ArticleProceedings of the National Academy of Sciences of the United States of America2018
Multiplicative mixing of object identity and image attributes in single inferior temporal neurons.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Viewpoint dependence and scene context effects generalize to depth rotated three-dimensional objects.Journal of vision · 2023Article
- Using compositionality to understand parts in whole objects.The European journal of neuroscience · 2022Review
- Direct comparison of contralateral bias and face/scene selectivity in human occipitotemporal cortex.Brain structure & function · 2022Article
- A separable neural code in monkey IT enables perfect CAPTCHA decoding.Journal of neurophysiology · 2022Article
- Joint encoding of facial identity, orientation, gaze, and expression in the middle dorsal face area.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Stable readout of observed actions from format-dependent activity of monkey's anterior intraparietal neurons.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Mechanisms underlying gain modulation in the cortex.Nature reviews. Neuroscience · 2020Review
- Are you from North or South India? A hard face-classification task reveals systematic representational differences between humans and machines.Journal of vision · 2019Article
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Authors and funding
2 authors.
Funding
Abstract
Object recognition is challenging because the same object can produce vastly different images, mixing signals related to its identity with signals due to its image attributes, such as size, position, rotation, etc. Previous studies have shown that both signals are present in high-level visual areas, but precisely how they are combined has remained unclear. One possibility is that neurons might encode identity and attribute signals multiplicatively so that each can be efficiently decoded without interference from the other. Here, we show that, in high-level visual cortex, responses of single neurons can be explained better as a product rather than a sum of tuning for object identity and tuning for image attributes. This subtle effect in single neurons produced substantially better population decoding of object identity and image attributes in the neural population as a whole. This property was absent both in low-level vision models and in deep neural networks. It was also unique to invariances: when tested with two-part objects, neural responses were explained better as a sum than as a product of part tuning. Taken together, our results indicate that signals requiring separate decoding, such as object identity and image attributes, are combined multiplicatively in IT neurons, whereas signals that require integration (such as parts in an object) are combined additively.
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