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How does aging affect recognition-based inference? A hierarchical Bayesian modeling approach.

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PubMed
Summary
This summary is machine-generated.

Younger and older adults adaptively use the recognition heuristic (RH) based on environmental recognition validity. However, older adults show a greater tendency to rely on the RH, especially in low-validity environments.

Keywords:
AgingHeuristicsHierarchical Bayesian modelingRecognition memoryStrategy selection

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Area of Science:

  • Cognitive Psychology
  • Decision Making
  • Human Aging

Background:

  • The recognition heuristic (RH) is a strategy for probabilistic inference where recognized objects are favored.
  • Understanding age-related differences in RH use and its adaptivity is crucial for cognitive aging research.

Purpose of the Study:

  • To quantify individual RH use using a hierarchical Bayesian extension of the multinomial r-model.
  • To compare RH strategy reliance between younger and older adults across varying environments.
  • To explore the relationship between r-model parameters and other measures of recognition and knowledge use.

Main Methods:

  • Application of a hierarchical Bayesian extension of the multinomial r-model.
  • Analysis of participant strategy reliance in environments with high and low recognition validity.
  • Correlation analysis with adherence rates and signal-detection theory (SDT) measures.

Main Results:

  • Both age groups demonstrated adaptive RH use, increasing reliance in high-validity environments.
  • Older adults showed a stronger tendency to use the RH in low-validity environments compared to younger adults.
  • Individual variability in RH use was higher in low-validity environments, but did not differ between age groups.

Conclusions:

  • Younger and older adults generally adapt their RH use to environmental feedback.
  • Cognitive aging may be linked to an increased propensity to employ the RH, particularly when environmental cues are less reliable.
  • The r-model and SDT measures offer complementary insights into recognition-based decision-making processes.