ArticleJournal of vision2018
Deep learning-Using machine learning to study biological vision.
Article in Journal of vision, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 43 citations in OpenAlex.
- Identification of letters distorted by physiologically-inspired spatial scrambling.PLoS computational biology · 2026Article
- The Comparison of Human and Machine Performance in Object Recognition.Behavioral sciences (Basel, Switzerland) · 2026Article
- Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification.Journal of chemical theory and computation · 2025Article
- Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification.Diagnostics (Basel, Switzerland) · 2024Review
- Article
- On the synthesis of visual illusions using deep generative models.Journal of vision · 2022Article
- Distinguishing mirror from glass: A "big data" approach to material perception.Journal of vision · 2022Article
- FP-nets as novel deep networks inspired by vision.Journal of vision · 2022Article
- ImageNet-trained deep neural networks exhibit illusion-like response to the Scintillating grid.Journal of vision · 2021Article
- A comparative biology approach to DNN modeling of vision: A focus on differences, not similarities.Journal of vision · 2021Article
- Binocular vision supports the development of scene segmentation capabilities: Evidence from a deep learning model.Journal of vision · 2021Article
- Five points to check when comparing visual perception in humans and machines.Journal of vision · 2021Article
- Performance vs. competence in human-machine comparisons.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Visual perception of liquids: Insights from deep neural networks.PLoS computational biology · 2020Article
- Humans can decipher adversarial images.Nature communications · 2019Article
- Fluorescent Biosensors for Neurotransmission and Neuromodulation: Engineering and Applications.Frontiers in cellular neuroscience · 2019Review
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 at 1 institution in 1 country.
Funding
Abstract
Many vision science studies employ machine learning, especially the version called "deep learning." Neuroscientists use machine learning to decode neural responses. Perception scientists try to understand how living organisms recognize objects. To them, deep neural networks offer benchmark accuracies for recognition of learned stimuli. Originally machine learning was inspired by the brain. Today, machine learning is used as a statistical tool to decode brain activity. Tomorrow, deep neural networks might become our best model of brain function. This brief overview of the use of machine learning in biological vision touches on its strengths, weaknesses, milestones, controversies, and current directions. Here, we hope to help vision scientists assess what role machine learning should play in their research.
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.