Individuals with internet or social media addictions have been found to demonstrate heightened sensitivity to social-related information and attentional biases toward social triggers (Brand et al., 2019; Yan et al., 2023). However, little is known about whether problematic social media users show distinct attentional patterns toward social–facial cues, particularly eye-gaze directions. Eye gaze is a powerful signal in social interactions. The gaze-cueing effect (GCE) refers to individuals’ tendency to attend toward the direction another person is gazing, and it can be modulated by contextual factors like facial expressions, which can reflect emotional states such as happiness, sadness, anger, fear, disgust, and surprise (Ekman, 1992), and individual differences (e.g., anxiety, gender) (Dalmaso et al., 2020). However, research on whether psychological traits like problematic social media use influence the GCE remains limited.

 

The GCE refers to faster reactions to targets appearing in the gaze direction compared to targets not in the gaze direction (Friesen & Kingstone, 1998; McKay et al., 2021). A meta-analysis by McKay et al. (2021) indicated that the GCE is modulated by direct-gaze presentation, task types, and emotional expressions. Previous research has also found that when the gaze cue and the facial expression of the gaze cue are processed unconsciously, the GCE still exists and is modulated by the expression itself (Liu et al., 2019). Further, Dalmaso et al. (2020) proposed a theoretical framework named the eyeTUNE for social attention, and concluded that the GCE can be determined by three dimensions including situational gains (e.g., observer–cue relationship in terms of familiarity, personal values, and political affiliation), individual constraints (e.g., age and internal states), and contextual factors like emotional expressions of the cueing face. Therefore, the GCE is determined by both individual and contextual/methodological factors.

 

Moreover, individual differences including anxiety and personality affect the GCE. For example, high state anxiety has been found to amplify the GCE for fearful facial expressions (Fox et al., 2007; Uono et al., 2009), although conflicting findings exist. Some researchers have reported that individual difference variables, such as anxiety, trait anxiety, depression, and gender, do not modulate the GCE (McCrackin & Itier, 2019; Talipski et al., 2021). Therefore, given the contradictory findings of previous studies, the impact of individual differences or personal characteristics on the GCE requires more investigation. It is necessary to further explore whether individuals with different psychological characteristics or behavioral patterns show differences in the GCE. Zhang et al. (2015) pointed out that the gaze cue can be processed in both subliminal and supraliminal perceptions, but that this might depend on different methods of perceptual processing (i.e., top-down and bottom-up). Whether this attentional effect will exist under both perceptual levels is worth exploring.

 

Problematic social media use (PSMU), characterized by excessive, uncontrollable social media use with addictive effects (e.g., mood modification, withdrawal, conflict) causing functional impairment (Kuss & Griffiths, 2017), represents a significant concern in studies of cyberpsychology and behavioral addictions. The interaction of person-affect-cognition-execution model indicates that problematic internet users exhibit attentional bias toward social information, particularly in advanced addiction stages (Brand et al., 2019). Further, empirical studies have shown attentional bias toward social media-related stimuli and social–interactive content in those exhibiting problematic social media use (Nikolaidou et al., 2019; Yan et al., 2023).

 

Given that (a) individual differences potentially influence the GCE but require further investigation, (b) those exhibiting PSMU show heightened sensitivity to social cues, and (c) prior research has linked the severity of PSMU to an attentional bias toward negative emotional information (Zhao et al., 2023), the present study investigated whether PSMU influences GCE. Using latent profile analysis, a person-centered statistical approach that identifies distinct subgroups (i.e., profiles) of individuals based on their patterns across multiple continuous observed variables rather than assuming the sample is homogenous (Collins & Lanza, 2010), instead of cut-off points (i.e., predetermined threshold scores on scales), we examined whether profiles of PSMU modulate the GCE across different facial expressions and presentation modes (i.e., subliminal or supraliminal). Therefore, we proposed the following research questions:

1. How can presentation mode and facial expressions influence the gaze-cueing effect?
2. How can profiles of problematic social media use affect the gaze-cueing effect?

We recruited participants through both online and offline poster advertisements. Our sample consisted of 61 participants, including 31 men and 30 women (M_age = 19.98 years, SD = 1.02), all of whom had normal or corrected-to-normal eyesight and were right-handed. The mean accuracy of the tasks performed was above 95% (M = 99.01, SD = 1.07), so all data were retained. Power analysis (MorePower software; Campbell & Thompson, 2012) indicated that within-group and mixed analysis of variance (ANOVA) with 61 participants could reach the effect size of η_p² = .048 and η_p² = .049, respectively (power = .8, α = .05).

 

All participants provided informed consent and received CNY 20 (USD 2.8) compensation for their participation. This study was approved by the ethics committee of Soochow University.

 

To test the validity of the gaze cue, we adopted a 2 × 2 × 5 within-group design: presentation mode (supraliminal/subliminal) × facial expression (fear/anger/disgust/neutrality/happiness) × gaze validity (valid: target stimuli presented at the same side of the gaze-cueing direction/invalid), measuring reaction time (RT) and accuracy. To explore the relationship between the GCE and PSMU, we used a 2 × 2 × 5 mixed design comparing high- and low-severity PSMU groups across presentation modes and expressions, with the standardized cueing effect (CE) index as the dependent variable: CE = (RT_invalid − RT_valid) / (RT_invalid + RT_valid) (Yuan et al., 2023).

 

The experiment comprised 432 trials (36 per condition; negative expressions were combined as one condition during the experiment). Participants sat 60 cm from the screen. Each trial presented a central fixation point (i.e., 600 ms), then a facial cue (i.e., a 300-ms supraliminal or 16-ms subliminal cue), followed by a 200-ms interstimulus interval, then a target square at a cued or noncued location. Participants judged target location (i.e., left/right keys). This was followed by a 1,000-ms blank screen. The stimulus sequence of the experiment is shown in Figure 1.

We used the Chinese version of the Bergen Social Media Addiction Scale (Leung et al., 2020) originally developed by Andreassen et al. (2016) to measure the severity of PSMU across six addiction components (i.e., salience, mood modification, tolerance, withdrawal, conflict, and relapse). An example item is “(I) tried to cut down on the use of social media without success.” Participants rate all items on a 5-point Likert scale ranging from 1 = very rarely to 5 = very often. Total scores are obtained by summing participants’ ratings for each item, with higher scores indicating greater severity of PSMU. In this study, the Cronbach’s alpha for the scale was .84.

 

We utilized the Chinese Facial Affective Picture System (Gong et al., 2011) to measure the GCE, using facial images displaying five expressions (i.e., fear, anger, disgust, neutrality, and happiness) with leftward/rightward gaze (mean emotional category agreement = 80.97%). Pilot Experiment 1 confirmed image recognition accuracy (M = 97.91%). According to the method of Mele et al. (2008), the original luminance of the images was set as 100%, and images with the original luminance were used as supraliminal stimuli; images set to 50% of the original luminance were used as subliminal stimuli. Pilot Experiment 2 was used to determine the time of subliminal presentation for gaze cues. Participants were asked whether they were aware of any changes in facial expressions and gaze direction. The presentation procedure of stimuli in Pilot Experiment 2 was the same as in the formal experiment (i.e., the subliminal part), but no target stimuli were presented. When the luminance was 50% and gaze cues were presented for 16 ms, no participants reported having clearly observed changes in facial expressions or gaze direction, indicating that they had reached the subliminal perceptual level. Hence, these parameters were used in the formal experiment. E-Prime (MacWhinney et al., 2001) was used to display stimuli on a 2,560 × 1,600 laptop monitor.

Descriptive statistics and ANOVA were conducted using JASP software (JASP Team, 2021). We conducted latent profile analysis using R (Version 4.2.3) to identify the profiles of PSMU.

Latent profile analysis identified two profiles of PSMU, high severity (n = 34) and low severity (n = 27), using the Akaike information criterion, the Bayesian information criterion, entropy (> .80), and the bootstrapped likelihood ratio test (Akogul & Erisoglu, 2017; Nylund et al., 2007). The results are shown in Table 1 and Figure 2.

To confirm gaze cue validity, we conducted a repeated-measures ANOVA on the average RT and average accuracy, with a 2 (gaze cue validity: valid or invalid) × 2 (presentation mode: supraliminal or subliminal) × 5 (facial expression: fear, anger, disgust, neutrality, or happiness) design. Results revealed that participants showed higher accuracy in valid (99.50 ± 0.08%) versus invalid conditions (98.90 ± 0.20%), F(1, 59) = 14.57, p < .001, η_p² = .20, and faster RTs (see Figure 3) for valid (296.05 ± 3.49 ms) versus invalid trials (305.03 ± 3.54 ms), F(1, 59) = 112.96, p < .001, η_p² = .66.

We conducted a mixed ANOVA on the CE, with a 2 (PSMU: high or low severity) × 2 (presentation mode: supraliminal or subliminal) × 5 (facial expression: fear, anger, disgust, neutrality, or happiness) design. The Greenhouse–Geisser correction method was used to correct the p values that were not in accordance with Mauchly’s test of sphericity, and they were labelled as pc. The results revealed a significant main effect of facial expression, F(4, 232) = 4.16, pc = .007, η_p² = .07. Post hoc t tests (least significant difference-corrected) showed stronger CEs for fearful faces compared to angry faces, disgusted faces, neutral faces, and happy faces, ps ≤ .014. Main effects of presentation mode and PSMU were not significant, F(1, 58) = 2.44, p = .124; F(1, 58) = 2.71, p = .105. The pairwise interactions between variables of presentation mode, facial expression, and PSMU, and the three-way interaction between these variables, were not significant, ps > .05.

 

To explore the impact of each factor on CEs, we used a simple effects analysis. Results showed that, under the fearful expression and supraliminal presentation, PSMU significantly influenced the CE, with high-severity PSMU > low-severity PSMU, F(1, 58) = 5.97, p = .018, η_p² = .09. Within the high-severity PSMU group, facial expression significantly influenced the CE under both the supraliminal, F(4, 128) = 3.26, p = .014, η_p² = .09, and subliminal conditions, F(4, 128) = 2.91, p = .024, η_p² = .08. Post hoc t tests (least significant difference-corrected; see Table 2) showed that the supraliminal fearful CE exceeded the disgust, neutrality and happiness conditions, ps ≤ .009, and that the subliminal afraid CE exceeded the anger and happiness conditions, ps ≤ .007. In the low-severity PSMU group, facial expression did not significantly affect CE, ps > .05 (see Figure 4).

The present research aimed to explore the relationship between PSMU and the GCE, and how this relationship is influenced by facial expressions and presentation mode (i.e., perception threshold). We used latent profile analysis to define different levels of problematic social media users, and ANOVA to examine how PSMU, facial expression, and presentation mode affected GCE.

 

The main effect of facial expression was significant on the GCE, with greater GCEs for fearful faces than happy and neutral faces. That is, the participants’ attention was more likely to be oriented by the eye gaze of fearful faces. This finding is in line with previous studies that identified greater GCEs for fearful faces (e.g., Yu et al., 2023). It shows that negative expressions (e.g., fearful faces), which usually communicate threatening or dangerous information to observers, can be processed faster with more cognitive or attentional resources allocated. From the perspective of evolutionary psychology, such vigilance towards the eye gazing of faces expressing negative emotions might be useful for individuals to make appropriate decisions in vital or survival situations (Liu et al., 2019).

 

The present study identified a significant difference in the GCEs between high- and low-severity profiles of PSMU for fearful faces under the supraliminal condition only. In other words, individuals with higher severity PSMU were more sensitive to fearful faces only when they could process these faces consciously. This indicates that the GCE can be modulated by presentation mode (i.e., under supraliminal or subliminal conditions), which is in line with previous findings by Dalmaso et al. (2020) and McKay et al. (2021). Individuals with high-severity PSMU who consciously recognized the fearful emotions could process the emotional faces in a top-down manner, and exhibit increased susceptibility to gaze direction cues from these faces. Such findings are also supported by previous studies that found a modulating effect of facial expressions on the GCE (e.g., Yu et al., 2023). Previous researchers have found that the rapid processing of threatening information by the human brain may be specific to the type of facial expression, as people will exhibit stronger processing bias for fearful faces compared to other threatening expressions (Song et al., 2023). Under the subliminal condition, the GCE was greater for the fearful faces among participants with the high-severity profile of PSMU in this study. This shows that individuals, especially those who use social media problematically, tend to be more sensitive to negative facial expressions even when the faces are not processed consciously. This may be attributed to individuals’ ability to process fearful facial expressions unconsciously (Wang et al., 2012).

 

The above results support the theoretical framework for social attention, which suggests that individuals’ visual attention as oriented by eye gaze can be influenced by multiple components, including the cueing face, the observer (i.e., participant), and the social relationship between the two (Dalmaso et al., 2020). Further, individual differences among the three components appear to be key moderators of the GCE, but this requires more investigation (McKay et al., 2021). For example, the GCE has been found to be greater for fearful faces among individuals with higher anxiety (e.g., Fox et al., 2007; Uono et al., 2009). Interestingly, the present study found that individuals’ PSMU profiles could potentially influence the GCE.

 

The moderating effect of the fearful facial expression on the GCE was only identified among the high-severity PSMU group, not among the low-severity PSMU group. This group difference in the moderating effect was consistently observed under both supraliminal and subliminal conditions. This suggests that individuals who use social media problematically tended to allocate more cognitive resources and pay more attention to fearful faces, as they were more likely to consciously recognize fearful emotions in top-down processing under supraliminal conditions. In other words, we found that participants who exhibited more PSMU were more responsive or attentive to the eye gaze directions of fearful facial expressions. Such an effect was also identified in the subliminal condition, when the problematic social media users recognized the faces unconsciously. This seems to support the findings of previous studies showing that PSMU or internet addiction is associated with higher social anxiety and fear of missing out (Elhai et al., 2016; Fabris et al., 2020). Further, these problematic social media users might be more sensitive to social cues and information, as they require greater or possibly excessive social reassurance (Billieux et al., 2015), as well as fear of missing out in social interactions.

Such findings could enhance understanding of the personal characteristics and cognitive bias towards certain social triggers of individuals who develop addictive behaviors, and contribute to the person-affect-cognition-execution model. That is, those who engage in problematic social media or internet use are probably more sensitive to or worry about others’ eye gaze, especially the fearful faces. These findings also contribute to the literature on the GCE in that PSMU (or other addictive behaviors) could be one of the individual difference variables that moderate how an individual’s visual attention shifting is modulated by others’ eye gaze. Additionally, for clinical screening and early intervention, our identification of heightened sensitivity to fearful gaze cues in individuals with higher severity PSMU provides a potential behavioral marker for PSMU. Although the link between individual differences (e.g., social anxiety) and the GCE has not been consistently identified in previous studies (e.g., Talipski et al., 2021), it seems necessary to further explore whether PSMU and social anxiety could moderate the GCE independently or interactively.

Limitations of this study include participant recruitment from one university, limiting generalizability, and use of only Chinese faces in a Chinese sample. Replication across cultures and developmental stages (i.e., adolescents, elderly) would strengthen the findings. Additionally, high-severity profiles of PSMU may not represent true addiction; future research should compare highly addicted versus recreational users.

This study identified that individuals with high-severity PSMU demonstrated enhanced GCEs for fearful facial expressions, suggesting heightened responsiveness to others’ eye gaze, particularly with negative emotional content. These findings contribute to understanding PSMU cognitive mechanisms, and identify PSMU as a potential individual difference variable modulating the GCE, complementing the eyeTUNE theoretical framework.

Akogul, S., & Erisoglu, M. (2017). An approach for determining the number of clusters in a model-based cluster analysis. Entropy, 19(9), 452. https://doi.org/10.3390/e19090452

 

Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30(2), 252–262. https://doi.org/10.1037/adb0000160

 

Billieux, J., Maurage, P., Lopez-Fernandez, O., Kuss, D. J., & Griffiths, M. D. (2015). Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Current Addiction Reports, 2(2), 156–162. https://doi.org/10.1007/s40429-015-0054-y

 

Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. (2019). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience and Biobehavioral Reviews, 104, 1–10. https://doi.org/10.1016/j.neubiorev.2019.06.032

 

Campbell, J. I. D., & Thompson, V. A. (2012). MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis. Behavior Research Methods, 44(4), 1255–1265. https://doi.org/10.3758/s13428-012-0186-0

 

Collins, L. M, & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. Wiley. https://doi.org/10.1002/9780470567333

 

Dalmaso, M., Castelli, L., & Galfano, G. (2020). Social modulators of gaze-mediated orienting of attention: A review. Psychonomic Bulletin & Review, 27(5), 833–855. https://doi.org/10.3758/s13423-020-01730-x

 

Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6(3–4), 169–200. https://doi.org/10.1080/02699939208411068

 

Elhai, J. D., Levine, J. C., Dvorak, R. D., & Hall, B. J. (2016). Fear of missing out, need for touch, anxiety and depression are related to problematic smartphone use. Computers in Human Behavior, 63, 509–516. https://doi.org/10.1016/j.chb.2016.05.079

 

Fabris, M. A., Marengo, D., Longobardi, C., & Settanni, M. (2020). Investigating the links between fear of missing out, social media addiction, and emotional symptoms in adolescence: The role of stress associated with neglect and negative reactions on social media. Addictive Behaviors, 106, Article 106364. https://doi.org/10.1016/j.addbeh.2020.106364

 

Fox, E., Mathews, A., Calder, A. J., & Yiend, J. (2007). Anxiety and sensitivity to gaze direction in emotionally expressive faces. Emotion, 7(3), 478–486. https://doi.org/10.1037/1528-3542.7.3.478

 

Friesen, C. K., & Kingstone, A. (1998). The eyes have it! Reflexive orienting is triggered by nonpredictive gaze. Psychonomic Bulletin & Review, 5(3), 490–495. https://doi.org/10.3758/BF03208827

 

Gong, X., Huang, Y., Wang, Y., & Luo, Y. (2011). Revision of the Chinese facial affective picture system [In Chinese]. Chinese Mental Health Journal, 25(1), 40–46.

 

JASP Team. (2021). JASP (Version 0.16) [Computer software]. https://jasp-stats.org/

 

Kuss, D. J., & Griffiths, M. D. (2017). Social networking sites and addiction: Ten lessons learned. International Journal of Environmental Research and Public Health, 14(3), Article 311. https://doi.org/10.3390/ijerph14030311

 

Leung, H., Pakpour, A. H., Strong, C., Lin, Y.-C., Tsai, M.-C., Griffiths, M. D., Lin, C.-Y., & Chen, I.-H. (2020). Measurement invariance across young adults from Hong Kong and Taiwan among three internet-related addiction scales: Bergen Social Media Addiction Scale (BSMAS), Smartphone Application-Based Addiction Scale (SABAS), and Internet Gaming Disorder Scale-Short Form (IGDS-SF9) (Study Part A). Addictive Behaviors, 101, Article 105969. https://doi.org/10.1016/j.addbeh.2019.04.027

 

Liu, X., Wang, B., & Tang, W. (2019). The impact of subliminal emotional faces on the gaze-cueing effect. [In Chinese]. Psychological Development and Education, 35(2), 129–137.

 

MacWhinney, B., St. James, J., Schunn, C., Li, P., & Schneider, W. (2001). STEP—A System for Teaching Experimental Psychology using E-Prime. Behavior Research Methods, Instruments, & Computers, 33(2), 287–296. https://doi.org/10.3758/bf03195379

 

McCrackin, S. D., & Itier, R. J. (2019). Individual differences in the emotional modulation of gaze-cuing. Cognition and Emotion, 33(4), 768–800. https://doi.org/10.1080/02699931.2018.1495618

 

McKay, K. T., Grainger, S. A., Coundouris, S. P., Skorich, D. P., Phillips, L. H., & Henry, J. D. (2021). Visual attentional orienting by eye gaze: A meta-analytic review of the gaze-cueing effect. Psychological Bulletin, 147(12), 1269–1289. https://doi.org/10.1037/bul0000353

 

Mele, S., Savazzi, S., Marzi, C. A., & Berlucchi, G. (2008). Reaction time inhibition from subliminal cues: Is it related to inhibition of return? Neuropsychologia, 46(3), 810–819. https://doi.org/10.1016/j.neuropsychologia.2007.11.003

 

Nikolaidou, M., Fraser, D. S., & Hinvest, N. (2019). Attentional bias in Internet users with problematic use of social networking sites. Journal of Behavioral Addictions, 8(4), 733–742. https://doi.org/10.1556/2006.8.2019.60

 

Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. https://doi.org/10.1080/10705510701575396

 

Song, S., Li, S., Zhao, S., Xiao, G., Zhang, J., & Zheng, Y. (2023). Sustained attentional bias of individuals with high social anxiety to facial expressions: Evidence from N2pc [In Chinese]. Chinese Journal of Clinical Psychology, 31(2), 267–273.

 

Talipski, L. A., Bell, E., Goodhew, S. C., Dawel, A., & Edwards, M. (2021). Examining the effects of social anxiety and other individual differences on gaze-directed attentional shifts. Quarterly Journal of Experimental Psychology, 74(4), 771–785. https://doi.org/10.1177/1747021820973954

 

Uono, S., Sato, W., Michimata, C., Yoshikawa, S., & Toichi, M. (2009). Facilitation of gaze-triggered attention orienting by a fearful expression and its relationship to anxiety. Psychologia, 52(3), 188–197. https://doi.org/10.2117/psysoc.2009.188

 

Wang, L. L., Fu, S. M., Feng, C. L., Luo, W. B., Zhu, X. R., & Luo, Y. J. (2012). The neural processing of fearful faces without attention and consciousness: An event-related potential study. Neuroscience Letters, 506(2), 317–321. https://doi.org/10.1016/j.neulet.2011.11.034

 

Yan, Z., Yang, Z., & Griffiths, M. D. (2023). ‘Danmu’ preference, problematic online video watching, loneliness and personality: An eye-tracking study and survey study. BMC Psychiatry, 23(1), Article 523. https://doi.org/10.1186/s12888-023-05018-x

 

Yu, C., Ishibashi, K., & Iwanaga, K. (2023). Effects of fearful face presentation time and observer’s eye movement on the gaze cue effect. Journal of Physiological Anthropology, 42(1), Article 8. https://doi.org/10.1186/s40101-023-00325-4

 

Yuan, T., Ji, H., Wang, L., & Jiang, Y. (2023). Happy is stronger than sad: Emotional information modulates social attention. Emotion, 23(4), 1061–1074. https://doi.org/10.1037/emo0001145

 

Zhang, M., Wei, P., & Zhang, Q. (2015). The impact of supra- and sub-liminal facial expressions on the gaze-cueing effect [In Chinese]. Acta Psychologica Sinica, 47(11), 1309–1317. https://doi.org/10.3724/SP.J.1041.2015.01309

 

Zhao, J., Zhou, Z., Lin, Z., Sun, B., Wu, X., & Fu, S. (2023). The role of attentional bias toward negative emotional information and social anxiety in problematic social media use. Journal of Psychosocial Nursing and Mental Health Services, 61(6), 33–42. https://doi.org/10.3928/02793695-20221122-02

Zeyang Yang, Department of Psychology, School of Education, Soochow University, Suzhou 215123, People’s Republic of China. Email: [email protected]