screen formore than half of the target window. Participants wereeliminated if they contributed data for fewer than 8 of the16 trials. Adoption of this criterion resulted in the exclu-sion of data from three participants.

Latency was calculated for the subset of trials in whichthe child was looking at the distracter image at wordonset (i.e. distracter-initial trials). Latency was defined asthe time to initiate a shift toward the target image indistracter-initial trials, calculated from noun onset. Aswith accuracy, trials with latencies of less than 300 ms ormore than 2000 ms were eliminated. A minimum crite-rion of two latency trials was imposed, which resulted inthe exclusion of data from an additional four childrenfrom latency analyses.

Index of age-of-acquisition. The proportion of 8-month-old infants reported to understand each targetword in the CDI normative database [Dale & Fenson,1996] was used as an index of age-of-acquisition. Datafrom 8-month-olds were used because of limited variabil-ity (i.e. ceiling effects) for comprehension of these wordsat older ages.

ResultsCharacterizing Real-Time Comprehension

The first objective of this study was to characterize real-time comprehension of familiar words in children withASD using the LWL paradigm. Recall that accuracy wasmeasured as the relative proportion of looking to thetarget image, 300–2000 ms after noun onset. The propor-tion of looking to target was also calculated at baseline—the 2 sec prior to noun onset—when children wereexpected to spend a similar amount of time looking ateach image. Each child (n = 31) contributed between 9and 16 accuracy trials (M = 13.32, SD = 2.09), for a total of413 trials.

Time course data are presented in Figure 1. Relativelooking to target was significantly higher after nounonset (M = 78.73%, SD = 9.72) than at baseline (M =52.51%, SD = 8.16; t(d.f. = 30) = -10.72, P < 0.001, d =1.93), meaning that children’s eye movements providedevidence of comprehension. Consistent with our predic-tions, mean accuracy varied considerably across children(55.09–97.38%). This means that one child looked at thetarget image nearly exclusively after hearing it named,whereas another child spent nearly the same amount oftime fixating the target and the distracter.

Interestingly, accuracy also varied a great deal acrossthe trials contributed by individual children. Several chil-dren had accuracy values of 0 on at least one trial,meaning that they never looked at the target imageduring the target window; all children had accuracyvalues of 1 on at least one trial, indicating that theylooked exclusively at the target image. To further illus-trate, one child had an accuracy of 0 on one trial forthe word ball and an accuracy of 1 on the other ball trial.This demonstrates the variability in children’s attentionacross trials, the challenge of interpreting children’s


performance on individual trials, and the impetus behindcalculating mean accuracy values by child.

Recall that latency was the time to initiate a shifttoward the target image in distracter-initial trials, calcu-lated from noun onset. Each child (n = 27) contributedbetween two and eight latency trials (M = 4.0 trials,SD = 1.49), for a total of 108 trials. On average, the firstshift away from the distracter image was initiated561.19 ms after word onset (SD = 183.12 ms). As withonline accuracy, variability in mean latency across chil-dren was substantial (333.50–1000.00 ms). Latency alsovaried considerably at the trial level. Similar to accuracy,children’s latency values on individual trials ranged from300 ms (the minimum) to 2000 ms (the maximum).Because latency was calculated only for the subset ofdistracter-initial trials, some children contributed veryfew trials to this variable, which may have produced anundesirable degree of noise. If possible, future studiesusing the LWL method with children with ASD shouldincorporate more trials to increase the likelihood of cap-turing meaningful information about their processingspeed.

In addition to calculating mean latency for distracter-initial trials, which is the standard measure of latencyused by Fernald et al. [2008], we also calculated latencyfor target-initial trials. As in the original latency analyses,trials with latencies outside of the target window wereeliminated. Latency of the target-initial trials indicatedthe amount of time it took children to initiate the firstshift from the target (correct) to the distracter (incorrect)picture, calculated from noun onset. Because childrenalready happened to be looking at the target picturewhen the target word was presented, we predicted thatmean latencies in target-initial trials would be longerthan mean latencies in the distracter-initial trials.

Consistent with our expectations, mean latency intarget-initial trials was 1023.95 ms (SD = 330.64), nearly500 ms longer than mean latency in distracter-initialtrials (561.19 ms). Of the 21 children who contributeddata for both latency variables, 19 (90.5%) had a longerlatency on the target-initial trials. This demonstrates thatchildren took a longer time to initiate a shift toward theincorrect image than they did to initiate a shift towardthe correct image, confirming that the latency measure iscapturing meaningful information about children’s pro-cessing speed. (Note that the standard measure of latencybased on distracter-initial trials was used in all subsequentanalyses.)

To determine whether children’s online accuracy andprocessing speed were related, a Pearson correlation wascalculated for the subset of children who contributedaccuracy and latency data (n = 27). A significant negativecorrelation was identified, r = -0.51, P = 0.01, meaningthat the children who were more accurate were alsofaster. Additionally, the partial correlation between accu-racy and latency remained significant when chronologi-cal age was statistically controlled, r = -0.47, P = 0.015,demonstrating that age did not fully account for therelationship between online accuracy and processingspeed.

Next, we felt it necessary to ensure that accuracy andlatency were not inherently correlated due to a potentialmeasurement confound. Because the length of the targetwindow was fixed (300–2000 ms after noun onset), trialsin which children had long latencies to initially shift tothe target image may have inherently had lower accuracy.To address this issue, we conducted a complementaryanalysis that rested on the creation of an alternative accu-racy variable containing only those trials that were notincluded in the latency analyses. This ensured that the

Figure 1. Time course of lexical comprehension. Error bars represent one standard error of the mean.


accuracy and latency variables relied on entirely indepen-dent subsets of trials, thus precluding an inherent corre-lation. Although the standard accuracy variable requiredeach child to contribute at least eight trials, some chil-dren contributed as few as six trials to the alternativeaccuracy variable because all latency trials had beeneliminated; the minimum criterion was not applied inorder to retain all 31 children in this analysis.

The correlation between the standard and alternativeaccuracy variables was r = 0.95, P < 0.001, indicating thatboth accuracy variables were capturing very similar infor-mation. Individual children’s mean accuracies changedvery little between the two variables, as 84% of thesample differed by only 0–3 percentage points. Next, weexamined the correlation between the alternative accu-racy variable and the standard latency variable; a one-tailed test was adopted because there was a clear expecteddirection of effect (i.e. that children who were more accu-rate would also be faster). The correlation between thealternative accuracy variable and latency was weakenedbut remained significant: r = -0.36, one-tailed P = 0.03.

This analysis, therefore, provides convincing evidencethat online accuracy and processing speed are associatedin children with ASD. The new accuracy variable con-sisted primarily of trials in which children were alreadylooking at the correct picture when they heard it named,which may represent a qualitatively different experiencethan distracter-initial trials. For this reason, the standardaccuracy variable was retained in all subsequent analysesunless indicated otherwise.

Online Processing and Child-Level Characteristics

Our second objective was to examine the relationshipbetween individual differences in online lexical process-ing (i.e. accuracy and latency) and several child-levelcharacteristics, both concurrently and retrospectively. Toprovide a meaningful context for interpreting our results,we also examined the concurrent relationship between

offline language comprehension (PLS-4 Auditory Compre-hension raw score) and these same characteristics. Ana-lyzing online and offline language comprehension in thisway allowed us to identify similarities and differences inthe factors that were most strongly related to the onlineand offline constructs of language comprehension.

Concurrent Analyses

Correlational analyses were conducted prior to the con-current analyses to affirm our selection of independentvariables for the regression models. Given our limitedsample size, it was imperative to limit the number ofpredictors entered into the models; our correlationalanalyses ensured selection of the most powerful predic-tors. Bivariate Pearson correlations were conductedbetween online accuracy and latency, offline comprehen-sion, and several child-level characteristics (see Table 3).Age was not significantly correlated with any variablestested, including language and cognitive raw scores(Ps > 0.12). Significance levels were unchanged and cor-relation strengths were nearly identical when age wasstatistically controlled through partial correlations foranalyses involving raw scores. Only bivariate correlationsare presented for the sake of clarity.

With the exception of online accuracy (describedearlier), online latency was not significantly associatedwith any child-level variable. Online accuracy wasstrongly correlated with offline language comprehension,offline language production (PLS-4 Expressive Communi-cation raw score), and nonverbal cognition (MullenVisual Reception raw score), but not with maternal edu-cation or autism severity. In contrast, offline languagecomprehension was strongly correlated with online accu-racy, offline language production, nonverbal cognition,and autism severity, and moderately correlated withmaternal education.

Next, regression analyses were conducted to identifyconcurrent predictors of online accuracy and offline

Table 3. Bivariate Correlations between Online Processing and Child Characteristics

Onlineaccuracy

Onlinelatency Age

Maternaleducation

Autismseverity

Nonverbalcognition

PLS-4comprehension

Online latency -0.51**Age -0.22 0.30Maternal education 0.13 0.17 0.07Autism severity -0.26 -0.21 -0.19 -0.21Nonverbal cognition 0.51** -0.10 0.27 0.18 -0.47**PLS-4 comprehension 0.62** -0.04 0.28 0.45* -0.58** 0.74**PLS-4 production 0.57** -0.001 0.24 0.42* -0.58** 0.74** 0.94**

Note. Autism severity was measured by calibrated ADOS severity scores. Nonverbal cognition was measured by Mullen Visual Reception raw score. PLS-4comprehensionwasmeasuredbyAuditoryComprehensionsubscale rawscore.PLS-4productionwasmeasuredbyExpressiveCommunicationsubscale rawscore.

*P < 0.05; **P < 0.01.ADOS, Autism Diagnostic Observation Schedule; PLS-4, Preschool Language Scale, 4th edition.


comprehension. Unlike the bivariate correlational analy-ses, the regression analyses allowed us to determinewhether certain variables explained unique variance inonline and offline language comprehension, controllingfor other correlated variables. Stepwise regression analy-ses were conducted because although we hypothesizedthat several factors would relate to online lexical process-ing, we did not have strong a priori hypotheses about thefactors that would uniquely predict online accuracy.The stepwise regression approach statistically identifiesthe most predictive independent variable and enters thatvariable into the model first, followed by the second mostpredictive, and so on. If a predictor does not account forunique variance in the dependent variable, it is automati-cally excluded from the model.

Two separate regression analyses of the concurrent pre-dictors were conducted—one with online accuracy as thedependent variable, and one with offline comprehensionas the dependent variable (see Table 4). Three-predictormodels were used. The three concurrent variables thatwere most strongly correlated with each dependent vari-able were entered as the independent variables, with oneexception: offline production was excluded becauseit was highly associated with offline comprehension(r = 0.94).

Offline comprehension, nonverbal cognition, andonline latency were entered as concurrent predictors of

online accuracy. The final model included offline com-prehension and online latency, and accounted for 62.9%of the variance in online accuracy. Partial regression plotsare presented in Figures 2 and 3. Offline comprehensionwas the strongest predictor of online accuracy, followedby online latency. Nonverbal cognition was statisticallyremoved from the stepwise regression model because itdid not explain unique variance in online accuracy. Thisregression analysis was also conducted using the alterna-tive accuracy variable that contained only the non-latency trials. This step was taken to ensure that themeasurement confound was not responsible for the vari-ance that online latency explained in online accuracy.Results of this analysis were consistent with the previousanalysis, such that offline comprehension and onlinelatency were both significant predictors of online accu-racy; again, nonverbal cognition was not a unique pre-dictor and was thus removed from the model.

Online accuracy, nonverbal cognition, and autismseverity were entered as concurrent predictors of offlinecomprehension. Maternal education was not includedbecause it was more weakly correlated with offline com-prehension than the other predictors. The final modelcontained all three predictors and accounted for 70.1% ofthe variance in offline comprehension. Nonverbal cogni-tion was the strongest predictor of offline comprehen-sion, followed by online accuracy and autism severity.

Table 4. Results of Concurrent Stepwise Regression Analyses Predicting Online Accuracy and Offline Language Comprehension

R2 F d.f. b SE b b* P

Online accuracyStep 1 0.40 15.80 25

Constant 0.59 0.05 < 0.001PLS-4 comprehension 0.004 0.001 0.63 0.001

Step 2 0.63 19.51 25Constant 0.75 0.06 < 0.001PLS-4 comp 0.004 0.001 0.61 < 0.001Online latency 0.000 0.000 -0.48 0.001

Offline comprehensionStep 1 0.55 33.94 29

Constant -27.87 12.83 0.04Nonverbal cognition 1.65 0.28 0.74 < 0.001

Step 2 0.63 23.28 29Constant -51.26 15.04 0.002Nonverbal cognition 1.27 0.30 0.57 < 0.001Online accuracy 51.14 20.45 0.34 0.02

Step 3 0.70 20.36 29Constant -19.70 18.93 0.31Nonverbal cognition 0.98 0.30 0.44 0.003Online accuracy 50.46 18.80 0.33 0.01Autism severity -2.54 1.04 -0.30 0.02

Note. PLS-4 comprehension was measured by Auditory Comprehension subscale raw score. Nonverbal cognition was measured by Visual Reception raw scoreon theMullen. Autism severitywasmeasured by calibratedADOS severity scores. Online accuracywas the proportion of looking to target in the LWL task. Onlinelatency was the speed of processing in the LWL task. Nonverbal cognition was a non-significant predictor of online accuracy.

ADOS, Autism Diagnostic Observation Schedule; LWL, looking-while-listening; PLS-4, Preschool Language Scale, 4th edition.


Retrospective Analyses

Finally, we examined whether online accuracy wasrelated to children’s early vocabulary comprehension asmeasured by parent report of words understood on theCDI at approximately age 2½. Only the children whoparticipated in the LWL task during their final visit in thebroader longitudinal study (at age 5½) were included inthis analysis (n = 26) because it had been approximatelythree years since the CDI had been completed at the startof the broader study. On average, 37 months (range =33–42 months) had lapsed between the CDI completiondate and the date of the LWL task; an “age difference”variable was created to account for this time lapse. Con-sistent with the concurrent analyses, the dependent vari-able of interest in the regression analyses was onlineaccuracy; online latency was not explored because it wasnot significantly correlated with children’s early abilities.The predictor variables of interest at age 2½ were thenumber of words understood on the CDI and nonverbalcognition (Mullen Visual Reception raw score); the agedifference variable was also included to control for theamount of time between visits.

The final model included only vocabulary comprehen-sion and accounted for 34.2% of the variance in later

online accuracy (see Table 5). Interestingly, despite the3-year time lapse, the amount of variance accounted forin online accuracy was similar to that explained by con-current offline language comprehension alone (39.7%).Nonverbal cognition and the age difference variable wereautomatically excluded from the model because they didnot explain significant unique variance in online accu-racy. Thus, the findings of the retrospective regressionanalyses were similar to the findings for the concurrentanalyses.

Online Processing and a Word-Level Characteristic

The final objective was to determine whether a feature ofthe target words—an index of age-of-acquisition—wasassociated with online lexical processing. Spearman cor-relations were conducted because histograms of accuracyand latency for the eight target words suggested a non-normal distribution. A significant correlation was identi-fied between the index of age-of-acquisition and onlinelatency, r = -0.72, P = 0.04, meaning that the words typi-cally acquired earlier in life were processed more quickly.The correlation with online accuracy was non-significant,r = 0.43, P = 0.28.

Figure 2. Partial relationship between online accuracy and offline comprehension, adjusting for online latency. The x-axis represents theresiduals obtained after regressing offline comprehension on latency. The y-axis represents the residuals obtained after regressing onlineaccuracy on latency.


DiscussionPatterns of Real-Time Lexical Processing in Childrenwith ASD

This study found that the eye-gaze patterns of 3- to6-year-old children with ASD provided evidence of theircomprehension of familiar words in real time. Specifi-cally, children looked relatively longer at the targetpicture after hearing it named (79%) than at baseline

(53%). Despite the significant group effect, there was agreat deal of variability in mean online accuracy (55–97%) and online latency (334–1000 ms) across children.This variability is consistent with our initial hypothesesand provides evidence that heterogeneity in languageskills in young children with ASD extends to real-timelexical comprehension.

Child-level individual differences in online accuracywere best explained by only two concurrent factors:offline language comprehension (PLS-4 Auditory Com-prehension raw score) and online processing speed (i.e.latency). Offline comprehension and online latency eachexplained unique variance in online accuracy when theother factor was controlled, revealing their independentcontributions; in combination, they accounted for 63%of the variance in online accuracy. Contrary to our expec-tations, nonverbal cognition did not explain uniquevariance in online accuracy. Notably, online lexical pro-cessing at age 5½ was also related retrospectively tovocabulary comprehension 3 years earlier, at age 2½—although it was not uniquely related to early nonverbalcognition. Lastly, faster online processing was associatedwith an index of age-of-acquisition of the target words,such that words typically learned earlier in life were pro-cessed more quickly.

Figure 3. Partial relationship between online accuracy and online latency, adjusting for offline comprehension. The x-axis represents theresiduals obtained after regressing latency on offline comprehension. The y-axis represents the residuals obtained after regressing onlineaccuracy on offline comprehension.

Table 5. Results of Retrospective Stepwise RegressionAnalysis Predicting Online Accuracy

R2 F d.f. b SE b b* P

Online accuracyStep 1 .34 12.47 25

Constant .69 .03 < .001CDI words understood .00 .00 .59 .002

Note. CDI words understood was the raw number of words understood onthe Communicative Development Inventory-Infant Form (CDI). Non-significant predictors were nonverbal cognition, as measured by VisualReception raw score on the Mullen; and age difference in months betweenwhen the CDI was completed and when the looking-while-listening task wascompleted.


In combination, these findings provide support for adimensional view of language abilities in ASD in a previ-ously unexplored area of language development: process-ing of familiar nouns in real time using the LWLparadigm. Specifically, the patterns of online languageprocessing that emerged in this sample of children withASD mirror those seen in previous work with typicallydeveloping children and children with language delays.This finding suggests that although children with ASDmay show more severe delays in language comprehen-sion, there are more qualitative similarities than differ-ences in their patterns of real-time lexical processing.

Even when a more conservative analysis was used, chil-dren with higher online accuracy were also faster toprocess the familiar nouns. Additionally, latency andoffline language ability were both unique predictors ofonline accuracy [also see Naigles et al., 2011]. Previousstudies have identified parallel relationships in youngerchildren without ASD [Eernisse et al., 2011; Fernald &Marchman, 2012; Fernald et al., 2006], suggesting thatlanguage knowledge, online accuracy, and online pro-cessing speed are closely linked in young children, regard-less of diagnostic status. In combination, these findingscontribute to a growing body of literature suggesting thatsome patterns of typical language acquisition are alsoevident in children with ASD [Goodwin et al., 2012;Luyster et al., 2008; Swensen et al., 2007; but see Teket al., 2008].

It was also interesting that children’s lexical processingspeed was related to the index of age-of-acquisition foreach of the target words. To our knowledge, previousstudies investigating real-time lexical processing inyoung children have not specifically investigated thisissue; however, results from previous studies point to aprominent role of age-of-acquisition in many languageprocessing tasks—for example, word recognition, picturenaming, and lexical decision tasks—in both children andadults [e.g. Garlock, Walley, & Metsala, 2001; Juhasz,2005]. Our finding that children with ASD processedearlier-learned words more quickly suggests that theymay be sensitive to the same factors that allow typicallydeveloping infants to learn some words more quicklyand easily than others. This finding is particularly inter-esting in the context of statistical learning theories oflanguage acquisition [Romberg & Saffran, 2010], whichemphasize the impact of linguistic structure on languagelearning. Future studies may identify similar effects basedon features such as word frequency or sound or syllablephonotactics.

This study is not the first to identify continuity inpatterns of language development across populations ofchildren with and without ASD that are consistent with adimensional view of language abilities. Several studies[Ellis Weismer et al., 2011; Rescorla & Safyer, 2013;Tager-Flusberg et al., 1990] have identified similar pat-

terns of lexical and grammatical development in toddlerswith ASD, and non-spectrum toddlers with languagedelay or developmental delay. Naigles et al. [2011] foundthat young children with ASD were capable of learningnovel transitive verbs through syntactic bootstrapping,leading the authors to conclude that syntactic bootstrap-ping is a robust language-learning mechanism in childrenwith and without ASD. Brock, Norbury, Einav, and Nation[2008] found that adolescents with poorer languageskills—regardless of ASD diagnosis—were less sensitive tosentence context in an online sentence processing task,even when nonverbal IQ was controlled. This findingpoints to similarities in the processing skills of childrenwith language deficits, regardless of etiology—which sup-ports the possibility that language impairments in ASDdo not represent a qualitatively distinct category.

Understanding Variability in Real-Time LexicalProcessing in ASD

Although it is clear that some children had a more diffi-cult time in the LWL task than others, it is not immedi-ately clear why this is the case. The target words wereearly emerging—indeed, all the children had certainlyheard words like ball, cup, and shoe thousands of timesduring their 3–6 years of life—and all words were reportedto be familiar. Why did such variability in online lexicalprocessing emerge?

We believe that the answer to this question lies in ourinterpretation of the construct of language processingitself. One interpretation—supported by the robust con-current and retrospective relationships between onlineaccuracy and offline language abilities—is that some chil-dren simply had a better understanding of the targetwords. As discussed by Fernald et al. [2006], vocabularyacquisition is not a black and white process; the LWLparadigm captures subtle differences not only in whatwords children understand, but also in how well theyunderstand them. From this perspective, the root of vari-ability in lexical processing might be seen as originatingin the richness or connectedness of children’s lexicalrepresentations. However, it is clear that factors otherthan language knowledge—namely, processing speed—also affected performance.

What does it mean that children’s lexical processingaccuracy was explained by processing speed and offlinelanguage comprehension? Although this study does notspeak directly to causality, it is likely that language knowl-edge and real-time lexical processing interact in complexways during development. For example, knowing certainwords may allow them to be processed more quickly;faster processing may free resources for learning newwords; and learning new words may facilitate faster pro-cessing of even more words [Fernald & Marchman, 2012].This cascading effect is one mechanism through which


language interventions may impact both current lexicalknowledge and sustained language learning in childrenwith ASD.

It is also noteworthy that our study identified a rela-tionship between children’s online accuracy for wordrecognition at age 5½ and their vocabulary comprehen-sion 3 years earlier, at age 2½. This finding, along withthe finding of Naigles et al. [2011] that early languageprocessing was predictive of later verb learning in youngchildren with ASD, suggests that language processingmay indeed play an important role throughout develop-ment in this population. An important next step is under-standing how slowed and less accurate processingmanifests over developmental time in children with ASD.For example, early language processing may have prog-nostic value for later vocabulary growth in young chil-dren with ASD, as it does for late-talking toddlers [Fernald& Marchman, 2012]. It is crucial to identify the childrenwith ASD who are most likely to exhibit persistent lan-guage challenges and to ensure that these children beginto receive intervention as early as possible; knowingabout their online language processing may inform thisprognosis.

Interestingly, online accuracy was not significantlyassociated with autism severity, maternal education, orage, and nonverbal cognition was not uniquely predictive[also see Goodwin et al., 2012]. Together, these findingssuggest that the LWL paradigm provides a measure oflanguage processing in individuals with ASD that isrelatively less affected by factors such as cognitionand autism symptomatology. This argument is furtherstrengthened by our finding that offline language com-prehension on the PLS-4 was associated with autismseverity and maternal education, and that nonverbal cog-nition was uniquely predictive—precisely the opposite ofthe findings for online accuracy.

The finding that nonverbal cognition was not a uniquepredictor of online accuracy deserves further attention.One potential reason that nonverbal cognition did notexplain unique variance in online accuracy is that theskills measured by the Mullen Visual Reception scale—the measure of nonverbal cognition adopted in thisstudy—do not directly align with the skills required foronline lexical processing accuracy as measured by theLWL paradigm. The majority of items on the MullenVisual Reception scale between 21 and 45 + monthsmeasure matching (e.g. of pictures or words) or memory(e.g. of one or three pictures). Although matching andmemory are certainly critical developmental skills, theirrelationship with real-time processing of familiar nounsmay be indirect.

For example, although successful performance in theLWL task requires children to “remember” that thenoun cup goes with the picture of the drinking recep-tacle with the handle, the task itself has limited

memory requirements because the auditory label mustbe retained for only a few seconds, and the two objectimages are visible throughout the trial. Although non-verbal cognition is certainly an important foundationfor language acquisition, there may be a tighter linkbetween real-time lexical comprehension and cumula-tive language knowledge (as measured by the PLS-4 orthe CDI) than general nonverbal cognition in childrenwith ASD. In fact, the language processing abilities cap-tured by the LWL paradigm may be what allow childrento accumulate the language knowledge that they dem-onstrate on measures like the PLS-4.

Although the LWL paradigm is a relatively implicitmeasure of vocabulary comprehension, it is important toacknowledge that successful performance relies on morethan vocabulary knowledge alone [Fernald et al., 2006].For example, fast and accurate recognition of a targetword in the LWL paradigm requires that childrensegment nouns from continuous speech, integrate audi-tory and visual modalities, and disengage and shift theirvisual attention [see Fernald & Marchman, 2012]. Whilethese skills do not generally pose a challenge for typicallydeveloping children, children with ASD may have diffi-culty integrating multimodal sensory input [Iarocci &McDonald, 2006] and disengaging visual attention [i.e.sticky attention; Landry & Bryson, 2004].

It is, therefore, possible that future studies mayuncover specific visual attentional factors—perhaps thosemore broadly related to nonverbal cognition or autismseverity—that impact online language processing in chil-dren with ASD. For example, although the static imagesused in the current study are unlikely to have elicitedsticky attention, such problems may occur when childrenare exposed to more complex and dynamic visual stimuli.In these situations, it is possible that the latency variablewould measure not only the speed with which childrenprocess verbal language, but also their tendency to disen-gage their visual attention. It is important to acknowl-edge, however, that one attentional factor not required byLWL or similar eye-gaze paradigms (e.g. intermodal pref-erential looking [IPL]; see discussion below) is joint atten-tion, which is an area of deficit in many children withASD. Thus, poor language processing skills in the LWLtask cannot be directly explained by poor joint attentionskills—which is not the case for many other assessmentsof early language ability.

Interestingly, Fernald et al. [2006] found no relation-ship between children’s speed of oculomotor responseduring a non-linguistic visual orienting task, and theirprocessing speed for familiar nouns, prompting them toemphasize the language-specific nature of the LWL task.Additionally, Kelly, Walker, and Norbury [2012] foundthat children with ASD and language impairment (8–14years of age) demonstrated no deficits in basic oculomotorcontrol; instead, deficits emerged only when higher-order


inhibition was required. Because it does not incorporatespecific instructions, the LWL task does not specificallyrequire inhibition of this type, but future work is neededto address this issue.

Methodological Implications

Our final objective was to evaluate the utility of the LWLmethod with children with ASD. Broadly, this study addsto the growing evidence that eye-gaze paradigms are afeasible method for investigating language processing inchildren with ASD, as over 90% of participants providedadequate data for analysis. Importantly, the results of thisstudy suggest that the LWL paradigm is appropriate forchildren with ASD who demonstrate a wide range oflanguage and cognitive abilities.

More specifically, this study makes an important meth-odological contribution by validating the LWL paradigmfor investigating familiar language processing in ASD.The LWL methodology has not been previously used withchildren with ASD, but we argue that the LWL paradigmrepresents a valuable tool that can be applied across manyareas of research pertinent to individuals with ASD. TheLWL paradigm is beneficial because it offers a windowinto real-time comprehension processes, while offeringthe practical advantage of limited task demands. The useof this method is also important because its focus on thetime course of comprehension more closely parallels chil-dren’s language comprehension during everyday learningopportunities. These conclusions are consistent withfindings from studies using the IPL paradigm [Goodwinet al., 2012; Naigles et al., 2011; Swensen et al., 2007; Teket al., 2008], a similar eye-gaze method using offlinecoding, which supports the broader implication thatimplicit eye-gaze methods provide insight into the lan-guage processing abilities of children with ASD.

Although the LWL paradigm is similar to automaticeye tracking and can accomplish similar objectives, itdiffers in several aspects that may be particularly impor-tant when working with young children with ASD. First,eye-gaze data are hand-coded, offline, by trained coders.Training and coding are time-intensive, but it isextremely valuable to have the flexibility to determinethe location of children’s eye gaze without requiringthem to remain in the relatively small tracking range ofan automatic eye tracker. Our experience with auto-matic eye tracking has revealed that some children withASD frequently move forward and back or side to sidein their chair, or squint their eyes, which can result indata loss. Despite the challenging behaviors exhibitedby several children in this study, hand coding of thedata resulted in the exclusion of only three of 34 chil-dren due to excessive inattention. Another possiblereason for the limited data loss may have been that chil-dren’s visual attention was captured and maintained by

the 58′ screen, which is much larger than most auto-matic eye-tracking screens.

Second, the LWL approach to measuring and analyzingthe constructs of online accuracy and processing speeddiffers from approaches taken in other types of eye-tracking studies—even those that are also designed tomeasure language comprehension. For example, themanner in which accuracy is calculated in the LWL para-digm differs from measurements of accuracy using firstlooks to target; we believe it offers a useful alternativebecause it allows children to switch their attentionbetween the two images, which may capture meaningfuluncertainty about the target words. Another differenceassociated with the LWL method is the emphasis on pro-cessing of familiar words. Although the LWL method iseasily adapted to measure word learning, many studies—including the current study—have revealed a meaningfulrelationship between lexical processing of familiar wordsand extant language skills, both concurrently and acrossseveral years.

The sensitivity of the LWL method is clear: despite thefact that children were reported to know the target words,striking individual differences emerged. Measuring real-time lexical processing abilities in children with ASDusing the LWL paradigm could have important implica-tions for individually identifying optimally effectiveinterventions, for designing treatment strategies, and formeasuring intervention outcomes. Importantly, measur-ing real-time language processing may help us under-stand the changes that occur during the course ofintervention that lead to positive—or, even more impor-tantly, negative—outcomes. If future studies are to realis-tically adopt this method, however, it is critical to firstunderstand the construct of language processing cap-tured by the LWL paradigm in children with ASD; thisstudy is a vital first step in that direction.

Conclusions and Future Directions

This study adopted an individual differences approach tocharacterizing real-time lexical processing in a heteroge-neous group of children with ASD using the LWL para-digm. Children with higher online accuracy processed thetarget words more quickly, and individual differences inonline accuracy were concurrently and retrospectivelyrelated to offline language abilities. Additionally, thetarget words typically acquired earlier in life were pro-cessed more quickly. The findings support a dimensionalaccount of language abilities in ASD and point to avenuesof future research that will further inform theories oflanguage processing in this population. Building on ourspecific findings, we propose that the LWL methodology isa valuable and sensitive tool that can be adapted to answermany questions about language development and atten-tion in this population.


Because the current study was focused on individualdifferences in children with ASD, a comparison group wasnot included; studies that examine lexical processing inchildren with typical development or other neurodevel-opmental disorders, as well as children with ASD, willinform theories of language development across thesepopulations. Future studies that test multiple exemplars oftarget objects may also be capable of exploring the extentto which children with ASD flexibly generalize wordlabels, although the limited number of trials used in thecurrent study precluded us from addressing this question.

Future work is also needed to determine how languageinterventions impact real-time language processing.Expressive language is often the primary target oflanguage intervention [Tager-Flusberg et al., 2005], butresearchers have begun to stress the importance oftargeting language comprehension in clinical settings[Charman et al., 2003; Ellis Weismer et al., 2010; Hudryet al., 2010]. Our finding that some children had a diffi-cult time accurately and efficiently comprehending evenfamiliar words in real time supports the suggestionthat language comprehension should continue to bean important intervention target for many childrenwith ASD.

Acknowledgments

First and foremost, we would like to thank the childrenand families who participated in this study. We appreciatetheir generosity and their commitment to this research.We would also like to thank Anna Dorrance, TarynStricker, and Meghan Davidson for their assistance witheye-gaze coding. Thanks also to Chris Potter, Casey Lew-Williams, Sara Kover, Brianna McMillan, and Erica Wojcikfor helpful discussions about the looking-while-listeningparadigm. Funding for this study was provided by theNational Institute on Deafness & Other CommunicationDisorders (R01DC007223, T32DC005359, F31DC009142)and the National Institute of Child Health & HumanDevelopment (R37HD037466, P30HD003352).

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Individual Differences in the Real-Time Comprehension of Children with ASD