Introduction

Questions concerning ontogenetic stability and change in the autism spectrum disorder (ASD) phenotype have received increased attention in recent years. This is due in large part to the appearance of long-term longitudinal studies. For example, studies have called the universal stability of an ASD diagnosis into question by identifying individuals who had an initial diagnosis of autism, yet later function within normal limits [1]. A long-term longitudinal study of individuals diagnosed with autism at 2-years separated the individuals into three outcome groups, based on distinct trajectories on measures of verbal and non-verbal IQ, social adaptation, social communication, repetitive sensory motor behaviors, and insistence on sameness [2]. These authors postulated that longitudinal trajectories of behavioral profiles may represent meaningful phenotypes, which researchers could leverage to parse the heterogeneity of ASD for genetic studies. A series of reports from the Pathways in ASD group (www.asdpathways.ca) has also refuted the universal stability assumption, quantifying heterogeneity in longitudinal trajectories from initial preschool diagnosis into school-age [3–5]. These studies underscore the benefits of implementing longitudinal designs to track variability of the natural history of ASD, but also highlight the need for new experimental measures that offer measurement equivalence [6] across broad age ranges, intellectual functioning and verbal abilities.

A common domain of inquiry in individuals with ASD, which has proven useful as a measure of treatment response in young children [7], is face processing. In general, the face processing literature is hindered by small sample sizes, variability in methodological considerations (e.g. dynamic vs. static stimuli, task parameters, etc.), variability in age of assessments and the assumption that findings from one age group should generalize to other ages, and the inherent heterogeneity of the phenotype [8, 9]. That said, studies with sufficiently large samples to approximate the heterogeneity of autism have begun to appear [10]. Early studies suggested children with autism use mouth information more than controls in face recognition tasks, while controls tend to rely more on information from the eye region to perform the same tasks [11]. This latter finding has been replicated in various studies using free viewing of video clips of complex social scenes [12], identifying emotional expression from faces [13] and free viewing of static face stimuli [14]. Other evidence suggests that young children with autism may show equivalent looking at the eye region while gazing less at the mouth when presented with static stimuli [15] indicating that a preference for the mouth region may not be universal in ASD [16, 17]. However, little is known about long-term longitudinal changes in face processing (but see [18] for findings from the first 2 years).

One useful task for assessing face processing is the “bubbles” technique [19]. This technique involves revealing faces through randomly selected Gaussian windows (“bubbles”) that are superimposed onto the facial images underneath, to assess the visual information used by an observer to make a categorical decision about the face (e.g. male vs. female, afraid vs. happy, etc.). Gaussian window locations that covary with higher performance on the task are considered to contain the information that is more important for making the correct categorization on the given task. Using this technique combined with eye tracking technology, Spezio et al. [20] found no difference between a group of adults with high functioning autism and age and IQ matched controls in terms of accuracy, reaction time, or number of bubbles required to perform the task (determining whether a face showed fear or happiness), indicating that task performance can appear equivalent between groups. However, despite equivalent performance on the task, the bubbles methodology revealed that the ASD group made more use of the mouth and less use of the eyes than controls when making judgments of facial expression, a finding that extended to a subgroup of parents who showed aloof features of the broader autism phenotype [21]. This finding underscores the importance of assessing gaze location in addition to basic task performance (i.e. accuracy, reaction time), in the study of autism. In other words, it demonstrates that in orienting tasks, it is just as important to investigate the “how” as it is to investigate the “how well” [9].

One limitation to the bubbles task is that some populations, such as infants and individuals with co-occurring intellectual disabilities, cannot provide responses (e.g. via key press) to complete the task as initially designed. Humphreys et al. [22] adapted the bubbles task for use with infants, creating a preferential looking task that paired a picture of the infant’s mother with a stranger to determine what information is used to identify a familiar face [22]. While this task provided a useful alternative for infant populations, it specifically tests identity processing and requires adaptations for each participant (i.e. using mother’s face). It is also unclear whether this version of the bubbles task would provide meaningful information across a broad age range. Hence, there is a need for face-orienting/processing tasks that can be implemented across a wide range of ages and functioning levels. Accordingly, we developed a paired visual comparison version of a modified bubbles task that can be used across ages and developmental abilities to investigate preferential orienting to facial features. Our task pairs two of three types of facial features: 1) eyes, 2) mouth, and 3) other facial parts (i.e. OFPs; nose, cheeks, forehead), revealed through static Gaussian windows (“bubbles”) (Fig 1).

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Fig 1. Bubbles paired visual comparison task.

a) Task procedure. b) Various pairings of face parts: Eyes vs. Mouth, Eyes vs. Other Face Parts (OFPs), Mouth vs. OFPs. OFPs consisted of forehead, nose, and/or cheeks.

https://doi.org/10.1371/journal.pone.0202875.g001

We designed this task with three primary goals in mind: 1) to make a first attempt at designing a task that can be implemented across a broad range of ages and developmental abilities, and to test the task by 2) evaluating the stability of patterns of preferential orienting to the eye region of static faces from infancy to school-age, and 3) comparing orienting patterns in individuals with autism to two typically developing groups (i.e. infants and school-age children). Our results demonstrate an interesting equivalence between groups in terms of early interest in eyes, and ultimately suggest that this modified bubbles task is useful for measuring preferential orienting to specific facial features in infancy, childhood, and in atypical populations.

Method

Results

First feature

There was a main effect of Feature Pair, F(2, 61) = 77.36, p<0.001 (Fig 2A). Post-hoc Bonferroni paired comparisons indicated a lower difference score across groups for OFPs vs. Mouth (14.0%) than for both Eyes vs. Mouth (60.8%) (p<0.001) and Eyes vs. OFP (47.4%) (p<0.001). There was no main effect of Group (p = 0.871), nor was there a Group x Feature Pair interaction (p = 0.938). This indicates that all participants tended to look at the Eyes first when paired with the Mouth (75.9% vs. 15.1%) or OFPs (71.0% vs. 23.6%), but showed less of a tendency to look at the OFPs first when paired with the Mouth (52.5% vs. 38.5%).

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Fig 2. Results.

a) Difference scores (in percent of trials) for which feature of a pair was looked at first. Difference scores were calculated by subtracting the percent of trials during which one feature was looked at first from the percent of trials during which the competing feature was looked at first. The direction of the bar indicates which feature of the pair was more frequently looked at first. b) Difference scores (in percent of total trial time) for time spent on competing features. Difference scores were calculated by subtracting the percent of the total trial time spent looking at one feature from the percent of total trial time spent looking at the competing feature. The direction of the bar indicates which feature of the pair had a higher dwell time measured as a percent of total trial time. Error bars represent +/- 1 standard error.

https://doi.org/10.1371/journal.pone.0202875.g002

In order to verify the statistical significance of the preference for the Eyes, we conducted 1-sample t-tests comparing the preference for Eyes (i.e. the difference score) to 0% for Eyes vs. Mouth and Eyes vs. OFPs, for each group. Indeed, for all groups, the preference for Eyes over other features was significantly greater than 0%, all ps < 0.001.

Discussion

The purpose of this study was to design and test a single task that can be implemented with a range of populations who have varying levels of age, verbal abilities, and cognitive functioning. We kept in mind three primary goals: 1) to make a first attempt at designing such a task, and to use the task to, 2) evaluate the stability of patterns of preferential orienting to the eye region of static faces from infancy to school-age, and 3) to compare orienting patterns in individuals with autism to two typically developing groups (i.e. infants and school-age children). Our approach was to design and test a paired visual comparison version of a bubbles face-orienting task [19, 22]. We monitored participants’ eye movements while they viewed pairs of facial features (eyes, mouth, and other facial parts (OFPs) such as nose, cheeks and forehead) that were briefly presented on opposite sides of fixation. Despite their inherent differences, all groups performed the task, and even had some interesting similarities in their looking behavior: all groups tended to look at the eyes first, regardless of the paired feature. Thus, across ages and developmental abilities, orienting to eyes is a behavior that emerges early and is stable over time. It also suggests a common attraction to particular social information that is present in typically developing children and preserved in autism. Differences between groups emerged when we examined dwell time. While all groups spent longer looking at the eyes than the mouth or OFPs, the tendency to dwell on eyes over OFPs was larger in infants compared to school-age children with and without autism. Infants also showed a larger preference for the eyes over the mouth than the ASD group. Thus, while early orienting is consistent across groups, dwell time shows differences between infants and school-age children. Interestingly, there were no differences between the typically developing children and children with ASD on either measure, suggesting a normative change in looking behavior with age, rather than with diagnosis.

The findings that both TYP and ASD groups look at the eyes first, and spend a larger proportion of time looking at them compared to the mouth, is inconsistent with some published findings that show that individuals with ASD tend to show the reverse pattern, a bias towards the mouth rather than the eyes (e.g. [11, 13, 14, 20, 29]). Although the literature on eyes vs. mouth looking in ASD is inconsistent (see, [16, 17]), one possible explanation for the difference between our findings and others is that our task is an orienting task rather than a classification or free viewing task where features appear in the context of a whole face or social scenes. Indeed, our findings are broadly consistent with findings from another rapid orienting task implemented with adults with autism [30]. Uniquely, our task puts facial features in direct competition with one another, with no other context or facial judgment requirements. This may lead to different behavioral patterns than when the participant is confronted with an entire face or with a task that requires goal-oriented cognitive processing of facial features. Another possible explanation for our findings is that our method of computing difference scores rather than comparing the time on the AOIs directly may have diluted differences between the ASD and TYP groups. To investigate this possibility, we calculated the average proportion of time spent on eyes and mouth, respectively, for each participant, ignoring what these AOIs were paired with on any given trial. Independent samples t-tests revealed no differences between the TYP to ASD children in terms of their mean proportion of time on the eyes (p = 0.299) or mouth (p = 0.972) throughout the task. Thus, this alternate analytic approach confirmed that the ASD children did indeed show a preference for the eyes over the mouth that was comparable to the TYP children.

The primary goal of the current study was to develop and test a procedure that can be implemented across a wide range of ages and developmental abilities, while maintaining measurement equivalence over time. The idea that an experimental task measures the same process across time and across heterogeneous samples is more often assumed than tested (but see [6]). As reliability and validity are not inherent properties of any measurement procedure, this type of examination promises to counter “detached validity” claims (c.f., [31]) that are pervasive in the field. We demonstrated that our task can be used successfully with infants and children, as well as special populations such as children with ASD. The task requires no goal-oriented instructions beyond passive viewing and can therefore be used with other, more severely impaired groups, such as individuals with below-average IQ, minimally verbal individuals, and individuals who are physically unable to make a key press or verbal response, allowing direct comparisons between groups. Thus, while this task may not be diagnostic in its current form, it illustrates an approach towards designing experimental tasks that can adequately test the assumption of ontogenetic stability in autism.

It is important to acknowledge some limitations of the current study. First, recruitment of the ASD group took place in the context of another study, and was therefore small, and not a truly random sample for the present study. That said, there were no existing characteristics of this group that would be expected to differentially affect their looking behavior during this task (we found no relationship between age or IQ and any of our dependent measures), and large sample sizes were not needed to achieve the primary goal of this study i.e. to demonstrate that it is possible to design a task that can be implemented across a broad range of ages and developmental abilities. We also wish to mention that even though we were able to achieve our goal with this task by demonstrating its feasibility, the final sample sizes for the school-age children would have ideally been larger to better represent the heterogeneity of the ASD and typical development. This task provides only a single measure of a given phenomenon and it would be beneficial to have a second task that measures the same behavior to provide converging evidence for orienting behaviors in these groups. Also, while the task presented here provides a simple, easy to use measure of basic looking behavior, it would be useful to design an alternate task with greater ecological validity to compare outcomes.

Another methodological issue worth discussing is data inclusion. We used a minimum valid trial criterion of 250 ms, meaning that trials were only included in the analysis if the participant spent at least 250 ms on-screen. We implemented this inclusion criterion to promote the inclusion of trials that were more likely to be representative of a participant’s true performance and exclude trials that likely include only spurious looks. We chose the minimum trial duration based on work from others [27] that has indicated that 200 ms is the minimum amount of time needed to characterize an orienting response. However, the mean time on-screen for each group was > 1000 ms, suggesting that for most participants, most trials included more than 250 ms of on-screen time. It is still important to consider why data loss may have occurred. Data loss in eye tracking reflects a quality control metric known as robustness. Robustness refers to the amount of eye tracking data recorded relative to data lost during recording. Data can be lost because the eye tracker is not able to detect the eye (e.g. because it failed to detect the corneal reflection), or because the participant is not looking at the screen (e.g. due to inattention). Infants have been reported to have less robust data than adults, and less robust data has been associated with longer response latencies [32]. In the present study we look at first feature fixated rather than first fixation, circumventing issues related to response latencies. More robust data has also been linked to longer average dwell times [32]. If the infant data in the present study is less robust than the data from school-age children, then this could have resulted in artificial differences in dwell times between the two groups. However, we computed and analyzed difference scores that reflect relative time on the different facial features within groups. This means that each group’s time on one feature, while proportional to their overall dwell time, is actually computed relative to that group’s time on the other feature(s). Within subjects, data loss can be assumed to be consistent and our groups were not directly compared in terms of absolute dwell time. This means that preference for a given feature within a group, and differences in preference between groups, should be unaffected by robustness.

Another methodological choice that warrants discussion is the size of the areas of interest. Specifically, the areas of interest are not matched for size such that the eyes region is twice the size of the mouth (i.e. each eye is matched in size to the mouth, but the eyes appear in pairs), and the other face parts are matched in size to the eyes on 60% of the trials, and matched to the mouth on 40% of the trials (i.e. the OFPs appear in pairs on 60% of the trials and appear alone on 40% of the trials). Thus, when the eyes are in competition with the mouth, participants may look at the eyes first because that region is larger than the mouth. We ultimately concluded that the current design of the stimuli is more ecologically valid than pairing single eyes with the mouth, or adjusting the data post-hoc (i.e. by area normalizing). We should also note that the eye region appeared above the center of the screen, though the mouth appeared below. While there is a possibility that dwell time on the eyes were driven by their location on the screen (i.e. a top-heavy stimulus), the placement of the facial features on the stimuli was chosen to reflect ecologically valid locations. However, the present results are likely unrelated to stimulus placement: a study investigating whether the location of the eyes on a face is what compels others to fixate them reported that the tendency to orient to eyes has more to do with the eyes themselves (i.e. their biological significance), than their positioning on the face [33]. Indeed, in the present study, the OFPs stimuli contained foreheads, which were even more top-heavy than the eyes, yet the preference for the eyes persisted.

The above caveats aside, the present study tested measurement invariance in a single eye-tracking task across a wide range of ages and ability levels. Our results suggest nominal age-related differences between the group of infants and school-aged children. Designing tasks that can be implemented across age, functioning level, and modality (e.g., eye tracking, EEG, fMRI) that provide meaningful individual differences that span the typical-atypical continuum is a major effort in our lab. Future work on measurement invariance/equivalence promises to inform long-term longitudinal studies that chart the natural history of autism spectrum disorders. Indeed, characterizing long-term stability and/or change in basic perceptual and attentional abilities promises to augment more traditional assessments of autistic symptoms or language level [2, 34]. If trajectories of development become a useful strategy to parse the heterogeneity of ASD, the utility of tasks suitable only for specific ages or functioning levels diminishes substantially. Further, many in the field are concerned with identifying biomarkers of treatment response, with a specific focus on eye-tracking and/or EEG [7]. Appreciating the fact that different interventions may be necessary for different ages, maintaining consistency in outcome biomarkers requires ensuring developmental invariance over time. While the task presented here did not reveal differences between children with and without autism, this in and of itself is interesting, especially considering other findings that suggest that individuals with autism have an abnormal tendency to fixate the mouth rather than the eyes (e.g. [11, 13, 14, 20, 29]). This task also serves as a proof-of-principle investigation of measurement invariance in a single eye-tracking task. More work is needed to develop a suite of tasks (eye-tracking and EEG tasks) that elicit meaningful individual differences between groups that could represent actionable metrics of treatment outcomes across a wide age range and broad functioning level.

Supporting information