Correction to "Understanding the nature of face processing in early autism: A prospective study" by Tye et al. (2022).

Reports an error in "Understanding the nature of face processing in early autism: A prospective study" by Charlotte Tye, Giorgia Bussu, Teodora Gliga, Mayada Elsabbagh, Greg Pasco, Kristinn Johnsen, Tony Charman, Emily J. H. Jones, Jan Buitelaar and Mark H. Johnson (, 2022[Aug], Vol 131[6], 542-555; see record 2022-84900-002). In the article (, 2022, Vol. 131, No. 6, pp. 542-555, https://doi .org/10.1037/abn0000648), in the second paragraph of the Group-Level Differences section of the Results, in the sentence beginning "Specifically, the EL-ASD group had longer P1 latency to gaze shifting away versus toward …" the phrase "away versus toward" should have read "toward versus away." In the same paragraph, "Finally, there was enhanced P400 amplitude to gaze shifting . . ." should have read "Finally, the TL group showed enhanced P400 amplitude to gaze shifting away versus toward the viewer . . . . " In Panels A, B, and C of Figure 2, the TL and EL-no ASD groups are incorrectly keyed to each other's colors. The corrected figure is provided. (The following abstract of the original article appeared in record 2022-84900-002.) Dimensional approaches to psychopathology interrogate the core neurocognitive domains interacting at the individual level to shape diagnostic symptoms. Embedding this approach in prospective longitudinal studies could transform our understanding of the mechanisms underlying neurodevelopmental disorders. Such designs require us to move beyond traditional group comparisons and determine which domain-specific alterations apply at the level of the individual, and whether they vary across distinct phenotypic subgroups. As a proof of principle, this study examines how the domain of face processing contributes to the emergence of autism spectrum disorder (ASD). We used an event-related potentials (ERPs) task in a cohort of 8-month-old infants with ( = 148) and without ( = 68) an older sibling with ASD, and combined traditional case-control comparisons with machine-learning techniques for prediction of social traits and ASD diagnosis at 36 months, and Bayesian hierarchical clustering for stratification into subgroups. A broad profile of alterations in the time-course of neural processing of faces in infancy was predictive of later ASD, with a strong convergence in ERP features predicting social traits and diagnosis. We identified two main subgroups in ASD, defined by distinct patterns of neural responses to faces, which differed on later sensory sensitivity. Taken together, our findings suggest that individual differences between infants contribute to the diffuse pattern of alterations predictive of ASD in the first year of life. Moving from group-level comparisons to pattern recognition and stratification can help to understand and reduce heterogeneity in clinical cohorts, and improve our understanding of the mechanisms that lead to later neurodevelopmental outcomes. (PsycInfo Database Record (c) 2025 APA, all rights reserved).