Unisensory temporal processing abilities across modalities in neurotypical children and young adults

Abstract

Sensory signals from multiple modalities presented close in time are often integrated, building a coherent and meaningful multisensory perceptual world. A better understanding of our perception requires characterization of how the nervous system detects and encodes unisensory cues in time. There are very few studies that have focused on the development and individual variabilities in temporal aspects of unisensory signal processing in neurotypical populations across modalities. Using a temporal order judgment (TOJ) task, this study explored individual differences in the temporal processing of unisensory (auditory, tactile, and visual) stimuli in neurotypical children and young adults. We examined whether the precision of unisensory temporal processing and perceptual synchrony for unisensory stimuli can be influenced by participants’ age, cognition, and sensory responsiveness profiles. Performance in each of the unisensory TOJ tasks, measured in temporal order judgment threshold (JND) and reaction time (RT), showed significant improvement with age. On the other hand, perceptual synchrony, measured in Point of Subjective Simultaneity (PSS), remained stable with age across modalities. Although cognitive abilities and sensory responsiveness patterns could not predict the individual variability in unisensory temporal precision or perceptual synchrony for this group of subjects, results from this study show a developmental trajectory of unisensory temporal sensitivity from childhood to young adulthood.

Keywords

unisensory temporal processing development age cognition sensory perception

Introduction

Information processing, defined as the detection and translation of information (e.g., sensory stimuli) to achieve a particular action (McCarthy & Warrington, 1999), is an essential part of complex human cognition. Successful integration of information from different sensory modalities to get a meaningful gestalt perception of the world around us requires recognition and discrimination of patterns of stimuli articulated in time and in space (Hirsh & Sherrick, 1961). Time, being a key aspect of our information processing system, makes it essential for us to understand how the nervous system processes temporal information (Bremner & Spence, 2008; Mauk & Buonomano, 2004). Temporal information processing (TIP), the patterning of the incoming sensory stimuli in time (Mauk & Buonomano, 2004; Nowak et al., 2016) to create a coherent and meaningful percept (e.g., binding of auditory and visual input in temporal proximity to understand a speech), is considered a crucial aspect of cognitive functioning. TIP contributes to the processes associated with perception, decision making, memory, and motor control (Pöppel, 1997; Pöppel, 2004; Salthouse, 1996; Szymaszek et al., 2009).

Studies have shown that temporal processing abilities can be influenced by various task or stimulus-related factors such as stimulus modality (e.g., auditory, visual, tactile) (Li et al., 2009; Rammsayer & Brandler, 2007), task (e.g., Simultaneity Judgement (SJ)/Temporal Order Judgment (TOJ)) and stimulus complexity (e.g., flash & beep/audiovisual speech) (Stevenson & Wallace, 2013) and intensity (Fister et al., 2016), mode (e.g., unimodal/bimodal) and order of stimulus presentation (Hillock-Dunn & Wallace, 2012; Stevenson & Wallace, 2013). For example, it is often suggested that the auditory modality provides better discriminability than the visual modality in temporal tasks (Grondin & Rousseau, 1991; Li et al., 2009; Rammsayer & Brandler, 2007; Rousseau et al., 1983), while visual stimuli are better identified in spatial judgment tasks (Hirsh & Sherrick, 1961; Howard & Templeton, 1966). As for stimulus complexity, Stevenson and Wallace (2013) illustrated that the temporal binding window (TBW), the period in which sensory signals from multiple modalities are likely integrated into one coherent percept (Baum & Stevenson, 2017; Hillock-Dunn & Wallace, 2012; Wallace & Stevenson, 2014) is wider for complex audiovisual speech stimulus in comparison to that elicited by simple non-speech (flash/beep) stimuli. Furthermore, this study (Stevenson & Wallace, 2013) also illustrated that TBW for the same pair of auditory and visual stimuli, measured as a function of stimulus onset asynchronies (SOA), varied depending on the task paradigm itself. The TBW was wider for the SJ task, in which participants decided whether auditory and visual stimuli were presented simultaneously, in comparison to the TOJ task, in which participants judged whether auditory or visual stimuli were presented first.

In addition to task and stimulus-related factors, subject-specific factors such as age (Szelag et al., 2004; Szymaszek et al., 2006; Szymaszek et al., 2009), sensory responsiveness (Poole et al., 2017), and cognitive abilities (Barutchu et al., 2011; Rammsayer & Brandler, 2007; Szymaszek et al., 2009) are known to contribute to individual differences in TIP abilities. Temporal processing ability is believed to develop initially within the first year of an infant's life. This is exemplified by an infant's ability to understand speech, which relies on the perception of rapid intensity and frequency changes in sound. However, efficient, adult-like usage of such auditory temporal information requires continued maturation of the central auditory neural pathway beyond infancy (Cabrera & Lau, 2022). Auditory temporal acuity has been shown to increase with age from child to adult (Grose et al., 1993; Irwin et al., 1985; Lowe & Campbell, 1965; Wightman et al., 1989), suggesting the developmental trajectory can be frequency dependent. While development of the visual and tactile temporal processing from childhood to adulthood are less studied, studies focused on adults and older adults have consistently revealed an age-related decline in temporal processing across auditory (Fink et al., 2005; Fitzgibbons & Gordon-Salant, 1998; Hirsh, 1959; Hirsh & Sherrick, 1961; Szymaszek et al., 2006), visual (Baltes & Lindenberger, 1997; Busey et al., 2010; Ulbritch et al., 2009), and tactile (Craig et al., 2010; Humes et al., 2009; Laasonen & Virsu, 2001; Verrilo, 1993) modalities.

Further, the ability to integrate temporal information is reliant on the development of time perception abilities, which are theorized to develop during middle childhood, more specifically, between ages 5 and 8 (Droit-Volet, 2013; Droit-Volet & Coull, 2016; Hallez et al., 2021). This is known because at these ages, children are more likely to demonstrate more consistent and accurate performances in tasks that involve temporal order judgments. (Droit-Volet, 2013; Droit-Volet & Coull, 2016; Hallez et al., 2021). These tasks enlist the use of many important cognitive functions tied to time perception, such as inhibition, working memory, and attention, which are essential functions for internal timing mechanisms that lie behind the ability to accurately estimate intervals of time (Droit-Volet, 2013; Droit-Volet & Coull, 2016; Hallez et al., 2021). Notably, attentional control is proven to modulate time perception in children as recent studies show that added attentional demand can alter timing judgements, skewing results (Charras et al., 2017; Hallez et al., 2023). A recent research theory (Hallez et al., 2023) alludes to the coordination of cognitive resources being the driving factor for the development of time perception. Individual differences in intelligence quotient scores (IQ), which we used as an index of cognitive function (Ganuthula & Sinha, 2019) in this study, have also been linked with differences in performance on tasks relating to time perception, suggesting the importance of the evolution of general cognitive abilities in quantifying temporal ability (Gourlat et al., 2023). Taken together, these studies highlight the importance of considering both developmental and individual cognitive factors when investigating mechanisms involved in time perception.

Cognitive abilities have also been found to be significant predictors of visual (Baltes & Lindenberger, 1997; Busey et al., 2010; Ulbritch et al., 2009; Zhou et al., 2020), auditory (Nowak et al., 2016; Szelag et al., 2010; Szymaszek et al., 2009), and tactile (Poole et al., 2015; Stevens et al., 1998) temporal processing in adult and older adult populations. While age-related decline in cognitive abilities (e.g., memory, attention, reasoning) is widely reported (e.g., Craik & Salthouse, 2011; Park & Schwarz, 2012; Rogers & Fisk, 2001; Salthouse, 1996, 1998), it has been shown that variabilities in cognitive capacity in adult and older populations can be well predicted by their visual and auditory temporal processing abilities (Baltes & Lindenberger, 1997; Szelag et al., 2010). It has been suggested by Baltes and Lindenberger (1997) that the connection between sensory processing and cognitive functioning is present throughout adult life but is intensified in old age. Szymaszek et al. (2009) illustrated in their study that age-induced performance declines in monaural TOJ tasks, which require an attentional shift to identify sequences of stimuli, were significantly correlated with cognitive functioning and mode-specific sensory processing. Cognitive resources like attention and memory play a considerable role in the development of temporal processing abilities (Cabrera & Lau, 2022; Hallez et al., 2021). Based on these prior findings, we hypothesized that young adults would perform better than their child and adolescent counterparts in tasks involving temporal processing, owing to their more developed and refined cognitive resources.

Sensory responsiveness is reportedly another important contributor to the individual differences in temporal processing abilities. For example, using a visual TOJ task, Zhou et al. (2020) found a positive association between atypical sensory response patterns and reduced temporal acuity in a group of neurotypical children and adolescents. Poole et al. (2015) investigated the relationship between tactile temporal separation thresholds and the autism spectrum quotient (AQ) in an adult non-clinical population. Researchers found that these self-reported AQ scores were positively correlated with their tactile thresholds, suggesting participants who showed a higher number of autistic traits related to sensory responsiveness, such as hyposensitivity and hypersensitivity, tended to show lower precision in tactile temporal separation tasks.

Individual differences in temporal processing have been largely studied using multisensory stimuli in both neurotypical (Barutchu et al., 2011; Baum & Stevenson, 2017; Hillock-Dunn & Wallace, 2012; Poole et al., 2017; Rammsayer & Brandler, 2007) and clinical populations (Harrar et al., 2014; Stevenson et al., 2014, 2018; Wallace et al., 2020). Temporal acuity, when in response to multisensory integration, relies on the underlying temporal processing of unisensory stimuli and can be improved with unisensory integration training (Stevenson & Wallace, 2013). Unisensory stimuli, which are the building blocks of our perceptual world, are therefore important when evaluating the development of temporal processing as we age. Earlier studies focused mostly on the effect of aging on the various aspects of sensory processing of adult and older adult subjects (e.g., Stevens et al., 1998, 2010) or evaluated the sensory responsiveness pattern associated with behavioral response of diagnostic and clinical groups such as children with ASD (e.g., Feldman et al., 2018). There have been very few studies that systematically compare unisensory temporal processing performance across multiple modalities within the same study group (Humes et al., 2009). The current study aimed to explore individual differences in unisensory temporal processing using simple auditory, visual, and tactile stimuli in neurotypical children and young adults. Specifically, we examined whether their temporal processing performances can be predicted by individual subject-related factors such as age, cognitive abilities, and sensory responsiveness patterns.

Materials and Methods

Participants

Fifty-five participants were recruited from the University of Nevada, Reno, and the surrounding areas for this study. Participants were screened for normal to corrected to normal vision and normal hearing. Participants were also screened verbally for a history of psychiatric disorders, neurological disorders, epilepsy, brain trauma, brain surgery, or having been prescribed an anxiolytic or antidepressant. Two participants did not pass the screening. Seven participants did not complete TOJ tasks, and their data were therefore excluded from the analysis. Forty-six participants completed all three unisensory TOJ tasks (age range of 8-25 years; gender: 13 male and 33 female; for age distribution, see Table 1 in Appendix A), 45 of them completed the cognitive task, and 44 of them completed the sensory profile. All participants were right-handed (self-reported), except for one. Each participant completed the study in one or two visits. Participants (or their guardians) provided signed informed consent before any experimentation and were financially compensated for their time. The protocol was reviewed and approved by the Institutional Review Board at the University of Nevada, Reno.

The age range of participants was determined by the available subject population and extensive pilot testing. Pilot testing revealed that lengthy trials in three different TOJ tasks were overly fatiguing for younger children. Therefore, we chose to set eight years as the minimum age for participation. As we wanted to focus our study on the progression of temporal processing through early adulthood, the age range was capped at 25 years. No power analysis was conducted as the sample size was limited by the impact of the COVID-19 pandemic.

Cognitive and Sensory Functioning Assessment

Wechsler Abbreviated Scale of Intelligence (WASI, 1999)

A WASI was administered to evaluate the cognitive functioning of the participants. WASI is a brief standardized tool for rapid evaluation of general cognitive ability, applicable for participants between 6 and 89 years old, which makes it useful to use for research and educational settings (Wechsler, 1999). It has four subsets: Vocabulary, Matrix Reasoning, Block Design, and Similarities, which are used to evaluate verbal intelligence quotient (IQ) (using vocabulary and similarities sections), performance IQ (using matrix reasoning and block design sections), and full-scale IQ (using all four subsets). WASI has standardized T-score and percentiles for both child and adult participants based on the raw scores from different subsets. The current study included full-scale IQ percentile and performance IQ percentile for each subject as the index of general cognitive ability.

Sensory Profile

Two different sensory profiles (Short sensory profile 2 and Adolescent/Adult sensory profile) were used to evaluate participants’ sensory responsiveness. For standardization purposes, raw scores from these profiles were transformed into z-scores.

SHORT Sensory Profile 2. This sensory profile is applicable for children aged 3–14.11 years and provides a rapid and reliable assessment of the child's sensory perception, making it suitable to use for screening and research purposes (Dunn, 2014). It includes a list of Likert scale questions about the sensory processing of the child in the context of their daily life (e.g., “is distracted when there is a lot of noise around) and their “behavioral response” associated with this processing (e.g., “struggles to pay attention”). For our participants aged from 8 to 14 years, this profile was completed by their parents/caregivers.

Adolescent/Adult Sensory Profile . This sensory profile (by Brown & Dunn, 2002) is a quick and reliable assessment of sensory processing applicable for individuals 11 years or older. This profile includes a list of Likert questions about individuals’ behavior/activity preferences to different sensory stimuli, including vision, audition, and touch. Participants from 15 to 25 years old completed this profile.

Temporal Processing Assessment

General Procedures

Temporal processing abilities were assessed using unisensory temporal order judgment tasks (TOJ) during which participants were asked to judge the order between two stimuli from the same sensory modality (auditory, visual, or tactile). Each of the three unisensory TOJ tasks was administered in four blocks with 115 trials per block. Each block lasted 5–8 min, with self-paced breaks between blocks. Stimulus Onset asynchrony (SOA), defined as the time gap between the onset of first stimulus and the onset of the second stimulus, varied between +400 ms and −400 ms at 23 levels (±400 ms, ±350 ms, ±300 ms, ±250 ms, ±200 ms, ±150 ms, ±75 ms, ±50 ms, ±35 ms, ±20 ms, ±10 ms, and 0 ms). Positive SOAs indicated the right stimulus was leading, while negative SOAs indicated the left stimulus was leading. A 0-ms SOA indicated that both right and left stimuli were presented simultaneously. Each SOA was repeated five times in each block in random order. Task orders were randomized for the participants. Before each unisensory TOJ, task participants were given a practice session with feedback.

SOA levels were selected for the TOJ tasks based on earlier literature (Baum et al. 2015) and pilot testing. For each task, subjects were positioned 60 cm from the computer screen. They were instructed to focus on a fixation cross on the computer screen, press any key to start, and give their responses as quickly as possible after the stimuli were presented. Instructions were also provided on the display, asking participants to press the corresponding keys/pedals to give a response. Each subsequent trial started 1.5 s after participants responded.

Apparatus and Stimuli

Auditory TOJ : Two auditory stimuli of the same frequency (1 kHz pure tone) and volume (25 dB) were presented for 50 ms via noise-canceling headphones to the right and left ear at different SOAs. Participants were asked to press the left arrow if the left sound came first and the right arrow if the right sound came first, using their dominant hand. Auditory stimuli were delivered through an Audiofile stimulus processor (Cambridge Research Systems, Rochester, UK).

Visual TOJ : Visual stimuli were generated using MATLAB (MathWorks, Natick, MA, USA) with the Psych toolbox extensions (Brainard, 1997; Pelli, 1997) and were presented on a Display++ LCD monitor. Visual stimuli were a pair of white circles with a diameter of 3.2° presented on a gray background for 50 ms in a dimly lit room. Stimuli were presented on each side of the fixation cross at 15.94° visual angle, at different SOAs. Participants were instructed to press the left arrow if the left stimulus came first and the right arrow if the right came first with the index finger of their dominant hand. Participants were positioned 60 cm in front of the computer screen, and a chin rest was used to minimize their head movements.

Tactile TOJ : Tactile stimuli were presented as mechanical vibrations delivered by a non-invasive tactile device from Engineering Acoustics Inc (EAI). Two vibrations at 50 Hz frequency (1.2 mm or 0.6 mm peak-to-peak displacement) were delivered to the index finger of the right and left hand for a 50 ms duration, at different SOAs between the two vibrations. Participants were instructed to provide a response as soon as possible by pressing the left pedal if the left stimulus came first and the right pedal if the right stimulus came first, using their dominant foot.

Analysis

Data from 40 subjects were finally included in the JND, response time (RT), and PSS analysis. Data from six subjects were excluded as either their performance did not yield an expected level of 75% accuracy in judging the right sequence of stimulus presentation, or their estimated JND exceeded the maximum level of SOAs used in the task. Participant responses in the unisensory TOJs were recorded along with the response time, and the proportion of “right first” responses was plotted as a function of SOAs. Individual data were fitted to a normal cumulative distribution function (CDF) (Burr et al., 2009; Fechner, 1860), and the goodness-of-fit for the model was assessed using adjusted R² for each participant in each task. The mean and standard deviation of the distribution were estimated. The mean represented the Point of Subjective Simultaneity (PSS), a measure of participants’ perceptual synchrony and any bias in their temporal judgments. The standard deviation represents a just noticeable difference (JND), the minimum temporal difference between two stimuli that a participant can detect reliably. Standard errors for both PSS and JND measures were estimated using the bootstrapping method (Efron & Tibshirani, 1994). The response time for each trial was recorded as the duration between the disappearance of the fixation point and the participant pressing the corresponding response key or pedal.

For each task, response time was averaged across SOA levels to obtain an average response time for each participant in the respective task. JND and RT values were transformed into their log values to achieve a more normalized distribution. Separate analyses (ANCOVA) were conducted for LogJND, LogRT, and PSS with age as a continuous independent variable and task modality (auditory, visual, and tactile) as a categorical independent variable. Multiple regression models were conducted with age, sensory profile, and IQ profiles to predict LogJND, LogRT, and PSS in each unisensory TOJ task, using data from the subjects (37) who had completed all the TOJ tasks, sensory profile, and WASI. Since two different sensory profiles have been used (Short Sensory Profile: 8–14 years old, Adult/Adolescent Sensory Profile: 15–25 years old), raw scores from these two profiles were standardized into Z-scores and used in the analysis. Pearson's Correlations between the dependent variables (JND/RT) were analyzed.

Studies exploring individual differences in metrics of sensory perception have used correlation and linear regression models to determine their predictors (e.g., Cecere et al., 2015; Stevenson et al., 2012; Venskus & Hughes, 2021; Zmigrod & Zmigrod, 2016). The current study applied similar correlation analyses and linear regression models to explore individual differences in temporal processing among neurotypical children and adults.

Results

The normal cumulative distribution function provided a good fit to the observed data in all three TOJ tasks, with a mean adjusted R² of 0.94 ± 0.08 for the visual task, 0.92 ± 0.06 for the tactile task, and 0.92 ± 0.05 for the auditory task. Figure 1 shows mean JND values (1A) and mean RT (1B) for the auditory, visual, and tactile TOJ tasks. The estimated marginal means of JNDs were 47.5 ± 71.37 ms for the visual TOJ task, 136 ± 68.38 ms for the auditory TOJ task, and 134 ± 83.9 ms for the tactile TOJ task. JNDs were transformed to log scale to obtain a more normalized distribution, and an ANCOVA (continuous independent variable: Age; categorical independent variable: Task Modality with three levels: auditory, tactile, and visual) was conducted (adjusted R² = .51, F(3,116) = 42.01, p < .001). ANCOVA showed significant main effects of age (F(1,40) = 26.53, p < .001, partial η2 = 0.19) and task (F(2,40) = 50.99, p < .001, partial η2 = 0.47) on LogJND. There was no significant interaction between age and task modality (F(2,40) = 0.50, p = .61, partial η2 = 0.008). Tukey's pairwise post-hoc analysis showed a significant LogJND difference between auditory and visual (p < .001), between visual and tactile (p < .001), but not between tactile and auditory TOJ tasks (p = .97). Linear regression lines showed similar decrease of LogJND with age in all three TOJ tasks (auditory task: F(1,38) = 8.75, p < .01, adjusted R²= .16, β −0.43; visual task: F(1,38)= 6.53, p < .05, adjusted R² = .12, β −0.38; tactile task: F(1,38)= 21.43, p < .001, adjusted R²=.34, β −0.60) (Figure 2).

The estimated marginal means of RT were 315 ± 191.82 ms for the visual TOJ task, 641 ± 401.46 ms for the tactile TOJ task, and 459 ± 323.29 ms for the auditory TOJ task. RTs were transformed to log scale to obtain a more normalized distribution, and a similar ANCOVA (continuous independent variable: age, and categorical independent variable: task modality with three levels) was conducted (adjusted R² = 0.54, F(3,116) = 48.62, p < .001). It showed a significant main effect of age (F(1,40) = 96, p < .001, η2 = 0.45) and task modality (F(2,40) = 24.65, p < .001, η2 = 0.30) for LogRT, with no interaction between age and task (F(2,40) = 0.36, p = .70, η2 = 0.006). Tukey's HSD post hoc analysis revealed significant differences for all three comparisons (between auditory and tactile: p < .001; between visual and tactile: p < .001; and between auditory and visual: p < .01). Linear regression also showed significant regression of LogRT with age in all three TOJ tasks (auditory: F(1,38) = 30.68, p < .001, adjusted R² = .43, β −0.67; visual: F(1,38) = 25.19, p < .001, adjusted R² = .38, β −0.63; tactile: F(1,38) = 43.19, p < .001, adjusted R² = .52, β −0.73) (Figure 3).

To examine whether participants’ performances in the TOJ tasks (LogJND, LogRT) were correlated between different modalities, Pearson's Correlation coefficients with Bonferroni adjustments were calculated. Significant correlations were found between tactile and auditory JNDs (r(38) = .74, p < .001), between tactile and visual JNDs (r(38) = .72, p < .001), and between visual and auditory JNDs (r(38) = .65, p < .001). Significant correlations were also found between tactile and auditory RTs (r(38) = .77, p < .001), between tactile and visual RTs (r(38) = .75, p < .001), and between visual and auditory RTs (r(38) = .77, p < .001).

Separate multiple linear regression models were conducted to examine whether independent variables (e.g., age, full-scale IQ percentile, performance IQ percentile, and sensory profile scores) can predict LogJND and LogRT in three modality TOJ tasks. Regression models were found significant for tactile JND (adjusted R² = 0.39, F(7,29) = 3.53, p < .01) and for visual JND (adjusted R² = 0.21, F(7,29) = 2.34, p < .05) with age being the only significant predictor in both instances (β −0.70, p < .001; β −0.52, p < .01, respectively). Age also significantly predicted JND in the auditory task (β −0.51, p < .01); however, the regression model did not reach significance (adjusted R²= 0.06, F(7,29) = 1.34, p > .05).

For reaction time, significant regression was found for auditory TOJ (adjusted R² = 0.37, F(7,29) = 4.02, p < .01), visual TOJ (adjusted R² = 0.33, F(7,29) = 3.50, p < .01), and tactile TOJ task(adjusted R² = .53, F(7,29) = 6.89, p < .001) with age being the only significant predictor in all three tasks (auditory: β −0.62, p < .001; visual: β −0.61, p < .001; tactile: β −0.75, p < .001).

Linear regression between independent variables including age, IQ percentiles, and sensory quadrant Z-scores (data from 37 subjects) had revealed a significant association between age and full-scale IQ percentile (F(1,35) = 4.33, p < .05, adjusted R² = 0.08, β −0.33,) (Figure 4A) suggesting full-scale IQ decreases slightly with age. Full-scale IQ percentile also showed a significant positive association with performance IQ percentile (F(1,35) = 113.9, p < .001, adjusted R² = 0.75, β 0.87) (Figure 4B), suggesting both IQ profiles develop simultaneously.

The estimated marginal means of PSS were 0.81 ± 12.7 ms for the visual TOJ task, −2.29 ± 31.1 ms for the tactile TOJ task, and 3.04 ± 26.2 ms for the auditory TOJ task (Figure 5A). ANCOVA (continuous independent variable: PSS, and categorical independent variable: task modality with three levels) was conducted (adjusted R² = −.014, F(3,116) = 0.42, p = .74). We did not find any main effect of age (F(1,40) = 0.32, p = .57, η2 = 0.003) and task modality (F(2,40) = 0.47, p = .62, η2 = 0.0.008) on PSS (Figure 5B), or any interaction between age and task modalities (F(2,40) = 1.42, p = .24, η2 = 0.02). Linear regression models also did not find any significant regression of PSS with age in any TOJ tasks (auditory: F(1,38)= 1.59, p = .21, adjusted R² = .01; visual: F(1,38)= 1.55, p = .22, adjusted R² = .01; tactile: F(1,38)= 0.59, p = .44, adjusted R² = −.01).

To examine whether PSS in the three TOJ tasks were correlated between different modalities, Pearson's Correlation coefficients with Bonferroni adjustment were calculated, but no significant correlation was found between modalities (auditory-visual: p = 0.43, visual-tactile: p = 1.0, tactile-auditory: p = .43). Furthermore, multiple linear regression models did not show any significant association between PSS and independent variables including age, IQ percentiles, and Sensory quadrant Z-scores (all p values >.05), except for one instance: PSS in tactile TOJ tasks shows significant regression with “sensory avoiding” Z-score (β= −0.53, p < .05), though regression model did not reach statistical significance (F(7,29) = 1.37, p = .25, adjusted R² = .06).

Discussion

It is important to characterize the individual differences in perceptual processing abilities in the neurotypical population, as this group is often used in control groups and compared to neurologically atypical groups. Individual variability within neurotypical control groups can affect observed differences between groups (Poole et al., 2015). This study, to the best of our knowledge, is the first study to evaluate temporal processing abilities in neurotypical children and young adults within and across all three sensory modalities (visual, auditory, tactile). This study offered to evaluate the contribution of participants’ age, cognitive abilities, and sensory responsiveness profiles to their temporal processing performances. Our results suggest that temporal sensitivity for unisensory signals improves with age in terms of accuracy and response speed from childhood to young adulthood, while PSS estimates in all three unisensory TOJ tasks remained stable. Although previous literature suggests superior judgment of auditory signals in temporal order of judgment tasks over the visual and tactile signals, our study finds superior judgment for visual cues. This study was not able to illustrate any association between temporal resolution, sensory response patterns, and cognitive abilities for this study sample.

Unisensory Temporal Sensitivity Improves With Age

Consistent with existing literature, the findings of our study showed the effect of age on unisensory temporal sensitivity in neurotypical children and young adults. While previous studies have reported a decline in temporal sensitivity across all major sensory modalities in adult and older adults (Busey et al., 2010; Craig et al., 2010; Fink et al., 2005; Ulbritch et al., 2009; Szymaszek et al., 2006), our study showed that temporal sensitivity in these sensory domains increases with age, reflecting the developmental trajectory of unisensory temporal processing from childhood to young adulthood. Note that reaction times in these TOJ tasks decreased with age in our study. The concurrent improvement in both TOJ task performance and speed with increasing age suggests that there was no “trade-off” between accuracy and speed up to and through early adulthood. Decrease in both threshold and reaction time with age was highest for the tactile task and was lowest for the visual modality. This might suggest that visual temporal processing evolves more steadily from childhood to adulthood compared to the other two sensory modalities.

Note that the perceptual synchrony, measured as PSS and reflecting subjective biases in temporal judgements, did not show significant changes with age in any of the TOJ tasks with unisensory stimuli. PSS in three TOJ tasks were not significantly correlated with each other, consistent with the previous finding that perceptual synchrony can vary depending on the surrounding situation as well as stimulus properties (Scurry et al., 2019).

Visual Temporal Sensitivity Was Superior to Auditory and Tactile

The temporal order judgment threshold was the lowest in the visual TOJ task, while the difference in threshold was not significant between the auditory and tactile tasks. As one of the goals of this study is to evaluate whether there are differences in temporal processing of sensory stimuli across modalities and within a wide age range of subjects, our TOJ task paradigms employed the presentation of identical and simple unisensory stimuli in different spaces rather than manipulating stimulus property (e.g., stimulus intensity, complexity), which itself can influence the temporal processing (Fister et al., 2016; Stevenson & Wallace, 2013). Our paradigm elicited a spatial component to the TOJ task, illustrated as “temporal order in space” by an early pioneer study using TOJ (Hirsh & Sherrick, 1961). The finding of superior visual temporal sensitivity, therefore, should be interpreted with caution given the spatial component in our paradigm. Our findings support the suggestions that visual modality is dominant in judging spatial cues (Foss-Feig, 2008; Hirsh & Sherrick, 1961; Howard & Templeton, 1966). However, the thresholds derived from the three unisensory TOJ tasks were significantly correlated with one another, suggesting a consistent pattern of improvement in the perceptual sensitivity of participants. The correlation between JNDs was strongest for auditory and tactile TOJ tasks, consistent with the suggestion that these two modalities exhibit significant resemblance in perceptual threshold levels, especially at low frequencies (Merchel & Altinsoy, 2020).

For the RTs, post-hoc analysis revealed significant differences among three modalities, with longer RT in the tactile TOJ task and the shortest RT in the visual TOJ task. Longer RT in tactile TOJ tasks compared to visual or auditory TOJ tasks could have resulted from the fact that our participants used foot pedals instead of hand in providing their responses for the tactile task. Reaction times in all three TOJ tasks were significantly correlated, suggesting a consistent pattern in temporal processing speed across the three sensory modalities.

Age, Cognitive Ability, and Sensory Sensitivity Predicting Unisensory Temporal Sensitivity

Earlier studies have reported the relationship between age-induced decline in cognition and perceptual abilities (Nowak et al., 2016; Szymaszek et al., 2009; Ulbritch et al., 2009) in older adults. These studies suggested a “common cause” theory for shared variance in the deterioration of cognition, sensation, and sensory-motor function because of aging. On the other hand, the continued improvement of cognitive resources like attention, memory, and processing speed through late childhood and into adolescence (Luna et al., 2004; Nelson & Dukette, 1998) may contribute to the improvement in TOJ function from childhood to young adulthood.

However, while the current study revealed an age-induced improvement in unisensory temporal processing abilities from childhood to young adulthood, IQ percentiles and sensory profile scores were unable to predict unisensory TOJ performance. Note that our study included a wide range of SOAs; therefore, lengthy trials might have resulted in fatigue and deterioration of the attentional span for the participants, contributing to the large variability seen in their performance. Additionally, the selection of age range (starting from 8 yrs old) in the current sample may lead to insufficient heterogeneity of cognitive abilities, hindering our ability to reveal a strong link between cognitive function and time perception development. Interestingly, full-scale IQ showed a slight decline with age for this group of subjects tested. It has been suggested that some aspects of cognitive aging (e.g., matrix reasoning, spatial visualization, and processing speed) could start as early as the 2nd or 3rd decade of life (Salthouse, 2009). Other studies have suggested that factors contributing to cognition, including neurobiological factors (e.g., brain volume (Pieperhoff et al., 2008), cortical thickness (Salat et al., 2004), and myelin integrity (Hsu et al., 2008)), as well as psychometric variables (e.g., vocabulary, reasoning, logical memory, spatial visualization (Salthouse et al., 2003)), show decline starting in early adulthood.

Possible Contributors to Age-Related Improvements in Temporal Sensitivity

One potential contributor to age-induced improvement in temporal processing is the development of myelination. The development of myelination is commonly studied as an estimator of neural maturation (Gibson, 1991). In some conditions that affect myelination, temporal acuity is impaired. For instance, multiple sclerosis, a condition that is associated with demyelination of white matter in the central nervous system, results in deficits in auditory and visual temporal resolution (Rappaport et al., 1994; White et al., 1983). A considerable number of studies concluded that there is a linear increase in the volume of the white matter of the brain, including myelination, until late adolescence (20/22 years) (Giedd et al., 1996; Giedd et al., 1999; Jernigan et al., 1991; Reiss et al., 1996). Maturation of myelin is also believed to be extended to adolescence (Chugani et al., 1987), with cortical layers involved with information processing seeming to develop more slowly than the other layers (Irwin et al., 1985). Nevertheless, the potential role of myelination may play in the development of temporal processing, while plausible, is rather speculative, given our study did not directly assess this structural brain change of age.

Another possible contributor to age-related improvements in temporal abilities is increased engagement in temporally structured experiences as we age such as music and social interactions. Events like these may facilitate the honing of the ability to judge time intervals accurately through practice effects (Droit-Volet & Coull, 2016). Additionally, the development and refinement of cognitive abilities likely play a role in the age-related improvement of temporal ability found in this study. Prior research suggests that improvements in cognitive domains such as working memory and attention within childhood commonly align with more accurate timing abilities and attributes this link to the importance of these specific cognitive domains for encoding duration information (Droit-Volet & Coull, 2016; Hallez & Droit-Volet, 2020). Others have elected to attribute the age-related increase in temporal abilities to cross-domain cognitive abilities such as spatial and mathematical skills (Bueti & Walsh, 2009; Walsh, 2003). “A Theory of Magnitude” alludes to the overlapping cortical networks that process temporal, spatial, and numerical information, all of which are skills that typically become more prevalent as we age with more access to formal educational opportunities. This theory has been supported by recent findings that performance in temporally related tasks correlates with spatial transformation and proficiency in mathematics (Bonato et al., 2012; Coull & Droit-Volet, 2018).

Conclusion

The findings of this study are consistent with our proposed hypothesis that temporal sensitivity for unisensory signals in all three sensory modalities (audio, visual, and tactile) becomes more refined from childhood to young adulthood, both in terms of precision and speed. Temporal order judgement thresholds in these three unisensory TOJ tasks were significantly correlated with one another, suggesting a consistent pattern of improvement in the sensory perception for this group of participants. Reaction times and thresholds for the visual modality were the shortest and lowest, respectively, in support of previous literature showing that visual cues are more dominant than other modality cues in spatial judgment paradigms.

While earlier studies examining temporal perception mostly focused on neurologically atypical groups (e.g., ASD and ADHD), results from our study offer new insights into natural development in the temporal processing, setting a unique benchmark for future relevant studies to mark “normal” or “deviation from normal.” One limitation of this study is that it has a relatively small sample size due to the impact of the COVID pandemic. Future studies with a larger group of participants and an extension of the age range tested are needed to confirm our findings.

Footnotes

Acknowledgments

We thank Dr. Alexandra Scurry for assisting in experiment programming and Jung Min Kim for assisting with data collection. This study has been adopted from a master's thesis titled “Individual Differences in the Temporal Processing of Neurotypical Children and Adults” by Chowdhury, Shahida. The thesis is made available through ProQuest Dissertations and Theses database (PQDT), in ProQuest/UMI's Dissertation Abstracts International (https://www.proquest.com/openview/07baad3f8b071abf1c6b9b7b38d8baea/1?pq-origsite=gscholar&cbl=18750&diss=y).

Author Contribution(s)

Shahida Chowdhury: Conceptualization; Formal analysis; Investigation; Methodology; Writing – original draft; Writing – review & editing.

Jillian Martin: Formal analysis; Writing – review & editing.

Jeffrey J. Hutsler: Conceptualization; Methodology; Writing – review & editing.

Fang Jiang: Conceptualization; Methodology; Writing – review & editing.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute of General Medical Studies of the National Institutes of Health (grant number P20 GM103650).

ORCID iDs

Shahida Chowdhury

Jillian Martin

Jeffrey J. Hutsler

Fang Jiang

Appendix A

Table 1.

Frequency distribution of the sample according to age.

Age (years)	Frequency
8	1
9	2
10	2
11	2
12	3
13	1
14	4
16	1
17	1
18	4
19	5
20	3
21	2
22	3
23	4
24	1
25	1

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