ReviewAnnals of the New York Academy of Sciences2021
From convolutional neural networks to models of higher-level cognition (and back again).
Review in Annals of the New York Academy of Sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Exploring the role of micro-valence in the phenomenal space: insights from similarity judgments and deep learning models.Neuroscience of consciousness · 2026Article
- Explaining Human Comparisons Using Alignment-Importance Heatmaps.Computational brain & behavior · 2025Article
- Using drawings and deep neural networks to characterize the building blocks of human visual similarity.Memory & cognition · 2025Article
- Artificial Intelligence and IBD: Where are We Now and Where Will We Be in the Future?Current gastroenterology reports · 2024Review
- Contiguity in perception: origins in cellular associative computations.Trends in neurosciences · 2024Review
- Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond.Pediatric radiology · 2024Review
- Human perception and machine vision reveal rich latent structure in human figure drawings.Frontiers in psychology · 2023Article
- The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance-A Comprehensive Review.Journal of clinical medicine · 2022Review
- Neural Network Model for Perceptual Evaluation of Product Modelling Design Based on Multimodal Image Recognition.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The remarkable successes of convolutional neural networks (CNNs) in modern computer vision are by now well known, and they are increasingly being explored as computational models of the human visual system. In this paper, we ask whether CNNs might also provide a basis for modeling higher-level cognition, focusing on the core phenomena of similarity and categorization. The most important advance comes from the ability of CNNs to learn high-dimensional representations of complex naturalistic images, substantially extending the scope of traditional cognitive models that were previously only evaluated with simple artificial stimuli. In all cases, the most successful combinations arise when CNN representations are used with cognitive models that have the capacity to transform them to better fit human behavior. One consequence of these insights is a toolkit for the integration of cognitively motivated constraints back into CNN training paradigms in computer vision and machine learning, and we review cases where this leads to improved performance. A second consequence is a roadmap for how CNNs and cognitive models can be more fully integrated in the future, allowing for flexible end-to-end algorithms that can learn representations from data while still retaining the structured behavior characteristic of human cognition.
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What 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.