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Decision-level processes in rapid numerosity estimation.

Trygve Solstad1, Eivind Kaspersen1, Elisabeth I Romijn1

  • 1Department of Teacher Education, Faculty of Social and Educational Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.

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|March 21, 2026
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Summary
This summary is machine-generated.

People rapidly estimate numbers using strategies like comparing, partitioning, and counting. These decision-level processes explain common human numerical judgment patterns under time constraints.

Keywords:
Number senseNumerical cognitionRelative judgementStrategies

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Area of Science:

  • Cognitive Psychology
  • Numerical Cognition

Background:

  • Human numerical judgments exhibit consistent patterns, including exact enumeration for small quantities and underestimation for large ones.
  • Current computational models explain these patterns based on stimulus representation, but decision-level processes remain less understood.

Purpose of the Study:

  • To investigate the decision-level cognitive processes individuals use when rapidly estimating numerosity.
  • To determine if these processes contribute to observed behavioral patterns in numerical estimation tasks.

Main Methods:

  • Combined qualitative interviews, quantitative surveys, and preregistered experiments with participants estimating dot patterns under brief viewing (100 ms) and masking conditions.
  • Participants' self-reported strategies (comparing, counting, partitioning) were correlated with task performance and response times.

Main Results:

  • Participants reported using strategies such as visual memory comparison, dot counting, and approximate group calculations.
  • Decision-level processes including comparing, partitioning, and counting were found to significantly contribute to established behavioral patterns in numerosity estimation.
  • These processes systematically correlated with numerosity, response time, and estimation accuracy.

Conclusions:

  • Rapid numerosity estimation patterns arise from the flexible deployment of decision-level cognitive processes, influenced by individual factors and task demands.
  • Integrating these decision-level mechanisms with representation-level models will advance our understanding of human numerical cognition.