Autistic traits modulate social attention: Evidence for cultural generalizability from a community sample in India

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Social attention is typically tested using preferential-looking paradigms in labs, which have demonstrated that autistic individuals attend less to social stimuli. Such studies are limited, by the fact that they have almost all been conducted in Western Europe and the USA. To address this gap, our objective was to test the cultural generalizability of these results by investigating whether autistic symptoms are negatively associated with social attention in a traditionally understudied sample: Indian adults. Additionally, we tested the specificity of this relation by investigating whether a similar association exists with the traits of attention-deficit/hyperactivity disorder (ADHD). Our study involved 121 young adults from Kerala, India. Autistic and ADHD traits were evaluated using the Autism Spectrum Quotient (AQ) and Adult ADHD Self-Report Scale (ASRS), respectively. The participants' gaze behavior was recorded during a preferential-looking task, where pairs of social and non-social images were presented simultaneously. Individuals with higher autistic traits exhibited a reduced preference for social stimuli. No such association of social attention was noted with ADHD traits. Follow-up analysis of AQ subscales indicated that the association between gaze duration and autistic traits was driven by the social, and not the attention to detail factor of autistic traits. Our results provide new evidence for the cultural generalizability of the social attention task and offer the potential for culture-agnostic phenotypic assessments for adults with autism. Biological sciences/Neuroscience/Social behaviour Biological sciences/Neuroscience/Social neuroscience ADHD ASD Eye tracking Indian population Social Attention Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Social cognition refers to a set of processes, including perception, interpretation, and response to any socially relevant information, and is influenced by arousal, attention, and emotion, modulating it at various levels (Adolphs & Minzenberg, 2009 ). Social cognition provides the bedrock for everyday social interactions, affecting how individuals perceive themselves and others, form relationships, and navigate social situations. Humans display an attentional bias toward social stimuli from an early age, which can have important consequences for social cognitive processes (Grossmann, 2015 ). Preferential orienting to and engagement with social stimuli can provide individuals with more information on social signals from others, which in turn can shape social behavior. Individuals with autism spectrum disorder (ASD) often face challenges in navigating the social world and show atypical visual attention toward social cues in lab-based tasks (Hedger et al., 2020 ; Frazier et al., 2017 ). Eye tracking is an efficient technique for mapping the explorative patterns of eye saccades and fixations, which provides an online measure of environmental sampling during different tasks (Laurence et al., 2023 ; Salvucci & Goldberg, 2000 ). This technology offers an effective method for evaluating social cognition and has been extensively used in studies involving individuals with ASD, including children and young adult cohorts (Guillon et al., 2014 ; Setien-Ramos et al., 2023 ). These studies focussed on understanding how individuals with ASD direct their attention toward social cues, such as faces and social settings, and the impact of joint attention on their social interactions. Notably, these lab-based studies have revealed an association between visual social attention and autism characteristics, demonstrating reduced preferential attention to social cues among individuals with ASD (Boraston & Blakemore, 2007 ; Chita-Tegmark, 2016 ; Frazier et al., 2017 ; Hedger et al., 2020 ; Setien-Ramos et al., 2023 ). One limitation of this literature is that the vast majority of these eye-tracking studies examining visual attention toward social cues in ASD have been observed in Western countries (Chakrabarti et al., 2017 ; Hedger et al., 2021; Clin et al., 2020 ; Constantino et al., 2017 ; Jones & Klin, 2013 ; Pierce et al., 2011 ; Plesa Skwerer et al., 2019 ). At the same time, it is widely recognized that cultural influences have a significant role in shaping social cognition and behavior (Lee et al., 2010 ), thereby influencing the perception, diagnosis, and support of individuals with ASD (Nayar et al., 2023 ). While two relatively recent studies have been conducted on children in Asian countries such as Qatar and China (Al-Shaban et al., 2023 ; Kou et al., 2019 ) there remains a notable absence of published reports on analogous studies in adults. Research on the intersection of culture and social cognition can provide valuable insights into how cultural factors influence the manifestation of autism-relevant traits (Carruthers et al., 2018 ). This continued exploration and understanding of cultural influences on autism-relevant phenotypic dimensions can, in principle, lead to more effective and culturally tailored support and diagnostic measures for autistic individuals worldwide. The present study employs the preferential-looking paradigm, a widely utilized method in developmental psychology, where the participants are presented with social and non-social images that compete for an observer’s attention. This specific paradigm has been tested and validated several times in autistic and non-autistic adults in the UK (Chakrabarti et al., 2017 ; Hedger et al., 2018 ; Hedger & Chakrabarti, 2021a , 2021b ). Our objective was to test the cultural generalizability of this paradigm by assessing whether adults in the Indian population showed a similar social attentional bias and whether autistic traits were negatively predictive of such social preference. In addition to testing the cultural generalizability of the results, we also aimed to test if the observed effects extend to other dimensional measures of psychopathology, particularly attention deficit hyperactivity disorder (ADHD). Despite the differences in underlying mechanism, ADHD is also associated with difficulties in social cognition (Cotter et al., 2018 ). Therefore, we tested the specificity of the relationship between ASD and social cognition, by examining the association between social attention and traits related to ADHD. This approach not only aids in understanding the specific role of AQ in modulating social attention that is not confounded by ADHD but also allows an assessment of the preferential-looking paradigm for indexing social attention across different neurodevelopmental conditions. MATERIALS AND METHODS Subject recruitment: We recruited 121 young adults (mean age: 25.09, SD: 5.43; 52 males) from Kerala, India, and included graduate and postgraduate students as well as working professionals. All participants were native speakers of Malayalam with normal or corrected-to-normal vision and no significant medical history, as confirmed by a short clinical interview. The study protocol was approved by the institutional ethics committee of IUCBR & SSH (IUCBR-IEC/Certificate/2019-5), and all participants provided informed written consent before their participation. The data collection and analysis were performed in accordance with relevant guidelines and regulations. The participants received non-monetary rewards like sweets, and stationary items as a gesture of appreciation for their participation. Assessment: We evaluated the autistic and ADHD traits of the participants using the Autism Spectrum Quotient (AQ) and the adult ADHD self-report scale (ASRS), respectively. AQ is a 50-item questionnaire that measures an individual’s autistic traits, with higher scores indicating higher autistic traits (Baron-Cohen et al., 2001). Similarly, ASRS is an 18-item tool used to assess ADHD traits in adults, where elevated scores indicate heightened ADHD traits (Kessler et al., 2005). Both questionnaires have been validated to assess traits in the general population (Adler et al., 2019; Baron-Cohen et al., 2001). We administered the questionnaire online in English since all participants were educated and comfortable with the language. AQ and its factor scores were computed based on a previous report (Hoekstra et al., 2008), while the ASRS scores were calculated as outlined in the original report (Kessler et al., 2005). Detailed demographic and trait score information of the participants is presented in Table 1. Stimuli: The study involved a paradigm presenting 30 pairs of social and non-social images side by side in the same pseudorandom sequence using MATLAB with Psychtoolbox extensions (Hedger & Chakrabarti, 2021a). Social images comprised humans (images of babies, couples, etc.), whereas non-social images consisted of food, natural scenery, and other objects. Images were drawn from the International Affective Picture System (Lang et al., 2008) and Flickr ( Flickr , n.d.), and each pair of images was carefully matched for psychological (arousal and valence) and stimulus parameters (Hedger & Chakrabarti, 2021a). To minimize the influence of low-level visual elements such as contrast and color on the participants' visual preferences during the experiment, a phase-scrambled version of each of these image pairs was also included in the trial (Hedger & Chakrabarti, 2021b). The spatial positions of social and non-social images were counterbalanced across the experiment. Data collection: During the experimental procedure, participants were seated at a distance ranging from 720 mm to 820 mm from the screen. Eye movements and fixations were recorded binocularly at 1000 Hz using an Eyelink 1000 plus eye tracker from SR Research Ltd. The stimuli were presented on a 410×230 mm screen with a resolution of 1366×768-pixels and a refresh rate of 60 Hz, as illustrated in Figure 1. Prior to the start of data collection, five-point calibration and validation processes were performed, and the process was repeated if there was a general difference of more than one degree of visual angle between the calibration and validation. Each trial began with a drift correction to confirm that the calibration was still valid. Then a central fixation dot was presented for 1000 milliseconds (ms), followed by the social/non-social image pairs for 5000 ms. The next trial followed after an inter-trial interval (ITI) of 100 ms. A schematic illustration of the trial sequence is shown in Figure 2. The participants were briefed that they would be presented with some images and were instructed to relax and take a good look at the presented images. The entire data collection process took 10-15 minutes. Analysis: The eye-tracking data were extracted using the Data Viewer software of SR Research Ltd. The display coordinates occupied by the social and non-social images during stimuli presentation were utilized to define their respective areas of interest (AOI). Dwell time, indicating the time spent in each AOI ms was calculated for each trial and then averaged across participants. Trials with over 60 percent track loss by participants were excluded from the analysis. On this basis, 224 of the 7260 trials (3.08%) were removed, equating to an average of 1.87 trials per participant. A linear mixed model analysis was implemented using Jamovi ( The Jamovi Project , 2024) to examine the impact of predictors on dwell time (DT). The model included age, sex (male, female), AQ score, ASRS score, stimulus type (intact, scrambled), and AOI (social, non-social) as fixed effects and random intercepts per subject (ID). The restricted maximum likelihood (REML) method was utilized to estimate the model fit. The model equation was as follows: DT ~ 1 + Age + Sex + Stimulus + AOI + AQ + ASRS + Stimulus:AOI + Stimulus:AOI:AQ + Stimulus:AOI:ASRS + ( 1 | ID ) We conducted a follow-up correlation analysis to explore whether the observed effect of autistic traits on dwell time was driven by the social or non-social factors of the AQ. The correlation matrix for the dwell time on the social AOI and AQ factor scores was obtained using GraphPad Prism ( GraphPad Prism , 2024). Further, to examine whether the impact of the AQ score on dwell time was influenced by the low-level image characteristics of the presented stimuli, we carried out a manipulation assessment. For this purpose, the structural similarity index measurement (SSIM) of the social/non-social image pairs was calculated using MATLAB. The SSIM metric evaluates the likeness between images by taking into account the low-level features of the images like luminance, contrast, and structure (Wang et al., 2004). In our manipulation check analysis, we employed the following model: Intact Social DT ~ 1 + SSIM + ‘Trial Number’ + AQ + SSIM:AQ+ ‘Trial Number’:AQ+ (1 | ID) RESULTS The outcome of the linear mixed model analysis is presented in Table 2. The analysis indicates a statistically significant main effect of AOI and AQ scores, while no significant effects were observed for age, sex, stimulus type, or ASRS score on dwell time. The analysis revealed a significant interaction effect between stimulus type and AOI [F(1, 354) = 13.9615, p<0.001]. Post hoc comparisons performed between AOI and stimulus type demonstrated that there was a significant difference in dwell time between intact social and non-social images [t(354) = -14.282, p≤0.001] with a strong bias towards the social image. Conversely, no significant difference was evident between scrambled social and non-social stimuli (t(354) = 0.287, p=1.000), as depicted in Figure 3. Moreover, a significant three-way interaction between the stimulus type, AOI, and AQ was identified [F(3, 354) = 3.9169, p=0.009]. No effect of age or sex was noted in this analysis. Specifically for intact stimuli, the AQ score showed a negative correlation with the social dwell time and a positive correlation with non-social dwell time, as illustrated in Figure 4A. However, no such differences were observed with scrambled stimuli, as shown in Figure 4B. The analysis did not reveal a significant interaction effect between stimulus type, AOI, and ASRS [F(3, 354) = 0.3661, p=0.778]. To test whether the significant association of dwell time with AQ was driven by one or both of the sub-factors of the AQ (social interaction and attention to detail), exploratory correlation analyses were conducted. This analysis revealed that the inverse relationship between the AQ score and social dwell time was predominantly influenced by the social interaction factor (r=-0.26, p=0.0036) rather than the attention to detail factor (r=0.02, p=0.7899). Notably, the disparity between these two correlations was statistically significant (Steigers Z= -2.0614, p=0.0393). The manipulation check analysis to investigate the influence of low-level image properties on dwell time in social AOI revealed that neither the SSIM scores of the image pair [F(1,3480)=0.1683, p=0.682] nor the interaction effect between AQ and SSIM scores [F(1,3480)=0.0227, p=0.880] had a significant effect on dwell time on the social image. Interestingly, the trial number had a significant positive effect on dwell time on the social image [F(1, 3480)=24.4201, p<0.001]. However, the interaction effect between the AQ score and trial number did not show a significant effect [F(1, 3480)=0.6470, p=0.421] suggesting that the effect of AQ was unlikely to be influenced by any loss of engagement over the course of the experiment. DISCUSSION The present study used a preferential-looking paradigm to test social visual attentional bias in a sample of young adults in India. Autistic traits were negatively associated with social attentional bias, while ADHD traits showed no such relationship. This report replicates similar findings reported in the UK using the same paradigm and thus demonstrates the cultural generalizability of these results. Across the whole sample, participants demonstrated a strong bias towards social images, as reported in previous studies with this paradigm in UK samples (Hedger & Chakrabarti, 2021a ). To test if this observed social bias was driven by low-level stimulus properties, two checks were implemented. First, phase-scrambled versions of the same image pairs were presented to the participants. No attentional bias was noted for the social images in phase scrambled versions. Second, structural similarity index measurements were calculated for each intact image pair. This metric showed no relationship with the observed attentional bias for social images. Together, there is sufficient strength of evidence to suggest that social attentional bias in this sample cannot be explained by stimulus properties alone. The observed negative relationship between autistic symptoms and social attentional bias replicates findings from several studies that have used similar preferential-looking paradigms in autistic and nonautistic samples primarily in Western Europe and USA (Frazier et al., 2017 ; Hedger et al., 2020 ). While the majority of these studies have been conducted in children, a similar pattern has also been observed in autistic and nonautistic adults (Hedger & Chakrabarti, 2021a ). Recent efforts to study similar paradigms in Asian samples involve using culturally appropriate stimuli (Al-Shaban et al., 2023 ; Kou et al., 2019 ). The current study presents three points of contrast from these recent efforts. First, it uses the same stimuli as used in the UK sample, thus demonstrating that the results are generalizable in an Indian sample even without cultural adaptation of the stimuli. This generalizability could, in principle, be attributed to the relatively high familiarity of the white faces and images via online and social media platforms and television. Second, both of these recent Asian studies were conducted in children. The current study shows that the results are generalizable to an older young adult sample. Third, neither of these previous studies have tested the potential impact of low-level stimulus features in the observed results. Further investigation of the negative relationship between autistic traits and social attention revealed that it was driven entirely by the social – and not the attentional factor of autistic traits. This finding is consistent with studies in children that suggest that reduced attention on social stimuli may be indicative of difficulties in social communication and interaction (Congiu et al., 2024 ). Social cognition differences, as measured by visual preference and joint attention, have been shown to be associated with social affect scores of ADOS, rather than scores related to restricted and repetitive behaviors (Kou et al., 2019 ; Nyström et al., 2019 ). Interestingly, no relationship was noted between social attentional bias and ADHD symptoms. While both autism and ADHD are often associated with difficulties in social cognition (Cotter et al., 2018 ; Uekermann et al., 2010 ), their underlying symptoms may manifest differently in lab-based tests of social attention. These findings also support previous research indicating that alterations in social visual attention measures are specific to ASD and are not evident in ADHD (Ioannou et al., 2020 ). No effect of age and sex on social attentional bias was noted in the current sample, which is consistent with the largest meta-analysis on social orienting paradigms that reported a null effect of age and sex (Hedger et al., 2020 ). While demonstrating the feasibility and generalizability of the current paradigm in an Indian sample, the study presents several opportunities for building on these initial findings. First, dynamic social stimuli involving interactions are known to elicit the largest effects in social attentional bias (Hedger et al., 2020 ; Kou et al., 2019 ). Future studies should move beyond static stimuli to index social attentional bias. Second, the current sample is drawn from a non-autistic student population. Future studies should test if these findings can be generalized to autistic individuals, especially the understudied subgroup with few/no words. Third, self-report accounts from some autistic individuals with high verbal ability suggest the presence of high social motivation, while lab-based measures such as the one in the current study do not pick this up (Jaswal & Akhtar, 2019 ). Future studies should investigate this apparent disconnect between self-reported and lab-based findings on social attention. CONCLUSION The current study demonstrates the generalisability of a preferential-looking paradigm to index social attentional bias in an Indian young adult sample. It also demonstrates that autistic traits are negatively associated with social attentional bias. No such relationship was noted with ADHD traits. It opens the possibility for creating culture-agnostic performance-based tasks for measuring the autistic phenotype using scalable measures (Dubey et al., 2024 ). Declarations Funding The work was supported by the grants of Ministry of Human Resource Development (MHRD), Government of India under the SPARC project sanctioned to UR, BC, GC & KPM (No.: SPARC/2018-2019/P1215/SL dated 15.03.2019) and the UK-India Education and Research Initiative (UKIERI) granted to BC & UR (P1215). BC acknowledges support from the Medical Research Council UK (MR/S036423/1) and the European Research Council (ref: 865568). The authors gratefully acknowledge the fellowship awards to KSN (3/1/2/117/Neuro/2019-NCD-1) and RLG (3/1/2/118/Neuro/2019-NCD-1)by Indian Council of Medical Research (ICMR), Government of India. Author Contributions Conceptualization: Usha Rajamma, Bhismadev Chakrabarti Funding acquisition: Usha Rajamma, Bhismadev Chakrabarti, Kochupurackal P Mohanakumar, Goutam Chandra Stimulus preparation & Experiment setup: Bhismadev Chakrabarti, Nicholas Hedger, Krishna S Nair Data collection: Krishna S Nair, Roana Liz George Data Analysis: Krishna S Nair, Bhismadev Chakrabarti, Usha Rajamma Writing-original draft: Krishna S Nair Writing-Review and Editing- Bhismadev Chakrabarti, Usha Rajamma, Nicholas Hedger, Kochupurackal P Mohanakumar, Goutam Chandra, Roana Liz George Conflict of Interest The authors declare no conflict of interest. Ethical Approval The study protocol was reviewed and approved by the Institutional Ethics Committee Data availability Data that support the findings of this work are available from the corresponding/co-corresponding author, upon reasonable request References Adler, L. A., Faraone, S. V., Sarocco, P., Atkins, N., & Khachatryan, A. (2019). Establishing US norms for the Adult ADHD Self-Report Scale (ASRS-v1.1) and characterising symptom burden among adults with self-reported ADHD. International Journal of Clinical Practice , 73 (1), e13260. https://doi.org/10.1111/ijcp.13260 Adolphs, R., & Minzenberg, M. (2009). Social Cognition. In Handbook of Neuroscience for the Behavioral Sciences . John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470478509.neubb002047 Al-Shaban, F. A., Ghazal, I., Thompson, I. R., Klingemier, E. W., Aldosari, M., Al-Shammari, H., Al-Faraj, F., El-Hag, S., Tolefat, M., Ali, M., Nasir, B., & Frazier, T. W. (2023). Development and validation of an Arabic language eye-tracking paradigm for the early screening and diagnosis of autism spectrum disorders in Qatar. Autism Research: Official Journal of the International Society for Autism Research , 16 (12), 2291–2301. https://doi.org/10.1002/aur.3046 Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The Autism-Spectrum Quotient (AQ): Evidence from Asperger Syndrome/High-Functioning Autism, Malesand Females, Scientists and Mathematicians. Journal of Autism and Developmental Disorders , 13 (1). https://doi.org/10.1023/a:1005653411471 Boraston, Z., & Blakemore, S.-J. (2007). The application of eye-tracking technology in the study of autism. The Journal of Physiology , 581 (3), 893–898. https://doi.org/10.1113/jphysiol.2007.133587 Carruthers, S., Kinnaird, E., Rudra, A., Smith, P., Allison, C., Auyeung, B., Chakrabarti, B., Wakabayashi, A., Baron-Cohen, S., Bakolis, I., & Hoekstra, R. A. (2018). A cross-cultural study of autistic traits across India, Japan and the UK. Molecular Autism , 9 , 52. https://doi.org/10.1186/s13229-018-0235-3 Chakrabarti, B., Haffey, A., Canzano, L., Taylor, C. P., & McSorley, E. (2017). Individual differences in responsivity to social rewards: Insights from two eye-tracking tasks. PLOS ONE , 12 (10), e0185146. https://doi.org/10.1371/journal.pone.0185146 Chita-Tegmark, M. (2016). Social attention in ASD: A review and meta-analysis of eye-tracking studies. Research in Developmental Disabilities , 48 , 79–93. https://doi.org/10.1016/j.ridd.2015.10.011 Clin, E., Maes, P., Stercq, F., & Kissine, M. (2020). No preference for direct versus averted gaze in autistic adults: A reinforced preferential looking paradigm. Molecular Autism , 11 (1), 91. https://doi.org/10.1186/s13229-020-00398-3 Congiu, S., Doneddu, G., & Fadda, R. (2024). Attention toward Social and Non-Social Stimuli in Preschool Children with Autism Spectrum Disorder: A Paired Preference Eye-Tracking Study. International Journal of Environmental Research and Public Health , 21 (4), 421. https://doi.org/10.3390/ijerph21040421 Constantino, J. N., Kennon-McGill, S., Weichselbaum, C., Marrus, N., Haider, A., Glowinski, A. L., Gillespie, S., Klaiman, C., Klin, A., & Jones, W. (2017). Infant viewing of social scenes is under genetic control and is atypical in autism. Nature , 547 (7663), 340–344. https://doi.org/10.1038/nature22999 Cotter, J., Granger, K., Backx, R., Hobbs, M., Looi, C. Y., & Barnett, J. H. (2018). Social cognitive dysfunction as a clinical marker: A systematic review of meta-analyses across 30 clinical conditions. Neuroscience & Biobehavioral Reviews , 84 , 92–99. https://doi.org/10.1016/j.neubiorev.2017.11.014 Dubey, M. J., Ghosh, R., Dubey, S., Das, S., Chakraborty, A. P., Chatterjee, S., Sengupta, S., & Benito-León, J. (2024). Integrating parental psychopathologies in autism spectrum disorder care: Toward a holistic family-centric approach. Indian Journal of Psychiatry , 66 (8), 751. https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_352_24 Flickr . (n.d.). Retrieved May 30, 2024, from https://www.flickr.com/ Frazier, T. W., Strauss, M., Klingemier, E. W., Zetzer, E. E., Hardan, A. Y., Eng, C., & Youngstrom, E. A. (2017). A Meta-Analysis of Gaze Differences to Social and Nonsocial Information Between Individuals With and Without Autism. Journal of the American Academy of Child & Adolescent Psychiatry , 56 (7), 546–555. https://doi.org/10.1016/j.jaac.2017.05.005 GraphPad Prism (Version Version 10.2.0). (2024). [Computer software]. GraphPad Software. www.graphpad.com Grossmann, T. (2015). The development of social brain functions in infancy. Psychological Bulletin , 141 (6), 1266–1287. https://doi.org/10.1037/bul0000002 Guillon, Q., Hadjikhani, N., Baduel, S., & Rogé, B. (2014). Visual social attention in autism spectrum disorder: Insights from eye tracking studies. Neuroscience and Biobehavioral Reviews , 42 , 279–297. https://doi.org/10.1016/j.neubiorev.2014.03.013 Hedger, N., & Chakrabarti, B. (2021a). Autistic differences in the temporal dynamics of social attention. Autism , 25 (6), 1615–1626. https://doi.org/10.1177/1362361321998573 Hedger, N., & Chakrabarti, B. (2021b). To covet what we see: Autistic traits modulate the relationship between looking and choosing. Autism Research , 14 (2), 289–300. https://doi.org/10.1002/aur.2349 Hedger, N., Dubey, I., & Chakrabarti, B. (2020). Social orienting and social seeking behaviors in ASD. A meta analytic investigation. Neuroscience & Biobehavioral Reviews , 119 , 376–395. https://doi.org/10.1016/j.neubiorev.2020.10.003 Hedger, N., Haffey, A., McSorley, E., & Chakrabarti, B. (2018). Empathy modulates the temporal structure of social attention. Proceedings of the Royal Society B: Biological Sciences . https://doi.org/10.1098/rspb.2018.1716 Hoekstra, R. A., Bartels, M., Cath, D. C., & Boomsma, D. I. (2008). Factor Structure, Reliability and Criterion Validity of the Autism-Spectrum Quotient (AQ): A Study in Dutch Population and Patient Groups. Journal of Autism and Developmental Disorders , 38 (8), 1555–1566. https://doi.org/10.1007/s10803-008-0538-x Ioannou, C., Seernani, D., Stefanou, M. E., Riedel, A., Tebartz van Elst, L., Smyrnis, N., Fleischhaker, C., Biscaldi-Schaefer, M., Boccignone, G., & Klein, C. (2020). Comorbidity Matters: Social Visual Attention in a Comparative Study of Autism Spectrum Disorder, Attention-Deficit/Hyperactivity Disorder and Their Comorbidity. Frontiers in Psychiatry , 11 , 545567. https://doi.org/10.3389/fpsyt.2020.545567 Jaswal, V. K., & Akhtar, N. (2019). Being versus appearing socially uninterested: Challenging assumptions about social motivation in autism. Behavioral and Brain Sciences , 42 , e82. https://doi.org/10.1017/S0140525X18001826 Jones, W., & Klin, A. (2013). Attention to eyes is present but in decline in 2–6-month-old infants later diagnosed with autism. Nature , 504 (7480), 427–431. https://doi.org/10.1038/nature12715 Kessler, R. C., Adler, L., Ames, M., Demler, O., Faraone, S., Hiripi, E., Howes, M. J., Jin, R., Secnik, K., Spencer, T., Ustun, T. B., & Walters, E. E. (2005). The World Health Organization Adult ADHD Self-Report Scale (ASRS): A short screening scale for use in the general population. Psychological Medicine , 35 (2), 245–256. https://doi.org/10.1017/s0033291704002892 Kou, J., Le, J., Fu, M., Lan, C., Chen, Z., Li, Q., Zhao, W., Xu, L., Becker, B., & Kendrick, K. M. (2019). Comparison of three different eye‐tracking tasks for distinguishing autistic from typically developing children and autistic symptom severity. Autism Research , 12 (10), 1529–1540. https://doi.org/10.1002/aur.2174 Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (2008). International Affective Picture System (IAPS): Affective ratings of pictures and instruction manual (A-8). University of Florida. Laurence, P. G., Lukasova, K., Alves, M. V. C., & de Macedo, E. C. (2023). What Our Eyes Can Tell Us About Our Social and Affective Brain? In P. S. Boggio, T. S. H. Wingenbach, M. L. da Silveira Coêlho, W. E. Comfort, L. Murrins Marques, & M. V. C. Alves (Eds.), Social and Affective Neuroscience of Everyday Human Interaction: From Theory to Methodology (pp. 271–281). Springer International Publishing. https://doi.org/10.1007/978-3-031-08651-9_16 Lee, C.-T., Beckert, T. E., & Goodrich, T. R. (2010). The Relationship Between Individualistic, Collectivistic, and Transitional Cultural Value Orientations and Adolescents’ Autonomy and Identity Status. Journal of Youth and Adolescence , 39 (8), 882–893. https://doi.org/10.1007/s10964-009-9430-z Nayar, K., Kang, X., Winston, M., Wong, P., & Losh, M. (2023). A cross-cultural study of visual attention in autism spectrum disorder. Child Neuropsychology , 29 (3), 413–444. https://doi.org/10.1080/09297049.2022.2094904 Nyström, P., Thorup, E., Bölte, S., & Falck-Ytter, T. (2019). Joint Attention in Infancy and the Emergence of Autism. Biological Psychiatry , 86 (8), 631–638. https://doi.org/10.1016/j.biopsych.2019.05.006 Pierce, K., Conant, D., Hazin, R., Stoner, R., & Desmond, J. (2011). Preference for Geometric Patterns Early in Life as a Risk Factor for Autism. Archives of General Psychiatry , 68 (1), 101. https://doi.org/10.1001/archgenpsychiatry.2010.113 Plesa Skwerer, D., Brukilacchio, B., Chu, A., Eggleston, B., Meyer, S., & Tager-Flusberg, H. (2019). Do minimally verbal and verbally fluent individuals with autism spectrum disorder differ in their viewing patterns of dynamic social scenes? Autism , 23 (8), 2131–2144. https://doi.org/10.1177/1362361319845563 Salvucci, D. D., & Goldberg, J. H. (2000). Identifying fixations and saccades in eye-tracking protocols. Proceedings of the Symposium on Eye Tracking Research & Applications - ETRA ’00 , 71–78. https://doi.org/10.1145/355017.355028 Setien-Ramos, I., Lugo-Marín, J., Gisbert-Gustemps, L., Díez-Villoria, E., Magán-Maganto, M., Canal-Bedia, R., & Ramos-Quiroga, J. A. (2023). Eye-Tracking Studies in Adults with Autism Spectrum Disorder: A Systematic Review and Meta-analysis. Journal of Autism and Developmental Disorders , 53 (6), 2430–2443. https://doi.org/10.1007/s10803-022-05524-z The jamovi project (Version Version 2.5). (2024). [Computer software]. https://www.jamovi.org Uekermann, J., Kraemer, M., Abdel-Hamid, M., Schimmelmann, B. G., Hebebrand, J., Daum, I., Wiltfang, J., & Kis, B. (2010). Social cognition in attention-deficit hyperactivity disorder (ADHD). Neuroscience & Biobehavioral Reviews , 34 (5), 734–743. https://doi.org/10.1016/j.neubiorev.2009.10.009 Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing , 13 (4), 600–612. https://doi.org/10.1109/TIP.2003.819861 Tables Table 1: Descriptive statistics of the demographic (age) and trait score (AQ and its subscale scores and ASRS scores) details. AQ: Autism spectrum quotient, ASRS: ADHD self-report scale. Range Mean Std. Deviation Age 16-44 25.09 5.431 AQ 76-141 112.4 11.91 Social interaction factor 56-112 87.09 11.33 Attention to detail factor 13-40 25.34 5.272 ASRS 0-17 5.289 3.282 Table 2: Parameter estimates for the fixed effect omnibus tests Fixed Effects Omnibus Tests F df df (res) p Age 0.8427 1 116 0.361 Sex 0.3318 1 116 0.566 Stimulus 0.0398 1 354 0.842 AOI 9.7659 1 354 0.002 AQ 6.6152 1 437 0.010 ASRS 0.0933 1 434 0.760 Stimulus ✻ AOI 13.9581 1 354 < .001 Stimulus ✻ AOI ✻ AQ 3.9169 3 354 0.009 Stimulus ✻ AOI ✻ ASRS 0.3661 3 354 0.778 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Feb, 2025 Reviews received at journal 03 Feb, 2025 Reviewers agreed at journal 31 Jan, 2025 Reviews received at journal 28 Jan, 2025 Reviewers agreed at journal 28 Jan, 2025 Reviews received at journal 28 Jan, 2025 Reviewers agreed at journal 26 Jan, 2025 Reviewers invited by journal 24 Jan, 2025 Editor assigned by journal 24 Jan, 2025 Editor invited by journal 13 Jan, 2025 Submission checks completed at journal 10 Jan, 2025 First submitted to journal 07 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5783350","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400511145,"identity":"5449475e-f897-4ff5-b1a3-06093666ca9b","order_by":0,"name":"Krishna S Nair","email":"","orcid":"","institution":"Inter University Centre for Biomedical Research \u0026 Super Speciality Hospital (IUCBR \u0026 SSH), MG University Campus at Thalappady","correspondingAuthor":false,"prefix":"","firstName":"Krishna","middleName":"S","lastName":"Nair","suffix":""},{"id":400511146,"identity":"8f7ff40b-cc54-4cab-93f5-a3dd2781022e","order_by":1,"name":"Nicholas Hedger","email":"","orcid":"","institution":"University of Reading","correspondingAuthor":false,"prefix":"","firstName":"Nicholas","middleName":"","lastName":"Hedger","suffix":""},{"id":400511147,"identity":"6047ea4c-2cf5-4483-bd24-6fab60c4d029","order_by":2,"name":"Roana Liz George","email":"","orcid":"","institution":"Inter University Centre for Biomedical Research \u0026 Super Speciality Hospital (IUCBR \u0026 SSH), MG University Campus at Thalappady","correspondingAuthor":false,"prefix":"","firstName":"Roana","middleName":"Liz","lastName":"George","suffix":""},{"id":400511148,"identity":"4493212a-3bd8-4dc8-bb9e-84b2a0a7da08","order_by":3,"name":"Goutam Chandra","email":"","orcid":"","institution":"Inter University Centre for Biomedical Research \u0026 Super Speciality Hospital (IUCBR \u0026 SSH), MG University Campus at Thalappady","correspondingAuthor":false,"prefix":"","firstName":"Goutam","middleName":"","lastName":"Chandra","suffix":""},{"id":400511149,"identity":"c90d4c32-9f07-4e6e-8c4f-0b5881b2d57c","order_by":4,"name":"Kochupurackal P Mohanakumar","email":"","orcid":"","institution":"Inter University Centre for Biomedical Research \u0026 Super Speciality Hospital (IUCBR \u0026 SSH), MG University Campus at Thalappady","correspondingAuthor":false,"prefix":"","firstName":"Kochupurackal","middleName":"P","lastName":"Mohanakumar","suffix":""},{"id":400511150,"identity":"aa1993e6-5aa8-434f-b2f7-5a752a4f0d14","order_by":5,"name":"Bhismadev Chakrabarti","email":"data:image/png;base64,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","orcid":"","institution":"University of Reading","correspondingAuthor":true,"prefix":"","firstName":"Bhismadev","middleName":"","lastName":"Chakrabarti","suffix":""},{"id":400511151,"identity":"433181a1-07d1-4927-b515-87910aec3496","order_by":6,"name":"Usha Rajamma","email":"","orcid":"","institution":"Inter University Centre for Biomedical Research \u0026 Super Speciality Hospital (IUCBR \u0026 SSH), MG University Campus at Thalappady","correspondingAuthor":false,"prefix":"","firstName":"Usha","middleName":"","lastName":"Rajamma","suffix":""}],"badges":[],"createdAt":"2025-01-07 17:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5783350/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5783350/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-23676-7","type":"published","date":"2025-10-28T15:58:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73760073,"identity":"8fdc931b-0220-482a-be6a-c857f8893687","added_by":"auto","created_at":"2025-01-14 11:21:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":578228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLayout of the trial presentation setup. \u003c/strong\u003eThe display had a resolution of 3.33 pixels/mm. A pair of scrambled images is given here.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5783350/v1/c5a5b27813b5dfd0f0fed62b.png"},{"id":73759170,"identity":"97128088-ec15-40e0-b99b-935efc519dc5","added_by":"auto","created_at":"2025-01-14 11:13:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156989,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic diagram of the trial sequence. \u003c/strong\u003eEach trial started with a drift correction, followed by the display of a central fixation dot. Subsequently, a pair of social/non-social images was presented. The next trial started after an inter trial interval (ITI). Thiry pairs of intact images and their corresponding scrambled images were displayed in a predefined pseudorandom sequence.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5783350/v1/87b82904c293d2ce4c7bdfa3.png"},{"id":73759168,"identity":"7530825b-d637-4fbc-9108-66bdf0a06555","added_by":"auto","created_at":"2025-01-14 11:13:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102172,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effect of stimuli and AOI on dwell time. \u003c/strong\u003eWhile scrambled images did not affect the dwell time (in ms), the intact images significantly affected the dwell time while viewing social and non-social images with a strong bias towards social image. The error bar signifies 95% confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5783350/v1/1dbe34857441f73d720b8659.png"},{"id":73759174,"identity":"fe4190b8-a5af-48ec-bfa0-376971cda0f2","added_by":"auto","created_at":"2025-01-14 11:13:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":314293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of AQ score on the dwell time in social and non-social area of interest (AOI) for intact and scrambled images.\u003c/strong\u003e The effect of AQ score on the social and nonsocial AOI (A) for intact images displayed an opposite correlation while viewing the two AOIs, (B) for scrambled images, the effect of AQ score on the dwell time of social and nonsocial images displayed a similar effect. The shaded error bar signifies 95% confidence interval and the dots represents individual data points.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5783350/v1/e513a4109aa7706b616e9f8d.png"},{"id":95040661,"identity":"b17135c0-bbe3-45ee-9957-2a4fa9ccd993","added_by":"auto","created_at":"2025-11-03 16:10:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2172857,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5783350/v1/4f6bd097-10d7-4261-88f0-6f21cddedc32.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Autistic traits modulate social attention: Evidence for cultural generalizability from a community sample in India","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSocial cognition refers to a set of processes, including perception, interpretation, and response to any socially relevant information, and is influenced by arousal, attention, and emotion, modulating it at various levels (Adolphs \u0026amp; Minzenberg, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Social cognition provides the bedrock for everyday social interactions, affecting how individuals perceive themselves and others, form relationships, and navigate social situations. Humans display an attentional bias toward social stimuli from an early age, which can have important consequences for social cognitive processes (Grossmann, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Preferential orienting to and engagement with social stimuli can provide individuals with more information on social signals from others, which in turn can shape social behavior. Individuals with autism spectrum disorder (ASD) often face challenges in navigating the social world and show atypical visual attention toward social cues in lab-based tasks (Hedger et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Frazier et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEye tracking is an efficient technique for mapping the explorative patterns of eye saccades and fixations, which provides an online measure of environmental sampling during different tasks (Laurence et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Salvucci \u0026amp; Goldberg, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This technology offers an effective method for evaluating social cognition and has been extensively used in studies involving individuals with ASD, including children and young adult cohorts (Guillon et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Setien-Ramos et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These studies focussed on understanding how individuals with ASD direct their attention toward social cues, such as faces and social settings, and the impact of joint attention on their social interactions. Notably, these lab-based studies have revealed an association between visual social attention and autism characteristics, demonstrating reduced preferential attention to social cues among individuals with ASD (Boraston \u0026amp; Blakemore, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Chita-Tegmark, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Frazier et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hedger et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Setien-Ramos et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne limitation of this literature is that the vast majority of these eye-tracking studies examining visual attention toward social cues in ASD have been observed in Western countries (Chakrabarti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hedger et al., 2021; Clin et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Constantino et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jones \u0026amp; Klin, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pierce et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Plesa Skwerer et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). At the same time, it is widely recognized that cultural influences have a significant role in shaping social cognition and behavior (Lee et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), thereby influencing the perception, diagnosis, and support of individuals with ASD (Nayar et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While two relatively recent studies have been conducted on children in Asian countries such as Qatar and China (Al-Shaban et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kou et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) there remains a notable absence of published reports on analogous studies in adults. Research on the intersection of culture and social cognition can provide valuable insights into how cultural factors influence the manifestation of autism-relevant traits (Carruthers et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This continued exploration and understanding of cultural influences on autism-relevant phenotypic dimensions can, in principle, lead to more effective and culturally tailored support and diagnostic measures for autistic individuals worldwide.\u003c/p\u003e \u003cp\u003eThe present study employs the preferential-looking paradigm, a widely utilized method in developmental psychology, where the participants are presented with social and non-social images that compete for an observer\u0026rsquo;s attention. This specific paradigm has been tested and validated several times in autistic and non-autistic adults in the UK (Chakrabarti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hedger et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hedger \u0026amp; Chakrabarti, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Our objective was to test the cultural generalizability of this paradigm by assessing whether adults in the Indian population showed a similar social attentional bias and whether autistic traits were negatively predictive of such social preference.\u003c/p\u003e \u003cp\u003eIn addition to testing the cultural generalizability of the results, we also aimed to test if the observed effects extend to other dimensional measures of psychopathology, particularly attention deficit hyperactivity disorder (ADHD). Despite the differences in underlying mechanism, ADHD is also associated with difficulties in social cognition (Cotter et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, we tested the specificity of the relationship between ASD and social cognition, by examining the association between social attention and traits related to ADHD. This approach not only aids in understanding the specific role of AQ in modulating social attention that is not confounded by ADHD but also allows an assessment of the preferential-looking paradigm for indexing social attention across different neurodevelopmental conditions.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eSubject recruitment:\u0026nbsp;\u003c/strong\u003eWe recruited 121 young adults (mean age: 25.09, SD: 5.43; 52 males) from Kerala, India, and included graduate and postgraduate students as well as working professionals. All participants were native speakers of Malayalam with normal or corrected-to-normal vision and no significant medical history, as confirmed by a short clinical interview. The study protocol was approved by the institutional ethics committee of IUCBR \u0026amp; SSH (IUCBR-IEC/Certificate/2019-5), and all participants provided informed written consent before their participation. The data collection and analysis were performed in accordance with relevant guidelines and regulations. The participants received non-monetary rewards like sweets, and stationary items as a gesture of appreciation for their participation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment:\u003c/strong\u003e We evaluated the autistic and ADHD traits of the participants using the Autism Spectrum Quotient (AQ) and the adult ADHD self-report scale (ASRS), respectively. AQ is a 50-item questionnaire that measures an individual’s autistic traits, with higher scores indicating higher autistic traits (Baron-Cohen et al., 2001). Similarly, ASRS is an 18-item tool used to assess ADHD traits in adults, where elevated scores indicate heightened ADHD traits (Kessler et al., 2005). Both questionnaires have been validated to assess traits in the general population (Adler et al., 2019; Baron-Cohen et al., 2001). We administered the questionnaire online in English since all participants were educated and comfortable with the language. AQ and its factor scores were computed based on a previous report \u0026nbsp;(Hoekstra et al., 2008), while the ASRS scores were calculated as outlined in the original report (Kessler et al., 2005). Detailed demographic and trait score information of the participants is presented in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStimuli:\u003c/strong\u003e The study involved a paradigm presenting 30 pairs of social and non-social images side by side in the same pseudorandom sequence using MATLAB with Psychtoolbox extensions (Hedger \u0026amp; Chakrabarti, 2021a). Social images comprised humans (images of babies, couples, etc.), whereas non-social images consisted of food, natural scenery, and other objects. Images were drawn from the International Affective Picture System (Lang et al., 2008) and Flickr (\u003cem\u003eFlickr\u003c/em\u003e, n.d.), and each pair of images was carefully matched for psychological (arousal and valence) and stimulus parameters (Hedger \u0026amp; Chakrabarti, 2021a). To minimize the influence of low-level visual elements such as contrast and color on the participants' visual preferences during the experiment, a phase-scrambled version of each of these image pairs was also included in the trial (Hedger \u0026amp; Chakrabarti, 2021b). The spatial positions of social and non-social images were counterbalanced across the experiment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection:\u003c/strong\u003e During the experimental procedure, participants were seated at a distance ranging from 720 mm to 820 mm from the screen. Eye movements and fixations were recorded binocularly at 1000 Hz using an Eyelink 1000 plus eye tracker from SR Research Ltd. The stimuli were presented on a 410×230 mm screen with a resolution of 1366×768-pixels and a refresh rate of 60 Hz, as illustrated in Figure 1. Prior to the start of data collection, five-point calibration and validation processes were performed, and the process was repeated if there was a general difference of more than one degree of visual angle between the calibration and validation. Each trial began with a drift correction to confirm that the calibration was still valid. Then a central fixation dot was presented for 1000 milliseconds (ms), followed by the social/non-social image pairs for 5000 ms. The next trial followed after an inter-trial interval (ITI) of 100 ms. A schematic illustration of the trial sequence is shown in Figure 2. The participants were briefed that they would be presented with some images and were instructed to relax and take a good look at the presented images. The entire data collection process took 10-15 minutes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis:\u003c/strong\u003e The eye-tracking data were extracted using the Data Viewer software of SR Research Ltd. The display coordinates occupied by the social and non-social images during stimuli presentation were utilized to define their respective areas of interest (AOI). Dwell time, indicating the time spent in each AOI ms was calculated for each trial and then averaged across participants. Trials with over 60 percent track loss by participants were excluded from the analysis. On this basis, 224 of the 7260 trials (3.08%) were removed, equating to an average of 1.87 trials per participant. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA linear mixed model analysis was implemented using Jamovi (\u003cem\u003eThe Jamovi Project\u003c/em\u003e, 2024) to examine the impact of predictors on dwell time (DT). The model included age, sex (male, female), AQ score, ASRS score, stimulus type (intact, scrambled), and AOI (social, non-social) as fixed effects and random intercepts per subject (ID). The restricted maximum likelihood (REML) method was utilized to estimate the model fit.\u003c/p\u003e\n\u003cp\u003eThe model equation was as follows:\u003c/p\u003e\n\u003cp\u003eDT ~ 1 + Age + Sex + Stimulus + AOI + AQ + ASRS + Stimulus:AOI + Stimulus:AOI:AQ + Stimulus:AOI:ASRS + ( 1 | ID )\u003c/p\u003e\n\u003cp\u003eWe conducted a follow-up correlation analysis to explore whether the observed effect of autistic traits on dwell time was driven by the social or non-social factors of the AQ. The correlation matrix for the dwell time on the social AOI and AQ factor scores was obtained using GraphPad Prism (\u003cem\u003eGraphPad Prism\u003c/em\u003e, 2024).\u003c/p\u003e\n\u003cp\u003eFurther, to examine whether the impact of the AQ score on dwell time was influenced by the low-level image characteristics of the presented stimuli, we carried out a manipulation assessment. For this purpose, the structural similarity index measurement (SSIM) of the social/non-social image pairs was calculated using MATLAB. The SSIM metric evaluates the likeness between images by taking into account the low-level features of the images like luminance, contrast, and structure (Wang et al., 2004). In our manipulation check analysis, we employed the following model:\u003c/p\u003e\n\u003cp\u003eIntact Social DT ~ 1 + SSIM + ‘Trial Number’ + AQ + SSIM:AQ+ ‘Trial Number’:AQ+ (1 | ID)\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe outcome of the linear mixed model analysis is presented in Table 2. The analysis indicates a statistically significant main effect of AOI and AQ scores, while no significant effects were observed for age, sex, stimulus type, or ASRS score on dwell time. The analysis revealed a significant interaction effect between stimulus type and AOI [F(1, 354) = 13.9615, p\u0026lt;0.001]. Post hoc comparisons performed between AOI and stimulus type demonstrated that there was a significant difference in dwell time between intact social and non-social images [t(354) = -14.282, p≤0.001] with a strong bias towards the social image. Conversely, no significant difference was evident between scrambled social and non-social stimuli (t(354) = 0.287, p=1.000), as depicted in Figure 3. Moreover, a significant three-way interaction between the stimulus type, AOI, and AQ was identified [F(3, 354) = 3.9169, p=0.009]. No effect of age or sex was noted in this analysis. Specifically for intact stimuli, the AQ score showed a negative correlation with the social dwell time and a positive correlation with non-social dwell time, as illustrated in Figure 4A. However, no such differences were observed with scrambled stimuli, as shown in Figure 4B. The analysis did not reveal a significant interaction effect between stimulus type, AOI, and ASRS [F(3, 354) = 0.3661, p=0.778].\u003c/p\u003e\n\u003cp\u003eTo test whether the significant association of dwell time with AQ was driven by one or both of the sub-factors of the AQ (social interaction and attention to detail), exploratory correlation analyses were conducted. This analysis revealed that the inverse relationship between the AQ score and social dwell time was predominantly influenced by the social interaction factor (r=-0.26, p=0.0036) rather than the attention to detail factor (r=0.02, p=0.7899). Notably, the disparity between these two correlations was statistically significant (Steigers Z= -2.0614, p=0.0393).\u003c/p\u003e\n\u003cp\u003eThe manipulation check analysis to investigate the influence of low-level image properties on dwell time in social AOI revealed that neither the SSIM scores of the image pair [F(1,3480)=0.1683, p=0.682] nor the interaction effect between AQ and SSIM scores [F(1,3480)=0.0227, p=0.880] had a significant effect on dwell time on the social image. Interestingly, the trial number had a significant positive effect on dwell time on the social image [F(1, 3480)=24.4201, p\u0026lt;0.001]. However, the interaction effect between the AQ score and trial number did not show a significant effect [F(1, 3480)=0.6470, p=0.421] suggesting that the effect of AQ was unlikely to be influenced by any loss of engagement over the course of the experiment.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study used a preferential-looking paradigm to test social visual attentional bias in a sample of young adults in India. Autistic traits were negatively associated with social attentional bias, while ADHD traits showed no such relationship. This report replicates similar findings reported in the UK using the same paradigm and thus demonstrates the cultural generalizability of these results.\u003c/p\u003e \u003cp\u003eAcross the whole sample, participants demonstrated a strong bias towards social images, as reported in previous studies with this paradigm in UK samples (Hedger \u0026amp; Chakrabarti, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). To test if this observed social bias was driven by low-level stimulus properties, two checks were implemented. First, phase-scrambled versions of the same image pairs were presented to the participants. No attentional bias was noted for the social images in phase scrambled versions. Second, structural similarity index measurements were calculated for each intact image pair. This metric showed no relationship with the observed attentional bias for social images. Together, there is sufficient strength of evidence to suggest that social attentional bias in this sample cannot be explained by stimulus properties alone.\u003c/p\u003e \u003cp\u003eThe observed negative relationship between autistic symptoms and social attentional bias replicates findings from several studies that have used similar preferential-looking paradigms in autistic and nonautistic samples primarily in Western Europe and USA (Frazier et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hedger et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While the majority of these studies have been conducted in children, a similar pattern has also been observed in autistic and nonautistic adults (Hedger \u0026amp; Chakrabarti, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). Recent efforts to study similar paradigms in Asian samples involve using culturally appropriate stimuli (Al-Shaban et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kou et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The current study presents three points of contrast from these recent efforts. First, it uses the same stimuli as used in the UK sample, thus demonstrating that the results are generalizable in an Indian sample even without cultural adaptation of the stimuli. This generalizability could, in principle, be attributed to the relatively high familiarity of the white faces and images via online and social media platforms and television. Second, both of these recent Asian studies were conducted in children. The current study shows that the results are generalizable to an older young adult sample. Third, neither of these previous studies have tested the potential impact of low-level stimulus features in the observed results.\u003c/p\u003e \u003cp\u003eFurther investigation of the negative relationship between autistic traits and social attention revealed that it was driven entirely by the social \u0026ndash; and not the attentional factor of autistic traits. This finding is consistent with studies in children that suggest that reduced attention on social stimuli may be indicative of difficulties in social communication and interaction (Congiu et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Social cognition differences, as measured by visual preference and joint attention, have been shown to be associated with social affect scores of ADOS, rather than scores related to restricted and repetitive behaviors (Kou et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nystr\u0026ouml;m et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterestingly, no relationship was noted between social attentional bias and ADHD symptoms. While both autism and ADHD are often associated with difficulties in social cognition (Cotter et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Uekermann et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), their underlying symptoms may manifest differently in lab-based tests of social attention. These findings also support previous research indicating that alterations in social visual attention measures are specific to ASD and are not evident in ADHD (Ioannou et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNo effect of age and sex on social attentional bias was noted in the current sample, which is consistent with the largest meta-analysis on social orienting paradigms that reported a null effect of age and sex (Hedger et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile demonstrating the feasibility and generalizability of the current paradigm in an Indian sample, the study presents several opportunities for building on these initial findings. First, dynamic social stimuli involving interactions are known to elicit the largest effects in social attentional bias (Hedger et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kou et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Future studies should move beyond static stimuli to index social attentional bias. Second, the current sample is drawn from a non-autistic student population. Future studies should test if these findings can be generalized to autistic individuals, especially the understudied subgroup with few/no words. Third, self-report accounts from some autistic individuals with high verbal ability suggest the presence of high social motivation, while lab-based measures such as the one in the current study do not pick this up (Jaswal \u0026amp; Akhtar, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Future studies should investigate this apparent disconnect between self-reported and lab-based findings on social attention.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe current study demonstrates the generalisability of a preferential-looking paradigm to index social attentional bias in an Indian young adult sample. It also demonstrates that autistic traits are negatively associated with social attentional bias. No such relationship was noted with ADHD traits. It opens the possibility for creating culture-agnostic performance-based tasks for measuring the autistic phenotype using scalable measures (Dubey et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported by the grants of Ministry of Human Resource Development (MHRD), Government of India under the SPARC project sanctioned to UR, BC, GC \u0026amp; KPM (No.: SPARC/2018-2019/P1215/SL dated 15.03.2019) and the UK-India Education and Research Initiative (UKIERI) granted to BC \u0026amp; UR (P1215). BC acknowledges support from the Medical Research Council UK (MR/S036423/1) and the European Research Council (ref: 865568). The authors gratefully acknowledge the fellowship awards to KSN (3/1/2/117/Neuro/2019-NCD-1) and RLG (3/1/2/118/Neuro/2019-NCD-1)by Indian Council of Medical Research (ICMR), Government of India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Usha Rajamma, Bhismadev Chakrabarti\u003c/p\u003e\n\u003cp\u003eFunding acquisition: Usha Rajamma, Bhismadev Chakrabarti, Kochupurackal P Mohanakumar, Goutam Chandra\u003c/p\u003e\n\u003cp\u003eStimulus preparation \u0026amp; Experiment setup: Bhismadev Chakrabarti, Nicholas Hedger, Krishna S Nair\u003c/p\u003e\n\u003cp\u003eData collection: Krishna S Nair, Roana Liz George\u003c/p\u003e\n\u003cp\u003eData Analysis: Krishna S Nair, Bhismadev Chakrabarti, Usha Rajamma\u003c/p\u003e\n\u003cp\u003eWriting-original draft: Krishna S Nair\u003c/p\u003e\n\u003cp\u003eWriting-Review and Editing- Bhismadev Chakrabarti, Usha Rajamma, Nicholas Hedger, Kochupurackal P Mohanakumar, Goutam Chandra, Roana Liz George\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Institutional Ethics Committee\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData that support the findings of this work are available from the corresponding/co-corresponding author, upon reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdler, L. A., Faraone, S. V., Sarocco, P., Atkins, N., \u0026amp; Khachatryan, A. (2019). Establishing US norms for the Adult ADHD Self-Report Scale (ASRS-v1.1) and characterising symptom burden among adults with self-reported ADHD. \u003cem\u003eInternational Journal of Clinical Practice\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(1), e13260. https://doi.org/10.1111/ijcp.13260\u003c/li\u003e\n\u003cli\u003eAdolphs, R., \u0026amp; Minzenberg, M. (2009). Social Cognition. In \u003cem\u003eHandbook of Neuroscience for the Behavioral Sciences\u003c/em\u003e. John Wiley \u0026amp; Sons, Ltd. https://doi.org/10.1002/9780470478509.neubb002047\u003c/li\u003e\n\u003cli\u003eAl-Shaban, F. A., Ghazal, I., Thompson, I. R., Klingemier, E. W., Aldosari, M., Al-Shammari, H., Al-Faraj, F., El-Hag, S., Tolefat, M., Ali, M., Nasir, B., \u0026amp; Frazier, T. W. (2023). Development and validation of an Arabic language eye-tracking paradigm for the early screening and diagnosis of autism spectrum disorders in Qatar. \u003cem\u003eAutism Research: Official Journal of the International Society for Autism Research\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(12), 2291\u0026ndash;2301. https://doi.org/10.1002/aur.3046\u003c/li\u003e\n\u003cli\u003eBaron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., \u0026amp; Clubley, E. (2001). The Autism-Spectrum Quotient (AQ): Evidence from Asperger Syndrome/High-Functioning Autism, Malesand Females, Scientists and Mathematicians. \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1). https://doi.org/10.1023/a:1005653411471\u003c/li\u003e\n\u003cli\u003eBoraston, Z., \u0026amp; Blakemore, S.-J. (2007). The application of eye-tracking technology in the study of autism. \u003cem\u003eThe Journal of Physiology\u003c/em\u003e, \u003cem\u003e581\u003c/em\u003e(3), 893\u0026ndash;898. https://doi.org/10.1113/jphysiol.2007.133587\u003c/li\u003e\n\u003cli\u003eCarruthers, S., Kinnaird, E., Rudra, A., Smith, P., Allison, C., Auyeung, B., Chakrabarti, B., Wakabayashi, A., Baron-Cohen, S., Bakolis, I., \u0026amp; Hoekstra, R. A. (2018). A cross-cultural study of autistic traits across India, Japan and the UK. \u003cem\u003eMolecular Autism\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 52. https://doi.org/10.1186/s13229-018-0235-3\u003c/li\u003e\n\u003cli\u003eChakrabarti, B., Haffey, A., Canzano, L., Taylor, C. P., \u0026amp; McSorley, E. (2017). Individual differences in responsivity to social rewards: Insights from two eye-tracking tasks. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(10), e0185146. https://doi.org/10.1371/journal.pone.0185146\u003c/li\u003e\n\u003cli\u003eChita-Tegmark, M. (2016). Social attention in ASD: A review and meta-analysis of eye-tracking studies. \u003cem\u003eResearch in Developmental Disabilities\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e, 79\u0026ndash;93. https://doi.org/10.1016/j.ridd.2015.10.011\u003c/li\u003e\n\u003cli\u003eClin, E., Maes, P., Stercq, F., \u0026amp; Kissine, M. (2020). No preference for direct versus averted gaze in autistic adults: A reinforced preferential looking paradigm. \u003cem\u003eMolecular Autism\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 91. https://doi.org/10.1186/s13229-020-00398-3\u003c/li\u003e\n\u003cli\u003eCongiu, S., Doneddu, G., \u0026amp; Fadda, R. (2024). Attention toward Social and Non-Social Stimuli in Preschool Children with Autism Spectrum Disorder: A Paired Preference Eye-Tracking Study. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(4), 421. https://doi.org/10.3390/ijerph21040421\u003c/li\u003e\n\u003cli\u003eConstantino, J. N., Kennon-McGill, S., Weichselbaum, C., Marrus, N., Haider, A., Glowinski, A. L., Gillespie, S., Klaiman, C., Klin, A., \u0026amp; Jones, W. (2017). Infant viewing of social scenes is under genetic control and is atypical in autism. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e547\u003c/em\u003e(7663), 340\u0026ndash;344. https://doi.org/10.1038/nature22999\u003c/li\u003e\n\u003cli\u003eCotter, J., Granger, K., Backx, R., Hobbs, M., Looi, C. Y., \u0026amp; Barnett, J. H. (2018). Social cognitive dysfunction as a clinical marker: A systematic review of meta-analyses across 30 clinical conditions. \u003cem\u003eNeuroscience \u0026amp; Biobehavioral Reviews\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e, 92\u0026ndash;99. https://doi.org/10.1016/j.neubiorev.2017.11.014\u003c/li\u003e\n\u003cli\u003eDubey, M. J., Ghosh, R., Dubey, S., Das, S., Chakraborty, A. P., Chatterjee, S., Sengupta, S., \u0026amp; Benito-Le\u0026oacute;n, J. (2024). Integrating parental psychopathologies in autism spectrum disorder care: Toward a holistic family-centric approach. \u003cem\u003eIndian Journal of Psychiatry\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e(8), 751. https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_352_24\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eFlickr\u003c/em\u003e. (n.d.). Retrieved May 30, 2024, from https://www.flickr.com/\u003c/li\u003e\n\u003cli\u003eFrazier, T. W., Strauss, M., Klingemier, E. W., Zetzer, E. E., Hardan, A. Y., Eng, C., \u0026amp; Youngstrom, E. A. (2017). A Meta-Analysis of Gaze Differences to Social and Nonsocial Information Between Individuals With and Without Autism. \u003cem\u003eJournal of the American Academy of Child \u0026amp; Adolescent Psychiatry\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(7), 546\u0026ndash;555. https://doi.org/10.1016/j.jaac.2017.05.005\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eGraphPad Prism\u003c/em\u003e (Version Version 10.2.0). (2024). [Computer software]. GraphPad Software. www.graphpad.com\u003c/li\u003e\n\u003cli\u003eGrossmann, T. (2015). The development of social brain functions in infancy. \u003cem\u003ePsychological Bulletin\u003c/em\u003e, \u003cem\u003e141\u003c/em\u003e(6), 1266\u0026ndash;1287. https://doi.org/10.1037/bul0000002\u003c/li\u003e\n\u003cli\u003eGuillon, Q., Hadjikhani, N., Baduel, S., \u0026amp; Rog\u0026eacute;, B. (2014). Visual social attention in autism spectrum disorder: Insights from eye tracking studies. \u003cem\u003eNeuroscience and Biobehavioral Reviews\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e, 279\u0026ndash;297. https://doi.org/10.1016/j.neubiorev.2014.03.013\u003c/li\u003e\n\u003cli\u003eHedger, N., \u0026amp; Chakrabarti, B. (2021a). Autistic differences in the temporal dynamics of social attention. \u003cem\u003eAutism\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(6), 1615\u0026ndash;1626. https://doi.org/10.1177/1362361321998573\u003c/li\u003e\n\u003cli\u003eHedger, N., \u0026amp; Chakrabarti, B. (2021b). To covet what we see: Autistic traits modulate the relationship between looking and choosing. \u003cem\u003eAutism Research\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(2), 289\u0026ndash;300. https://doi.org/10.1002/aur.2349\u003c/li\u003e\n\u003cli\u003eHedger, N., Dubey, I., \u0026amp; Chakrabarti, B. (2020). Social orienting and social seeking behaviors in ASD. A meta analytic investigation. \u003cem\u003eNeuroscience \u0026amp; Biobehavioral Reviews\u003c/em\u003e, \u003cem\u003e119\u003c/em\u003e, 376\u0026ndash;395. https://doi.org/10.1016/j.neubiorev.2020.10.003\u003c/li\u003e\n\u003cli\u003eHedger, N., Haffey, A., McSorley, E., \u0026amp; Chakrabarti, B. (2018). Empathy modulates the temporal structure of social attention. \u003cem\u003eProceedings of the Royal Society B: Biological Sciences\u003c/em\u003e. https://doi.org/10.1098/rspb.2018.1716\u003c/li\u003e\n\u003cli\u003eHoekstra, R. A., Bartels, M., Cath, D. C., \u0026amp; Boomsma, D. I. (2008). Factor Structure, Reliability and Criterion Validity of the Autism-Spectrum Quotient (AQ): A Study in Dutch Population and Patient Groups. \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(8), 1555\u0026ndash;1566. https://doi.org/10.1007/s10803-008-0538-x\u003c/li\u003e\n\u003cli\u003eIoannou, C., Seernani, D., Stefanou, M. E., Riedel, A., Tebartz van Elst, L., Smyrnis, N., Fleischhaker, C., Biscaldi-Schaefer, M., Boccignone, G., \u0026amp; Klein, C. (2020). Comorbidity Matters: Social Visual Attention in a Comparative Study of Autism Spectrum Disorder, Attention-Deficit/Hyperactivity Disorder and Their Comorbidity. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 545567. https://doi.org/10.3389/fpsyt.2020.545567\u003c/li\u003e\n\u003cli\u003eJaswal, V. K., \u0026amp; Akhtar, N. (2019). Being versus appearing socially uninterested: Challenging assumptions about social motivation in autism. \u003cem\u003eBehavioral and Brain Sciences\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e, e82. https://doi.org/10.1017/S0140525X18001826\u003c/li\u003e\n\u003cli\u003eJones, W., \u0026amp; Klin, A. (2013). Attention to eyes is present but in decline in 2\u0026ndash;6-month-old infants later diagnosed with autism. \u003cem\u003eNature\u003c/em\u003e, \u003cem\u003e504\u003c/em\u003e(7480), 427\u0026ndash;431. https://doi.org/10.1038/nature12715\u003c/li\u003e\n\u003cli\u003eKessler, R. C., Adler, L., Ames, M., Demler, O., Faraone, S., Hiripi, E., Howes, M. J., Jin, R., Secnik, K., Spencer, T., Ustun, T. B., \u0026amp; Walters, E. E. (2005). The World Health Organization Adult ADHD Self-Report Scale (ASRS): A short screening scale for use in the general population. \u003cem\u003ePsychological Medicine\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(2), 245\u0026ndash;256. https://doi.org/10.1017/s0033291704002892\u003c/li\u003e\n\u003cli\u003eKou, J., Le, J., Fu, M., Lan, C., Chen, Z., Li, Q., Zhao, W., Xu, L., Becker, B., \u0026amp; Kendrick, K. M. (2019). Comparison of three different eye‐tracking tasks for distinguishing autistic from typically developing children and autistic symptom severity. \u003cem\u003eAutism Research\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(10), 1529\u0026ndash;1540. https://doi.org/10.1002/aur.2174\u003c/li\u003e\n\u003cli\u003eLang, P. J., Bradley, M. M., \u0026amp; Cuthbert, B. N. (2008). \u003cem\u003eInternational Affective Picture System (IAPS): Affective ratings of pictures and instruction manual\u003c/em\u003e (A-8). University of Florida.\u003c/li\u003e\n\u003cli\u003eLaurence, P. G., Lukasova, K., Alves, M. V. C., \u0026amp; de Macedo, E. C. (2023). What Our Eyes Can Tell Us About Our Social and Affective Brain? In P. S. Boggio, T. S. H. Wingenbach, M. L. da Silveira Co\u0026ecirc;lho, W. E. Comfort, L. Murrins Marques, \u0026amp; M. V. C. Alves (Eds.), \u003cem\u003eSocial and Affective Neuroscience of Everyday Human Interaction: From Theory to Methodology\u003c/em\u003e (pp. 271\u0026ndash;281). Springer International Publishing. https://doi.org/10.1007/978-3-031-08651-9_16\u003c/li\u003e\n\u003cli\u003eLee, C.-T., Beckert, T. E., \u0026amp; Goodrich, T. R. (2010). The Relationship Between Individualistic, Collectivistic, and Transitional Cultural Value Orientations and Adolescents\u0026rsquo; Autonomy and Identity Status. \u003cem\u003eJournal of Youth and Adolescence\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(8), 882\u0026ndash;893. https://doi.org/10.1007/s10964-009-9430-z\u003c/li\u003e\n\u003cli\u003eNayar, K., Kang, X., Winston, M., Wong, P., \u0026amp; Losh, M. (2023). A cross-cultural study of visual attention in autism spectrum disorder. \u003cem\u003eChild Neuropsychology\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(3), 413\u0026ndash;444. https://doi.org/10.1080/09297049.2022.2094904\u003c/li\u003e\n\u003cli\u003eNystr\u0026ouml;m, P., Thorup, E., B\u0026ouml;lte, S., \u0026amp; Falck-Ytter, T. (2019). Joint Attention in Infancy and the Emergence of Autism. \u003cem\u003eBiological Psychiatry\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e(8), 631\u0026ndash;638. https://doi.org/10.1016/j.biopsych.2019.05.006\u003c/li\u003e\n\u003cli\u003ePierce, K., Conant, D., Hazin, R., Stoner, R., \u0026amp; Desmond, J. (2011). Preference for Geometric Patterns Early in Life as a Risk Factor for Autism. \u003cem\u003eArchives of General Psychiatry\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(1), 101. https://doi.org/10.1001/archgenpsychiatry.2010.113\u003c/li\u003e\n\u003cli\u003ePlesa Skwerer, D., Brukilacchio, B., Chu, A., Eggleston, B., Meyer, S., \u0026amp; Tager-Flusberg, H. (2019). Do minimally verbal and verbally fluent individuals with autism spectrum disorder differ in their viewing patterns of dynamic social scenes? \u003cem\u003eAutism\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(8), 2131\u0026ndash;2144. https://doi.org/10.1177/1362361319845563\u003c/li\u003e\n\u003cli\u003eSalvucci, D. D., \u0026amp; Goldberg, J. H. (2000). Identifying fixations and saccades in eye-tracking protocols. \u003cem\u003eProceedings of the Symposium on Eye Tracking Research \u0026amp; Applications - ETRA \u0026rsquo;00\u003c/em\u003e, 71\u0026ndash;78. https://doi.org/10.1145/355017.355028\u003c/li\u003e\n\u003cli\u003eSetien-Ramos, I., Lugo-Mar\u0026iacute;n, J., Gisbert-Gustemps, L., D\u0026iacute;ez-Villoria, E., Mag\u0026aacute;n-Maganto, M., Canal-Bedia, R., \u0026amp; Ramos-Quiroga, J. A. (2023). Eye-Tracking Studies in Adults with Autism Spectrum Disorder: A Systematic Review and Meta-analysis. \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(6), 2430\u0026ndash;2443. https://doi.org/10.1007/s10803-022-05524-z\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eThe jamovi project\u003c/em\u003e (Version Version 2.5). (2024). [Computer software]. https://www.jamovi.org\u003c/li\u003e\n\u003cli\u003eUekermann, J., Kraemer, M., Abdel-Hamid, M., Schimmelmann, B. G., Hebebrand, J., Daum, I., Wiltfang, J., \u0026amp; Kis, B. (2010). Social cognition in attention-deficit hyperactivity disorder (ADHD). \u003cem\u003eNeuroscience \u0026amp; Biobehavioral Reviews\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(5), 734\u0026ndash;743. https://doi.org/10.1016/j.neubiorev.2009.10.009\u003c/li\u003e\n\u003cli\u003eWang, Z., Bovik, A. C., Sheikh, H. R., \u0026amp; Simoncelli, E. P. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. \u003cem\u003eIEEE Transactions on Image Processing\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(4), 600\u0026ndash;612. https://doi.org/10.1109/TIP.2003.819861\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Descriptive statistics of the demographic (age) and trait score (AQ and its subscale scores and ASRS scores) details. AQ: Autism spectrum quotient, ASRS: ADHD self-report scale.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e16-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e25.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e5.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e76-141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e112.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e11.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial interaction factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e56-112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e87.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e11.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttention to detail factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e13-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e25.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e5.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e5.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2: Parameter estimates for the fixed effect omnibus tests\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" width=\"585\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 581px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed Effects Omnibus Tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003edf (res)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStimulus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAOI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.7659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.6152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eASRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStimulus\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e✻\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;AOI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.9581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStimulus\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e✻\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;AOI\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e✻\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;AQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.9169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStimulus\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e✻\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;AOI\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e✻\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;ASRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ADHD, ASD, Eye tracking, Indian population, Social Attention","lastPublishedDoi":"10.21203/rs.3.rs-5783350/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5783350/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e The ability to attend to social stimuli is fundamental for processing social cues and shaping social behavior, yet cultural variability in this capacity remains relatively unexplored. Social attention is typically tested using preferential-looking paradigms in labs, which have demonstrated that autistic individuals attend less to social stimuli. Such studies are limited, by the fact that they have almost all been conducted in Western Europe and the USA. To address this gap, our objective was to test the cultural generalizability of these results by investigating whether autistic symptoms are negatively associated with social attention in a traditionally understudied sample: Indian adults. Additionally, we tested the specificity of this relation by investigating whether a similar association exists with the traits of attention-deficit/hyperactivity disorder (ADHD). Our study involved 121 young adults from Kerala, India. Autistic and ADHD traits were evaluated using the Autism Spectrum Quotient (AQ) and Adult ADHD Self-Report Scale (ASRS), respectively. The participants' gaze behavior was recorded during a preferential-looking task, where pairs of social and non-social images were presented simultaneously. Individuals with higher autistic traits exhibited a reduced preference for social stimuli. No such association of social attention was noted with ADHD traits. Follow-up analysis of AQ subscales indicated that the association between gaze duration and autistic traits was driven by the social, and not the attention to detail factor of autistic traits. Our results provide new evidence for the cultural generalizability of the social attention task and offer the potential for culture-agnostic phenotypic assessments for adults with autism.\u003c/p\u003e","manuscriptTitle":"Autistic traits modulate social attention: Evidence for cultural generalizability from a community sample in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-14 11:13:40","doi":"10.21203/rs.3.rs-5783350/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-05T07:06:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-03T12:30:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335941180153114826306785907685797502334","date":"2025-01-31T09:52:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-28T21:53:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99193816716895223058932529571526410249","date":"2025-01-28T21:02:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-28T06:55:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101749837132442470012470864058564550506","date":"2025-01-27T03:08:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-24T21:49:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-24T21:32:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-13T10:08:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-10T13:51:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-01-07T17:20:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67380066-4fae-4e53-8376-fe7b61609253","owner":[],"postedDate":"January 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42688317,"name":"Biological sciences/Neuroscience/Social behaviour"},{"id":42688318,"name":"Biological sciences/Neuroscience/Social neuroscience"}],"tags":[],"updatedAt":"2025-11-03T16:07:16+00:00","versionOfRecord":{"articleIdentity":"rs-5783350","link":"https://doi.org/10.1038/s41598-025-23676-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-28 15:58:18","publishedOnDateReadable":"October 28th, 2025"},"versionCreatedAt":"2025-01-14 11:13:40","video":"","vorDoi":"10.1038/s41598-025-23676-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-23676-7","workflowStages":[]},"version":"v1","identity":"rs-5783350","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5783350","identity":"rs-5783350","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}