Human biases limit cumulative innovation
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Abstract
Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here, we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large ( n = 1250) behavioural study using a transmission chain design, participants searched for virtual technologies in one of four environments after inheriting a solution from previous generations. Participants converged on worse solutions in environments misaligned with their biases. These results substantiate a mathematical model of cumulative innovation in Bayesian agents, highlighting formal relationships between cultural evolution and distributed stochastic optimization. Our findings provide experimental evidence that human biases can limit the advancement of knowledge in a controlled laboratory setting, reinforcing concerns about bias in creative, scientific and educational contexts.
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This can restrict the spectrum of solutions that a population can derive, and even highly effective solutions may not be sustained if they conflict with pre-existing biases.
“This can restrict the spectrum of solutions that a population can derive, and even highly effective solutions may not be sustained if they conflict with pre-existing biases 106 .”
From Machine culture (2023) · cites this paper as [106]
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Stereotypes as Bayesian prediction of social groups
Solanki, P., & Cesario, J. (2024) · Journal of Social Psychology · doi:10.1080/00224545.2024.2368017 · Cited by 3 · see citing works
Norms emerge through iterated learning
Partington, S. W., Kamtekar, R., & Nichols, S. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2504178122 · Cited by 3 · see citing works
Understanding the Role of Naive Learners in Cultural Change
Chimento, M., & Aplin, L. M. (2024) · The American Naturalist · doi:10.1086/730110 · Cited by 3 · see citing works
A quantum model for serial reproduction
Huang, J. A., Zhang, Q., Xie, X., Breithaupt, F. A., & Busemeyer, J. R. (2025) · Cognition · doi:10.1016/j.cognition.2025.106266 · Cited by 2 · see citing works
Kendal, R. L., & Watson, R. L. (2023) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198869252.013.14 · Cited by 4 · see citing works
Cultural Evolution of Ineffective Medicine
de Barra, M. (2023) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198869252.013.71 · Cited by 2 · see citing works
Nakawake, Y., & Kobayashi, Y. (2024) · Royal Society Open Science · doi:10.1098/rsos.231108 · Cited by 2 · see citing works
From Data to Theory: Understanding the Role of Naive Learners in Cultural ChangePreprint
Chimento, M., & Aplin, L. M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.20.492808 · Cited by 2 · see citing works
A Quantum Model for Serial ReproductionPreprint
Huang, J. A., Zhang, Q., Xie, X., Breithaupt, F. A., & Busemeyer, J. R. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4977921 · Cited by 2 · see citing works
Childhood exploration drives population-level innovation in cultural evolutionPreprint
Miu, E., Andersen, M. M., Lew‐Levy, S., & Riede, F. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.28.645934 · Cited by 2 · see citing works
The relationship between childhood exploration and population-level innovation in cultural evolution
Miu, E., Andersen, M. M., Lew‐Levy, S., & Riede, F. (2026) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2025.2122 · Cited by 1 · see citing works
Propagation and preservation of AI-discovered problem-solving strategies in human culture
Brinkmann, L. U. L., Müller, T. F., Nußberger, A.-M., Derex, M., Bonati, S., Chirkov, V., & Rahwan, I. (2026) · Nature Communications · doi:10.1038/s41467-026-76113-2 · Cited by 1 · see citing works
Thompson, B. (2022) · Science · doi:10.1126/science.ade3128 · Cited by 1 · see citing works
Self-citationMultidimensional Stereotypes Emerge Spontaneously When Exploration is CostlyPreprint
Bai, X., Griffiths, T. L., & Fiske, S. T. (2022) · doi:10.31234/osf.io/mbuhv · Cited by 1 · see citing works
Self-citationThe Bayesian iterated learning modelPreprint
Ferdinand, V. (2024) · doi:10.31234/osf.io/kwhcp · Cited by 1 · see citing works
The Bayesian iterated learning model
Ferdinand, V. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780192886491.013.15 · Cited by 1 · see citing works
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- Authors
- Thompson, B., & Griffiths, T. L.
- Affiliations
- Princeton University (US)
- Corresponding author
- Thompson, Bill
- Venue
- Proceedings of the Royal Society B: Biological Sciences
- Publisher
- The Royal Society
- Published
- 2021-03-10
- Volume
- 288
- Issue
- 1946
- Pages
- 20202752
- ISSN
- 0080-4649 (ISSN-L)
- 0962-8452
- 1471-2954
- PMC
- PMC7944091
- OpenAlex
- W3133873474
- Cited by
- 40
- References
- 100
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 11 · 2023: 8 · 2022: 11 · 2021: 2
- FWCI
- 43.75
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Proceedings of the Royal Society B Biological Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- National Science Foundation (1456709)
- Defense Advanced Research Projects Agency (D17AC00004)
- Topics
- Social Sciences › Cultural Studies › Language and cultural evolution
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Keywords
- cumulative innovation, knowledge transmission, cultural evolution, Bayesian agents, distributed stochastic optimization, technological progress
- MeSH terms
- Bayes Theorem, Creativity, Cultural Evolution, Humans, Learning, Bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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