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Compressed representations and attentional competition in numeric integration for average estimations.

Yongming Sun1, Alice Mason2, Sebastian Olschewski3

  • 1Zhejiang University, PR China; University of Warwick, UK.

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|December 26, 2025
PubMed
Summary
This summary is machine-generated.

Estimating averages from number streams is key to cognition. This study found that while competing information has limited impact, the Compressed Mental Number Line (CMNL) model better explains average estimation than Selective Integration (SI).

Keywords:
Average EstimationCompressed Mental Number LineInformation SamplingMean EstimationNumeric CognitionSelective Integration

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Area of Science:

  • Cognitive psychology
  • Numerical cognition
  • Decision science

Background:

  • Gauging averages is fundamental to numeric cognition and decision-making.
  • Previous research focused on integrating numerical information from single sources.
  • The impact of competing information sources on average estimation remains less understood.

Purpose of the Study:

  • To investigate average estimation when presented with competing information streams.
  • To compare the predictive power of the Compressed Mental Number Line (CMNL) and Selective Integration (SI) theories.
  • To analyze how competing information affects cognitive processes in average estimation.

Main Methods:

  • Four experiments were conducted to test average estimation under single- and dual-stream conditions.
  • Participants' estimations were analyzed to evaluate two competing theories of numeric value integration.
  • Computational modeling was employed to assess the fit of the CMNL and SI models to the observed data.

Main Results:

  • Significant underestimation of averages was observed in both single- and dual-stream conditions.
  • Competing information had a limited impact on average estimation accuracy.
  • The CMNL model provided a better overall account of estimation behavior, though SI explained approximately one-third of participants.
  • Integration noise increased in dual-stream conditions, but representational compression remained unaffected.

Conclusions:

  • The CMNL model offers a robust explanation for average estimation, even with competing information.
  • While CMNL is generally superior, a subset of individuals' behavior is better described by the SI model.
  • Limitations exist in processing multiple information streams, impacting integration noise but not representational compression.