Using a 3-D Multiple Object Tracking (MOT) Task to Assess Attentional Abilities in Autism

Friday, May 12, 2017: 5:00 PM-6:30 PM
Golden Gate Ballroom (Marriott Marquis Hotel)
B. Levy1,2, D. Tullo1,2, L. Mottron, M.D.3, J. Faubert4 and A. Bertone1,2, (1)McGill University, Montreal, QC, Canada, (2)Perceptual Neuroscience Lab (PNLab) for Autism and Development, Montreal, QC, Canada, (3)University of Montreal Center of Excellence for Pervasive Developmental Disorders (CETEDUM), Montreal, QC, Canada, (4)Laboratoire de Psychophysique et de Perception Visuelle, Université de Montréal, Montréal, QC, Canada
Background: Autism spectrum disorder (ASD) is characterized by differences in visuo-spatial perceptual and attentional abilities. Multiple object tracking (MOT) paradigms are useful in assessing both these abilities, as this task demands the use of sustained, selective, and distributed attention to dynamic visual information. Outcomes of previous MOT studies in ASD have been interpreted within either perceptual (e.g., van de Hallen, 2015) or attentional (e.g., Koldewyn et al., 2013) contexts. As suggested by the Flexible Resource Model (Alvarez and Franconeri, 2007), attentional capacity is increasingly taxed during MOT as the number of target items (i.e., load) of the task increases. We used a 3D-MOT task to manipulate load in an effort to assess dynamic visual attention in individuals on the autism spectrum. We also assessed whether typically beneficial trial-by-trial feedback differentially affected performance.

Objectives: (1) To assess the effect of cognitive load on 3D-MOT performance in individuals on the autism spectrum. (2) To investigate whether performance differs (a) at different levels of cognitive loading, and (b) with or without feedback.

Methods:  Individuals on the autism spectrum of average intelligence (n=16; Mage=21.82 years; MFSIQ=105.65) and neurotypicals (n=40; Mage =23.93 years; MFSIQ=104.75) tracked two blocks of 1, 2, 3, & 4 target items (spheres) among 8 in 3-D virtual space using a wearable head-mounted display. Half were shown the correct answers for each trial (Feedback groups), and half were not (No Feedback groups). Performance was defined as the average speed at which participants could successfully track all target spheres (Speed Threshold) at each load level (1, 2, 3, & 4 target items).

Results: ANOVAs revealed significant differences across all levels of Load (# tracked items) for both the ASD (p < .001) and TD (p < .001) groups; collapsed across feedback condition. No load by feedback group interaction was found for either diagnostic group. Analyses also revealed no interaction effects between diagnostic and feedback groups and load levels. However, when collapsing across load levels, a significant effect of diagnostic group was found (p=0.008); with the TD group outperforming the ASD group. Feedback significantly hindered performance in the TD group when speed threshold was collapsed across load p=0.010). When comparing performance between the first and second test block (collapsed across load level), improved performance on the second block was found only for the ASD Feedback group (p=0.020).

Conclusions:  Results support the use of our 3D-MOT task to target attentional abilities in ASD; as all groups demonstrated a decrease in performance as the number of tracked target items increased. Though a significantly lower overall average 3D-MOT performance was found in the ASD groups, we are not, at present, proposing that this difference is due to a differential ability to flexibly allocate attention across target items in ASD. The feedback-enhanced performance across test blocks does suggest that feedback may be important to consider in the study of attention in ASD. Our findings will be discussed within the context of current theories of the role of attention in perceptual abilities in ASD (e.g., Remington et al., 2009).