Untangling the animacy organization of occipitotemporal cortex

Ritchie, J. Brendan; Zeman, Astrid; Bosmans, Joyce; Sun, Shuo; Verhaegen, Kirsten; Beeck, Hans Op de

doi:10.1101/2020.07.17.206896

Cited by 4 publications

(3 citation statements)

References 86 publications

(48 reference statements)

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“…As an example, view-invariant features represented in face- [ 54 , 55 ] and hand-selective regions [ 56 , 57 ] reflect domain-specific computations: the former to support identity recognition [ 58 ], the latter to support action understanding [ 56 ]. In agreement, our results show that in addition to a large division between animal and scene representations, within each domain, representational content reflects the type of computations these networks support: animacy features in animal-selective areas [ 4 , 5 , 59 ] and layout navigational properties in scene-selective areas [ 7 , 60 ]. We can show this representational diversity because in our study we included separate behavioral-relevant dimensions for objects (i.e., animacy continuum) and background scenes (i.e., navigational properties), while this was typically not done in previous studies.…”

Section: Discussionsupporting

confidence: 83%

“…For instance, in scene selective areas, the degree of navigational layout well characterizes its representational content which is relevant for naviation [ 7 , 8 ]. In a similar fashion, in animal selective areas, the degree of animacy [ 47 , 48 ], and the animal-specific features [ 4 , 5 ] might be relevant to support social-related computations. In our results, DCNN’s mid-layers show domain division for animals and scenes ( Fig 3A ), but does this division embed rich domain-specific object spaces like those observed in the human visual cortex?…”

Section: Resultsmentioning

confidence: 99%

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The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities

et al. 2023

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Human vision is still largely unexplained. Computer vision made impressive progress on this front, but it is still unclear to which extent artificial neural networks approximate human object vision at the behavioral and neural levels. Here, we investigated whether machine object vision mimics the representational hierarchy of human object vision with an experimental design that allows testing within-domain representations for animals and scenes, as well as across-domain representations reflecting their real-world contextual regularities such as animal-scene pairs that often co-occur in the visual environment. We found that DCNNs trained in object recognition acquire representations, in their late processing stage, that closely capture human conceptual judgements about the co-occurrence of animals and their typical scenes. Likewise, the DCNNs representational hierarchy shows surprising similarities with the representational transformations emerging in domain-specific ventrotemporal areas up to domain-general frontoparietal areas. Despite these remarkable similarities, the underlying information processing differs. The ability of neural networks to learn a human-like high-level conceptual representation of object-scene co-occurrence depends upon the amount of object-scene co-occurrence present in the image set thus highlighting the fundamental role of training history. Further, although mid/high-level DCNN layers represent the category division for animals and scenes as observed in VTC, its information content shows reduced domain-specific representational richness. To conclude, by testing within- and between-domain selectivity while manipulating contextual regularities we reveal unknown similarities and differences in the information processing strategies employed by human and artificial visual systems.

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Section: Discussionsupporting

confidence: 83%

Section: Resultsmentioning

confidence: 99%

The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities

et al. 2023

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“…Neural RDMs for the different ROIs, for each subject, were constructed using the (non-crossvalidated) Mahalanobis distance as the dissimilarity metric, characterized as the pairwise distance along the discriminant between conditions for the beta weight patterns in an ROI (Ritchie and Op de Beeck, 2019;Walther et al 2016). To assess the between-subject reliability of these RDMs, the RDM of one subject was left-out, and those of the remaining subjects averaged and Pearson's r correlated with the left-out subject's RDM.…”

Section: Representational Similarity Analysismentioning

confidence: 99%

Untangling the Animacy Organization of Occipitotemporal Cortex

et al. 2021

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Some of the most impressive functional specialization in the human brain is found in occipitotemporal cortex (OTC), where several areas exhibit selectivity for a small number of visual categories, such as faces and bodies, and spatially cluster based on stimulus animacy.Previous studies suggest this animacy organization reflects the representation of an intuitive taxonomic hierarchy, distinct from the presence of face-and body-selective areas in OTC.Using human fMRI, we investigated the independent contribution of these two factors -the face-body division and taxonomic hierarchy -in accounting for the animacy organization of OTC, and whether they might also be reflected in the architecture of several deep neural networks. We found that graded selectivity based on animal resemblance to human faces and bodies masquerades as an apparent animacy continuum, which suggests that taxonomy is not a separate factor underlying the organization of the ventral visual pathway.

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The role of animal faces in the animate-inanimate distinction in the ventral temporal cortex

Proklova

Goodale

2020

Preprint

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Animate and inanimate objects elicit distinct response patterns in the human ventral temporal cortex (VTC), but the exact features driving this distinction are still poorly understood. One prominent feature that distinguishes typical animals from inanimate objects and that could potentially explain the animate-inanimate distinction in the VTC is the presence of a face. In the current fMRI study, we investigated this possibility by creating a stimulus set that included animals with faces, faceless animals, and inanimate objects, carefully matched in order to minimize other visual differences. We used both searchlight-based and ROI-based representational similarity analysis (RSA) to test whether the presence of a face explains the animate-inanimate distinction in the VTC. The searchlight analysis revealed that when animals with faces were removed from the analysis, the animate-inanimate distinction almost disappeared. The ROI-based RSA revealed a similar pattern of results, but also showed that, even in the absence of faces, information about agency (a combination of animal’s ability to move and think) is present in parts of the VTC that are sensitive to animacy. Together, these analyses showed that animals with faces do elicit a stronger animate/inanimate response in the VTC, but that this effect is driven not by faces per se, or the visual features of faces, but by other factors that correlate with face presence, such as the capacity for self-movement and thought. In short, the VTC appears to treat the face as a proxy for agency, a ubiquitous feature of familiar animals.Significance StatementMany studies have shown that images of animals are processed differently from inanimate objects in the human brain, particularly in the ventral temporal cortex (VTC). However, what features drive this distinction remains unclear. One important feature that distinguishes many animals from inanimate objects is a face. Here, we used fMRI to test whether the animate/inanimate distinction is driven by the presence of faces. We found that the presence of faces did indeed boost activity related to animacy in the VTC. A more detailed analysis, however, revealed that it was the association between faces and other attributes such as the capacity for self-movement and thinking, not the faces per se, that was driving the activity we observed.

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Untangling the animacy organization of occipitotemporal cortex

Cited by 4 publications

References 86 publications

The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities

The representational hierarchy in human and artificial visual systems in the presence of object-scene regularities

Untangling the Animacy Organization of Occipitotemporal Cortex

The role of animal faces in the animate-inanimate distinction in the ventral temporal cortex

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