ArticlePloS one2019
Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Unlocking the benefits of transparent and reusable science for climate risk management.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- What is critical metascience and why is it important?Frontiers in psychology · 2026Review
- Computational complexity as a potential limitation on brain-behaviour mapping.The European journal of neuroscience · 2025Review
- What Makes a Good Theory? Interdisciplinary Perspectives.Computational brain & behavior · 2024Article
- The Problem-Ladenness of Theory.Computational brain & behavior · 2024Article
- Statistics in Service of Metascience: Measuring Replication Distance with Reproducibility Rate.Entropy (Basel, Switzerland) · 2024Article
- Modelling dataset bias in machine-learned theories of economic decision-making.Nature human behaviour · 2024Article
- A guide for social science journal editors on easing into open science.Research integrity and peer review · 2024Article
- Better Accuracy for Better Science . . . Through Random Conclusions.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Article
- Is N-Hacking Ever OK? The consequences of collecting more data in pursuit of statistical significance.PLoS biology · 2023Article
- Introducing tomsup: Theory of mind simulations using Python.Behavior research methods · 2023Article
- Rigorous exploration in a model-centric science via epistemic iteration.Journal of applied research in memory and cognition · 2023Article
- The logical structure of experiments lays the foundation for a theory of reproducibility.Royal Society open science · 2023Article
- On the Role of Theory and Modeling in Neuroscience.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- From Puzzle to Progress: How Engaging With Neurodiversity Can Improve Cognitive Science.Cognitive science · 2023Article
- The inverted U-shaped relationship between knowledge diversity of researchers and societal impact.Scientific reports · 2022Article
- Transparent, Open, and Reproducible Prevention Science.Prevention science : the official journal of the Society for Prevention Research · 2022Article
- The natural selection of good science.Nature human behaviour · 2021Article
- Estimating the reproducibility of social learning research published between 1955 and 2018.Royal Society open science · 2021Article
- Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics.PLoS computational biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Consistent confirmations obtained independently of each other lend credibility to a scientific result. We refer to results satisfying this consistency as reproducible and assume that reproducibility is a desirable property of scientific discovery. Yet seemingly science also progresses despite irreproducible results, indicating that the relationship between reproducibility and other desirable properties of scientific discovery is not well understood. These properties include early discovery of truth, persistence on truth once it is discovered, and time spent on truth in a long-term scientific inquiry. We build a mathematical model of scientific discovery that presents a viable framework to study its desirable properties including reproducibility. In this framework, we assume that scientists adopt a model-centric approach to discover the true model generating data in a stochastic process of scientific discovery. We analyze the properties of this process using Markov chain theory, Monte Carlo methods, and agent-based modeling. We show that the scientific process may not converge to truth even if scientific results are reproducible and that irreproducible results do not necessarily imply untrue results. The proportion of different research strategies represented in the scientific population, scientists' choice of methodology, the complexity of truth, and the strength of signal contribute to this counter-intuitive finding. Important insights include that innovative research speeds up the discovery of scientific truth by facilitating the exploration of model space and epistemic diversity optimizes across desirable properties of scientific discovery.
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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.