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Despite an increasing interest in detecting early signs of Autism Spectrum Disorders (ASD), the pathogenesis of the social impairments characterizing ASD is still largely unknown. Atypical visual attention to social stimuli is a potential early marker of the social and communicative deficits of ASD. Some authors hypothesized that such impairments are present from birth, leading to a decline in the subsequent typical functioning of the learning-mechanisms. Others suggested that these early deficits emerge during the transition from subcortically to cortically mediated mechanisms, happening around 2-3 months of age. The present study aimed to provide additional evidence on the origin of the early visual attention disturbance that seems to characterize infants at high risk (HR) for ASD. Four visual preference tasks were used to investigate social attention in 4-month-old HR, compared to low-risk (LR) infants of the same age. Visual attention differences between HR and LR infants emerged only for stimuli depicting a direct eye-gaze, compared to an adverted eye-gaze. Specifically, HR infants showed a significant visual preference for the direct eye-gaze stimulus compared to LR infants, which may indicate a delayed development of the visual preferences normally observed at birth in typically developing infants. No other differences were found between groups. Results are discussed in the light of the hypotheses on the origins of early social visual attention impairments in infants at risk for ASD.
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http://dx.doi.org/10.1038/s41598-021-95418-4 | DOI Listing |
Exp Brain Res
September 2025
Siena Brain Investigation and Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Siena, Italy.
Postdiction is a perceptual phenomenon where the perception of an earlier stimulus is influenced by a later one. This effect is commonly studied using the 'rabbit illusion', in which temporally regular, but spatially irregular, stimuli are perceived as equidistant. While previous research has focused on short inter-stimulus intervals (100-200 ms), the role of longer intervals, which may engage late attentional processes, remains unexplored.
View Article and Find Full Text PDFCogn Neuropsychiatry
September 2025
Department of Psychology, Faculty of Arts, Comenius University in Bratislava, Bratislava, Slovakia.
Introduction: Schizophrenia (SCZ) spectrum is characterised by aberrant processing of social cues. However, little is known about the specific stages of visual attention and their connection to subclinical and clinical symptoms in psychosis. This study aimed to investigate the visual processing of social and non-social parts of naturalistic scenes, and its link to positive and negative symptoms.
View Article and Find Full Text PDFAm Psychol
September 2025
State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences.
In cluttered and complex natural scenes, selective attention enables the visual system to prioritize relevant information. This process is guided not only by perceptual cues but also by imagined ones. The current research extends the imagery-induced attentional bias to the unconscious level and reveals its cross-category applicability between different social cues (e.
View Article and Find Full Text PDFElife
September 2025
Center for Mind and Brain, University of California, Davis, Davis, United States.
Visual search relies on the ability to use information about the target in working memory to guide attention and make target-match decisions. The 'attentional' or 'target' template is thought to be encoded within an inferior frontal junction (IFJ)-visual attentional network. While this template typically contains veridical target features, behavioral studies have shown that target-associated information, such as statistically co-occurring object pairs, can also guide attention.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
School of Information Science and Automation, Northeastern University, Shenyang, 110819 China.
Accurate prediction of drug-target interactions (DTIs) is crucial for improving the efficiency and success rate of drug development. Despite recent advancements, existing methods often fail to leverage interaction features at multiple granular levels, resulting in suboptimal data utilization and limited predictive performance. To address these challenges, we propose CF-DTI, a coarse-to-fine drug-target interaction model that integrates both coarse-grained and fine-grained features to enhance predictive accuracy.
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