ArticleProceedings of the National Academy of Sciences of the United States of America2020
Performance vs. competence in human-machine comparisons.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Behavioral differences between humans and machines arise early in visual processing.Journal of vision · 2026Article
- Review
- The Comparison of Human and Machine Performance in Object Recognition.Behavioral sciences (Basel, Switzerland) · 2026Article
- A Suite of LMs Comprehend Puzzle Statements as Well or Better Than Humans.Open mind : discoveries in cognitive science · 2026Article
- On Benchmarking Human-like Intelligence in Machines.Open mind : discoveries in cognitive science · 2026Article
- Revealing emergent human-like conceptual representations from language prediction.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Testing for completions that simulate altruism in early language models.Nature human behaviour · 2025Article
- Re-evaluating Theory of Mind evaluation in large language models.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning.Nature communications · 2025Article
- Deep neural networks and humans both benefit from compositional language structure.Nature communications · 2024Article
- Embers of autoregression show how large language models are shaped by the problem they are trained to solve.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- How Can Deep Neural Networks Inform Theory in Psychological Science?Current directions in psychological science · 2024Article
- AI Psychometrics: Assessing the Psychological Profiles of Large Language Models Through Psychometric Inventories.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Article
- Geometric feature extraction in nanofiber membrane image based on convolution neural network for surface roughness prediction.Heliyon · 2024Article
- Testing theory of mind in large language models and humans.Nature human behaviour · 2024Article
- (Ir)rationality and cognitive biases in large language models.Royal Society open science · 2024Article
- Subtle adversarial image manipulations influence both human and machine perception.Nature communications · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- When will AI misclassify? Intuiting failures on natural images.Journal of vision · 2023Article
- Toward better AFIS practice and process in the forensic fingerprint environment.Forensic science international. Synergy · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach "human-level" accuracy in an astounding variety of domains, and even predict human brain activity-raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines
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.