ArticlePLoS computational biology2019
Beyond core object recognition: Recurrent processes account for object recognition under occlusion.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Systematic image perturbations reveal persistent gaps between human and machine vision.iScience · 2026Article
- The neural processes of illusory occlusion in object recognition.Journal of vision · 2026Article
- Recurrent connections facilitate occluded object recognition by explaining-away.Nature communications · 2026Article
- Effects of Partial Occlusion on Response Dynamics and Interregional Processing within Primate Superior Temporal Sulcus.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Top-down perceptual inference shaping the activity of early visual cortex.Nature communications · 2025Article
- Recurrent issues with deep neural network models of visual recognition.Scientific reports · 2025Article
- Neural oscillations and top-down connectivity are modulated by object-scene congruency.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Neuronal signatures of successful one-shot memory in mid-level visual cortex.bioRxiv : the preprint server for biology · 2025Article
- Recurrence affects the geometry of visual representations across the ventral visual stream in the human brain.PLoS biology · 2025Article
- An fMRI Dataset on Occluded Image Interpretation for Human Amodal Completion Research.Scientific data · 2025Article
- Misspelled-Word Reading Modulates Late Cortical Dynamics.Human brain mapping · 2025Article
- Unraveling the complexity of rat object vision requires a full convolutional network and beyond.Patterns (New York, N.Y.) · 2025Article
- How does the primate brain combine generative and discriminative computations in vision?ArXiv · 2024Article
- Seeing social interactions.Trends in cognitive sciences · 2023Review
- Recurrent connectivity supports higher-level visual and semantic object representations in the brain.Communications biology · 2023Article
- Brain Functional Representation of Highly Occluded Object Recognition.Brain sciences · 2023Article
- Hybrid predictive coding: Inferring, fast and slow.PLoS computational biology · 2023Article
- The effect of familiarity on behavioral oscillations in face perception.Scientific reports · 2023Article
- Article
- Independent spatiotemporal effects of spatial attention and background clutter on human object location representations.NeuroImage · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the other hand, object recognition under more challenging conditions (i.e. beyond the core recognition problem) is less characterized. One such example is object recognition under occlusion. It is unclear to what extent feedforward and recurrent processes contribute in object recognition under occlusion. Furthermore, we do not know whether the conventional deep neural networks, such as AlexNet, which were shown to be successful in solving core object recognition, can perform similarly well in problems that go beyond the core recognition. Here, we characterize neural dynamics of object recognition under occlusion, using magnetoencephalography (MEG), while participants were presented with images of objects with various levels of occlusion. We provide evidence from multivariate analysis of MEG data, behavioral data, and computational modelling, demonstrating an essential role for recurrent processes in object recognition under occlusion. Furthermore, the computational model with local recurrent connections, used here, suggests a mechanistic explanation of how the human brain might be solving this problem.
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