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Processes of developmental change

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Umberto D'Ovidio

on 25 March 2014

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Transcript of Processes of developmental change

U. D'Ovidio, A. Visalli
Representational change
Patterns of activation within the brain that correspond to aspects of the external environment
Constraints on learning
govern WHAT can be learned and WHEN
apply across different domains (memory, face processing, word learning)
intrinsic specificity
encoding specificity hypothesis(tulving & thomson)
mobile conjugate
internal lexical constraints when determining meaning of new words

whole-object constraint (blicket)

alternative explanation:
reasoning about intentions of others
infant learning biased by:
infant-directed teaching
teaching-context cues
we have a predisposition to pay attention to humans since birth

specific configuration for face is controversial

non specific top heavy configuration bias
Increased specialization
and localization

Causal maps
Processing becomes increasingly specialized

Role of experience

Developmental disorders:
affect functional specialization (neural network studies)

The modularity of the brain is a product of development

children are scientists,
building intuitive
causal maps about
the world

increasing segregation of processing streams

"Divide and conquer strategy"

synaptic pruning

it reflects a process of specialization of different neural structures

e.g. color input to motion processing is stronger in the immature visual system
streams of information get integrated with development

e.g. numerical cognition

mediation of language

e.g. whole brain model (O'Reilly)
Learning mechanisms
goal of reducing errors
(target state-actual state)

teacher corrections
expectations provided by the environment
infant's goals

learning reduces plasticity
less sensitivity to error signals due to expertise

e.g. second language learning

neural network modelling framework
1) forming representations that capture important aspects of the environmental structure

2) not error-shaped

3) simultaneous activation among nodes

4) hebbian learning algorithms


learning after sensitive period may be counterproductive
e.g. phoneme discrimination

extraction of statistical regularities

present both in self-organizing
and error-driven algorithms

e.g. language acquisition :

learner use the natural statistical properties of language to deduce its structure
-learner as active agent

-information shaped by behaviour (eye-gaze)

- models can grow (dynamic network) as a function of errors

face bias
object continuity
encoding specificity
lexical constraints
objects exist and move continuously in time and space
"naïve physics"
thanks for your attention!!
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