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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Human Memory

Background:

  • Understanding how humans represent numerical information in memory is crucial for cognitive modeling.
  • Previous models have not fully captured the nuances of number representation and decision-making processes.

Purpose of the Study:

  • To evaluate three computational models for number representation in memory.
  • To integrate these models with the diffusion decision model to explain recognition memory data.
  • To investigate the influence of numerical distance on accuracy and response times.

Main Methods:

  • A recognition memory experiment using two-digit numbers as stimuli.
  • Integration of three number representation models (two overlap-based, one exponential gradient) with the diffusion decision model.
  • Monte Carlo simulations to assess parameter estimate variability.

Main Results:

  • All integrated models explained distance/confusability effects, where numerically close numbers led to lower accuracy and longer response times (RTs).
  • The exponential gradient model demonstrated a slightly better fit to the experimental data compared to overlap models.
  • Simulations indicated that individual differences in parameter estimates are more substantial than variability from short-term data collection.
  • A second experiment revealed differing distance effects for number memory (exponential-like) and number discrimination (linear).

Conclusions:

  • Numerical representations in memory are not uniform and differ between memory and discrimination tasks.
  • The exponential gradient model offers a promising framework for understanding number representation in recognition memory.
  • Computational modeling, combined with experimental data, can effectively elucidate individual differences in numerical cognition.