ReviewNature human behaviour2024
Building machines that learn and think with people.
Review in Nature human behaviour, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Mental bootstrapping enables human-level concept learning in self-supervised deep models.Science advances · 2026Article
- Article
- Fractional-Order Spiking Bayesian Neural Model for Cognitive Computations.Neuroinformatics · 2026Article
- A large-scale evaluation of commonsense knowledge in humans and large language models.PNAS nexus · 2026Article
- Toward a science of human-AI teaming for decision making: A complementarity framework.PNAS nexus · 2026Article
- Double training promotes retinotopic transfer of category learning.NPJ science of learning · 2026Article
- From symptom-based heterogeneity to mechanism-based profiling in youth ADHD: the promise of computational psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Computational hermeneutics: evaluating generative AI as a cultural technology.Frontiers in artificial intelligence · 2026Article
- On Benchmarking Human-like Intelligence in Machines.Open mind : discoveries in cognitive science · 2026Article
- Distributionally robust free energy principle for decision-making.Nature communications · 2025Article
- Addressing Autonomy Risks in Generative Chatbots with the Socratic Method.Science and engineering ethics · 2025Article
- Revealing emergent human-like conceptual representations from language prediction.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Evidential deep learning-based drug-target interaction prediction.Nature communications · 2025Article
- A generalist medical language model for disease diagnosis assistance.Nature medicine · 2025Article
- Transforming clinical reasoning-the role of AI in supporting human cognitive limitations.Frontiers in digital health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
What do we want from machine intelligence? We envision machines that are not just tools for thought but partners in thought: reasonable, insightful, knowledgeable, reliable and trustworthy systems that think with us. Current artificial intelligence systems satisfy some of these criteria, some of the time. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called 'thought partners', systems built to meet our expectations and complement our limitations. We lay out several modes of collaborative thought in which humans and artificial intelligence thought partners can engage, and we propose desiderata for human-compatible thought partnerships. Drawing on motifs from computational cognitive science, we motivate an alternative scaling path for the design of thought partners and ecosystems around their use through a Bayesian lens, whereby the partners we construct actively build and reason over models of the human and world.
Indexed as
Identifiers
39438684What 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.