Evidence map›Paper›PMID 33715436›Full record

ArticleProceedings. Biological sciences2021

Human biases limit cumulative innovation.

Bill Thompson, Thomas L Griffiths

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Norms emerge through iterated learning.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Cultural evolution: Where we have been and where we are going (maybe).Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  5. Article
  6. Article
  7. Machine culture.Nature human behaviour · 2023
    Review
  8. Superhuman artificial intelligence can improve human decision-making by increasing novelty.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  9. Hybrid social learning in human-algorithm cultural transmission.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022
    Article
  10. Article
  11. Human cumulative culture and the exploitation of natural phenomena.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Article
  12. Article
  13. Cultural selection and biased transformation: two dynamics of cultural evolution.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Bill ThompsonDepartments of Psychology and Computer Science, Princeton University, Princeton, NJ 08544, USA.
Thomas L GriffithsDepartments of Psychology and Computer Science, Princeton University, Princeton, NJ 08544, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 (

Indexed as

Cultural EvolutionBayes TheoremBiasCreativityHumansLearningBayesiancultural evolutionfunction learninginductive biasinnovationoptimization

Identifiers

PMID33715436
PMCPMC7944091

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.