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  1. Home
  2. Are Current Etiological Theories Of Alzheimer's Disease Falsifiable? An Epistemological Assessment.
  1. Home
  2. Are Current Etiological Theories Of Alzheimer's Disease Falsifiable? An Epistemological Assessment.

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Are current etiological theories of Alzheimer's disease falsifiable? An epistemological assessment.

Tommaso Costa1,2,3, Donato Liloia1,4

  • 1Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.

Frontiers in Aging Neuroscience
|December 15, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Alzheimer's disease research struggles with competing theories. This study proposes Bayesian inference for a more comparative and predictive approach to evaluating scientific models in Alzheimer's disease (AD) research.

Keywords:
Bayesian inferenceeliminative inductionepistemology of sciencefalsifiabilitypredictive modelingscientific modelsscientific rationalitytheory comparison

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Area of Science:

  • Neuroscience
  • Philosophy of Science
  • Medical Research Methodology

Background:

  • Alzheimer's disease (AD) research faces challenges due to numerous competing etiological theories lacking systematic comparison.
  • The traditional falsifiability criterion in scientific research is insufficient for effectively evaluating AD hypotheses.
  • Factors like biological complexity, lack of biomarkers, and institutional inertia hinder paradigm shifts in AD research.

Purpose of the Study:

  • To examine the limitations of the falsifiability criterion in Alzheimer's disease (AD) research.
  • To propose an alternative framework for evaluating competing scientific theories in AD research.
  • To advocate for a more quantitative and comparative approach to scientific progress in AD.

Main Methods:

  • Critical analysis of the epistemological limitations of Karl Popper's falsifiability criterion in the context of AD.
  • Introduction of Bayesian inference as a framework for eliminative induction.
  • Modeling scientific theories as probabilistic hypotheses with gradable plausibility.
  • Main Results:

    • Falsifiability proves ineffective as an operational standard in AD research due to biological complexity and other factors.
    • Bayesian inference offers a pragmatic approach, evaluating models based on predictive performance rather than absolute truth.
    • The proposed framework facilitates rational distinction among competing models using quantitative criteria.

    Conclusions:

    • A shift from proving/disproving hypotheses to rationally distinguishing between competing models is crucial for AD research progress.
    • Bayesian inference and eliminative induction provide a more robust framework for advancing Alzheimer's disease (AD) science.
    • Promoting comparative, predictive, and transparent scientific practices will enhance the evaluation of AD theories.