Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • News & Views
  • Published:

Computational cognitive science

Artificial intelligence tackles the nature–nurture debate

Nature Machine Intelligence volume 6, pages 381–382 (2024)Cite this article

Subjects

A classic question in cognitive science is whether learning requires innate, domain-specific inductive biases to solve visual tasks. A recent study trained machine-learning systems on the first-person visual experiences of children to show that visual knowledge can be learned in the absence of innate inductive biases about objects or space.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: AI models for studying the origins of intelligence.

References

  1. Orhan, A. E. & Lake, B. M. Nat. Mach. Intell. https://doi.org/10.1038/s42256-024-00802-0 (2024).

    Article  Google Scholar 

  2. Azevedo, F. A. C. et al. J. Comp. Neurol. 513, 532–541 (2009).

    Article  Google Scholar 

  3. Adolph, K. E. et al. Trends Cogn. Sci. 22, 699–711 (2018).

    Article  Google Scholar 

  4. Dosovitskiy, A. et al. In 9th Int. Conf. Learning Representations https://openreview.net/forum?id=YicbFdNTTy (2021).

  5. Zhuang, C. et al. Proc. Natl Acad. Sci. USA 118, e2014196118 (2021).

    Article  Google Scholar 

  6. Sheybani, S., Hansaria, H., Wood, J. N., Smith, L., & Tiganj, Z. In 37th Conf. Neural Information Processing Systems https://openreview.net/forum?id=zkfyOkBVpz (2023).

  7. Pandey, L., Wood, S. M. W., & Wood, J. N. In 36th Conf. Neural Information Processing Systems https://openreview.net/forum?id=W23ZTdsabj (2023).

  8. Smith, L. B., Jayaraman, S., Clerkin, E. & Yu, C. Trends Cogn. Sci. 22, 325–336 (2018).

    Article  Google Scholar 

  9. Kuhn, T. The Structure of Scientific Revolution (Univ. Chicago Press, 1962).

  10. Buckner, C. From Deep Learning to Rational Machines (Oxford Univ. Press, 2023).

  11. Tong, Z., Song, Y., Wang, J., & Wang, L. In 36th Conf. Neural Information Processing Systems https://openreview.net/forum?id=AhccnBXSne (2022).

Download references

Author information

Authors and Affiliations

  1. Informatics Department, Indiana University Bloomington, Bloomington, IN, USA

    Justin N. Wood

Authors
  1. Justin N. Wood
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Justin N. Wood.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wood, J.N. Artificial intelligence tackles the nature–nurture debate. Nat Mach Intell 6, 381–382 (2024). https://doi.org/10.1038/s42256-024-00828-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-024-00828-4

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing