thông tin chi tiết - Computational neuroscience - # Representation of Object Real-World Size

Human Brain and Artificial Neural Networks Reveal Distinct Representations of Object Real-World Size in Natural Images

Q: How do the representations of object real-world size, retinal size, and real-world depth interact and influence each other in the human brain and artificial models

In the study, the representations of object real-world size, retinal size, and real-world depth were investigated in both the human brain and artificial models. The findings revealed a distinct timeline of processing for these visual features. Real-world depth information was represented first, followed by retinal size, and finally, real-world size. This suggests that the human brain has dissociated mechanisms for processing these features, with real-world size emerging as a stable and higher-level dimension in object space. In artificial models, such as ResNet and CLIP, similar patterns were observed. The early layers of the models primarily encoded retinal size information, while real-world size representations were more prominent in the late layers. This aligns with the hierarchical processing observed in the human brain, where higher-level information is needed to form representations of object real-world size. The interactions between these representations in the human brain and artificial models indicate a complex interplay between low-level visual features, semantic information, and object size perception. The findings suggest that object real-world size is a multifaceted dimension that integrates both visual and semantic information to form a comprehensive representation of objects.

Q: What are the potential limitations of the current study, and how could future research address them to further our understanding of object size and depth processing

One potential limitation of the current study is the reliance on 2D images to investigate object size and depth processing. While the use of naturalistic images provided ecologically valid stimuli, the study focused on perceived real-world size and depth, which may differ from absolute physical size and depth. Future research could incorporate 3D stimuli or virtual reality environments to explore how depth perception influences object size representation in a more immersive setting. Another limitation is the focus on a specific set of visual features (real-world size, retinal size, real-world depth) without considering other potentially relevant dimensions of object space. Future studies could explore how these features interact with additional dimensions, such as shape, texture, or animacy, to provide a more comprehensive understanding of object representation in the human brain and artificial models. To address these limitations, future research could also investigate the neural mechanisms underlying the integration of different visual features in object processing. By using advanced neuroimaging techniques, such as fMRI or MEG, researchers could examine the neural networks involved in processing object size and depth information and how these networks interact to form a coherent representation of objects in the visual system.

Q: Given the importance of object size and depth information for visual perception and cognition, how might these findings inform the development of more brain-inspired artificial intelligence systems

The findings from this study have significant implications for the development of more brain-inspired artificial intelligence systems, particularly in the field of computer vision. By understanding how the human brain processes object size and depth information, researchers can design AI models that more closely mimic the neural mechanisms of visual perception. One key insight is the importance of hierarchical processing in both the human brain and artificial models. By incorporating multiple layers that encode different levels of visual and semantic information, AI systems can better capture the complexity of object representation. This hierarchical approach can lead to more robust and accurate object recognition systems that are capable of understanding objects in their real-world context. Additionally, the study highlights the role of semantic information in object size perception. By integrating semantic embedding models, such as Word2Vec, into AI systems, researchers can enhance the ability of these models to understand the conceptual dimensions of objects. This integration of visual and semantic information can lead to more sophisticated AI systems that can interpret objects in a more human-like manner. Overall, the findings from this study can guide the development of AI systems that not only excel in object recognition tasks but also demonstrate a deeper understanding of the underlying principles of visual perception. By bridging the gap between human brain processing and artificial intelligence, researchers can create more advanced and brain-inspired AI systems that push the boundaries of computer vision technology.

Khái niệm cốt lõi

Human brains and artificial neural networks contain distinct representations of object real-world size, retinal size, and real-world depth, with real-world size emerging as a stable and higher-level dimension in object space.

Tóm tắt

The study used a combination of high-temporal resolution human EEG data, naturalistic visual stimuli, and artificial neural networks to investigate the neural representations of object real-world size, retinal size, and real-world depth.

Key findings:

EEG data revealed a representational timeline, with real-world depth represented first, followed by retinal size, and then real-world size.
Artificial neural networks (ANNs) also showed dissociable representations of these visual features, with real-world size emerging more strongly in later layers.
Removing background context from the images reduced the representations of retinal size and real-world depth in ANNs, but the real-world size representation was more stable.
A semantic language model (Word2Vec) also showed a significant representation of real-world size, suggesting it is a higher-level dimension in object space.

The study provides a detailed characterization of the temporal dynamics and computational mechanisms underlying the processing of different object size and depth properties in both biological and artificial visual systems.

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Thống kê

"The range of possible size ratings was from 0 to 519 in their online size rating task, with the actual mean ratings across subjects ranging from 100.03 ('sand') to 423.09 ('subway')."
"We calculated the perceived depth based on the measured retinal size index and behavioral real-world size ratings, such that real-world depth / visual image depth = real-world size / retinal size."

Trích dẫn

"Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective."
"Consistent with the human EEG findings, we also successfully disentangled representation of object real-world size from retinal size and real-world depth in all three types of artificial neural networks (visual-only ResNet, visual-language CLIP, and language-only Word2Vec)."
"Even though the magnitude of representational similarity for object real-world size decreased when we removed the background, this high-level representation was not entirely eliminated."

Thông tin chi tiết chính được chắt lọc từ

Human EEG and artificial neural networks reveal disentangled representations of object real-world size in natural images

by Lu,Z., Golom... lúc www.biorxiv.org 08-21-2023

https://www.biorxiv.org/content/10.1101/2023.08.19.553999v5

Yêu cầu sâu hơn

How do the representations of object real-world size, retinal size, and real-world depth interact and influence each other in the human brain and artificial models

In the study, the representations of object real-world size, retinal size, and real-world depth were investigated in both the human brain and artificial models. The findings revealed a distinct timeline of processing for these visual features. Real-world depth information was represented first, followed by retinal size, and finally, real-world size. This suggests that the human brain has dissociated mechanisms for processing these features, with real-world size emerging as a stable and higher-level dimension in object space.
In artificial models, such as ResNet and CLIP, similar patterns were observed. The early layers of the models primarily encoded retinal size information, while real-world size representations were more prominent in the late layers. This aligns with the hierarchical processing observed in the human brain, where higher-level information is needed to form representations of object real-world size.
The interactions between these representations in the human brain and artificial models indicate a complex interplay between low-level visual features, semantic information, and object size perception. The findings suggest that object real-world size is a multifaceted dimension that integrates both visual and semantic information to form a comprehensive representation of objects.

What are the potential limitations of the current study, and how could future research address them to further our understanding of object size and depth processing

One potential limitation of the current study is the reliance on 2D images to investigate object size and depth processing. While the use of naturalistic images provided ecologically valid stimuli, the study focused on perceived real-world size and depth, which may differ from absolute physical size and depth. Future research could incorporate 3D stimuli or virtual reality environments to explore how depth perception influences object size representation in a more immersive setting.
Another limitation is the focus on a specific set of visual features (real-world size, retinal size, real-world depth) without considering other potentially relevant dimensions of object space. Future studies could explore how these features interact with additional dimensions, such as shape, texture, or animacy, to provide a more comprehensive understanding of object representation in the human brain and artificial models.
To address these limitations, future research could also investigate the neural mechanisms underlying the integration of different visual features in object processing. By using advanced neuroimaging techniques, such as fMRI or MEG, researchers could examine the neural networks involved in processing object size and depth information and how these networks interact to form a coherent representation of objects in the visual system.

Given the importance of object size and depth information for visual perception and cognition, how might these findings inform the development of more brain-inspired artificial intelligence systems

The findings from this study have significant implications for the development of more brain-inspired artificial intelligence systems, particularly in the field of computer vision. By understanding how the human brain processes object size and depth information, researchers can design AI models that more closely mimic the neural mechanisms of visual perception.
One key insight is the importance of hierarchical processing in both the human brain and artificial models. By incorporating multiple layers that encode different levels of visual and semantic information, AI systems can better capture the complexity of object representation. This hierarchical approach can lead to more robust and accurate object recognition systems that are capable of understanding objects in their real-world context.
Additionally, the study highlights the role of semantic information in object size perception. By integrating semantic embedding models, such as Word2Vec, into AI systems, researchers can enhance the ability of these models to understand the conceptual dimensions of objects. This integration of visual and semantic information can lead to more sophisticated AI systems that can interpret objects in a more human-like manner.
Overall, the findings from this study can guide the development of AI systems that not only excel in object recognition tasks but also demonstrate a deeper understanding of the underlying principles of visual perception. By bridging the gap between human brain processing and artificial intelligence, researchers can create more advanced and brain-inspired AI systems that push the boundaries of computer vision technology.