ReviewFrontiers in computational neuroscience2020
Attention in Psychology, Neuroscience, and Machine Learning.
Review in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT06782360 (CAMSAN), which is not on this map. Cited by 60 papers, 2 of them syntheses that pooled it.
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.
CAMSAN: Cognitive Augmentation Via Multimodal Sensing and Auricular Neurostimulation
Who cites it
60 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Obstructive sleep apnea and attention deficits: A systematic review of magnetic resonance imaging biomarkers and neuropsychological assessments.Brain and behavior · 2023Pooled it
- The neurobiology of functional neurological disorders characterised by impaired awareness.Frontiers in psychiatry · 2023Pooled it
- Child training in the Child ViReal Support Program: Combining iVR-based cognitive training and CBT techniques in a pilot study.PloS one · 2026Trial
- Deficient executive control in transformer attention.PNAS nexus · 2026Article
- The role of stimulus characteristics in visual attention: an eye-tracking study.BMC psychology · 2026Article
- Modeling attention and binding in the brain through bidirectional recurrent gating.Nature communications · 2026Article
- Article
- Modulation of feature attention by reward prediction error explains value learning behavior.bioRxiv : the preprint server for biology · 2026Article
- Active Task Engagement Enhances Auditory Brain-Behaviour Prediction From Single-Trial EEG Compared With Passive Listening.The European journal of neuroscience · 2026Article
- Prompt Engineering for Organic Chemistry Education: A Digital Loom-Based Approach.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Effects of School-Based Neurofeedback Training on Attention in Students with Autism and Intellectual Disabilities.Journal of autism and developmental disorders · 2025Article
- Comparison of Post-Consumption Attention Between Two Different Aroma Types of Baijiu Using the Stroop Task.Foods (Basel, Switzerland) · 2025Article
- Gaze cluster analysis reveals heterogeneity in attention allocation and predicts learning outcomes.Scientific reports · 2025Article
- Attention-driven UNet enhancement for accurate segmentation of bacterial spore outgrowth in microscopy images.Scientific reports · 2025Article
- Benchmarking the speed-accuracy tradeoff in object recognition by humans and neural networks.Journal of vision · 2025Article
- A mathematical framework for modelling the dynamic nature of ADHD symptoms.Frontiers in psychiatry · 2025Article
- From generative AI to the brain: five takeaways.Frontiers in computational neuroscience · 2025Article
- Article
- Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks.Bioengineering (Basel, Switzerland) · 2024Article
- Improvement of persistent impairments in executive functions and attention following electroconvulsive therapy in a case control longitudinal follow up study.BMC psychiatry · 2024Observational
Corrections and comments
- Erratum issued
Authors and funding
1 author.
Funding
Abstract
Attention is the important ability to flexibly control limited computational resources. It has been studied in conjunction with many other topics in neuroscience and psychology including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This review starts by providing an overview of how attention is conceptualized in the neuroscience and psychology literature. It then covers several use cases of attention in machine learning, indicating their biological counterparts where they exist. Finally, the ways in which artificial attention can be further inspired by biology for the production of complex and integrative systems is explored.
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.