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Phases of learning: How skill acquisition impacts cognitive processing.

Caitlin Tenison1, Jon M Fincham1, John R Anderson1

  • 1Department of Psychology, Carnegie Mellon University, United States.

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Summary
This summary is machine-generated.

Learning to solve math problems involves distinct cognitive shifts. This study reveals three learning phases and their brain signatures, showing how practice accelerates problem-solving through changes in encoding, solving, and responding stages.

Keywords:
ACT-RCognitive modelingSkill acquisitionfMRI

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Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging
  • Mathematical Cognition

Background:

  • Repeated practice significantly improves cognitive task performance, particularly in mathematical problem-solving.
  • Understanding the neural mechanisms underlying learning and skill acquisition is crucial for educational psychology and cognitive science.
  • Previous research often models learning as a continuous process, potentially overlooking discrete shifts in cognitive strategies.

Purpose of the Study:

  • To investigate the discrete changes in cognitive processing during repeated mathematical problem-solving using functional Magnetic Resonance Imaging (fMRI).
  • To identify distinct learning phases and their associated cognitive stages (Encoding, Solving, Responding) and neural signatures.
  • To model the progression through these learning phases and cognitive stages over practice.

Main Methods:

  • Employing fMRI to capture brain activity patterns during repeated mathematical problem-solving.
  • Utilizing multi-voxel pattern analysis (MVPA) and hidden semi-Markov modeling (HSMM) to identify cognitive stage durations from neural data.
  • Developing and simulating an ACT-R cognitive architecture model to represent and track learning phases and stages.

Main Results:

  • Identified three distinct learning phases characterized by changes in information processing.
  • Each learning phase comprises three cognitive stages (Encoding, Solving, Responding), each with a unique brain signature.
  • Demonstrated that practice-induced speedup primarily results from discrete shifts, such as transitioning from computation-heavy solving to answer retrieval, and then to automatic response generation.

Conclusions:

  • Learning mathematical problems involves discrete shifts in cognitive processing across identifiable learning phases.
  • The transition between phases is marked by changes in the duration and nature of cognitive stages, particularly the Solving stage.
  • These findings provide a neural and cognitive framework for understanding skill acquisition in mathematical domains.