Unveiling Face Recognition Challenges and Awareness in Autism Spectrum Disorder: Insights from the Italian Famous Face Test (IT-FFT)

Unveiling Face Recognition Challenges and Awareness in Autism Spectrum Disorder: Insights from the Italian Famous Face Test (IT-FFT) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unveiling Face Recognition Challenges and Awareness in Autism Spectrum Disorder: Insights from the Italian Famous Face Test (IT-FFT) Martina Ventura, Valerio Manippa, Alessandro Oronzo Caffò, Giovanni Cicinelli, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5676642/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Accurate face recognition is crucial for navigating social interactions. While neurotypical individuals generally show no issues with face processing, persons with Autism Spectrum Disorder (PwASD) often exhibit impairments in this area. This study explores the extent of these face recognition deficits in autistic adults, focusing on their ability to identify famous faces, along with the awareness (metacognition) of their face recognition skills. Using the Italian Famous Face Test (IT-FFT) and the Prosopagnosia Index-20 (PI-20), to compare face recognition performance and self-awareness of face recognition abilities between 50 neurotypical individuals (NTs) and 49 PwASD. Our results showed that PwASD had significantly lower face identification scores and greater difficulties recognizing famous faces than NT participants. Additionally, PwASD reported more face recognition challenges on the PI-20, highlighting their awareness of these deficits. These findings suggest that face recognition impairments in PwASD extend to famous faces and underscore the importance of further research to explore targeted interventions aimed at improving different aspects of face recognition processes in ASD. Psychology Prosopagnosia developmental disorders face recognition metacognition social cognition autism Figures Figure 1 Figure 2 1. Introduction The human face represents the stimulus we rely on the most for social interactions, as it provides rich social information such as identity, emotions, and intentions (Haxby et al., 2000 ). Thus, accurate and rapid face processing and recognition are necessary for building and maintaining social relationships, as they allow one to modulate behavior, and intent, and provide an appropriate social response (Avery et al., 2016 ). Indeed, it has been shown that superior social skills (e.g., extraversion personality trait) are linked to better face identification (Li et al., 2010 ). In contrast, deficiencies in face processing can lead to anxiety and social situation avoidance (Yardley et al., 2008 ). Difficulty with face recognition is a characteristic associated with various conditions, including Autism Spectrum Disorder (ASD). ASD is a developmental disorder characterized by compromised communication and basic social development, along with narrowed interests and stereotyped, repetitive behaviors (American Psychiatric Association & Association, 2013 ). Numerous studies have consistently highlighted abnormalities in face processing among autistic individuals, manifesting with a wide range of severity challenges, from mild to moderate impairments (Teunisse & de Gelder, 2003 ; Zhao et al., 2016 ), differently affecting face memory, face perception, or facial expression recognition (Stantić et al., 2022 ; Tanaka et al., 2012 ). This, together with other social deficits, significantly impacts persons with ASD (PwASD) ability to navigate human interactions (Constantino, 2011 ) - but see Tracy et al., 2011 and Naumann et al., 2018 for a different perspective. Despite these variations, a recent meta-analysis by Griffin et al., 2021 consistently points towards deficits in face recognition skills among PwASD. Notably, face recognition impairment in PwASD shares similarities with prosopagnosia, a condition characterized by the inability to explicitly recognize familiar people’s identities based on their faces (Manippa et al., 2023 ; Rivolta et al., 2013 ). Recent research suggests that up to one-third of PwASD show prosopagnosia-like symptoms (Cygan et al., 2018 ). However, in everyday life, we view faces in a range of contexts where the recognition of familiar or unfamiliar faces is required. That implies that we deal with different kinds of familiarity; for instance, differentiating between a family member and a stranger or a TV star. Research indicates that our ability to recognize faces varies significantly depending on their familiarity (Matthews et al., 2018 ). Herzmann et al., ( 2004 ) found that reaction times were quicker for both famous and personally familiar faces compared to unknown faces, but only the personally familiar triggered a strong skin conductance response. Additionally, the neurological basis for recognizing different types of faces varies significantly, with several studies showing distinct brain activation patterns or modulation for unknown, famous, and personally familiar faces (Andrews et al., 2017 ; Caharel et al., 2014 ; Gosling & Eimer, 2011 ; Keyes et al., 2010 ). This suggests the need to distinguish between experimentally learned/unknown, famous, and personally familiar faces when assessing face perception, as some conditions may show difficulties in some, but not all, aspects of face processing (Liccione et al., 2014 ). However, ASDs studies on face recognition usually employed experimental familiarization techniques (Ventura et al., 2023) with initially unfamiliar face stimuli (e.g., Cambridge Face Memory test - CFMT, (Duchaine & Nakayama, 2006 )), mostly using Old-new paradigms, and only a few studies used famous or personally familiar identities (Weigelt et al., 2012 ). Thus, even though it's not a formal diagnosis criterion, improving research on how PwASD (and more broadly, people with recognition impairment) process different types of faces could offer a unique perspective on comprehending the mechanisms behind their different social activity and inform the development of targeted interventions. An additional relevant aspect to consider is metacognition, broadly defined as the ability to be aware of one's mental processes, skills, and performance (Metcalfe & Shimamura, 1994 ). Metacognition studies on PwASD demonstrate an overall metacognitive difficulty in various cognitive domains, such as memory (Teunisse & de Gelder, 2003 ; Zhao et al., 2016 ). However, as ASD represents a heterogeneous condition for the etiology, phenotype, and outcome (Masi et al., 2017 ), not all PwASD may experience the same type of metacognitive difficulties (Embon et al., 2023 ). Specifically, visual metacognition is the ability to evaluate one’s performance on visual perceptual tasks, such as recognizing our ability to distinguish between colors or shapes accurately. What is so far fairly unexplored is whether PwASD have insights into their face recognition skills, with only one study (Gehdu et al., 2024 ) showing a statistically significant correlation between the Prosopagnosia Index-20 scores of PwASD and their performance on two variants of the CFMT. Assessing this aspect is important at the theoretical level since it informs cognitive theories of ASD, but also at the practical level since working on metacognition could represent an intervention tool in the domain of face recognition, as studies suggest that metacognitive skills can be trained (Fleur et al., 2021 ; Taouki et al., 2022 ). Thus, our study aims to (i) ascertain whether PwASD level 1 according to DSM 5 criteria (APA, 2013) show famous face recognition deficits through our recently developed Italian Famous Face Test (IT-FFT, Ventura et al., 2024 ); and (ii) whether PwASD have insights into their overall face identification skills. Consequently, we also evaluate the potential of using the IT-FFT as a tool to highlight face recognition deficits, further enhancing our diagnostic capabilities and intervention strategies. 2. Methods 2.1 Participants A total of 99 adult participants (ages 18–57 years, M = 29.41, SD = 10.98) were included in the study, divided into 49 clinical and 50 control participants. Their years of schooling ranged from 7 to 21 years (M = 14.20, SD = 2.70), and all had normal or corrected-to-normal vision. The clinical group consisted of 49 PwASD level 1 recruited from the Regional Center for Adults with Autism in the Piedmont region (Italy). Each of them was diagnosed with level 1 ASD by psychologists and psychiatrists of the Center according to DSM-5 criteria (American Psychiatric Association & Association, 2013 ) based on clinical anamnesis, clinical interview, psychiatric interview, psychopathology assessment, cognitive assessment with the Wechsler Adult Intelligence Scale (WAIS-IV, (Wechsler, 2012 ) or Leiter-3 (Roid & Miller, 1997 ), diagnostic evaluation with the Autism Diagnostic Interview (ADI-R, Rutter et al., 2003 ), and Autism Diagnostic Observation Schedule - Second Edition (ADOS-2, Lord et al., 2000 ) or Autism Asperger Diagnostic Scale (RAADS, Ritvo et al., 2011 ; Keller et al, 2020 ). The control sample consisted of 50 Neurotypical individuals (NTs), stratified to match the ASD group's age, sex, and education level (see Table 1 ). To ensure that no individual selected for the NT sample fell within the ASD spectrum, NTs completed the Italian version of the Autism-Spectrum Quotient (AQ; (Baron-Cohen et al., 2001 ; Ruta et al., 2012 ). All NTs scored between 12 and 26, which is below the recommended cut-off of 29, indicative of clinically significant autism traits ( M = 16.09, SD = 4.40). Individuals with a history of neurological diseases, cerebral stroke, epilepsy or epileptic seizures, head injury with loss of consciousness, severe medical conditions or psychiatric disorders (this latter criterion was not applied to PwASD, as they often present comorbidity), and alcohol or drug abuse were excluded. Table 1 Descriptive statistics of persons with Autism Spectrum Disorder (PwASD) and neurotypical individuals (NTs). The two samples did not differ in sex distribution, age, and years of schooling. PwASD ( N = 49) NTs ( N = 50) p-value Sex (N) 22 F, 27 M 26 F, 24 M 0.480 Age (M ± SD) 29.47 ± 10.12 29.36 ± 11.86 0.961 Schooling (M ± SD) 13.77 ± 2.27 14.62 ± 3.04 0.121 2.2 Procedure and measurement The study was approved by the Ethical Committee of the University of Bari (protocol number: ET-19-01) and was performed in accordance with the Helsinki Declaration and its later amendments. After providing informed consent and socio-demographic information, each participant took the Italian version of the Prosopagnosia Index-20 (PI-20) (Tagliente et al., 2023 ), a self-report assessment of face recognition ability. The PI-20 consists of twenty statements that represent facial recognition experiences. Respondents rate the accuracy of these statements on a five-point scale (1 being "Totally disagree" and 5 being "Totally agree"). Scores range from 20 to 100, with higher scores indicating greater difficulties in facial recognition. After, each participant completed the Italian Famous Face Test (IT-FFT), administered through a computer-based interface. The IT-FFT comprises 100 images: 50 depicting celebrities and 50 unknown people. Faces were presented one at a time in randomized order at the center of the screen. Participants were instructed to view each photograph carefully and i) evaluate whether the face was famous or not ( Face familiarity ), and ii) provide the name of the celebrity if they thought the face was famous or specific information about the character ( Face identification ). At the end, the final checklist containing all the 50 names belonging to the famous people included in the IT-FFT was shown, and participants had to indicate the names of celebrities they were unfamiliar with (i.e., people they do not know and, thus, they cannot recognize) ( Name familiarity ). A detailed description of the stimuli, procedures, index measures, and normative data on NT participants can be found in Ventura et al., ( 2024 ). Face Identification score was computed by summing the number of correctly identified celebrities, which could be achieved either by naming the celebrity or providing specific biographical information, such as referring to them as "the main character of the film...". Minor name spelling errors were not counted as mistakes. However, responses that were missing, incorrect, or too general (e.g., simply stating "actor" or "singer") were given a score of 0. The final accuracy for face identification was expressed as a percentage, calculated by dividing the face identification score by the name familiarity score (assessed through the checklist) and then multiplying by 100. This method ensured that participants' accuracy reflected their performance relative to their self-reported knowledge of famous personalities, rather than a fixed number of targets. The Face Familiarity scores, instead, were based on signal detection theory, which categorizes outcomes into four types: false alarms (incorrectly identifying non-famous faces as famous), correct rejections (correctly identifying non-famous faces as non-famous), misses (failing to recognize famous faces), and hits (correctly recognizing famous faces). The sensitivity measure, d-prime ( d' ), was calculated from these outcomes, with higher values indicating greater sensitivity to recognizing famous faces. Additionally, response bias ( β ) was computed to understand participants' tendencies: a β of 1.00 indicates no bias, values below 1.00 indicate a liberal tendency (more likely to report faces as famous), and values above 1.00 indicate a conservative tendency (more likely to report faces as non-famous). 2.3 Data analysis Our dependent variables were PI-20 scores and the IF-FFT Face familiarity and Face identification scores. To determine whether PwASD exhibit famous face recognition deficits and whether they have insight into their overall face identification skills, we conducted 4 generalized linear models, one for each dependent variable (Face identification, PI-20 score, Face familiarity ( d’ ) and response bias (β) scores). For each dependent variable, we run an Analysis of Covariance (ANCOVA) with group (PwASD vs. NT) and sex (F vs. M) as between factors, and age and schooling as covariates. Additionally, we computed a 2 x 2 Chi-squared analysis to assess the distribution of PwASD and NT samples above (ES > 1) and below (ES = 0) the FFT-IT Face Identification cutoff scores (as defined by Ventura et al., 2024 ). Finally, Pearson’s correlation between Face Identification and PI-20 was calculated for each sample to evaluate whether PwASD and NT sample have insights into their overall face identification skills. 3. Results The ANCOVA on Face identification scores showed a main effect of the group (F 1,94 = 19.049, p < .001, η p 2 = .167, see Fig. 1 a) with a higher face identification score for NTs compared to PwASD, and a significant effect of participants’ age (F 1,94 = 10.790, p = .001, η p 2 = .103), indicating that face identification increased along with participants’ age. The ANCOVA conducted on PI-20 score showed a main effect of the group (see Fig. 1 b) (F 1,94 = 6.854, p = .010, η p 2 = .078), with PwASD scoring significantly higher on PI-20 than NT participants. The ANCOVA conducted on Face familiarity ( d’ ), showed a main effect of group (F 1,94 = 4.352, p = .039, η p 2 = .062) with higher d’ score for NTs compared to PwASD (see Fig. 1 c), and a main effect of participants’ age (F 1,94 = 11.598, p < .001, η p 2 = .099) with increasing d’ scores along with participants’ age. The ANCOVA on face familiarity response bias ( β ) showed no main or interaction effect (see Fig. 1 d). The 2 x 2 chi-squared tests conducted to assess the distribution of PwASD and NTs above and below the IT-FFT Face Identification cut-off, demonstrated a higher proportion of ASDs distributed below the cut-off score ( N = 21, 42.8%) compared to the NTs ( N = 3.6%) (χ 2 1 = 18.305; p <. 001) (see Fig. 2 a). Descriptive data and comparisons are reported in Table 2. Finally, a significant negative correlation was found between Face identification and PI-20 scores (r= -0.314, p = .002, see Fig. 2 b), driven primarily by the PwASD (r = -0.343, p = .016). No significant correlation was observed in the NT group (r = -0.139, p = .336). 4. Discussion In this study, we investigated potential differences in face recognition skills between PwASD level 1 and NTs using the IT-FFT. We also assessed their level of insight into their own face recognition ability through the PI-20. Our findings showed that PwASD performed significantly worse than NTs in the IT-FFT and reported more subjective face recognition difficulties to the PI-20. Furthermore, PI-20 and IT-FFT accuracy scores correlated (negatively) in PwASD but not in NTs. This pattern highlights a connection between perceived abilities and objective performance in face identification: while the latter seems strongly impaired, the former appears to be unaffected by the presence of ASD. The significant main effect showing higher face identification accuracy in the NT group suggests that PwASD struggle more with recognizing famous faces, consistent with previous research on facial recognition difficulties in ASD (Tanaka et al., 2012 ; Weigelt et al., 2012 ). Notably, more than 40% of PwASD (compared to just 4% of the NT sample) scored below the IT-FFT cut-off (Ventura et al., 2024 ), indicating pronounced and significant difficulties in facial recognition skills. Additionally, the NT sample achieved higher d' scores compared to the ASD sample, reinforcing the idea that PwASD demonstrate reduced discriminative abilities when judging face familiarity. It is important to note that response bias, as measured by ꞵ score, was comparable between PwASD and NTs, suggesting that the difference in d' scores is not due to differences in response tendencies. These findings emphasize the need to consider a weakened sense of familiarity as a contributing factor to face processing impairments in PwASD. This underlines the heterogeneous nature of face recognition challenges within the ASD population, where some people may experience more severe impairments than others. Impaired face recognition not only affects social interactions and communication but also contributes to difficulties in forming social bonds, which are critical to navigating everyday life (Davis, 2010 ; Lane et al., 2018 ). Given these challenges, it is crucial to develop tailored assessments and targeted face recognition training programs to effectively address these issues and enhance the social functioning of PwASD. While both face identification accuracy and face familiarity sensitivity were influenced by participant sex, according to the normative data of the IT-FFT (Ventura et al., 2024 ), the absence of significant interaction effects suggests that factors such as sex, age, and education did not significantly modulate the observed group differences. Moreover, we assessed the recognition ability insight of PwASD, which has rarely been investigated. Metacognition is a broad concept that includes several components (Jia et al., 2019 ); among these, one important aspect is the individuals’ thoughts, beliefs, and reflections on one’s cognitive capacity, which could also be defined as metacognitive monitoring (Carpenter & Williams, 2023 ). Metacognition studies in ASD led to mixed results; some research reports no differences between NTs and PwASD metacognition level (Nicholson et al., 2021 ; Williams et al., 2018 ), while others suggest that they show several difficulties in metacognitive abilities (Maras et al., 2020 ; Sawyer et al., 2014 ). However, it is unclear whether metacognition is domain-general or domain-specific (Faivre et al., 2018 ; Rouault et al., 2018 ). Considering metacognition to be domain-specific, it may be possible that this ability in PwASD may be disrupted for some domains or tasks and not for others, and this could explain the variability in results observed among ASD individuals. To this aim, we administered the Italian version of the PI-20. Our results align with previous findings, showing that PwASD had higher PI-20 scores compared to the NTs, indicating that PwASD are aware of their difficulties with face recognition. This result aligns with previous literature that showed no impaired metacognition among PwASD (Adams et al., 2020 ; Ambrus et al., 2019 ; Migo et al., 2012 ) and specifically, Gehdu et al., ( 2024 ), which observed significant correlations between PwASD PI-20 scores and their performance on two variants of the CMFT. Additionally, a moderate negative correlation between PI-20 scores and face recognition accuracy was observed when considering all participants together. However, this correlation was significant only within PwASD and not within the NT sample. This suggests that PwASD who perceive themselves as having difficulties in recognizing faces tend to perform worse in face recognition tasks, aligning their self-awareness with actual performance. This higher metacognitive ability observed in individuals with ASD can be partly attributed to the lack of extreme scores reported by the NT sample. In contrast, PwASD are characterized by greater variability in face recognition abilities (mostly impaired), allowing for a more pronounced correlation between self-reported difficulties in face recognition and actual performance. Accordingly, the correlation between PI-20 scores and face recognition performance seems primarily driven by those with either very low (e.g., PwASD) or very high face recognition abilities (Bindemann et al., 2014 ; Estudillo & Wong, 2021 ; Palermo et al., 2017 ). Thus, while the PI-20 can serve as a useful tool for initial screening and assessing individuals' insight into their face recognition skills, its discriminative power for identifying prosopagnosia-level performance on objective assessments may be limited. This highlights the need to balance caution against overreliance on self-report measures with recognizing their utility in specific contexts. Integrating subjective and objective assessments is crucial for comprehensively understanding face recognition difficulties (Arizpe et al., 2019 ). 4.1 Conclusions, limitations, and future research This study found that PwASD level 1 performed worse than matched NT to the IT-FFT, coherently with the PI-20 assessing self-reported face recognition difficulties. IT-FFT and PI-20 negatively correlated mainly in ASD samples, demonstrating that PwASD level 1 are aware of their face recognition abilities. Therefore, metacognitive skills related to facial recognition appear to be preserved in PwASD; however, this finding is specific to facial recognition and does not encompass other aspects of metacognition. Future studies should consider exploring additional metacognitive abilities, using IQ as a covariate, as higher support levels are often associated with intellectual disabilities, making broader comparisons challenging. Furthermore, while this test can provide insights into the ability to recognize well-known faces and assess identification skills and familiarity, our data analysis strategy may only partially capture the comprehensive aspects of face recognition abilities, as other tasks related to face recognition were not assessed. Our test and others such as face-matching tests or the CFMT (Duchaine et al., 2006 ; Duchaine & Nakayama, 2006 ) can provide a more comprehensive insight into individual face identification dimensions (i.e., face perception, face memory, and face recognition). In conclusion, this study highlights the IT-FFT as a valuable tool for detecting face recognition deficits in ASD and potentially other conditions, such as neurodegenerative disorders (e.g., Alzheimer's disease and frontotemporal dementia), or prosopagnosia, which often present challenges in facial recognition (Manippa et al., 2023 ). While preliminary results are encouraging, further research is needed to evaluate the IT-FFT’s effectiveness across a broader range of conditions to enhance our diagnostic capabilities and understanding. Declarations The authors did not receive support from any organization for the submitted work and have no competing interests to declare that are relevant to the content of this article. References Adams, A., Hills, P. J., Bennetts, R. J., & Bate, S. (2020). Coping strategies for developmental prosopagnosia. Neuropsychological Rehabilitation , 30 (10), 1996–2015. https://doi.org/10.1080/09602011.2019.1623824 Ambrus, G. G., Kaiser, D., Cichy, R. M., & Kovács, G. (2019). The Neural Dynamics of Familiar Face Recognition. Cerebral Cortex . https://doi.org/10.1093/cercor/bhz010 American Psychiatric Association, D., & Association, A. P. (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (Vol. 5). American psychiatric association Washington, DC. https://www.academia.edu/download/38718268/csl6820_21.pdf Andrews, S., Burton, A. M., Schweinberger, S. R., & Wiese, H. (2017). Event-related potentials reveal the development of stable face representations from natural variability. Quarterly Journal of Experimental Psychology , 70 (8), 1620–1632. https://doi.org/10.1080/17470218.2016.1195851 Arizpe, J. M., Saad, E., Douglas, A. O., Germine, L., Wilmer, J. B., & DeGutis, J. M. (2019). Self-reported face recognition is highly valid, but alone is not highly discriminative of prosopagnosia-level performance on objective assessments. Behavior Research Methods , 51 (3), 1102–1116. https://doi.org/10.3758/s13428-018-01195-w Avery, S. N., VanDerKlok, R. M., Heckers, S., & Blackford, J. U. (2016). Impaired face recognition is associated with social inhibition. Psychiatry Research , 236 , 53–57. https://doi.org/10.1016/j.psychres.2015.12.035 Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders , 31 (1), 5–17. https://doi.org/10.1023/a:1005653411471 Bindemann, M., Attard, J., & Johnston, R. A. (2014). Perceived ability and actual recognition accuracy for unfamiliar and famous faces. Cogent Psychology , 1 (1), 986903. https://doi.org/10.1080/23311908.2014.986903 Caharel, S., Ramon, M., & Rossion, B. (2014). Face familiarity decisions take 200 msec in the human brain: Electrophysiological evidence from a go/no-go speeded task. Journal of Cognitive Neuroscience , 26 (1), 81–95. https://doi.org/10.1162/jocn_a_00451 Carpenter, K. L., & Williams, D. M. (2023). A meta-analysis and critical review of metacognitive accuracy in autism. Autism , 27 (2), 512–525. https://doi.org/10.1177/13623613221106004 Constantino, J. N. (2011). The quantitative nature of autistic social impairment. Pediatric research , 69 (8), 55–62. Cygan, H. B., Okuniewska, H., Jednoróg, K., Marchewka, A., Wypych, M., & Nowicka, A. (2018). Face processing in a case of high functioning autism with developmental prosopagnosia. Acta Neurobiologiae Experimentalis , 78 (2), 114–131. Davis, C. (2010). Attention-deficit/hyperactivity disorder: Associations with overeating and obesity. Current Psychiatry Reports , 12 (5), 389–395. https://doi.org/10.1007/s11920-010-0133-7 Duchaine, B. C., & Nakayama, K. (2006). Developmental prosopagnosia: A window to content-specific face processing. Current opinion in neurobiology , 16 (2), 166–173. Duchaine, B. C., Yovel, G., Butterworth, E. J., & Nakayama, K. (2006). Prosopagnosia as an impairment to face-specific mechanisms: Elimination of the alternative hypotheses in a developmental case. Cognitive Neuropsychology , 23 (5), 714–747. https://doi.org/10.1080/02643290500441296 Embon, I., Cukier, S., Iorio, A., Barttfeld, P., & Solovey, G. (2023). Is visual metacognition associated with autistic traits? A regression analysis shows no link between visual metacognition and Autism-Spectrum Quotient scores. Consciousness and Cognition , 110 , 103502. https://doi.org/10.1016/j.concog.2023.103502 Estudillo, A. J., & Wong, H. K. (2021). Associations between self-reported and objective face recognition abilities are only evident in above- and below-average recognisers. PeerJ , 9 , e10629. https://doi.org/10.7717/peerj.10629 Faivre, N., Filevich, E., Solovey, G., Kühn, S., & Blanke, O. (2018). Behavioral, Modeling, and Electrophysiological Evidence for Supramodality in Human Metacognition. The Journal of Neuroscience , 38 (2), 263–277. https://doi.org/10.1523/JNEUROSCI.0322-17.2017 Fleur, D. S., Bredeweg, B., & Van Den Bos, W. (2021). Metacognition: Ideas and insights from neuro- and educational sciences. Npj Science of Learning , 6 (1), 13. https://doi.org/10.1038/s41539-021-00089-5 Gehdu, B. K., Press, C., Gray, K. L. H., & Cook, R. (2024). Autistic adults have insight into their relative face recognition ability. Scientific Reports , 14 (1), 17802. https://doi.org/10.1038/s41598-024-67649-8 Gosling, A., & Eimer, M. (2011). An event-related brain potential study of explicit face recognition. Neuropsychologia , 49 (9), 2736–2745. Griffin, J. W., Bauer, R., & Scherf, K. S. (2021). A quantitative meta-analysis of face recognition deficits in autism: 40 years of research. Psychological Bulletin , 147 (3), 268–292. https://doi.org/10.1037/bul0000310 Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. (2000). The distributed human neural system for face perception. Trends in cognitive sciences , 4 (6), 223–233. Herzmann, G., Schweinberger, S. R., Sommer, W., & Jentzsch, I. (2004). What’s special about personally familiar faces? A multimodal approach. Psychophysiology , 41 (5), 688–701. https://doi.org/10.1111/j.1469-8986.2004.00196.x Keller R, Chieregato S, Bari S, Castaldo R, Rutto F, Chiocchetti A, Dianzani U. Autism in Adulthood: Clinical and Demographic Characteristics of a Cohort of Five Hundred Persons with Autism Analyzed by a Novel Multistep Network Model. Brain Sci. 2020 Jul 1;10(7):416. doi: 10.3390/brainsci10070416. PMID: 32630229; PMCID: PMC7407178. Jia, X., Li, W., & Cao, L. (2019). The Role of Metacognitive Components in Creative Thinking. Frontiers in Psychology , 10 , 2404. https://doi.org/10.3389/fpsyg.2019.02404 Keyes, H., Brady, N., Reilly, R. B., & Foxe, J. J. (2010). My face or yours? Event-related potential correlates of self-face processing. Brain and Cognition , 72 (2), 244–254. https://doi.org/10.1016/j.bandc.2009.09.006 Lane, J., Rohan, E. M., Sabeti, F., Essex, R. W., Maddess, T., Dawel, A., Robbins, R. A., Barnes, N., He, X., & McKone, E. (2018). Impacts of impaired face perception on social interactions and quality of life in age-related macular degeneration: A qualitative study and new community resources. PLoS One , 13 (12), e0209218. Li, J., Tian, M., Fang, H., Xu, M., Li, H., & Liu, J. (2010). Extraversion predicts individual differences in face recognition. Communicative & Integrative Biology , 3 (4), 295–298. https://doi.org/10.4161/cib.3.4.12093 Liccione, D., Moruzzi, S., Rossi, F., Manganaro, A., Porta, M., Nugrahaningsih, N., Caserio, V., & Allegri, N. (2014). Familiarity is not notoriety: Phenomenological accounts of face recognition. Frontiers in Human Neuroscience , 8 , 672. https://doi.org/10.3389/fnhum.2014.00672 Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C., Pickles, A., & Rutter, M. (2000). The Autism Diagnostic Observation Schedule—Generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of autism and developmental disorders , 30 , 205–223. Manippa, V., Palmisano, A., Ventura, M., & Rivolta, D. (2023). The Neural Correlates of Developmental Prosopagnosia: Twenty-Five Years on. Brain Sciences , 13 (10), 1399. https://doi.org/10.3390/brainsci13101399 Maras, K., Norris, J. E., & Brewer, N. (2020). Metacognitive Monitoring and Control of Eyewitness Memory Reports in Autism. Autism Research , 13 (11), 2017–2029. https://doi.org/10.1002/aur.2278 Masi, A., DeMayo, M. M., Glozier, N., & Guastella, A. J. (2017). An Overview of Autism Spectrum Disorder, Heterogeneity and Treatment Options. Neuroscience Bulletin , 33 (2), 183–193. https://doi.org/10.1007/s12264-017-0100-y Matthews, C. M., Davis, E. E., & Mondloch, C. J. (2018). Getting to know you: The development of mechanisms underlying face learning. Journal of Experimental Child Psychology , 167 , 295–313. Metcalfe, J., & Shimamura, A. P. (1994). Metacognition: Knowing about knowing . MIT press. https://books.google.com/books?hl=it&lr=&id=Ci0TDgAAQBAJ&oi=fnd&pg=PR5&dq=Metcalfe,+J.,+%26+Shimamura,+A.+P.+(Eds.).+(1994).+Metacognition:+Knowing+about+knowing.+MIT+press.&ots=qH5HazPzWq&sig=EIFMssK5hzsYaZV_f2j8EPePzmc Migo, E. M., Mayes, A. R., & Montaldi, D. (2012). Measuring recollection and familiarity: Improving the remember/know procedure. Consciousness and Cognition , 21 (3), 1435–1455. https://doi.org/10.1016/j.concog.2012.04.014 Naumann, S., Senftleben, U., Santhosh, M., McPartland, J., & Webb, S. J. (2018). Neurophysiological correlates of holistic face processing in adolescents with and without autism spectrum disorder. Journal of Neurodevelopmental Disorders , 10 (1), 27. https://doi.org/10.1186/s11689-018-9244-y Nicholson, T., Williams, D. M., Lind, S. E., Grainger, C., & Carruthers, P. (2021). Linking metacognition and mindreading: Evidence from autism and dual-task investigations. Journal of Experimental Psychology: General , 150 (2), 206–220. https://doi.org/10.1037/xge0000878 Palermo, R., Rossion, B., Rhodes, G., Laguesse, R., Tez, T., Hall, B., Albonico, A., Malaspina, M., Daini, R., Irons, J., Al-Janabi, S., Taylor, L. C., Rivolta, D., & McKone, E. (2017). Do People Have Insight into their Face Recognition Abilities? Quarterly Journal of Experimental Psychology , 70 (2), 218–233. https://doi.org/10.1080/17470218.2016.1161058 Ritvo, R. A., Ritvo, E. R., Guthrie, D., Ritvo, M. J., Hufnagel, D. H., McMahon, W., Tonge, B., Mataix-Cols, D., Jassi, A., Attwood, T., & Eloff, J. (2011). The Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R): A Scale to Assist the Diagnosis of Autism Spectrum Disorder in Adults: An International Validation Study. Journal of Autism and Developmental Disorders , 41 (8), 1076–1089. https://doi.org/10.1007/s10803-010-1133-5 Rivolta, D., Palermo, R., & Schmalzl, L. (2013). What is overt and what is covert in congenital prosopagnosia? Neuropsychology Review , 23 (2), 111–116. https://doi.org/10.1007/s11065-012-9223-0 Roid, G. H., & Miller, L. J. (1997). Leiter international performance scale-revised (Leiter-R). Wood Dale, IL: Stoelting , 10 . Rouault, M., McWilliams, A., Allen, M. G., & Fleming, S. M. (2018). Human metacognition across domains: Insights from individual differences and neuroimaging. Personality neuroscience , 1 , e17. Ruta, L., Mazzone, D., Mazzone, L., Wheelwright, S., & Baron-Cohen, S. (2012). The Autism-Spectrum Quotient--Italian version: A cross-cultural confirmation of the broader autism phenotype. Journal of Autism and Developmental Disorders , 42 (4), 625–633. https://doi.org/10.1007/s10803-011-1290-1 Rutter, M., Le Couteur, A., & Lord, C. (2003). Autism diagnostic interview-revised. Los Angeles, CA: Western Psychological Services , 29 (2003), 30. Sawyer, A. C. P., Williamson, P., & Young, R. (2014). Metacognitive Processes in Emotion Recognition: Are They Different in Adults with Asperger’s Disorder? Journal of Autism and Developmental Disorders , 44 (6), 1373–1382. https://doi.org/10.1007/s10803-013-1999-0 Stantić, M., Ichijo, E., Catmur, C., & Bird, G. (2022). Face memory and face perception in autism. Autism , 26 (1), 276–280. https://doi.org/10.1177/13623613211027685 Tagliente, S., Passarelli, M., D’Elia, V., Palmisano, A., Dunn, J. D., Masini, M., Lanciano, T., Curci, A., & Rivolta, D. (2023). Self-reported face recognition abilities moderately predict face-learning skills: Evidence from Italian samples. Heliyon , 9 (3), e14125. https://doi.org/10.1016/j.heliyon.2023.e14125 Tanaka, J. W., Wolf, J. M., Klaiman, C., Koenig, K., Cockburn, J., Herlihy, L., Brown, C., Stahl, S. S., South, M., McPartland, J. C., Kaiser, M. D., & Schultz, R. T. (2012). The perception and identification of facial emotions in individuals with autism spectrum disorders using the Let’s Face It! Emotion Skills Battery. Journal of Child Psychology and Psychiatry, and Allied Disciplines , 53 (12), 1259–1267. https://doi.org/10.1111/j.1469-7610.2012.02571.x Taouki, I., Lallier, M., & Soto, D. (2022). The role of metacognition in monitoring performance and regulating learning in early readers. Metacognition and Learning , 17 (3), 921–948. https://doi.org/10.1007/s11409-022-09292-0 Teunisse, J.-P., & de Gelder, B. (2003). Face processing in adolescents with autistic disorder: The inversion and composite effects. Brain and Cognition , 52 (3), 285–294. https://doi.org/10.1016/S0278-2626(03)00042-3 Tracy, J. L., Robins, R. W., Schriber, R. A., & Solomon, M. (2011). Is Emotion Recognition Impaired in Individuals with Autism Spectrum Disorders? Journal of Autism and Developmental Disorders , 41 (1), 102–109. https://doi.org/10.1007/s10803-010-1030-y Ventura, M., Caffò, A. O., Manippa, V., & Rivolta, D. (2024). Normative data of the Italian Famous Face Test. Scientific Reports , 14 (1), 15276. https://doi.org/10.1038/s41598-024-66252-1 Wechsler, D. (2012). Wechsler Adult Intelligence Scale—Fourth Edition [Dataset]. https://doi.org/10.1037/t15169-000 Weigelt, S., Koldewyn, K., & Kanwisher, N. (2012). Face identity recognition in autism spectrum disorders: A review of behavioral studies. Neuroscience & Biobehavioral Reviews , 36 (3), 1060–1084. Williams, D. M., Bergström, Z., & Grainger, C. (2018). Metacognitive monitoring and the hypercorrection effect in autism and the general population: Relation to autism(-like) traits and mindreading. Autism , 22 (3), 259–270. https://doi.org/10.1177/1362361316680178 Yardley, L., McDermott, L., Pisarski, S., Duchaine, B., & Nakayama, K. (2008). Psychosocial consequences of developmental prosopagnosia: A problem of recognition. Journal of Psychosomatic Research , 65 (5), 445–451. https://doi.org/10.1016/j.jpsychores.2008.03.013 Zhao, Y., Li, J., Liu, X., Song, Y., Wang, R., Yang, Z., & Liu, J. (2016). Altered spontaneous neural activity in the occipital face area reflects behavioral deficits in developmental prosopagnosia. Neuropsychologia , 89 , 344–355. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5676642","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392363574,"identity":"e3453f76-01b2-4f58-beaa-3e54849a8b69","order_by":0,"name":"Martina Ventura","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"","lastName":"Ventura","suffix":""},{"id":392363575,"identity":"9ba9cfcb-75c7-4f34-937d-1406960559e9","order_by":1,"name":"Valerio Manippa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYFAC5gYGBgMI8wAQyzEwMLYBaQk8WhhRtRjDtODRA9KCBBKBXDYGfNYYHD/Y+LiiwIZBt/3swcOFO+zSN9xubnvwg8GiDqeWM4nNhmcM0hjMzuQlHJ55Jjl3w52D7YY9eBwm2ZDYJtlgcJjB7ECOwWHeNubcDTcS2yR48Gnpf9j+E6zl/BuQlvp0A6AWyT94tPBLJLYxgrXcANtyOAGkRRqfLfwSD5uBDkvjMbsBtGXmmeOGM28kthvLGEhINuDQwsaffPBjwx8bObPzOcafC3dUy/PdSH/28E1FHT8uW2CAB0QwI+LIgJAGKGBGi9ZRMApGwSgYBWAAAOTyWJg8XXwDAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Valerio","middleName":"","lastName":"Manippa","suffix":""},{"id":392363576,"identity":"c8476397-da4a-440b-8263-75b4973a3446","order_by":2,"name":"Alessandro Oronzo Caffò","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"Oronzo","lastName":"Caffò","suffix":""},{"id":392363577,"identity":"469ec394-1eb4-4c07-b720-891c6eeb1f7e","order_by":3,"name":"Giovanni Cicinelli","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Cicinelli","suffix":""},{"id":392363578,"identity":"c1ff8ac5-f724-42df-addd-cd4077b60822","order_by":4,"name":"Emanuela Nobile","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Emanuela","middleName":"","lastName":"Nobile","suffix":""},{"id":392363579,"identity":"71c70990-2e0a-4ca5-a560-5c34da4a09b9","order_by":5,"name":"Roberto Keller","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"","lastName":"Keller","suffix":""},{"id":392363580,"identity":"38490bdc-fe89-4fec-a064-bcea75382849","order_by":6,"name":"Davide Rivolta","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Davide","middleName":"","lastName":"Rivolta","suffix":""}],"badges":[],"createdAt":"2024-12-19 12:12:37","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5676642/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5676642/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72204939,"identity":"f5df4e7b-3813-481c-99a5-06a532a74f88","added_by":"auto","created_at":"2024-12-23 16:29:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80322,"visible":true,"origin":"","legend":"\u003cp\u003eThe boxplots show the comparisons between persons with Autism Spectrum Disorder (ASD) and Neurotypical (NT) individuals, derived from the 4 ANCOVAs conducted on: a) Face identification accuracy (%)—higher in NT compared with ASD, b) Prosopagnosia Index-20 (PI-20) scores—higher in ASD than NT, c) Face familiarity sensitivity (d')—significantly lower in the ASD group compared to the NT group, and d) Face familiarity response bias (β)—showing no group difference. Each boxplot illustrates the spread of individual data points, median, and interquartile ranges.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5676642/v1/aba6bab2be93d7e4dcb75e02.png"},{"id":72204934,"identity":"b6afa802-ffb4-4a8a-a661-d4dcc440150a","added_by":"auto","created_at":"2024-12-23 16:29:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77264,"visible":true,"origin":"","legend":"\u003cp\u003ea) Distribution of persons with Autism Spectrum Disorder (ASD, in red) and Neurotypical individuals (NT, in blue) above and below the IT-FFT Face Identification cut-off: a significantly higher number of ASD individuals score below the cut-off compared to the NTs; b) Correlation between Face Identification and PI-20 scores: the negative correlation is significant within the ASD sample but not within the NT sample.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5676642/v1/e2b8979b93a10e6f65bf4b91.png"},{"id":72207584,"identity":"9055e252-c2cb-4325-a245-3b11d923f360","added_by":"auto","created_at":"2024-12-23 16:53:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":569405,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5676642/v1/331d7ac5-ff1e-4069-82a8-094aeb5625a7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUnveiling Face Recognition Challenges and Awareness in Autism Spectrum Disorder: Insights from the Italian Famous Face Test (IT-FFT)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe human face represents the stimulus we rely on the most for social interactions, as it provides rich social information such as identity, emotions, and intentions (Haxby et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Thus, accurate and rapid face processing and recognition are necessary for building and maintaining social relationships, as they allow one to modulate behavior, and intent, and provide an appropriate social response (Avery et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Indeed, it has been shown that superior social skills (e.g., extraversion personality trait) are linked to better face identification (Li et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In contrast, deficiencies in face processing can lead to anxiety and social situation avoidance (Yardley et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Difficulty with face recognition is a characteristic associated with various conditions, including Autism Spectrum Disorder (ASD). ASD is a developmental disorder characterized by compromised communication and basic social development, along with narrowed interests and stereotyped, repetitive behaviors (American Psychiatric Association \u0026amp; Association, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous studies have consistently highlighted abnormalities in face processing among autistic individuals, manifesting with a wide range of severity challenges, from mild to moderate impairments (Teunisse \u0026amp; de Gelder, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), differently affecting face memory, face perception, or facial expression recognition (Stantić et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tanaka et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This, together with other social deficits, significantly impacts persons with ASD (PwASD) ability to navigate human interactions (Constantino, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) - but see Tracy et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e and Naumann et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e for a different perspective. Despite these variations, a recent meta-analysis by Griffin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e consistently points towards deficits in face recognition skills among PwASD. Notably, face recognition impairment in PwASD shares similarities with prosopagnosia, a condition characterized by the inability to explicitly recognize familiar people\u0026rsquo;s identities based on their faces (Manippa et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rivolta et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Recent research suggests that up to one-third of PwASD show prosopagnosia-like symptoms (Cygan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, in everyday life, we view faces in a range of contexts where the recognition of familiar or unfamiliar faces is required. That implies that we deal with different kinds of familiarity; for instance, differentiating between a family member and a stranger or a TV star. Research indicates that our ability to recognize faces varies significantly depending on their familiarity (Matthews et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Herzmann et al., (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) found that reaction times were quicker for both famous and personally familiar faces compared to unknown faces, but only the personally familiar triggered a strong skin conductance response. Additionally, the neurological basis for recognizing different types of faces varies significantly, with several studies showing distinct brain activation patterns or modulation for unknown, famous, and personally familiar faces (Andrews et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Caharel et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gosling \u0026amp; Eimer, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Keyes et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This suggests the need to distinguish between experimentally learned/unknown, famous, and personally familiar faces when assessing face perception, as some conditions may show difficulties in some, but not all, aspects of face processing (Liccione et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, ASDs studies on face recognition usually employed experimental familiarization techniques (Ventura et al., 2023) with initially unfamiliar face stimuli (e.g., Cambridge Face Memory test - CFMT, (Duchaine \u0026amp; Nakayama, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)), mostly using Old-new paradigms, and only a few studies used famous or personally familiar identities (Weigelt et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Thus, even though it's not a formal diagnosis criterion, improving research on how PwASD (and more broadly, people with recognition impairment) process different types of faces could offer a unique perspective on comprehending the mechanisms behind their different social activity and inform the development of targeted interventions.\u003c/p\u003e \u003cp\u003eAn additional relevant aspect to consider is metacognition, broadly defined as the ability to be aware of one's mental processes, skills, and performance (Metcalfe \u0026amp; Shimamura, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Metacognition studies on PwASD demonstrate an overall metacognitive difficulty in various cognitive domains, such as memory (Teunisse \u0026amp; de Gelder, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, as ASD represents a heterogeneous condition for the etiology, phenotype, and outcome (Masi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), not all PwASD may experience the same type of metacognitive difficulties (Embon et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Specifically, visual metacognition is the ability to evaluate one\u0026rsquo;s performance on visual perceptual tasks, such as recognizing our ability to distinguish between colors or shapes accurately. What is so far fairly unexplored is whether PwASD have insights into their face recognition skills, with only one study (Gehdu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showing a statistically significant correlation between the Prosopagnosia Index-20 scores of PwASD and their performance on two variants of the CFMT. Assessing this aspect is important at the theoretical level since it informs cognitive theories of ASD, but also at the practical level since working on metacognition could represent an intervention tool in the domain of face recognition, as studies suggest that metacognitive skills can be trained (Fleur et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Taouki et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, our study aims to (i) ascertain whether PwASD level 1 according to DSM 5 criteria (APA, 2013) show famous face recognition deficits through our recently developed Italian Famous Face Test (IT-FFT, Ventura et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); and (ii) whether PwASD have insights into their overall face identification skills. Consequently, we also evaluate the potential of using the IT-FFT as a tool to highlight face recognition deficits, further enhancing our diagnostic capabilities and intervention strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eA total of 99 adult participants (ages 18\u0026ndash;57 years, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29.41, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.98) were included in the study, divided into 49 clinical and 50 control participants. Their years of schooling ranged from 7 to 21 years (M\u0026thinsp;=\u0026thinsp;14.20, SD\u0026thinsp;=\u0026thinsp;2.70), and all had normal or corrected-to-normal vision.\u003c/p\u003e \u003cp\u003eThe clinical group consisted of 49 PwASD level 1 recruited from the Regional Center for Adults with Autism in the Piedmont region (Italy). Each of them was diagnosed with level 1 ASD by psychologists and psychiatrists of the Center according to DSM-5 criteria (American Psychiatric Association \u0026amp; Association, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) based on clinical anamnesis, clinical interview, psychiatric interview, psychopathology assessment, cognitive assessment with the Wechsler Adult Intelligence Scale (WAIS-IV, (Wechsler, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) or Leiter-3 (Roid \u0026amp; Miller, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), diagnostic evaluation with the Autism Diagnostic Interview (ADI-R, Rutter et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and Autism Diagnostic Observation Schedule - Second Edition (ADOS-2, Lord et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) or Autism Asperger Diagnostic Scale (RAADS, Ritvo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Keller et al, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The control sample consisted of 50 Neurotypical individuals (NTs), stratified to match the ASD group's age, sex, and education level (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To ensure that no individual selected for the NT sample fell within the ASD spectrum, NTs completed the Italian version of the Autism-Spectrum Quotient (AQ; (Baron-Cohen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ruta et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). All NTs scored between 12 and 26, which is below the recommended cut-off of 29, indicative of clinically significant autism traits (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.09, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.40).\u003c/p\u003e \u003cp\u003eIndividuals with a history of neurological diseases, cerebral stroke, epilepsy or epileptic seizures, head injury with loss of consciousness, severe medical conditions or psychiatric disorders (this latter criterion was not applied to PwASD, as they often present comorbidity), and alcohol or drug abuse were excluded.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of persons with Autism Spectrum Disorder (PwASD) and neurotypical individuals (NTs). The two samples did not differ in sex distribution, age, and years of schooling.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePwASD (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNTs (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 F, 27 M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 F, 24 M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.47\u0026thinsp;\u0026plusmn;\u0026thinsp;10.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.36\u0026thinsp;\u0026plusmn;\u0026thinsp;11.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchooling (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.62\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Procedure and measurement\u003c/h2\u003e \u003cp\u003e The study was approved by the Ethical Committee of the University of Bari (protocol number: ET-19-01) and was performed in accordance with the Helsinki Declaration and its later amendments. After providing informed consent and socio-demographic information, each participant took the Italian version of the Prosopagnosia Index-20 (PI-20) (Tagliente et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), a self-report assessment of face recognition ability. The PI-20 consists of twenty statements that represent facial recognition experiences. Respondents rate the accuracy of these statements on a five-point scale (1 being \"Totally disagree\" and 5 being \"Totally agree\"). Scores range from 20 to 100, with higher scores indicating greater difficulties in facial recognition.\u003c/p\u003e \u003cp\u003eAfter, each participant completed the Italian Famous Face Test (IT-FFT), administered through a computer-based interface. The IT-FFT comprises 100 images: 50 depicting celebrities and 50 unknown people. Faces were presented one at a time in randomized order at the center of the screen. Participants were instructed to view each photograph carefully and i) evaluate whether the face was famous or not (\u003cem\u003eFace familiarity\u003c/em\u003e), and ii) provide the name of the celebrity if they thought the face was famous or specific information about the character (\u003cem\u003eFace identification\u003c/em\u003e). At the end, the final checklist containing all the 50 names belonging to the famous people included in the IT-FFT was shown, and participants had to indicate the names of celebrities they were unfamiliar with (i.e., people they do not know and, thus, they cannot recognize) (\u003cem\u003eName familiarity\u003c/em\u003e). A detailed description of the stimuli, procedures, index measures, and normative data on NT participants can be found in Ventura et al., (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFace Identification\u003c/em\u003e score was computed by summing the number of correctly identified celebrities, which could be achieved either by naming the celebrity or providing specific biographical information, such as referring to them as \"the main character of the film...\". Minor name spelling errors were not counted as mistakes. However, responses that were missing, incorrect, or too general (e.g., simply stating \"actor\" or \"singer\") were given a score of 0. The final accuracy for face identification was expressed as a percentage, calculated by dividing the face identification score by the name familiarity score (assessed through the checklist) and then multiplying by 100. This method ensured that participants' accuracy reflected their performance relative to their self-reported knowledge of famous personalities, rather than a fixed number of targets. The \u003cem\u003eFace Familiarity\u003c/em\u003e scores, instead, were based on signal detection theory, which categorizes outcomes into four types: false alarms (incorrectly identifying non-famous faces as famous), correct rejections (correctly identifying non-famous faces as non-famous), misses (failing to recognize famous faces), and hits (correctly recognizing famous faces). The sensitivity measure, d-prime (\u003cem\u003ed'\u003c/em\u003e), was calculated from these outcomes, with higher values indicating greater sensitivity to recognizing famous faces. Additionally, response bias (\u003cem\u003eβ\u003c/em\u003e) was computed to understand participants' tendencies: a \u003cem\u003eβ\u003c/em\u003e of 1.00 indicates no bias, values below 1.00 indicate a liberal tendency (more likely to report faces as famous), and values above 1.00 indicate a conservative tendency (more likely to report faces as non-famous).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data analysis\u003c/h2\u003e \u003cp\u003eOur dependent variables were PI-20 scores and the IF-FFT \u003cem\u003eFace familiarity\u003c/em\u003e and \u003cem\u003eFace identification\u003c/em\u003e scores. To determine whether PwASD exhibit famous face recognition deficits and whether they have insight into their overall face identification skills, we conducted 4 generalized linear models, one for each dependent variable (Face identification, PI-20 score, Face familiarity (\u003cem\u003ed\u0026rsquo;\u003c/em\u003e) and response bias (β) scores). For each dependent variable, we run an Analysis of Covariance (ANCOVA) with group (PwASD vs. NT) and sex (F vs. M) as between factors, and age and schooling as covariates. Additionally, we computed a 2 x 2 Chi-squared analysis to assess the distribution of PwASD and NT samples above (ES\u0026thinsp;\u0026gt;\u0026thinsp;1) and below (ES\u0026thinsp;=\u0026thinsp;0) the FFT-IT \u003cem\u003eFace Identification\u003c/em\u003e cutoff scores (as defined by Ventura et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Finally, Pearson\u0026rsquo;s correlation between Face Identification and PI-20 was calculated for each sample to evaluate whether PwASD and NT sample have insights into their overall face identification skills.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe ANCOVA on \u003cem\u003eFace identification\u003c/em\u003e scores showed a main effect of the group (F\u003csub\u003e1,94\u003c/sub\u003e= 19.049, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.167, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) with a higher face identification score for NTs compared to PwASD, and a significant effect of participants\u0026rsquo; age (F\u003csub\u003e1,94\u003c/sub\u003e= 10.790, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.103), indicating that face identification increased along with participants\u0026rsquo; age. The ANCOVA conducted on PI-20 score showed a main effect of the group (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) (F\u003csub\u003e1,94\u003c/sub\u003e= 6.854, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.010, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.078), with PwASD scoring significantly higher on PI-20 than NT participants. The ANCOVA conducted on Face familiarity (\u003cem\u003ed\u0026rsquo;\u003c/em\u003e), showed a main effect of group (F\u003csub\u003e1,94\u003c/sub\u003e= 4.352, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.039, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.062) with higher \u003cem\u003ed\u0026rsquo;\u003c/em\u003e score for NTs compared to PwASD (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), and a main effect of participants\u0026rsquo; age (F\u003csub\u003e1,94\u003c/sub\u003e= 11.598, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003eη\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.099) with increasing \u003cem\u003ed\u0026rsquo;\u003c/em\u003e scores along with participants\u0026rsquo; age. The ANCOVA on face familiarity response bias (\u003cem\u003eβ\u003c/em\u003e) showed no main or interaction effect (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The 2 x 2 chi-squared tests conducted to assess the distribution of PwASD and NTs above and below the IT-FFT Face Identification cut-off, demonstrated a higher proportion of ASDs distributed below the cut-off score (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21, 42.8%) compared to the NTs (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.6%) (χ\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;18.305; \u003cem\u003ep\u003c/em\u003e\u0026lt;. 001) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Descriptive data and comparisons are reported in Table\u0026nbsp;2. Finally, a significant negative correlation was found between Face identification and PI-20 scores (r= -0.314, p\u0026thinsp;=\u0026thinsp;.002, see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), driven primarily by the PwASD (r = -0.343, p\u0026thinsp;=\u0026thinsp;.016). No significant correlation was observed in the NT group (r = -0.139, p\u0026thinsp;=\u0026thinsp;.336).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we investigated potential differences in face recognition skills between PwASD level 1 and NTs using the IT-FFT. We also assessed their level of insight into their own face recognition ability through the PI-20. Our findings showed that PwASD performed significantly worse than NTs in the IT-FFT and reported more subjective face recognition difficulties to the PI-20. Furthermore, PI-20 and IT-FFT accuracy scores correlated (negatively) in PwASD but not in NTs. This pattern highlights a connection between perceived abilities and objective performance in face identification: while the latter seems strongly impaired, the former appears to be unaffected by the presence of ASD.\u003c/p\u003e \u003cp\u003eThe significant main effect showing higher face identification accuracy in the NT group suggests that PwASD struggle more with recognizing famous faces, consistent with previous research on facial recognition difficulties in ASD (Tanaka et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Weigelt et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Notably, more than 40% of PwASD (compared to just 4% of the NT sample) scored below the IT-FFT cut-off (Ventura et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), indicating pronounced and significant difficulties in facial recognition skills. Additionally, the NT sample achieved higher \u003cem\u003ed'\u003c/em\u003e scores compared to the ASD sample, reinforcing the idea that PwASD demonstrate reduced discriminative abilities when judging face familiarity. It is important to note that response bias, as measured by \u003cem\u003eꞵ\u003c/em\u003e score, was comparable between PwASD and NTs, suggesting that the difference in d' scores is not due to differences in response tendencies. These findings emphasize the need to consider a weakened sense of familiarity as a contributing factor to face processing impairments in PwASD. This underlines the heterogeneous nature of face recognition challenges within the ASD population, where some people may experience more severe impairments than others. Impaired face recognition not only affects social interactions and communication but also contributes to difficulties in forming social bonds, which are critical to navigating everyday life (Davis, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lane et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Given these challenges, it is crucial to develop tailored assessments and targeted face recognition training programs to effectively address these issues and enhance the social functioning of PwASD. While both face identification accuracy and face familiarity sensitivity were influenced by participant sex, according to the normative data of the IT-FFT (Ventura et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the absence of significant interaction effects suggests that factors such as sex, age, and education did not significantly modulate the observed group differences.\u003c/p\u003e \u003cp\u003eMoreover, we assessed the recognition ability insight of PwASD, which has rarely been investigated. Metacognition is a broad concept that includes several components (Jia et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); among these, one important aspect is the individuals\u0026rsquo; thoughts, beliefs, and reflections on one\u0026rsquo;s cognitive capacity, which could also be defined as metacognitive monitoring (Carpenter \u0026amp; Williams, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Metacognition studies in ASD led to mixed results; some research reports no differences between NTs and PwASD metacognition level (Nicholson et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Williams et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), while others suggest that they show several difficulties in metacognitive abilities (Maras et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sawyer et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, it is unclear whether metacognition is domain-general or domain-specific (Faivre et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rouault et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Considering metacognition to be domain-specific, it may be possible that this ability in PwASD may be disrupted for some domains or tasks and not for others, and this could explain the variability in results observed among ASD individuals.\u003c/p\u003e \u003cp\u003eTo this aim, we administered the Italian version of the PI-20. Our results align with previous findings, showing that PwASD had higher PI-20 scores compared to the NTs, indicating that PwASD are aware of their difficulties with face recognition. This result aligns with previous literature that showed no impaired metacognition among PwASD (Adams et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ambrus et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Migo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and specifically, Gehdu et al., (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which observed significant correlations between PwASD PI-20 scores and their performance on two variants of the CMFT.\u003c/p\u003e \u003cp\u003eAdditionally, a moderate negative correlation between PI-20 scores and face recognition accuracy was observed when considering all participants together. However, this correlation was significant only within PwASD and not within the NT sample. This suggests that PwASD who perceive themselves as having difficulties in recognizing faces tend to perform worse in face recognition tasks, aligning their self-awareness with actual performance. This higher metacognitive ability observed in individuals with ASD can be partly attributed to the lack of extreme scores reported by the NT sample. In contrast, PwASD are characterized by greater variability in face recognition abilities (mostly impaired), allowing for a more pronounced correlation between self-reported difficulties in face recognition and actual performance. Accordingly, the correlation between PI-20 scores and face recognition performance seems primarily driven by those with either very low (e.g., PwASD) or very high face recognition abilities (Bindemann et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Estudillo \u0026amp; Wong, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Palermo et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Thus, while the PI-20 can serve as a useful tool for initial screening and assessing individuals' insight into their face recognition skills, its discriminative power for identifying prosopagnosia-level performance on objective assessments may be limited. This highlights the need to balance caution against overreliance on self-report measures with recognizing their utility in specific contexts. Integrating subjective and objective assessments is crucial for comprehensively understanding face recognition difficulties (Arizpe et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Conclusions, limitations, and future research\u003c/h2\u003e \u003cp\u003eThis study found that PwASD level 1 performed worse than matched NT to the IT-FFT, coherently with the PI-20 assessing self-reported face recognition difficulties. IT-FFT and PI-20 negatively correlated mainly in ASD samples, demonstrating that PwASD level 1 are aware of their face recognition abilities. Therefore, metacognitive skills related to facial recognition appear to be preserved in PwASD; however, this finding is specific to facial recognition and does not encompass other aspects of metacognition. Future studies should consider exploring additional metacognitive abilities, using IQ as a covariate, as higher support levels are often associated with intellectual disabilities, making broader comparisons challenging. Furthermore, while this test can provide insights into the ability to recognize well-known faces and assess identification skills and familiarity, our data analysis strategy may only partially capture the comprehensive aspects of face recognition abilities, as other tasks related to face recognition were not assessed. Our test and others such as face-matching tests or the CFMT (Duchaine et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Duchaine \u0026amp; Nakayama, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) can provide a more comprehensive insight into individual face identification dimensions (i.e., face perception, face memory, and face recognition).\u003c/p\u003e \u003cp\u003eIn conclusion, this study highlights the IT-FFT as a valuable tool for detecting face recognition deficits in ASD and potentially other conditions, such as neurodegenerative disorders (e.g., Alzheimer's disease and frontotemporal dementia), or prosopagnosia, which often present challenges in facial recognition (Manippa et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While preliminary results are encouraging, further research is needed to evaluate the IT-FFT\u0026rsquo;s effectiveness across a broader range of conditions to enhance our diagnostic capabilities and understanding.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors did not receive support from any organization for the submitted work and have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams, A., Hills, P. J., Bennetts, R. J., \u0026amp; Bate, S. (2020). Coping strategies for developmental prosopagnosia. \u003cem\u003eNeuropsychological Rehabilitation\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(10), 1996\u0026ndash;2015. https://doi.org/10.1080/09602011.2019.1623824\u003c/li\u003e\n\u003cli\u003eAmbrus, G. G., Kaiser, D., Cichy, R. M., \u0026amp; Kov\u0026aacute;cs, G. (2019). The Neural Dynamics of Familiar Face Recognition. \u003cem\u003eCerebral Cortex\u003c/em\u003e. https://doi.org/10.1093/cercor/bhz010\u003c/li\u003e\n\u003cli\u003eAmerican Psychiatric Association, D., \u0026amp; Association, A. P. (2013). \u003cem\u003eDiagnostic and statistical manual of mental disorders: DSM-5\u003c/em\u003e (Vol. 5). American psychiatric association Washington, DC. https://www.academia.edu/download/38718268/csl6820_21.pdf\u003c/li\u003e\n\u003cli\u003eAndrews, S., Burton, A. M., Schweinberger, S. R., \u0026amp; Wiese, H. (2017). Event-related potentials reveal the development of stable face representations from natural variability. \u003cem\u003eQuarterly Journal of Experimental Psychology\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(8), 1620\u0026ndash;1632. https://doi.org/10.1080/17470218.2016.1195851\u003c/li\u003e\n\u003cli\u003eArizpe, J. M., Saad, E., Douglas, A. O., Germine, L., Wilmer, J. B., \u0026amp; DeGutis, J. M. (2019). Self-reported face recognition is highly valid, but alone is not highly discriminative of prosopagnosia-level performance on objective assessments. \u003cem\u003eBehavior Research Methods\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(3), 1102\u0026ndash;1116. https://doi.org/10.3758/s13428-018-01195-w\u003c/li\u003e\n\u003cli\u003eAvery, S. N., VanDerKlok, R. M., Heckers, S., \u0026amp; Blackford, J. U. (2016). Impaired face recognition is associated with social inhibition. \u003cem\u003ePsychiatry Research\u003c/em\u003e, \u003cem\u003e236\u003c/em\u003e, 53\u0026ndash;57. https://doi.org/10.1016/j.psychres.2015.12.035\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, males and females, scientists and mathematicians. \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(1), 5\u0026ndash;17. https://doi.org/10.1023/a:1005653411471\u003c/li\u003e\n\u003cli\u003eBindemann, M., Attard, J., \u0026amp; Johnston, R. A. (2014). Perceived ability and actual recognition accuracy for unfamiliar and famous faces. \u003cem\u003eCogent Psychology\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), 986903. https://doi.org/10.1080/23311908.2014.986903\u003c/li\u003e\n\u003cli\u003eCaharel, S., Ramon, M., \u0026amp; Rossion, B. (2014). Face familiarity decisions take 200 msec in the human brain: Electrophysiological evidence from a go/no-go speeded task. \u003cem\u003eJournal of Cognitive Neuroscience\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(1), 81\u0026ndash;95. https://doi.org/10.1162/jocn_a_00451\u003c/li\u003e\n\u003cli\u003eCarpenter, K. L., \u0026amp; Williams, D. M. (2023). A meta-analysis and critical review of metacognitive accuracy in autism. \u003cem\u003eAutism\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(2), 512\u0026ndash;525. https://doi.org/10.1177/13623613221106004\u003c/li\u003e\n\u003cli\u003eConstantino, J. N. (2011). The quantitative nature of autistic social impairment. \u003cem\u003ePediatric research\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e(8), 55\u0026ndash;62.\u003c/li\u003e\n\u003cli\u003eCygan, H. B., Okuniewska, H., Jednor\u0026oacute;g, K., Marchewka, A., Wypych, M., \u0026amp; Nowicka, A. (2018). Face processing in a case of high functioning autism with developmental prosopagnosia. \u003cem\u003eActa Neurobiologiae Experimentalis\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e(2), 114\u0026ndash;131.\u003c/li\u003e\n\u003cli\u003eDavis, C. (2010). Attention-deficit/hyperactivity disorder: Associations with overeating and obesity. \u003cem\u003eCurrent Psychiatry Reports\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(5), 389\u0026ndash;395. https://doi.org/10.1007/s11920-010-0133-7\u003c/li\u003e\n\u003cli\u003eDuchaine, B. C., \u0026amp; Nakayama, K. (2006). Developmental prosopagnosia: A window to content-specific face processing. \u003cem\u003eCurrent opinion in neurobiology\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 166\u0026ndash;173.\u003c/li\u003e\n\u003cli\u003eDuchaine, B. C., Yovel, G., Butterworth, E. J., \u0026amp; Nakayama, K. (2006). Prosopagnosia as an impairment to face-specific mechanisms: Elimination of the alternative hypotheses in a developmental case. \u003cem\u003eCognitive Neuropsychology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(5), 714\u0026ndash;747. https://doi.org/10.1080/02643290500441296\u003c/li\u003e\n\u003cli\u003eEmbon, I., Cukier, S., Iorio, A., Barttfeld, P., \u0026amp; Solovey, G. (2023). Is visual metacognition associated with autistic traits? A regression analysis shows no link between visual metacognition and Autism-Spectrum Quotient scores. \u003cem\u003eConsciousness and Cognition\u003c/em\u003e, \u003cem\u003e110\u003c/em\u003e, 103502. https://doi.org/10.1016/j.concog.2023.103502\u003c/li\u003e\n\u003cli\u003eEstudillo, A. J., \u0026amp; Wong, H. K. (2021). Associations between self-reported and objective face recognition abilities are only evident in above- and below-average recognisers. \u003cem\u003ePeerJ\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, e10629. https://doi.org/10.7717/peerj.10629\u003c/li\u003e\n\u003cli\u003eFaivre, N., Filevich, E., Solovey, G., K\u0026uuml;hn, S., \u0026amp; Blanke, O. (2018). Behavioral, Modeling, and Electrophysiological Evidence for Supramodality in Human Metacognition. \u003cem\u003eThe Journal of Neuroscience\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(2), 263\u0026ndash;277. https://doi.org/10.1523/JNEUROSCI.0322-17.2017\u003c/li\u003e\n\u003cli\u003eFleur, D. S., Bredeweg, B., \u0026amp; Van Den Bos, W. (2021). Metacognition: Ideas and insights from neuro- and educational sciences. \u003cem\u003eNpj Science of Learning\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 13. https://doi.org/10.1038/s41539-021-00089-5\u003c/li\u003e\n\u003cli\u003eGehdu, B. K., Press, C., Gray, K. L. H., \u0026amp; Cook, R. (2024). Autistic adults have insight into their relative face recognition ability. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 17802. https://doi.org/10.1038/s41598-024-67649-8\u003c/li\u003e\n\u003cli\u003eGosling, A., \u0026amp; Eimer, M. (2011). An event-related brain potential study of explicit face recognition. \u003cem\u003eNeuropsychologia\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(9), 2736\u0026ndash;2745.\u003c/li\u003e\n\u003cli\u003eGriffin, J. W., Bauer, R., \u0026amp; Scherf, K. S. (2021). A quantitative meta-analysis of face recognition deficits in autism: 40 years of research. \u003cem\u003ePsychological Bulletin\u003c/em\u003e, \u003cem\u003e147\u003c/em\u003e(3), 268\u0026ndash;292. https://doi.org/10.1037/bul0000310\u003c/li\u003e\n\u003cli\u003eHaxby, J. V., Hoffman, E. A., \u0026amp; Gobbini, M. I. (2000). The distributed human neural system for face perception. \u003cem\u003eTrends in cognitive sciences\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(6), 223\u0026ndash;233.\u003c/li\u003e\n\u003cli\u003eHerzmann, G., Schweinberger, S. R., Sommer, W., \u0026amp; Jentzsch, I. (2004). What\u0026rsquo;s special about personally familiar faces? A multimodal approach. \u003cem\u003ePsychophysiology\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(5), 688\u0026ndash;701. https://doi.org/10.1111/j.1469-8986.2004.00196.x\u003c/li\u003e\n\u003cli\u003eKeller R, Chieregato S, Bari S, Castaldo R, Rutto F, Chiocchetti A, Dianzani U. Autism in Adulthood: Clinical and Demographic Characteristics of a Cohort of Five Hundred Persons with Autism Analyzed by a Novel Multistep Network Model. Brain Sci. 2020 Jul 1;10(7):416. doi: 10.3390/brainsci10070416. PMID: 32630229; PMCID: PMC7407178. \u003c/li\u003e\n\u003cli\u003eJia, X., Li, W., \u0026amp; Cao, L. (2019). The Role of Metacognitive Components in Creative Thinking. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 2404. https://doi.org/10.3389/fpsyg.2019.02404\u003c/li\u003e\n\u003cli\u003eKeyes, H., Brady, N., Reilly, R. B., \u0026amp; Foxe, J. J. (2010). My face or yours? Event-related potential correlates of self-face processing. \u003cem\u003eBrain and Cognition\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(2), 244\u0026ndash;254. https://doi.org/10.1016/j.bandc.2009.09.006\u003c/li\u003e\n\u003cli\u003eLane, J., Rohan, E. M., Sabeti, F., Essex, R. W., Maddess, T., Dawel, A., Robbins, R. A., Barnes, N., He, X., \u0026amp; McKone, E. (2018). Impacts of impaired face perception on social interactions and quality of life in age-related macular degeneration: A qualitative study and new community resources. \u003cem\u003ePLoS One\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(12), e0209218.\u003c/li\u003e\n\u003cli\u003eLi, J., Tian, M., Fang, H., Xu, M., Li, H., \u0026amp; Liu, J. (2010). Extraversion predicts individual differences in face recognition. \u003cem\u003eCommunicative \u0026amp; Integrative Biology\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(4), 295\u0026ndash;298. https://doi.org/10.4161/cib.3.4.12093\u003c/li\u003e\n\u003cli\u003eLiccione, D., Moruzzi, S., Rossi, F., Manganaro, A., Porta, M., Nugrahaningsih, N., Caserio, V., \u0026amp; Allegri, N. (2014). Familiarity is not notoriety: Phenomenological accounts of face recognition. \u003cem\u003eFrontiers in Human Neuroscience\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 672. https://doi.org/10.3389/fnhum.2014.00672\u003c/li\u003e\n\u003cli\u003eLord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C., Pickles, A., \u0026amp; Rutter, M. (2000). The Autism Diagnostic Observation Schedule\u0026mdash;Generic: A standard measure of social and communication deficits associated with the spectrum of autism. \u003cem\u003eJournal of autism and developmental disorders\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e, 205\u0026ndash;223.\u003c/li\u003e\n\u003cli\u003eManippa, V., Palmisano, A., Ventura, M., \u0026amp; Rivolta, D. (2023). The Neural Correlates of Developmental Prosopagnosia: Twenty-Five Years on. \u003cem\u003eBrain Sciences\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(10), 1399. https://doi.org/10.3390/brainsci13101399\u003c/li\u003e\n\u003cli\u003eMaras, K., Norris, J. E., \u0026amp; Brewer, N. (2020). Metacognitive Monitoring and Control of Eyewitness Memory Reports in Autism. \u003cem\u003eAutism Research\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(11), 2017\u0026ndash;2029. https://doi.org/10.1002/aur.2278\u003c/li\u003e\n\u003cli\u003eMasi, A., DeMayo, M. M., Glozier, N., \u0026amp; Guastella, A. J. (2017). An Overview of Autism Spectrum Disorder, Heterogeneity and Treatment Options. \u003cem\u003eNeuroscience Bulletin\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(2), 183\u0026ndash;193. https://doi.org/10.1007/s12264-017-0100-y\u003c/li\u003e\n\u003cli\u003eMatthews, C. M., Davis, E. E., \u0026amp; Mondloch, C. J. (2018). Getting to know you: The development of mechanisms underlying face learning. \u003cem\u003eJournal of Experimental Child Psychology\u003c/em\u003e, \u003cem\u003e167\u003c/em\u003e, 295\u0026ndash;313.\u003c/li\u003e\n\u003cli\u003eMetcalfe, J., \u0026amp; Shimamura, A. P. (1994). \u003cem\u003eMetacognition: Knowing about knowing\u003c/em\u003e. MIT press. https://books.google.com/books?hl=it\u0026amp;lr=\u0026amp;id=Ci0TDgAAQBAJ\u0026amp;oi=fnd\u0026amp;pg=PR5\u0026amp;dq=Metcalfe,+J.,+%26+Shimamura,+A.+P.+(Eds.).+(1994).+Metacognition:+Knowing+about+knowing.+MIT+press.\u0026amp;ots=qH5HazPzWq\u0026amp;sig=EIFMssK5hzsYaZV_f2j8EPePzmc\u003c/li\u003e\n\u003cli\u003eMigo, E. M., Mayes, A. R., \u0026amp; Montaldi, D. (2012). Measuring recollection and familiarity: Improving the remember/know procedure. \u003cem\u003eConsciousness and Cognition\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(3), 1435\u0026ndash;1455. https://doi.org/10.1016/j.concog.2012.04.014\u003c/li\u003e\n\u003cli\u003eNaumann, S., Senftleben, U., Santhosh, M., McPartland, J., \u0026amp; Webb, S. J. (2018). Neurophysiological correlates of holistic face processing in adolescents with and without autism spectrum disorder. \u003cem\u003eJournal of Neurodevelopmental Disorders\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 27. https://doi.org/10.1186/s11689-018-9244-y\u003c/li\u003e\n\u003cli\u003eNicholson, T., Williams, D. M., Lind, S. E., Grainger, C., \u0026amp; Carruthers, P. (2021). Linking metacognition and mindreading: Evidence from autism and dual-task investigations. \u003cem\u003eJournal of Experimental Psychology: General\u003c/em\u003e, \u003cem\u003e150\u003c/em\u003e(2), 206\u0026ndash;220. https://doi.org/10.1037/xge0000878\u003c/li\u003e\n\u003cli\u003ePalermo, R., Rossion, B., Rhodes, G., Laguesse, R., Tez, T., Hall, B., Albonico, A., Malaspina, M., Daini, R., Irons, J., Al-Janabi, S., Taylor, L. C., Rivolta, D., \u0026amp; McKone, E. (2017). Do People Have Insight into their Face Recognition Abilities? \u003cem\u003eQuarterly Journal of Experimental Psychology\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(2), 218\u0026ndash;233. https://doi.org/10.1080/17470218.2016.1161058\u003c/li\u003e\n\u003cli\u003eRitvo, R. A., Ritvo, E. R., Guthrie, D., Ritvo, M. J., Hufnagel, D. H., McMahon, W., Tonge, B., Mataix-Cols, D., Jassi, A., Attwood, T., \u0026amp; Eloff, J. (2011). The Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R): A Scale to Assist the Diagnosis of Autism Spectrum Disorder in Adults: An International Validation Study. \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(8), 1076\u0026ndash;1089. https://doi.org/10.1007/s10803-010-1133-5\u003c/li\u003e\n\u003cli\u003eRivolta, D., Palermo, R., \u0026amp; Schmalzl, L. (2013). What is overt and what is covert in congenital prosopagnosia? \u003cem\u003eNeuropsychology Review\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(2), 111\u0026ndash;116. https://doi.org/10.1007/s11065-012-9223-0\u003c/li\u003e\n\u003cli\u003eRoid, G. H., \u0026amp; Miller, L. J. (1997). Leiter international performance scale-revised (Leiter-R). \u003cem\u003eWood Dale, IL: Stoelting\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eRouault, M., McWilliams, A., Allen, M. G., \u0026amp; Fleming, S. M. (2018). Human metacognition across domains: Insights from individual differences and neuroimaging. \u003cem\u003ePersonality neuroscience\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e, e17.\u003c/li\u003e\n\u003cli\u003eRuta, L., Mazzone, D., Mazzone, L., Wheelwright, S., \u0026amp; Baron-Cohen, S. (2012). The Autism-Spectrum Quotient--Italian version: A cross-cultural confirmation of the broader autism phenotype. \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(4), 625\u0026ndash;633. https://doi.org/10.1007/s10803-011-1290-1\u003c/li\u003e\n\u003cli\u003eRutter, M., Le Couteur, A., \u0026amp; Lord, C. (2003). Autism diagnostic interview-revised. \u003cem\u003eLos Angeles, CA: Western Psychological Services\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(2003), 30.\u003c/li\u003e\n\u003cli\u003eSawyer, A. C. P., Williamson, P., \u0026amp; Young, R. (2014). Metacognitive Processes in Emotion Recognition: Are They Different in Adults with Asperger\u0026rsquo;s Disorder? \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(6), 1373\u0026ndash;1382. https://doi.org/10.1007/s10803-013-1999-0\u003c/li\u003e\n\u003cli\u003eStantić, M., Ichijo, E., Catmur, C., \u0026amp; Bird, G. (2022). Face memory and face perception in autism. \u003cem\u003eAutism\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(1), 276\u0026ndash;280. https://doi.org/10.1177/13623613211027685\u003c/li\u003e\n\u003cli\u003eTagliente, S., Passarelli, M., D\u0026rsquo;Elia, V., Palmisano, A., Dunn, J. D., Masini, M., Lanciano, T., Curci, A., \u0026amp; Rivolta, D. (2023). Self-reported face recognition abilities moderately predict face-learning skills: Evidence from Italian samples. \u003cem\u003eHeliyon\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), e14125. https://doi.org/10.1016/j.heliyon.2023.e14125\u003c/li\u003e\n\u003cli\u003eTanaka, J. W., Wolf, J. M., Klaiman, C., Koenig, K., Cockburn, J., Herlihy, L., Brown, C., Stahl, S. S., South, M., McPartland, J. C., Kaiser, M. D., \u0026amp; Schultz, R. T. (2012). The perception and identification of facial emotions in individuals with autism spectrum disorders using the Let\u0026rsquo;s Face It! Emotion Skills Battery. \u003cem\u003eJournal of Child Psychology and Psychiatry, and Allied Disciplines\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(12), 1259\u0026ndash;1267. https://doi.org/10.1111/j.1469-7610.2012.02571.x\u003c/li\u003e\n\u003cli\u003eTaouki, I., Lallier, M., \u0026amp; Soto, D. (2022). The role of metacognition in monitoring performance and regulating learning in early readers. \u003cem\u003eMetacognition and Learning\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(3), 921\u0026ndash;948. https://doi.org/10.1007/s11409-022-09292-0\u003c/li\u003e\n\u003cli\u003eTeunisse, J.-P., \u0026amp; de Gelder, B. (2003). Face processing in adolescents with autistic disorder: The inversion and composite effects. \u003cem\u003eBrain and Cognition\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(3), 285\u0026ndash;294. https://doi.org/10.1016/S0278-2626(03)00042-3\u003c/li\u003e\n\u003cli\u003eTracy, J. L., Robins, R. W., Schriber, R. A., \u0026amp; Solomon, M. (2011). Is Emotion Recognition Impaired in Individuals with Autism Spectrum Disorders? \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(1), 102\u0026ndash;109. https://doi.org/10.1007/s10803-010-1030-y\u003c/li\u003e\n\u003cli\u003eVentura, M., Caff\u0026ograve;, A. O., Manippa, V., \u0026amp; Rivolta, D. (2024). Normative data of the Italian Famous Face Test. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 15276. https://doi.org/10.1038/s41598-024-66252-1\u003c/li\u003e\n\u003cli\u003eWechsler, D. (2012). \u003cem\u003eWechsler Adult Intelligence Scale\u0026mdash;Fourth Edition\u003c/em\u003e [Dataset]. https://doi.org/10.1037/t15169-000\u003c/li\u003e\n\u003cli\u003eWeigelt, S., Koldewyn, K., \u0026amp; Kanwisher, N. (2012). Face identity recognition in autism spectrum disorders: A review of behavioral studies. \u003cem\u003eNeuroscience \u0026amp; Biobehavioral Reviews\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(3), 1060\u0026ndash;1084.\u003c/li\u003e\n\u003cli\u003eWilliams, D. M., Bergstr\u0026ouml;m, Z., \u0026amp; Grainger, C. (2018). Metacognitive monitoring and the hypercorrection effect in autism and the general population: Relation to autism(-like) traits and mindreading. \u003cem\u003eAutism\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(3), 259\u0026ndash;270. https://doi.org/10.1177/1362361316680178\u003c/li\u003e\n\u003cli\u003eYardley, L., McDermott, L., Pisarski, S., Duchaine, B., \u0026amp; Nakayama, K. (2008). Psychosocial consequences of developmental prosopagnosia: A problem of recognition. \u003cem\u003eJournal of Psychosomatic Research\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(5), 445\u0026ndash;451. https://doi.org/10.1016/j.jpsychores.2008.03.013\u003c/li\u003e\n\u003cli\u003eZhao, Y., Li, J., Liu, X., Song, Y., Wang, R., Yang, Z., \u0026amp; Liu, J. (2016). Altered spontaneous neural activity in the occipital face area reflects behavioral deficits in developmental prosopagnosia. \u003cem\u003eNeuropsychologia\u003c/em\u003e, \u003cem\u003e89\u003c/em\u003e, 344\u0026ndash;355.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Bari Aldo Moro","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prosopagnosia, developmental disorders, face recognition, metacognition, social cognition, autism","lastPublishedDoi":"10.21203/rs.3.rs-5676642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5676642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate face recognition is crucial for navigating social interactions. While neurotypical individuals generally show no issues with face processing, persons with Autism Spectrum Disorder (PwASD) often exhibit impairments in this area. This study explores the extent of these face recognition deficits in autistic adults, focusing on their ability to identify famous faces, along with the awareness (metacognition) of their face recognition skills. Using the Italian Famous Face Test (IT-FFT) and the Prosopagnosia Index-20 (PI-20), to compare face recognition performance and self-awareness of face recognition abilities between 50 neurotypical individuals (NTs) and 49 PwASD. Our results showed that PwASD had significantly lower face identification scores and greater difficulties recognizing famous faces than NT participants. Additionally, PwASD reported more face recognition challenges on the PI-20, highlighting their awareness of these deficits. These findings suggest that face recognition impairments in PwASD extend to famous faces and underscore the importance of further research to explore targeted interventions aimed at improving different aspects of face recognition processes in ASD.\u003c/p\u003e","manuscriptTitle":"Unveiling Face Recognition Challenges and Awareness in Autism Spectrum Disorder: Insights from the Italian Famous Face Test (IT-FFT)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-23 16:29:17","doi":"10.21203/rs.3.rs-5676642/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13644606-5a2a-4859-b490-9081519a9021","owner":[],"postedDate":"December 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41824271,"name":"Psychology"}],"tags":[],"updatedAt":"2024-12-23T16:29:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-23 16:29:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5676642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5676642","identity":"rs-5676642","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}