Journal
JOURNAL OF NEUROSCIENCE
Volume 39, Issue 33, Pages 6513-6525Publisher
SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.1714-18.2019
Keywords
animacy; deep neural networks; fMRI; MVPA; object representations; occipitotemporal cortex
Categories
Funding
- FWO (Fonds Wetenschappelijk Onderzoek) [12S1317N, 1505518N]
- Horizon 2020 via a FWO [PEGASUS]2 Marie Sklodowska-Curie fellowship [12T9217N]
- European Research Council [ERC-2011-StG-284101]
- KU Leuven Research Council [C14/16/031]
- Hercules grant [ZW11_10]
- Excellence of Science grant [G0E8718N]
- [IUAP-P7/11]
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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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