ArticleAdvances in neural information processing systems2022
Harmonizing the object recognition strategies of deep neural networks with humans.
Article in Advances in neural information processing systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Quantifying task-relevant representational similarity using decision variable correlation.ArXiv · 2026Article
- Behavioral differences between humans and machines arise early in visual processing.Journal of vision · 2026Article
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- The Comparison of Human and Machine Performance in Object Recognition.Behavioral sciences (Basel, Switzerland) · 2026Article
- Quantifying Task-relevant Similarities in Representations Using Decision Variable Correlations.Advances in neural information processing systems · 2025Article
- Recurrent issues with deep neural network models of visual recognition.Scientific reports · 2025Article
- Human-like monocular depth biases in deep neural networks.PLoS computational biology · 2025Article
- Computational Urban Ecology of New York City Rats.bioRxiv : the preprint server for biology · 2025Article
- Article
- Unraveling the complexity of rat object vision requires a full convolutional network and beyond.Patterns (New York, N.Y.) · 2025Article
- Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Bird song comparison using deep learning trained from avian perceptual judgments.PLoS computational biology · 2024Article
- Article
- Scaling models of visual working memory to natural images.Communications psychology · 2024Article
- What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods.Advances in neural information processing systems · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The many successes of deep neural networks (DNNs) over the past decade have largely been driven by computational scale rather than insights from biological intelligence. Here, we explore if these trends have also carried concomitant improvements in explaining the visual strategies humans rely on for object recognition. We do this by comparing two related but distinct properties of visual strategies in humans and DNNs:
Identifiers
37465369PMC10353762What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.