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Neuroscience, Computational Modelling and Education: Reflections on Neil Burgess’ talk Gert Westermann

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  • 1. Neuroscience, Computational Modelling and Education: Reflections on Neil Burgess talk Gert Westermann

2. Modelling already featured in Neils talk: x j x i w ij e.g.w ij->w ij+ x jx i w ij->w ij+ w ijx j w j Different learningrulesin hippocampus and striatum? 3. So what can modelling offer to neuroscience and education? 4. BEHAVIOUR Huge gap! Computational models 5. Models

  • help us to understandhowlearning changes the brain
  • (to characterize the process of change)
  • Basic idea:
  • We observe a process (e.g., brain-behaviour correlates),
  • or more relevant here, a behaviouralchange
  • We develop a computational model that displays the same behaviour
  • We know how the model works, and this becomes our theory of how the process works in real life
  • But this is not always followed.

/rIt/ write 6. Neural network (connectionist) models

  • Added (important) benefit:
  • Functionality of these models is inspired by how neurons work

Although we should stay alert to the limits of this analogy. 7. Characterizing constraints on change

  • Models as a tool to explore what affects change:
  • Environment
  • frequency of exposure
  • order of exposure (age of acquisition)
  • type of exposure (e.g., similarity between stimuli)

8. Characterizing constraints on change

  • Genes/internal constraints
  • Structure/resources of the learning system
  • (critical periods, developmental disorders, speed-accuracy
  • trade-off in learning)

= 0.1 9. Characterizing constraints on change

  • Links between brain and cognitive development
  • Effect of environmental exposure on development of
  • functional structures
  • Effect of the integration of subsystems on behaviour
  • Maturation and experience-dependent plasticity

10.

  • These aspects of models should be constrained by neuroscience:
    • Mechanisms of synaptic change
    • Interplay of functional brain regions
  • and give rise to relevant behaviour.
  • Converging evidence

11. Bridging the gap

  • Models can be built at different levels of abstraction.
  • Is there a level that is acceptable both to neuroscientists and psychologists?
  • I think: yes, if we constantly remind ourselves what a model is for.