- 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)
- 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:
- 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
= 0.1 9. Characterizing constraints on change
- Links between brain and cognitive development
- Effect of environmental exposure on development of
- 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.
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.