ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2019
The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Animate Categories Show Higher Cross-Duration Representational Selectivity in Ventral Occipitotemporal Cortex Under Brief Visual Input.Brain sciences · 2026Article
- Image type reveals evolutionarily shaped perceptual and conceptual mechanisms of pareidolia.Scientific reports · 2026Article
- Coordinating multiple mental faculties during learning.Scientific reports · 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
- A computational deep learning investigation of animacy perception in the human brain.Communications biology · 2024Article
- Brief category learning distorts perceptual space for complex scenes.Psychonomic bulletin & review · 2024Article
- Language with vision: A study on grounded word and sentence embeddings.Behavior research methods · 2024Article
- Movement trajectories as a window into the dynamics of emerging neural representations.Scientific reports · 2024Article
- Empirically Identifying and Computationally Modeling the Brain-Behavior Relationship for Human Scene Categorization.Journal of cognitive neuroscience · 2023Article
- Effects of AR-Based Home Appliance Agents on User's Perception and Maintenance Behavior.Sensors (Basel, Switzerland) · 2023Article
- Category Trumps Shape as an Organizational Principle of Object Space in the Human Occipitotemporal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities.PLoS computational biology · 2023Article
- Neural representations of the perception of handwritten digits and visual objects from a convolutional neural network compared to humans.Human brain mapping · 2023Article
- Deep Neural Networks and Visuo-Semantic Models Explain Complementary Components of Human Ventral-Stream Representational Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Pulvinar Response Profiles and Connectivity Patterns to Object Domains.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- The representational dynamics of the animal appearance bias in human visual cortex are indicative of fast feedforward processing.Imaging neuroscience (Cambridge, Mass.) · 2023Article
- A broadly tuned network for affective body language in the macaque brain.Science advances · 2022Article
- Disentangling five dimensions of animacy in human brain and behaviour.Communications biology · 2022Article
- Images of the unseen: extrapolating visual representations for abstract and concrete words in a data-driven computational model.Psychological research · 2022Article
- Face neurons encode nonsemantic features.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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4 authors.
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Abstract
Recent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual and cognitive levels, often shaped by learning history and evolutionary constraints. Here, we explore one such perceptual phenomenon, perceiving animacy, and use the performance of neural networks as a benchmark. We performed an fMRI study that dissociated object appearance (what an object looks like) from object category (animate or inanimate) by constructing a stimulus set that includes animate objects (e.g., a cow), typical inanimate objects (e.g., a mug), and, crucially, inanimate objects that look like the animate objects (e.g., a cow mug). Behavioral judgments and deep neural networks categorized images mainly by animacy, setting all objects (lookalike and inanimate) apart from the animate ones. In contrast, activity patterns in ventral occipitotemporal cortex (VTC) were better explained by object appearance: animals and lookalikes were similarly represented and separated from the inanimate objects. Furthermore, the appearance of an object interfered with proper object identification, such as failing to signal that a cow mug is a mug. The preference in VTC to represent a lookalike as animate was even present when participants performed a task requiring them to report the lookalikes as inanimate. In conclusion, VTC representations, in contrast to neural networks, fail to represent objects when visual appearance is dissociated from animacy, probably due to a preferred processing of visual features typical of animate objects.
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