ReviewTrends in cognitive sciences2015
Understanding What We See: How We Derive Meaning From Vision.
Review in Trends in cognitive sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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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.
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Who cites it
60 citing papers in PubMed.
- The contribution of semantic control to elaborative episodic encoding.Memory & cognition · 2026Article
- Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding.Journal of vision · 2026Article
- Semantic properties and categorization norms for the 260 Snodgrass and Vanderwart objects: A 45-year conceptual update to a classic set.Behavior research methods · 2026Article
- Bridging Continuous and Discrete Models of the Anterior Temporal Lobe via Cortical Gradients.Research square · 2026Article
- Controlled Semantic Cognition: Precision Recordings Converge With in Silico Experiments to Reveal the Inner Workings of the Anterior Temporal Lobe Hub.Neurobiology of language (Cambridge, Mass.) · 2026Review
- Not all objects are created equal: The object benefit in visual working memory is supported by greater recollection-like memory, but only for memorable objects.Memory & cognition · 2025Article
- Object fine-grained discrimination as a sensitive cognitive marker of transentorhinal integrity.Communications biology · 2025Article
- Fabrication of a 3D Corneal Model Using Collagen Bioink and Human Corneal Stromal Cells.Journal of functional biomaterials · 2025Article
- The Scope and Limits of Fine-Grained Image and Category Information in the Ventral Visual Pathway.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- The brain prioritizes the basic level of object category abstraction.Scientific reports · 2025Article
- Clinical Manifestations and Neural Basis of Semantic Dementia: Converging Evidences From Brain Imaging Studies.Clinical interventions in aging · 2025Review
- Object color knowledge representation occurs in the macaque brain despite the absence of a developed language system.PLoS biology · 2024Article
- Reconstructing Spatiotemporal Trajectories of Visual Object Memories in the Human Brain.eNeuro · 2024Article
- MEG Evidence That Modality-Independent Conceptual Representations Contain Semantic and Visual Features.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Using deep neural networks to disentangle visual and semantic information in human perception and memory.Nature human behaviour · 2024Article
- Representation of event and object concepts in ventral anterior temporal lobe and angular gyrus.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- How to grow a self: development of self-representation in the Bayesian brain.Frontiers in human neuroscience · 2024Article
- Explainable artificial intelligence (xAI) in neuromarketing/consumer neuroscience: an fMRI study on brand perception.Frontiers in human neuroscience · 2024Article
- Recurrent connectivity supports higher-level visual and semantic object representations in the brain.Communications biology · 2023Article
- Effects of Aging on Successful Object Encoding: Enhanced Semantic Representations Compensate for Impaired Visual Representations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Recognising objects goes beyond vision, and requires models that incorporate different aspects of meaning. Most models focus on superordinate categories (e.g., animals, tools) which do not capture the richness of conceptual knowledge. We argue that object recognition must be seen as a dynamic process of transformation from low-level visual input through categorical organisation to specific conceptual representations. Cognitive models based on large normative datasets are well-suited to capture statistical regularities within and between concepts, providing both category structure and basic-level individuation. We highlight recent research showing how such models capture important properties of the ventral visual pathway. This research demonstrates that significant advances in understanding conceptual representations can be made by shifting the focus from studying superordinate categories to basic-level concepts.
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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.