A large-scale examination of inductive biases shaping high-level visual representation in brains and machines

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Oct. 30, 2024

Language: Английский

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Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition

Proceedings of the National Academy of Sciences, Journal Year: 2023, Volume and Issue: 120(32)

Published: July 31, 2023

Human face recognition is highly accurate and exhibits a number of distinctive well-documented behavioral "signatures" such as the use characteristic representational space, disproportionate performance cost when stimuli are presented upside down, drop in accuracy for faces from races participant less familiar with. These other phenomena have long been taken evidence that "special". But why does human perception exhibit these properties first place? Here, we deep convolutional neural networks (CNNs) to test hypothesis all signatures result optimization task recognition. Indeed, predicted by this hypothesis, found CNNs trained on recognition, but not object even additionally detect while matching amount experience. To whether principle specific faces, optimized CNN car discrimination tested it upright inverted images. As perception, car-trained network showed vs. cars. Similarly, produced an inversion effect. findings show reflect well explained nature computations underlying may be so special after all.

Language: Английский

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CNNs reveal the computational implausibility of the expertise hypothesis

iScience, Journal Year: 2023, Volume and Issue: 26(2), P. 105976 - 105976

Published: Jan. 14, 2023

Face perception has long served as a classic example of domain specificity mind and brain. But an alternative "expertise" hypothesis holds that putatively face-specific mechanisms are actually domain-general, can be recruited for the other objects expertise (e.g., cars car experts). Here, we demonstrate computational implausibility this hypothesis: Neural network models optimized generic object categorization provide better foundation expert fine-grained discrimination than do face recognition.

Language: Английский

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Explanatory models in neuroscience, Part 2: Functional intelligibility and the contravariance principle

Cognitive Systems Research, Journal Year: 2023, Volume and Issue: 85, P. 101200 - 101200

Published: Dec. 28, 2023

Language: Английский

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Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(1), P. e1012751 - e1012751

Published: Jan. 27, 2025

The human visual system possesses a remarkable ability to detect and process faces across diverse contexts, including the phenomenon of face pareidolia—–seeing in inanimate objects. Despite extensive research, it remains unclear why employs such broadly tuned detection capabilities. We hypothesized that pareidolia results from system’s optimization for recognizing both To test this hypothesis, we used task-optimized deep convolutional neural networks (CNNs) evaluated their alignment with behavioral signatures responses, measured via magnetoencephalography (MEG), related processing. Specifically, trained CNNs on tasks involving combinations identification, detection, object categorization, detection. Using representational similarity analysis, found included categorization training represented faces, real matched objects more similarly responses than those did not. Although these showed similar overall data, closer examination internal representations revealed specific had distinct effects how were layers. Finally, interpretability methods only CNN identification relied face-like features—such as ‘eyes’—to classify stimuli mirroring findings perception. Our suggest human-like may emerge within context generalized categorization.

Language: Английский

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Animal models of the human brain: Successes, limitations, and alternatives

Current Opinion in Neurobiology, Journal Year: 2025, Volume and Issue: 90, P. 102969 - 102969

Published: Feb. 1, 2025

Language: Английский

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A study comparing energy consumption and environmental emissions in ostrich meat and egg production

Journal of Agricultural Engineering, Journal Year: 2025, Volume and Issue: 56(1)

Published: Feb. 11, 2025

The assessment of energy usage in the production ostrich meat and eggs provides a comprehensive analysis consumption efficiency. per 1000 units is 1,086,825.54 MJ for 1,197,794.25 egg. When considering protein supply, egg seems to be more justifiable terms efficiency compared production. This study delves into impact on human health, revealing slight difference 0.23 disability adjusted life years (DALY), hinting that could potentially have marginally negative health effects than Artificial neural network (ANN) indicates optimizing machinery, diesel fuel, can enhance productivity It also suggests there possibility greater resource as opposed production, highlighting focus within yield positive environmental benefits. Additionally, coefficient determination adaptive neuro-fuzzy inference system (ANFI) 4 model favorable outcome factors related those Moreover, low mean squared error value reflects high accuracy results obtained analysis.

Language: Английский

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Hybrid Approach based Optimal Low Voltage Ride through Capability in DFIG-Based Wind Energy Systems

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135541 - 135541

Published: March 1, 2025

Language: Английский

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Brain-like border ownership signals support prediction of natural videos

iScience, Journal Year: 2025, Volume and Issue: 28(4), P. 112199 - 112199

Published: March 12, 2025

To make sense of visual scenes, the brain must segment foreground from background. This is thought to be facilitated by neurons that signal border ownership (BOS), which indicate side a in their receptive field owned an object. How these signals emerge without teaching what remains unclear. Here we find many units PredNet, self-supervised deep neural network trained predict future frames natural videos, are selective for BOS. They share key properties with BOS brain, including robustness object transformations and hysteresis. Ablation revealed contribute more prediction than other videos moving objects. Our findings suggest might due evolutionary or developmental pressure input natural, complex dynamic environments, even explicit requirement

Language: Английский

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Deep learning models challenge the prevailing assumption that face-like effects for objects of expertise support domain-general mechanisms

Proceedings of the Royal Society B Biological Sciences, Journal Year: 2023, Volume and Issue: 290(1998)

Published: May 10, 2023

The question of whether task performance is best achieved by domain-specific, or domain-general processing mechanisms fundemental for both artificial and biological systems. This has generated a fierce debate in the study expert object recognition. Because humans are experts face recognition, face-like neural cognitive effects objects expertise were considered support mechanisms. However, domain, experience level categorization, confounded human studies, which may lead to erroneous inferences. To overcome these limitations, we trained deep learning algorithms on different domains (objects, faces, birds) levels categorization (basic, sub-ordinate, individual), matched amount experience. Like humans, models larger inversion effect faces than objects. Importantly, was found individual-based non-faces (birds) but only network specialized that domain. Thus, contrary prevalent assumptions, do not originate from domain-specific More generally, show how can be used dissociate factors inherently natural environment organisms test hypotheses about their isolated contributions cognition behaviour.

Language: Английский

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