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A unified account of numerosity perception

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Listed:
  • Samuel J. Cheyette

    (UC Berkeley)

  • Steven T. Piantadosi

    (UC Berkeley)

Abstract

People can identify the number of objects in sets of four or fewer items with near-perfect accuracy but exhibit linearly increasing error for larger sets. Some researchers have taken this discontinuity as evidence of two distinct representational systems. Here, we present a mathematical derivation showing that this behaviour is an optimal representation of cardinalities under a limited informational capacity, indicating that this behaviour can emerge from a single system. Our derivation predicts how the amount of information accessible to viewers should influence the perception of quantity for both large and small sets. In a series of four preregistered experiments (N = 100 each), we varied the amount of information accessible to participants in number estimation. We find tight alignment between the model and human performance for both small and large quantities, implicating efficient representation as the common origin behind key phenomena of human and animal numerical cognition.

Suggested Citation

  • Samuel J. Cheyette & Steven T. Piantadosi, 2020. "A unified account of numerosity perception," Nature Human Behaviour, Nature, vol. 4(12), pages 1265-1272, December.
    • Handle: RePEc:nat:nathum:v:4:y:2020:i:12:d:10.1038_s41562-020-00946-0
    DOI: 10.1038/s41562-020-00946-0
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    Cited by:

    1. Santiago Alonso-Diaz, 2024. "A human-like artificial intelligence for mathematics," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 23(1), pages 79-97, December.
    2. Simon Ciranka & Juan Linde-Domingo & Ivan Padezhki & Clara Wicharz & Charley M. Wu & Bernhard Spitzer, 2022. "Asymmetric reinforcement learning facilitates human inference of transitive relations," Nature Human Behaviour, Nature, vol. 6(4), pages 555-564, April.

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