ReviewNature human behaviour2021
Capturing the objects of vision with neural networks.
Review in Nature human behaviour, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed.
- Modeling attention and binding in the brain through bidirectional recurrent gating.Nature communications · 2026Article
- Perceptual and conceptual influences on memory judgments.Communications psychology · 2026Article
- What makes memories vivid?Journal of experimental psychology. General · 2026Article
- Data-efficient image transformer for landscape character classification and visual comfort prediction in Chinese hospitals.Frontiers in artificial intelligence · 2026Article
- Grouping signals in primate visual cortex.Neuron · 2025Article
- The influence of a moving object's location on object identity judgments.Journal of experimental psychology. Human perception and performance · 2025Article
- Beyond binding: from modular to natural vision.Trends in cognitive sciences · 2025Review
- Parallelizing analog in-sensor visual processing with arrays of gate-tunable silicon photodetectors.Nature communications · 2025Article
- Lightweight error-tolerant edge detection using memristor-enabled stochastic computing.Nature communications · 2025Article
- Efficient Carbon-Based Optoelectronic Synapses for Dynamic Visual Recognition.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Unraveling the complexity of rat object vision requires a full convolutional network and beyond.Patterns (New York, N.Y.) · 2025Article
- Orthogonal neural representations support perceptual judgments of natural stimuli.Scientific reports · 2025Article
- A robotics-inspired scanpath model reveals the importance of uncertainty and semantic object cues for gaze guidance in dynamic scenes.Journal of vision · 2025Article
- Capturing forceful interaction with deformable objects using a deep learning-powered stretchable tactile array.Nature communications · 2024Article
- A Multifunctional Tactile Sensory System for Robotic Intelligent Identification and Manipulation Perception.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Factorized visual representations in the primate visual system and deep neural networks.eLife · 2024Article
- Orthogonal neural representations support perceptual judgements of natural stimuli.bioRxiv : the preprint server for biology · 2024Article
- The attentive reconstruction of objects facilitates robust object recognition.PLoS computational biology · 2024Article
- Visual Recognition Memory of Scenes Is Driven by Categorical, Not Sensory, Visual Representations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Grouping cells in primate visual cortex.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human visual perception carves a scene at its physical joints, decomposing the world into objects, which are selectively attended, tracked and predicted as we engage our surroundings. Object representations emancipate perception from the sensory input, enabling us to keep in mind that which is out of sight and to use perceptual content as a basis for action and symbolic cognition. Human behavioural studies have documented how object representations emerge through grouping, amodal completion, proto-objects and object files. By contrast, deep neural network models of visual object recognition remain largely tethered to sensory input, despite achieving human-level performance at labelling objects. Here, we review related work in both fields and examine how these fields can help each other. The cognitive literature provides a starting point for the development of new experimental tasks that reveal mechanisms of human object perception and serve as benchmarks driving the development of deep neural network models that will put the object into object recognition.
Indexed as
Identifiers
34545237What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.