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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Cognitive components underpinning the development of model-based learning.

Tracey C S Potter1, Nessa V Bryce1, Catherine A Hartley1

  • 1Sackler Institute for Developmental Psychobiology, Weill Cornell Medicine, New York, NY 10065, United States; New York University, Department of Psychology, New York, NY 10003, United States.

Developmental Cognitive Neuroscience
|November 10, 2016
PubMed
Summary
This summary is machine-generated.

Fluid reasoning, not statistical learning or working memory, drives the development of model-based learning in young people. This cognitive skill helps in flexibly choosing goal-directed actions as individuals mature.

Keywords:
Fluid reasoningModel-basedReinforcement learningStatistical learning

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Area of Science:

  • Cognitive Neuroscience
  • Developmental Psychology
  • Learning Theory

Background:

  • Reinforcement learning theory differentiates model-free (reflexive) from model-based (flexible, goal-directed) learning.
  • Model-free learning is consistent across development, while model-based learning increases with age.
  • The cognitive underpinnings of developing model-based learning are not well understood.

Purpose of the Study:

  • To investigate if cognitive abilities predict age-related increases in model-based choice.
  • To examine the roles of statistical learning, working memory, and fluid reasoning in model-based learning development.

Main Methods:

  • Assessed model-based choice in participants aged 9-25.
  • Measured statistical learning, working memory capacity, and fluid reasoning.
  • Used mediation analysis to link cognitive abilities to age-related model-based learning.

Main Results:

  • Statistical learning improvements did not mediate the age-model-based learning relationship.
  • Working memory performance was at ceiling, precluding analysis.
  • Fluid reasoning improvements significantly mediated the developmental increase in model-based strategy use.

Conclusions:

  • Fluid reasoning development is a key factor in the emergence of model-based learning.
  • This highlights the importance of higher-order cognitive processes in sophisticated learning strategies.
  • Understanding these developmental trajectories can inform educational and therapeutic interventions.