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Continuous and discrete proportion elicit different cognitive strategies.

Michelle A Hurst1, Steven T Piantadosi2

  • 1Rutgers University, New Brunswick.

Cognition
|August 17, 2024
PubMed
Summary
This summary is machine-generated.

People use different strategies for proportional reasoning depending on the task. Continuous proportions are best explained by proportion models, while discrete proportions are better explained by numerator comparison models.

Keywords:
Bayesian analysisModel comparisonProportionStrategy

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Area of Science:

  • Cognitive psychology
  • Developmental psychology
  • Mathematical cognition

Background:

  • Proportional reasoning is crucial but lacks a unified explanation.
  • Apparent contradictions exist: proportion is sometimes easy, sometimes difficult.
  • Individual differences in strategy may explain varied performance.

Purpose of the Study:

  • To investigate if different strategies are used for proportional reasoning across various contexts.
  • To computationally model and quantitatively compare these strategies.
  • To examine strategy use across different age groups and stimulus types.

Main Methods:

  • Computational implementation of different reasoning strategies.
  • Quantitative comparison using Bayesian tools.
  • Analysis of data from continuous (pie charts) and discrete (dots) stimuli.
  • Testing across age groups: preschoolers, 2nd graders, 5th graders, and adults.

Main Results:

  • Proportion strategy models best fit performance on continuous proportion tasks.
  • Numerator comparison models best fit performance on discrete proportion tasks.
  • Systematic differences in strategy use were observed based on stimulus type.

Conclusions:

  • There is no single explanation for proportional reasoning success or failure.
  • Multiple strategies are employed, chosen based on task context.
  • Understanding these varied strategies is key to explaining proportional reasoning behavior.