How Problematic Short Video Use Alters Time Perception: Neural Insights from a Prospective Temporal Estimation fNIRS Study

How Problematic Short Video Use Alters Time Perception: Neural Insights from a Prospective Temporal Estimation fNIRS Study | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 11 September 2025 V1 Latest version Share on How Problematic Short Video Use Alters Time Perception: Neural Insights from a Prospective Temporal Estimation fNIRS Study Authors : Tongshu Li , Run Hu , Huafang Liu , Pu Yang 0009-0000-3798-0194 , and Xiaolong Liu 0000-0002-3814-5606 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175758028.83496033/v1 461 views 198 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Short video platforms have dramatically altered users’ experiences of time, yet the effects of problematic use of short videos (PUSV) on prospective time estimation remain underexplored. This study investigated how PUSV impacts temporal perception and its neural correlates. A total 76 Chinese university students were categorized as either PUSV or control based on standardized scales. Participants performed a prospective time range estimation task while watching short or neutral videos, with concurrent monitoring of dorsolateral prefrontal cortex (DLPFC) activation using functional near-infrared spectroscopy (fNIRS). Compared to neutral content, short videos elicited more positive emotions, higher arousal, and marked overestimation of time. Notably, these effects were accentuated in the PUSV group, who also exhibited stronger right DLPFC activation when viewing short videos. This study provides novel evidence that PUSV can distort prospective time estimation and is linked to specific neural activity patterns. These findings deepen the understanding of the psychological and neural mechanisms underlying PUSV and inform future research on behavioral addictions related to social media. How Problematic Short Video Use Alters Time Perception: Neural Insights from a Prospective Temporal Estimation fNIRS Study Tongshu Li 1 , Run Hu 1 , Huafang Liu 1 , Pu Yang 1 , and Xiaolong Liu 1 * Email: [email protected] *; [email protected] ; [email protected] ; [email protected] ; [email protected] Conflict of Interest: None 1 Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China Abstract Short video platforms have dramatically altered users’ experiences of time, yet the effects of problematic use of short videos (PUSV) on prospective time estimation remain underexplored. This study investigated how PUSV impacts temporal perception and its neural correlates. A total 76 Chinese university students were categorized as either PUSV or control based on standardized scales. Participants performed a prospective time range estimation task while watching short or neutral videos, with concurrent monitoring of dorsolateral prefrontal cortex (DLPFC) activation using functional near-infrared spectroscopy (fNIRS). Compared to neutral content, short videos elicited more positive emotions, higher arousal, and marked overestimation of time. Notably, these effects were accentuated in the PUSV group, who also exhibited stronger right DLPFC activation when viewing short videos. This study provides novel evidence that PUSV can distort prospective time estimation and is linked to specific neural activity patterns. These findings deepen the understanding of the psychological and neural mechanisms underlying PUSV and inform future research on behavioral addictions related to social media. Keywords: PUSV, fNIRS, temporal estimation, neural mechanisms, DLPFC * Corresponding author Background The rapid expansion of the internet and widespread adoption of smartphones have revolutionized online engagement patterns, particularly with the emergence of short video apps. These apps, characterized by their concise format, ease of consumption, and instant gratification, have become the dominant mode of internet content dissemination in the past decade, filling mobile users’ brief leisure moments. The 55th China Internet Network Information Center (CNNIC) Statistical Report indicates that the short video user base has reached 1.04 billion, accounting for 93.8% of the total internet users. TikTok (”Douyin” in China), launched in 2016, is the most popular short video app. Statista’s 2024 data shows it has become the world’s most popular mobile video app with 825.48 million downloads by 2024. Beyond their popularity, short video apps have drawn attention to associated issues. Some users exhibit uncontrollable and excessive engagement. Mu et al. (2022) conducted a confirmatory study using behavioral addiction diagnostic criteria and termed this phenomenon Short-Form Video Addiction (SFV) as it meets the five canonical clinical criteria of behavioral addiction. Further research supports classifying SFV as a technological addiction subset (Tian et al., 2023). However, as SFV isn’t included in ICD-11 or DSM-5, a more rigorous term is Problematic Use of Short Video (PUSV) (Wang et al., 2021). Existing research shows PUSV adversely impacts various aspects of life. It affects mental health, causing anxiety and depression (Zhang et al., 2019); work and study, leading to attention distraction, reduced study time, and poor time management (Hong et al., 2014). It also causes higher social anxiety and is linked to bullying victimization. Currently, functional near-infrared spectroscopy (fNIRS) remains underutilized in exploring the neurobiological foundations of substance and behavioral addictions. The research on brain mechanisms of substance addiction and behavioral addiction continues to predominantly rely on electroencephalography (EEG) with high temporal resolution, magnetic resonance imaging (MRI) with high spatial resolution (Campanella et al., 2025), and functional magnetic resonance imaging (fMRI) (He et al., 2017; Shen et al., 2023; Wang et al., 2024; Weinstein & Lejoyeux, 2015). Despite its potential, this neuroimaging modality has primarily served as a superficial cortical monitoring tool in existing addiction research, focusing on investigating substance addiction mechanisms and corresponding interventions. However, fNIRS offers several advantages in studying PUSV. As a non-invasive neuroimaging technique with high temporal resolution and moderate spatial resolution, fNIRS allows for naturalistic experimental settings (Li et al., 2022), making it ideal for investigating the neural correlates of behaviors like watching short videos. It can measure hemodynamic responses in cortical regions, such as the dorsolateral prefrontal cortex (DLPFC), which is implicated in cognitive control and time perception, providing valuable insights into the neural mechanisms underlying PUSV (Pinti et al., 2015). Although fNIRS has limitations, such as its restricted penetration depth preventing subcortical measurements compared to fMRI, its safety profile, cost-effectiveness, and patient-friendly nature make it a promising tool for addiction research (Chen et al., 2020; Li et al., 2022). Recent studies have demonstrated fNIRS’s capability in elucidating neural mechanisms underlying various behavioral addictions, such as internet gaming disorder (Cho et al., 2022), smartphone addiction (Liu et al., 2023, 2024) and pornography addiction (Shu et al., 2025), with findings indicating heightened neural activation in the dorsolateral prefrontal cortex during addictive behaviors. The application of fNIRS in PUSV research could further our understanding of the neurocognitive processes involved in this phenomenon and contribute to the development of more effective intervention strategies. In summary, there is considerable scope for further exploration of PUSV. Previous research on PUSV has predominantly focused on behavioral studies, with limited integration of neuroimaging techniques (Chen et al., 2023; Gu et al., 2022; Husin et al., 2022; Jiang et al., 2024). In studies examining PUSV and time estimation, the prospective time estimation paradigm is more commonly employed, and existing findings suggest that individuals who frequently use short videos tend to overestimate the duration of their usage (Yang, 2024). However, there is a notable absence of specific research conclusions regarding the underlying mechanisms of PUSV, particularly the neural mechanisms, as well as its intervention and treatment strategies. Investigating the behavioral performance and brain activation patterns of individuals exhibiting PUSV using fNIRS holds significant theoretical and practical implications. The rise of short video platforms has significantly altered how users perceive and experience time. However, the impact of PUSV on prospective time estimation remains underexplored. This study bridges this gap by investigating the effects of PUSV on temporal perception and its neural correlates. Participants The prospective, parallel randomized controlled trial received approval from the Institutional Review Board of Sichuan Normal University (2023LS008). The research was conducted in strict adherence to the Declaration of Helsinki principles (World Medical Association, 2013). All procedures were carried out in compliance with relevant guidelines and regulations. Informed consent was secured from all participants. Participants were recruited through an online questionnaire, resulting in a total of 76 college students participating in the formal experiment. The sample included 36 males (average age 20.4 ± 2.55 years) and 40 females (average age 20.3 ± 1.53 years). All participants were right-handed, had normal or corrected-to-normal vision, and normal hearing. They had no history of traumatic brain injury, substance addiction, or neurological or psychiatric disorders, and had not previously participated in similar experiments. These criteria ensured the sample’s homogeneity and suitability for the study. All participants signed an informed consent form and received a participation fee upon completion of the experiment. Sample size determination followed Cohen’s (1988) guidelines for medium effect sizes, ensuring adequate statistical power for detecting significant effects. Procedure Participants were recruited and completed an online survey that collected demographic information, including age, gender, education level, preference for short video applications, and weekly duration of short video usage. They then completed the ”College Student Short Video Addiction Scale” (Qin et al., 2019) and the ”Short-Form Video Problematic Use Scale” (Chen et al., 2023). Based on the results of these scales, participants were categorized into either the ”Problematic Use of Short Video Group” (PUSV Group) or the ”Control Group” (CG). The PUSV Group was composed of 18 males and 20 females, while the CG included 18 males and 20 females. The experimental materials consisted of neutral landscape videos and ”Douyin” short videos. The neutral videos were evaluated and selected by 20 college students (10 males and 10 females) to ensure moderate arousal and emotional valence. Both types of videos had four presentation durations: 15s, 20s, 24s, and 30s, with the order and timing randomized. Neutral videos were presented on the computer monitor, while short videos were displayed via the ”Douyin” app on the mobile device. To ensure valid prospective duration estimates, participants were told in advance that they would have to judge the length of every video. Fig. 1 gives the trial structure. Each trial began with a fixation cross (200–500ms) on the computer monitor. Participants then viewed either a neutral video presented on the same monitor or a short video shown on a mobile phone that stood 50 cm to their right (Fig.2); only one type of video was shown per trial. After the clip ended, a blank screen appeared for 2-5s, followed by three consecutive tasks: (1) reproduce the video duration, (2) rate arousal on a 9-point Likert scale (1 = very calm, 9 = very excited), and (3) rate emotional valence on a 9-point Likert scale (1 = very unpleasant, 9 = very pleasant). A 2-5s inter-trial interval preceded the next trial. 36 trials were administered. Fig.1. Depicts the timeline for a single trial in the prospective time-estimation task with emotional ratings. Participants viewed either a neutral video (computer screen) or a short video (mobile phone), then estimated duration and rated arousal/valence. The sequence was repeated for 36 trials. fNIRS instrumentation and data acquisition During the experiment, fNIRS data were collected from the PFC regions of the participants using a portable near-infrared functional brain imaging device, NirSmart-3000A (Huichuang, China), with data captured at 11 Hz. The light source detectors were arranged with 8 light sources (Source) and 7 receivers (Detector), forming 22 channels that covered the participant’s head according to the international 10-20 system (Hoshi, 2003). The emitters produced light at wavelengths of 760 nm and 850 nm to monitor changes in PFC oxygenated (HbO) and deoxygenated (HbR) hemoglobin concentrations. The laboratory employed a computer screen (resolution: 1920×1080, refresh rate: 60 Hz) for presenting experimental paradigms. For the display of authentic short video materials, a Huawei Mate 40 Pro mobile device was utilized.The experimental setup is illustrated in Fig. 2. Fig.2. (a) 22-channel fNIRS array over the prefrontal cortex aligned with the international 10–20 system. (b) Participant placement and experimental setup. Data analysis fNIRS data processing fNIRS data were analyzed using the NirSpark software (Huichuang, China) and HOMER3, an open-source MATLAB-based toolbox compatible with MATLAB R2017b. Preprocessing steps included converting light intensity to optical density, applying band-pass filtering (0.01–0.5 Hz) to eliminate physiological noise, and transforming optical density to hemoglobin concentration changes using the modified Beer-Lambert law with a path-length factor (Molavi & Dumont, 2012). Motion artifacts were addressed using spline interpolation within HOMER3. The hemodynamic response function (HRF) was modeled by convolving a canonical HRF with the experimental paradigm. This model was incorporated into a general linear model (GLM) framework in HOMER3 to estimate beta ( β ) values for each channel and condition, which were then used for statistical analyses and activation map generation via EasyTopo software (Tian et al., 2013). Behavioral data analysis Behavioral data were analyzed using a 2 (Video Type: “Douyin” vs. Neutral) × 2 (Group: PUSV vs. CG) × 4 (Duration: 15s, 20s, 24s, 30s) repeated-measures ANOVA. Significant interactions were followed up with simple effects analysis and post hoc comparisons with Bonferroni correction to maintain strict significance levels across multiple comparisons. Time estimation accuracy was assessed using relative error (RE), calculated as: RE = (|EV - SV|) / SV where EV is the estimated duration and SV is the standard duration. Lower RE values indicate more accurate time estimation (Wittmann & Paulus, 2008). A repeated-measures ANOVA was also used to compare arousal, emotional valence, and RE between groups. Results Behavioral result Fig. 3 and 4 present the differential results of participants across the three behavioral tasks. For arousal ratings, participants perceived short videos as significantly more arousing than neutral videos across all durations. Repeated-measures ANOVA revealed significant main effects of video type for all durations: 15s ( F (1,75) = 108.916, p < 0.001, ηp² = 0.595), 20s ( F (1,75) = 79.74, p < 0.001, ηp² = 0.519), 24s ( F (1,75) = 160.699, p < 0.001, ηp² = 0.685), and 30s ( F (1,75) = 66.346, p < 0.001, ηp² = 0.473). For emotional valence ratings, short videos were rated significantly higher than neutral videos across all durations. The main effect of video type was significant for: 15s ( F (1,75) = 20.536, p < 0.001, ηp² = 0.217), 20s ( F (1,75) = 6.487, p < 0.05, ηp² = 0.081), 24s ( F (1,75) = 18.084, p < 0.001, ηp² = 0.196), and 30s ( F (1,75) = 13.41, p < 0.001, ηp² = 0.153). For time estimation bias, participants overestimated the duration of both short and neutral videos. However, this overestimation was significantly higher for neutral videos compared to short videos at 24s ( F (1,75) = 10.56, p < 0.005, ηp² = 0.125) and 30s ( F (1,75) = 16.31, p < 0.001, ηp² = 0.181). The results of arousal, emotional valence and time estimation bias repeated measures analysis of variance for the three are shown in table 1. Table 1. Results of repeated-measures ANOVA for arousal, emotional valence, and time-estimation bias. Arousal 15 108.916 < 0.001 0.595 20 79.74 < 0.001 0.519 24 160.699 < 0.001 0.685 30 66.346 < 0.001 0.473 Emotional Valence 15 20.536 < 0.001 0.217 20 6.487 < 0.05 0.081 24 18.084 < 0.001 0.196 30 13.41 < 0.001 0.153 Time Estimation 24 10.56 < 0.005 0.125 30 16.31 < 0.001 0.181 Fig.3. (a) Arousal and (b) emotional-valence differences between participants who watched short videos versus those who viewed neutral videos. Fig. 4. Time-estimation bias differences between participants who watched short videos and those who viewed neutral videos. Results of fNIRS data A repeated measures ANOVA was performed using SPSS 27 on paired fNIRS data to investigate brain activation differences during video watching and time estimation tasks. The analysis included data from four durations of “Douyin” and neutral videos-each corresponding to a specific measurement marker as well as a pair of markers for time estimation involving both short and neutral videos, resulting in a total of five marker pairs. Within the 15-second video condition, significant main effects of video type were observed in channels 2, 15, 16, 17, and 18, with further interactions between video type and group emerging in channels 2 and 5. The repeated measures ANOVA results for different channels are shown in table 2. Channel 2: Main effect of video type: F (1,75) = 55.98, p < 0.01, ηp² = 0.431. Interaction between video type and group: Post-hoc analysis revealed that both the PUSV group and the control group showed significantly higher activation when watching “Douyin” videos compared to neutral videos (PUSV: t = 6.844, p < 0.05; control group: t = 3.737, p < 0.05). The PUSV group exhibited a more pronounced upward trend (Fig. 5a); Channel 15: Main effect of video type: F (1,75) = 20.82, p < 0.001, ηp² = 0.22. Interaction between video type and group: Post-hoc analysis found that the PUSV group showed significantly higher activation in this channel when watching “Douyin” videos compared to neutral videos ( t = 5.01, p < 0.001) (Fig. 5b); Channel 16: Main effect of video type: F (1,75) = 10.33, p < 0.005, ηp² = 0.122. Interaction between video type and group: Post-hoc analysis revealed that the PUSV group showed significantly higher activation in this channel when watching “Douyin” videos compared to neutral videos ( t = 4.022, p < 0.001) (Fig. 5c); Channel 17: Main effect of video type: F (1,75) = 40.11, p < 0.001, ηp² = 0.352. Interaction between video type and group: Post-hoc analysis found that both the PUSV group and the control group showed significantly higher activation in this channel when watching “Douyin” videos compared to neutral videos (PUSV: t = 5.939, p < 0.001; control group: t = 3.018, p < 0.05). The PUSV group exhibited a more pronounced upward trend (Fig. 5d); Channel 18: Main effect of video type: F (1,75) = 47.92, p < 0.001, ηp² = 0.393. Interaction between video type and group: Post-hoc analysis revealed that both the PUSV group and the control group showed significantly higher activation in this channel when watching “Douyin” videos compared to neutral videos (PUSV: t = 6.699, p < 0.001; control group: t = 3.091, p < 0.05). The PUSV group exhibited a more pronounced upward trend (Fig. 5e); Channel 5: Interaction between video type and group: F (1,75) = 5.526, p < 0.05, ηp² = 0.069. Post-hoc analysis found that the PUSV group showed significantly higher activation in channel 5 when estimating the duration of “Douyin” videos compared to neutral videos ( t = 2.22, p < 0.05). However, when estimating the duration of neutral videos, the activation in channel 5 was significantly lower in the PUSV group compared to the control group ( t = -2.339, p < 0.05) (Fig. 5f). Table 2. Repeated-measures ANOVA results for video-type effects across channels (15-s Video Condition) 2 55.98 6.844 0.002** 0.431 15 20.82 5.010 < 0.001*** 0.220 16 10.33 4.022 0.016* 0.122 17 40.11 5.939 < 0.001*** 0.352 18 47.92 6.699 < 0.001*** 0.393 5 5.526 2.220 0.031* 0.069 Fig. 5. (a–e) illustrate between-group activation differences in channels 2, 15, 16, 17, and 18 during video viewing; Fig. 5f shows the corresponding difference in channel 5 during the subsequent video-evaluation phase. Brain maps were created to visualize the differential activation between the two groups. Fig.6 illustrates the activation differences between the PUSV and control groups in channels 2, 15, 16, 17, and 18 during the viewing of short and neutral videos. Fig. 6. (a, b) PUSV and control group brain-activation maps while viewing Douyin videos (Ch 2, 15, 16, 17, 18). (c, d) Activation maps of the same groups while viewing neutral videos (Ch 2, 15, 16, 17, 18). Fig. 7 shows the activation differences between the two groups in channel 5 during the duration estimation task for the two types of videos. These brain maps highlight that the PUSV group exhibited significantly stronger activation in specific channels during short video viewing, while the control group showed stronger activation during neutral video viewing. Fig. 7. (a, b) Brain-activation patterns of the PUSV and control groups during duration estimation of Douyin videos (Ch 5). (c, d) Activation patterns of the same groups during duration estimation of neutral videos (Ch 5). Discussion This study investigated the behavioral and neural correlates of Problematic Use of Short Video (PUSV) individuals by employing functional near-infrared spectroscopy (fNIRS) alongside a prospective time estimation paradigm. Our findings yield several notable insights that contribute to the growing body of literature on behavioral addiction, particularly as it relates to emerging media consumption habits. The results observed that individuals in the PUSV group experienced heightened positive emotions and greater physiological arousal in response to short videos, relative to control participants. This result aligns with our initial hypothesis and corroborates previous research indicating that short video consumption plays a significant role in emotional regulation within this population (Mu et al., 2022; Tian et al., 2023). The emotional and arousal states elicited by short videos may enhance their reinforcing value, promoting repeated use and potentially facilitating the transition from recreational engagement to compulsive or problematic patterns of behavior (Brand et al., 2014). These findings support the theoretical framework positioning PUSV within the spectrum of behavioral addictions, in which media stimuli assume a regulatory function in mood and affect. In contrast to earlier studies on behavioral addictions that frequently report pronounced overestimation of time spent engaging in addictive behaviors (Turel & Cavagnaro, 2019; Tobin et al., 2010; Yang, 2024), our results demonstrated a larger time-overestimation bias for neutral videos compared to short videos in the PUSV group. While both groups tended to overestimate the duration of short video exposure, this bias was less marked than for neutral content. This discrepancy may be partially attributable to methodological factors, such as the relatively short duration of video stimuli (15–30 seconds) used in the present study. Prior research employing longer tasks or stimuli (ranging from 5 to 60 minutes) may have exacerbated time estimation distortions. Moreover, the viewing habits typical in short video app usage—frequent video-switching and incomplete viewing—could attenuate the awareness of time passage, possibly by fostering a state of “flow” or temporal dissociation (Csikszentmihalyi, 2014). The engendered positive affect and heightened arousal in response to short videos may further contribute to reduced time estimation bias, as immersive experiences are known to alter the perception of time (Wittmann & Paulus, 2008). Thus, our findings refine the understanding of temporal distortions in behavioral addiction, suggesting that contextual factors such as the characteristics of media content and viewing patterns must be carefully considered. Neuroimaging results revealed that the PUSV group exhibited increased activation in the right dorsolateral prefrontal cortex (rDLPFC) during short video viewing—an area implicated in executive functions, including inhibitory control and emotional regulation (Su et al., 2021; Hughes et al., 2012, 2013, 2014; Lefaucheur et al., 2017). This pattern of neural activity is highly consistent with established neural signatures observed in various behavioral addictions, providing compelling evidence for conceptual parallels between PUSV and other forms of non-substance-related addictive behaviors. The heightened engagement of the rDLPFC may reflect compensatory efforts to regulate impulses or manage emotional responses during short video exposure, or it may indicate the increased cognitive demands imposed by managing compulsive reward-driven behaviors (Robbins et al., 2012). Collectively, these neural findings strengthen the case for understanding PUSV through the lens of behavioral addiction neuroscience and highlight critical targets for intervention. This study’s limitations should be acknowledged. Due to its cross-sectional design, causal relationships cannot be inferred from the observed associations. The reliance on a prospective time estimation paradigm may have been influenced by individual differences in baseline time perception and susceptibility to practice effects across trials. Furthermore, the sample predominantly consisted of young individuals and was characterized by gender imbalance, restricting the generalizability of our findings. Lastly, while fNIRS technology permits non-invasive assessment of cortical activity, its limited penetration depth precludes investigation of deeper brain structures, such as the striatum or limbic system, which are integral to reward processing and addiction (Koob & Volkow, 2016). Future research should address these limitations by incorporating longitudinal and experimental designs to determine causal mechanisms, recruiting demographically diverse samples to ensure broader applicability, and utilizing advanced neuroimaging modalities—such as functional MRI—to map activation in subcortical regions relevant to reward, habit formation, and emotional regulation. In addition, more ecologically valid paradigms or real-world data collection (e.g., digital phenotyping) may offer further insight into the dynamic nature of short video consumption and its psychological effects (Huckins et al., 2020). In summary, this study advances our understanding of the affective, behavioral, and neural correlates of PUSV. Our results underscore the unique appeal and regulatory function of short videos among vulnerable individuals, delineate contextual nuances in altered time perception, and establish neurobiological commonalities between PUSV and established behavioral addictions. Such insights are vital for informing the development of targeted prevention and intervention strategies addressing the mental health impacts of pervasive short video consumption in contemporary society. Conclusion This study provides novel insights into the behavioral and neurofunctional characteristics of individuals with Problematic Use of Short Video (PUSV). Behaviorally, PUSV individuals exhibited heightened positive emotional experiences and arousal levels when viewing short videos, suggesting that these videos hold particular appeal and may serve an emotional regulation function. In terms of temporal perception, PUSV individuals demonstrated a greater overestimation bias for neutral videos compared to short videos. This pattern may be attributed to the high refresh frequency and brief viewing duration typical of short video platforms, which can induce a flow state characterized by positive emotions and elevated arousal, potentially leading to systematic underestimation of viewing duration. Neurofunctionally, PUSV individuals showed significantly enhanced activation in the dorsolateral prefrontal cortex (DLPFC), particularly the right DLPFC, a region critical for decision-making, inhibitory control, and emotion regulation. This differential activation pattern was observed not only during short video viewing but also extended to temporal estimation tasks involving short video stimuli. These findings align with previous research on behavioral addictions and highlight the neurofunctional parallels between PUSV and established behavioral addictions. Informed Consent Statement Not applicable. Data Availability Statement The data presented in this review are available upon request from the corresponding author. Conflicts of Interest The authors declare no conflict of interest. Authors’ contribution Tongshu Li: Conceptualization, Methodology, Validation, Formal analysis, Data Curation, Writing Original Draft, Project administration, Funding acquisition. Run Hu: Validation, Formal analysis. Huafang Liu: Validation, Review & Editing, Pu Yang: Review & Editing. Xiaolong Liu: Validation, Review & Editing, Project administration, Funding acquisition. All authors contributed to and approved the final version of the manuscript and take responsibility for the integrity of the data and the accuracy of the data analysis. Ethics The study and its procedures had full approval by the local ethics committee and adhered to the most recent version of the Declaration of Helsinki and all participants were required to provide informed consent. Reference Brand, M., Young, K. S., & Laier, C. (2014). Prefrontal control and internet addiction: a theoretical model and review of neuropsychological and neuroimaging findings. Frontiers in human neuroscience , 8 , 375. https://doi.org/10.3389/fnhum.2014.00375. Csikszentmihalyi, M. (2014). Flow and the Foundations of Positive Psychology: The Collected Works of Mihaly Csikszentmihalyi. Springer Netherlands . https://doi.org/10.1007/978-94-017-9088-8 Campanella, S., Arikan, M. K., Ilhan, R., Sanader Vukadinivic, B., & Pogarell, O. (2025). New Insights in the Treatment of Substance Use Disorders Thanks to Electrophysiological Tools. Clinical EEG and Neuroscience , 15500594251324506. https://doi.org/10.1177/15500594251324506. Chen, W.-L., Julie C. Wagner, Wagner, J. C., Heugel, N., Sugar, J., Lee, Y.-W., Conant, L. L., Malloy, M., Heffernan, J., Quirk, B. J., Zinos, A., Scott A. Beardsley, Beardsley, S. A., Prost, R. W., Harry T. Whelan, & Whelan, H. T. (2020). Functional Near-Infrared Spectroscopy and Its Clinical Application in the Field of Neuroscience: Advances and Future Directions. Frontiers in Neuroscience , 14 , 724. https://doi.org/10.3389/fnins.2020.00724. Chen, Y., Li, M., Guo, F., & Wang, X. (2023). The effect of short-form video addiction on users’ attention. Behaviour & Information Technology , 42 (16), 2893–2910. https://doi.org/10.1080/0144929X.2022.2151512. Cho, T. H., Nah, Y., Park, S. H., & Han, S. (2022). Prefrontal cortical activation in Internet Gaming Disorder Scale high scorers during actual real-time internet gaming: A preliminary study using fNIRS. Journal of Behavioral Addictions . https://doi.org/10.1556/2006.2022.00017. Gu, L., Gao, X., & Li, Y. (2022). What drives me to use TikTok: A latent profile analysis of users’ motives. Frontiers in Psychology , 13 , 992824. https://doi.org/10.3389/fpsyg.2022.992824. He J., Nie Y., Zhou Z., & Chai Y. (2017). Are both neural mechanisms of Internet gaming and heroin addicts the same? Research evidence based on MRI. Advances in Psychological Science , 25 (8), 1327. https://doi.org/10.3724/SP.J.1042.2017.01327. Hong, F. Y., Huang, D. H., Lin, H. Y., & Chiu, S. L. (2014). Analysis of the psychological traits, Facebook usage, and Facebook addiction model of Taiwanese university students. Telematics and Informatics, 31 (4), 597-606. https://doi.org/10.1016/j.tele.2014.01.001. Huckins, J. F., daSilva, A. W., Wang, W., Hedlund, E., Rogers, C., Nepal, S. K., Wu, J., Obuchi, M., Murphy, E. I., Meyer, M. L., Wagner, D. D., Holtzheimer, P. E., & Campbell, A. T. (2020). Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study. Journal of medical Internet research, 22 (6), e20185. https://doi.org/10.2196/20185. Hughes, M. E., Budd, T. W., Fulham, W. R., Lancaster, S., Woods, W., Rossell, S. L., & Michie, P. T. (2014). Sustained brain activation supporting stop‐signal task performance. European Journal of Neuroscience, 39 (8), 1363–1369. https://doi.org/10.1111/ejn.12497. Hughes, M. E., Fulham, W. R., Johnston, P. J., & Michie, P. T. (2012). Stop-signal response inhibition in schizophrenia: Behavioural, event-related potential and functional neuroimaging data. Biological Psychology, 89 (1), 220–231. https://doi.org/10.1016/j.biopsycho.2011.10.013. Hughes, M. E., Johnston, P. J., Fulham, W. R., Budd, T. W., & Michie, P. T. (2013). Stop-signal task difficulty and the right inferior frontal gyrus. Behavioural Brain Research, 256 , 205–213. https://doi.org/10.1016/j.bbr.2013.08.026. Husin, N. A., Khairi, M. S. H., Nazeri, N. S. M., & Mariyanti, E. (2022). Why university students are addicted to short video TikTok?: A Malaysian case. Sociometry Journal of Social Science, Art and Humanity, 2 (2). Hoshi, Y. (2003). Functional near‐infrared optical imaging: Utility and limitations in human brain mapping. Psychophysiology , 40 (4), 511–520. https://doi.org/10.1111/1469-8986.00053 Jiang, Y., Feng, Y., Qi, J., He, R., & Chao, M. (2024). The relationship between bullying victimization and academic performance among adolescents: The chain mediating roles of social anxiety and short video addiction. Psychology in the Schools , 61 (10), 3859–3872. https://doi.org/10.1002/pits.23267 Koob, G. F., & Volkow, N. D. (2016). Neurobiology of addiction: a neurocircuitry analysis. The lancet. Psychiatry, 3 (8), 760–773. https://doi.org/10.1016/S2215-0366(16)00104-8 Li, R., Yang, D., Fang, F., Hong, K. S., Reiss, A. L., & Zhang, Y. (2022). Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review. Sensors (Basel, Switzerland) , 22 (15), 5865. https://doi.org/10.3390/s22155865 Lefaucheur, J.-P., Antal, A., Ayache, S. S., Benninger, D. H., Brunelin, J., Cogiamanian, F., Cotelli, M., De Ridder, D., Ferrucci, R., Langguth, B., Marangolo, P., Mylius, V., Nitsche, M. A., Padberg, F., Palm, U., Poulet, E., Priori, A., Rossi, S., Schecklmann, M., … Paulus, W. (2017). Evidence-based guidelines on the therapeutic use of transcranial direct current stimulation (tDCS). Clinical Neurophysiology , 128 (1), 56–92. https://doi.org/10.1016/j.clinph.2016.10.087 Liu, X., Tian, R., Bai, X., Liu, H., Li, T., Zhou, X., & Lei, Y. (2024). Exploring the impact of smartphone addiction on decision-making behavior in college students: An fNIRS study based on the Iowa Gambling Task. Frontiers in Psychiatry , 15 , 1342521. https://doi.org/10.3389/fpsyt.2024.1342521 Liu, X., Tian, R., Liu, H., Bai, X., & Lei, Y. (2023). Exploring the Impact of Smartphone Addiction on Risk Decision-Making Behavior among College Students Based on fNIRS Technology. Brain Sciences , 13 (9), 1330. https://doi.org/10.3390/brainsci13091330 Molavi, B., & Dumont, G. A. (2012). Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiological Measurement , 33 (2), 259–270. https://doi.org/10.1088/0967-3334/33/2/259 Mu, H., Jiang, Q., Xu, J., & Chen, S. (2022). Drivers and Consequences of Short-Form Video (SFV) Addiction amongst Adolescents in China: Stress-Coping Theory Perspective. International journal of environmental research and public health, 19 (21), 14173. https://doi.org/10.3390/ijerph192114173 Pinti, P., Cardone, D., & Merla, A. (2015). Simultaneous fNIRS and thermal infrared imaging during cognitive task reveal autonomic correlates of prefrontal cortex activity. Scientific Reports , 5 (1), 17471. https://doi.org/10.1038/srep17471. Qin H., Li X., Zeng M., Yuxue H., & Mulan H. (2019). The Preliminary Development of Short Video Addiction Scale among College Students. Psychology of China, 1 (8), 586–598. https://doi.org/10.35534/pc.0108037. Robbins, T. W., Gillan, C. M., Smith, D. G., de Wit, S., & Ersche, K. D. (2012). Neurocognitive endophenotypes of impulsivity and compulsivity: towards dimensional psychiatry. Trends in cognitive sciences, 16 (1), 81–91. https://doi.org/10.1016/j.tics.2011.11.009. Su, C., Zhou, H., Gong, L., Teng, B., Geng, F., & Hu, Y. (2021). Viewing personalized video clips recommended by TikTok activates default mode network and ventral tegmental area. NeuroImage , 237 , 118136. https://doi.org/10.1016/j.neuroimage.2021.118136. Shen, X., Li, Z., Sheng, J., Zhou, X., & Wang, J. (2023). Functional MRI of inhibitory control processing in problematic mobile video gamers. Psychiatry Research , 325 , 115220. https://doi.org/10.1016/j.psychres.2023.115220. Shu, Q., Tang, S., Wu, Z., Feng, J., Lv, W., Huang, M., & Xu, F. (2025). The impact of internet pornography addiction on brain function: A functional near-infrared spectroscopy study. Frontiers in Human Neuroscience , 19 , 1477914. https://doi.org/10.3389/fnhum.2025.1477914. Tian, F., Lin, Z. J., & Liu, H. (2013, March). EasyTopo: A toolbox for rapid diffuse optical topography based on a standard template of brain atlas. In Optical tomography and spectroscopy of tissue X (Vol. 8578, pp. 458-467). SPIE. https://doi.org/10.1117/12.2003907. Turel, O., & R. Cavagnaro, D. (2019). Effect of Abstinence from Social Media on Time Perception: Differences between Low- and At-Risk for Social Media “Addiction” Groups. The Psychiatric Quarterly, 90 (1), 217–227. https://doi.org/10.1007/s11126-018-9614-3. Tobin, S., Bisson, N., & Grondin, S. (2010). An Ecological Approach to Prospective and Retrospective Timing of Long Durations: A Study Involving Gamers. PloS one , 5 (2), e9271. https://doi.org/10.1371/journal.pone.0009271. Tian, X., Bi, X., & Chen, H. (2023). How short-form video features influence addiction behavior? Empirical research from the opponent process theory perspective. Information Technology & People , 36 (1), 387–408. https://doi.org/10.1108/ITP-04-2020-0186. Wang, T., Li, R., Chen, D., Xie, M., Li, Z., Mao, H., Ling, Y., Liang, X., Xu, G., & Zhang, J. (2024). Modulation of High‐Frequency rTMS on Reward Circuitry in Individuals with Nicotine Dependence: A Preliminary fMRI Study. Neural Plasticity , 2024 (1), 5673579. https://doi.org/10.1155/2024/5673579. Wittmann, M., & Paulus, M. P. (2008). Decision making, impulsivity and time perception. Trends in Cognitive Sciences, 12 (1), 7–12. https://doi.org/10.1016/j.tics.2007.10.004. Wang, X., Zhao, S., Zhang, M. X., Chen, F., & Chang, L. (2021). Life History Strategies and Problematic Use of Short-Form Video Applications. Evolutionary Psychological Science, 7 (1), 39–44. https://doi.org/10.1007/s40806-020-00255-9. Weinstein, A., & Lejoyeux, M. (2015). New developments on the neurobiological and pharmaco‐genetic mechanisms underlying internet and videogame addiction. The American Journal on Addictions , 24 (2), 117–125. https://doi.org/10.1111/ajad.12110. Yang, Y., Liu, R. D., Ding, Y., Lin, J., Ding, Z., & Yang, X. (2024). Time distortion for short-form video users. Computers in human behavior , 151 , 108009. https://doi.org/10.1016/j.chb.2023.108009. Zhang, X., Wu, Y., & Liu, S. (2019). Exploring short-form video application addiction: Socio-technical and attachment perspectives. Telematics and Informatics, 42 , 101243. https://doi.org/10.1016/j.tele.2019.101243. Information & Authors Information Version history V1 Version 1 11 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Tongshu Li Sichuan Normal University View all articles by this author Run Hu Sichuan Normal University View all articles by this author Huafang Liu Sichuan Normal University View all articles by this author Pu Yang 0009-0000-3798-0194 Sichuan Normal University View all articles by this author Xiaolong Liu 0000-0002-3814-5606 [email protected] Sichuan Normal University View all articles by this author Metrics & Citations Metrics Article Usage 461 views 198 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Tongshu Li, Run Hu, Huafang Liu, et al. How Problematic Short Video Use Alters Time Perception: Neural Insights from a Prospective Temporal Estimation fNIRS Study. Authorea . 11 September 2025. DOI: https://doi.org/10.22541/au.175758028.83496033/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175758028.83496033/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00369421f650700',t:'MTc3OTUzMjQ5OA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();