ReviewNeuron2020
Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks.
Review in Neuron, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 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
114 citing papers in PubMed.
- Effectiveness of a Biofeedback Intervention Targeting Mental and Physical Health Among College Students Through Speech and Physiology as Biomarkers Using Machine Learning: A Randomized Controlled Trial.Applied psychophysiology and biofeedback · 2024Trial
- The neuroscience of transformers.Cognitive neurodynamics · 2026Article
- Review
- Article
- Spontaneous emergence of context-dependent statistical learning in humans and neural networks.iScience · 2026Article
- A solution to generalized learning from small training sets found in infants' repeated visual experiences of individual objects.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Can neural networks model the human perception of geometric shapes?PLoS computational biology · 2026Article
- A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks.bioRxiv : the preprint server for biology · 2026Article
- Sensory encoding of emotion conveyed by the face and visual context.Social cognitive and affective neuroscience · 2026Article
- How do infant brains fold? Sulcal deepening is linked to development of sulcal span, thickness, curvature, and microstructure.Communications biology · 2026Article
- Beyond the dichotomy between controlled and naturalistic paradigms: modeling psychological constructs in cognitive neuroscience.Frontiers in psychology · 2026Article
- The neurobiology of human sequential signal prediction: Insights from language, music, and mathematics.Neuroscience research · 2026Review
- Same movies, different stories: aberrant brain state dynamics during naturalistic emotional stimuli in depression.Journal of translational medicine · 2025Article
- Computational origins of shape perception.PLoS computational biology · 2025Article
- Adaptive stretching of representations across brain regions and deep learning model layers.Nature communications · 2025Article
- Increasing alignment of large language models with language processing in the human brain.Nature computational science · 2025Article
- The cost of unmodeled biological complexity in artificial neural networks.Patterns (New York, N.Y.) · 2025Review
- Integrating Life-Cycle Assessment (LCA) and Artificial Neural Networks (ANNs) for Optimizing the Inclusion of Supplementary Cementitious Materials (SCMs) in Eco-Friendly Cementitious Composites: A Literature Review.Materials (Basel, Switzerland) · 2025Review
- Skillful subseasonal soil moisture drought forecasts with deep learning-dynamic models.Nature communications · 2025Article
- Text-related functionality and dynamics of visual human pre-frontal activations revealed through neural network convergence.Communications biology · 2025Article
54 more citing papers are in PubMed but not listed here.
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
Evolution is a blind fitting process by which organisms become adapted to their environment. Does the brain use similar brute-force fitting processes to learn how to perceive and act upon the world? Recent advances in artificial neural networks have exposed the power of optimizing millions of synaptic weights over millions of observations to operate robustly in real-world contexts. These models do not learn simple, human-interpretable rules or representations of the world; rather, they use local computations to interpolate over task-relevant manifolds in a high-dimensional parameter space. Counterintuitively, similar to evolutionary processes, over-parameterized models can be simple and parsimonious, as they provide a versatile, robust solution for learning a diverse set of functions. This new family of direct-fit models present a radical challenge to many of the theoretical assumptions in psychology and neuroscience. At the same time, this shift in perspective establishes unexpected links with developmental and ecological psychology.
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.