Over the past years, much interest has been devoted to understanding how individuals differ in their ability to process facial identity. Fast periodic visual stimulation (FPVS) is a promising technique to obtain objective and highly sensitive neural correlates of face processing across various populations, from infants to neuropsychological patients. Here, we use FPVS to investigate how neural face identity discrimination varies in amplitude and topography across observers. To ascertain more detailed inter-individual differences, we parametrically manipulated the visual input fixated by observers across ten viewing positions (VPs). Specifically, we determined the inter-session reliability of VP-dependent neural face discrimination responses, both across and within observers (6-month inter-session interval). All observers exhibited idiosyncratic VP-dependent neural response patterns, with reliable individual differences in terms of response amplitude for the majority of VPs. Importantly, the topographical reliability varied across VPs and observers, the majority of which exhibited reliable responses only for specific VPs. Crucially, this topographical reliability was positively correlated with the response magnitude over occipito-temporal regions: observers with stronger responses also displayed more reliable response topographies. Our data extend previous findings of idiosyncrasies in visuo-perceptual processing. They highlight the need to consider intra-individual neural response reliability in order to better understand the functional role(s) and underlying basis of such inter-individual differences.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.neuroimage.2019.01.023 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting.
Sci Rep
December 2024
Department of Civil Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, have become popular for modeling traffic sensor networks. However, traditional graph convolutional networks (GCNs) face limitations in capturing long-range spatial correlations, which can hinder accurate long-term predictions.
View Article and Find Full Text PDFFunctional resilience of the neural visual recognition system post-pediatric occipitotemporal resection.
iScience
December 2024
Department of Psychology and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Neural representations for visual stimuli typically emerge with a bilateral distribution across occipitotemporal cortex (OTC)? Pediatric patients undergoing unilateral OTC resection offer an opportunity to evaluate whether representations for visual stimulus individuation can sufficiently develop in a single OTC. Here, we assessed the non-resected hemisphere of patients with pediatric resection within ( = 9) and outside ( = 12) OTC, as well as healthy controls' two hemispheres ( = 21). Using functional magnetic resonance imaging, we mapped category selectivity (CS), and representations for visual stimulus individuation (for faces, objects, and words) with repetition suppression (RS).
View Article and Find Full Text PDFDynamic domain generalization for medical image segmentation.
Neural Netw
December 2024
School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China; Suzhou Research Institute of Shandong University, Suzhou, 215123, China. Electronic address:
Domain Generalization-based Medical Image Segmentation (DGMIS) aims to enhance the robustness of segmentation models on unseen target domains by learning from fully annotated data across multiple source domains. Despite the progress made by traditional DGMIS methods, they still face several challenges. First, most DGMIS approaches rely on static models to perform inference on unseen target domains, lacking the ability to dynamically adapt to samples from different target domains.
View Article and Find Full Text PDFAge- and sex-related differences in social competence and emotion labeling in pre-adolescence.
Dev Cogn Neurosci
December 2024
Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands.
Identification of facial expressions is important to navigate social interactions and associates with developmental outcomes. It is presumed that social competence, behavioral emotion labeling and neural emotional face processing are related, but this has rarely been studied. Here, we investigated these interrelations and their associations with age and sex, in the YOUth cohort (1055 children, 8-11 years old).
View Article and Find Full Text PDFEnhancing consistency and mitigating bias: A data replay approach for incremental learning.
Neural Netw
December 2024
Department of Automation, Tsinghua University, Beijing 100084, China. Electronic address:
Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks, as old data from previous tasks is unavailable when learning a new task. To address this, some methods propose replaying data from previous tasks during new task learning, typically using extra memory to store replay data. However, it is not expected in practice due to memory constraints and data privacy issues.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!