Date of Award
Campus Access Dissertation
Doctor of Philosophy (PhD)
Developmental and Brain Sciences
The visual system must organize dynamic input into useful percepts across time, balancing between stability and sensitivity to change. The Temporal Integration Window (TIW) has been hypothesized to underlie this balance: if two or more stimuli fall within the same TIW, they are integrated into a single percept; those that fall in different windows are segmented (Arnett & Di Lollo, 1979; Wutz, Muschter, van Koningsbruggen, Weisz, & Melcher, 2016). Visual TIWs have mainly been studied in adults, showing average windows of 65 ms (Wutz et al., 2016); however it is unclear how temporal windows develop throughout early childhood. Differences in TIWs can influence high level cognitive and perceptual processes that require well-adapted timing, such as object individuation (Drewes, Zhu, Wutz, & Melcher, 2015; Wutz & Melcher, 2014), visual working memory (Wutz & Melcher, 2013; Wutz, Weisz, Braun, & Melcher, 2014), apparent motion (Fairhall, Albi, & Melcher, 2014; Honey et al., 2012), action sequence perception (Faivre & Koch, 2014), language processing (Hillock-Dunn, Grantham, & Wallace, 2016), action planning (Hommel, Müsseler, Aschersleben, & Prinz, 2001), and pragmatic aspects of communication, such as interactional synchrony (Trevarthen & Daniel, 2005). Because of the fundamental role temporal processing plays in visual perception, it is important then, to understand not only the trajectory of how TIWs change over typical development (TD) but also neurodevelopmental disorders like autism spectrum disorder (ASD). A series of three experiments (Chapters 1-3) investigated the development of visual temporal processing (measured by the TIW) in typical development and toddlers diagnosed with ASD. In Chapter 1, we measured TIWs in 5-to 7-year-old children and adults, using a variant of the missing dot task (V. Di Lollo, 1980; Wutz et al., 2016), where integration and segmentation thresholds were measured within the same participant, using the same stimuli. Participants saw a sequence of two displays separated by an inter-stimulus interval (ISI) that determined the visibility of a visual search target. Longer ISIs increased the likelihood of detecting a segmentation target (but decreased detection for the integration target) while shorter ISIs increased the likelihood of detecting the integration target (but decreased detection of the segmentation target). We computed the crossover point of the integration and segmentation performance functions for each group, an estimate of the temporal integration window (TIW). We found that children’s TIWs (M = 68 ms) were comparable to adults’ (M = 73 ms), with no appreciable age trend within our sample, indicating that TIWs reach adult levels by approximately 5 years of age.
In Chapter 2, we investigated temporal processing in 2-year-old toddlers diagnosed with ASD and age-matched typically developing (TD) toddlers. We used a visual search, eye-tracking task where the visibility of the target was determined by the pace of a display sequence. We measured the percent of trials when participants fixated the target as a function of the stimulus onset asynchrony (SOA) between displays. We found that both groups of toddlers had significantly longer TIWs (125 ms) than 5-7-year-old children and adults (70 ms) (Freschl et al., 2019; Wutz et al., 2016), and that toddlers with ASD had significantly shorter TIWs (108 ms) than chronologically age-matched typically developing controls (142 ms). In Chapter 3, we used the same eye-tracking paradigm to investigate temporal processing in infants (5-to 8-months and 10.5-to 13.5-months). Data collection is ongoing due to the COVID-19 pandemic, however preliminary results show that infants’ TIW was found to be at 191 ms which is longer than 2-year-old toddlers (142 ms) and 5-7-year-old children (70 ms) - suggesting temporal windows narrow across development.
Supporting this, recent work has shown that alpha frequency have been implicated as a ‘clock’ on visual temporal processing. Conventional wisdom holds that alpha frequency increases across development, however, no work has explicitly tracked the precise development of alpha frequency and whether it is consistent with the developmental trajectory of temporal processing itself. Here, in Chapter 4, we conducted a meta-analysis examining the development of occipital peak alpha frequency (PAF) and its role in temporal processing from infancy to adolescence. We found an increase in PAF from infancy (reaching 6.1 Hz at 6 months to 7.6 Hz at 36 months) to adolescence (reaching 9.4 Hz at 10 years to 10 Hz at 18 years), with an asymptote at 10.1 Hz, matching adult levels. These results pin down the precise developmental trajectory of PAF, which was consistent with our behavioral measures of the development of visual temporal processing (Freschl et al., 2019, 2020).
Together, I show that temporal processing (measured by TIWs) gradually becomes more finely tuned within the first few years of life, with TIWs that are much longer in infants (Chapter 3) than typically developing toddlers (142 ms) (Chapter 2), that reach adult like levels by approximately 5 years of age (~70 ms) (Chapter 1). Then, working with 2-year-old toddlers diagnosed with ASD, I uncovered a contributing factor to the well-established ‘local bias’ in spatial processing: toddlers with ASD had shorter TIWs (108 ms) than age-matched controls (142 ms) (Chapter 2). In a local-to-global processing cascade, individuals with ASD prioritize, temporally, the processing of local information (Freschl et al. 2020; Van der Hallen et al. 2015). In addition, I also pinned down the developmental trajectory of peak alpha frequency which is consistent with behavioral measures of the development of visual temporal processing (Chapter 4).
Freschl, Julie, "The Development of Visual Temporal Processing" (2021). Graduate Doctoral Dissertations. 656.