ArticlePloS one2021
Joint representation of color and form in convolutional neural networks: A stimulus-rich network perspective.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
7 citing papers in PubMed.
- A Kernel-Aware Regularization Model for Chromatic-Robust Detection: Analysis of Grayscale-to-RGB Generalization.Journal of imaging · 2026Article
- Interactive shape and color representation in visual working memory for colored objects in the human occipitotemporal cortex.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A Long Skip Connection for Enhanced Color Selectivity in CNN Architectures.Sensors (Basel, Switzerland) · 2023Article
- Multiple visual objects are represented differently in the human brain and convolutional neural networks.Scientific reports · 2023Article
- Comparing the Dominance of Color and Form Information across the Human Ventral Visual Pathway and Convolutional Neural Networks.Journal of cognitive neuroscience · 2023Article
- Understanding transformation tolerant visual object representations in the human brain and convolutional neural networks.NeuroImage · 2022Article
- The contribution of object identity and configuration to scene representation in convolutional neural networks.PloS one · 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
2 authors.
Funding
Abstract
To interact with real-world objects, any effective visual system must jointly code the unique features defining each object. Despite decades of neuroscience research, we still lack a firm grasp on how the primate brain binds visual features. Here we apply a novel network-based stimulus-rich representational similarity approach to study color and form binding in five convolutional neural networks (CNNs) with varying architecture, depth, and presence/absence of recurrent processing. All CNNs showed near-orthogonal color and form processing in early layers, but increasingly interactive feature coding in higher layers, with this effect being much stronger for networks trained for object classification than untrained networks. These results characterize for the first time how multiple basic visual features are coded together in CNNs. The approach developed here can be easily implemented to characterize whether a similar coding scheme may serve as a viable solution to the binding problem in the primate brain.
Indexed as
Identifiers
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.