Skip to main content
Intended for healthcare professionals
Restricted access
Research article
First published online February 28, 2020

Abstractions are good for brains and machines: A commentary on Ambridge (2020)

Abstract

In ‘Against Stored Abstractions,’ Ambridge uses neural and computational evidence to make his case against abstract representations. He argues that storing only exemplars is more parsimonious – why bother with abstraction when exemplar models with on-the-fly calculation can do everything abstracting models can and more – and implies that his view is well supported by neuroscience and computer science. We argue that there is substantial neural, experimental, and computational evidence to the contrary: while both brains and machines can store exemplars, forming categories and storing abstractions is a fundamental part of what they do.

Get full access to this article

View all access and purchase options for this article.

References

Ambridge B. (2020). Against stored abstractions: A radical exemplar model of language acquisition. First Language 40(5-6): 509–559.
Amodei D., Ananthanarayanan S., Anubhai R., Bai J., Battenberg E., Case C., Casper J., Catanzaro B., Cheng Q., Chen G., Chen J., Chen J., Chen Z., Chrzanowski M., Coates A., Diamos G., Ding K., Du N., Elsen E., . . . Zhu Z. (2016). Deep speech 2: End-to-end speech recognition in English and Mandarin. In Lawrence N., Reid M. (Eds.). International Conference on Machine Learning (pp. 173–182). International Machine Learning Society.
Caplan S., Hafri A., Trueswell J. (2019, July 24–27). Speech processing does not involve acoustic maintenance. Paper presented at the 41st Annual Meeting of the Cognitive Science Society, Montreal, Canada.
Devlin J., Chang M.-W., Lee K., Toutanova K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv [cs.CL]. arXiv. http://arxiv.org/abs/1810.04805
Freedman D. J., Riesenhuber M., Poggio T., Miller E. K. (2003). A comparison of primate prefrontal and inferior temporal cortices during visual categorization. The Journal of Neuroscience, 23(12), 5235–5246.
Hampson R. E., Pons T. P., Stanford T. R., Deadwyler S. A. (2004). Categorization in the monkey hippocampus: A possible mechanism for encoding information into memory. Proceedings of the National Academy of Sciences of the United States of America, 101(9), 3184–3189.
Jesse A., McQueen J. M. (2011). Positional effects in the lexical retuning of speech perception. Psychonomic Bulletin & Review, 18(5), 943–950.
Jiang X., Bradley E., Rini R. A., Zeffiro T., Vanmeter J., Riesenhuber M. (2007). Categorization training results in shape- and category-selective human neural plasticity. Neuron, 53(6), 891–903.
Kraljic T., Samuel A. G. (2007). Perceptual adjustments to multiple speakers. Journal of Memory and Language, 56(1), 1–15.
Kreiman G., Koch C., Fried I. (2000). Category-specific visual responses of single neurons in the human medial temporal lobe. Nature Neuroscience, 3(9), 946–953.
Le Q. V. (2013, May 26–31). Building high-level features using large scale unsupervised learning. Paper presented at the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canada.
Mack M. L., Preston A. R., Love B. C. (2013). Decoding the brain’s algorithm for categorization from its neural implementation. Current Biology, 23(20), 2023–2027.
Medin D. L., Wattenmaker W. D., Hampson S. E. (1987). Family resemblance, conceptual cohesiveness, and category construction. Cognitive Psychology, 19(2), 242–279.
Meyers E. M., Freedman D. J., Kreiman G., Miller E. K., Poggio T. (2008). Dynamic population coding of category information in inferior temporal and prefrontal cortex. Journal of Neurophysiology, 100(3), 1407–1419.
Mikolov T., Chen K., Corrado G., Dean J. (2013). Efficient estimation of word representations in vector space. arXiv [cs.CL]. arXiv. http://arxiv.org/abs/1301.3781
Miller E. K., Buschman T. J. (2007). Rules through recursion: How interactions between the frontal cortex and basal ganglia may build abstract, complex rules from concrete, simple ones. In Neuroscience of rule-guided behavior (pp. 419–440). https://doi.org/10.1093/acprof:oso/9780195314274.003.0022
O’Reilly R. C., Munakata Y. (2000). Computational explorations in cognitive neuroscience: Understanding the mind by simulating the brain. The MIT Press.
Rayner K. (2009). Eye movements and attention in reading, scene perception, and visual search. The Quarterly Journal of Experimental Psychology, 62(8), 1457–1506.
Rehder B., Hoffman A. B. (2005). Eyetracking and selective attention in category learning. Cognitive Psychology, 51(1), 1–41.
Seger C. A., Miller E. K. (2010). Category learning in the brain. Annual Review of Neuroscience, 33, 203–219.
Shepard R. N., Hovland C. I., Jenkins H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75(13), 1–42.

Cite article

Cite article

Cite article

OR

Download to reference manager

If you have citation software installed, you can download article citation data to the citation manager of your choice

Share options

Share

Share this article

Share with email
EMAIL ARTICLE LINK
Share on social media

Share access to this article

Sharing links are not relevant where the article is open access and not available if you do not have a subscription.

For more information view the SAGE Journals article sharing page.

Information, rights and permissions

Information

Published In

Article first published online: February 28, 2020
Issue published: October-December 2020

Keywords

  1. Abstraction
  2. child language acquisition
  3. exemplar account
  4. natural language processing
  5. neuroscience

Rights and permissions

© The Author(s) 2020.
Request permissions for this article.

History

Published online: February 28, 2020
Issue published: October-December 2020

Authors

Affiliations

Jordan Kodner
University of Pennsylvania, USA

Notes

Kathryn D. Schuler, Department of Linguistics, University of Pennsylvania, Room 314C, 3401-C Walnut Street, Philadelphia, PA 19104, USA. Email: [email protected]

Metrics and citations

Metrics

Journals metrics

This article was published in First Language.

VIEW ALL JOURNAL METRICS

Article usage*

Total views and downloads: 496

*Article usage tracking started in December 2016

Articles citing this one

Web of Science: 2 view articles Opens in new tab

Crossref: 1

  1. Now You Hear Me, Later You Don’t: The Immediacy of Linguistic Computat...
    Go to citation Crossref Google ScholarPub Med

Figures and tables

Figures & Media

Tables

View Options

Get access

Access options

If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:


Alternatively, view purchase options below:

Purchase 24 hour online access to view and download content.

Access journal content via a DeepDyve subscription or find out more about this option.

View options

PDF/ePub

View PDF/ePub

Full Text

View Full Text