Rethinking Innatenessasks the question, "What does it really mean to say that a behavior is innate?" The authors describe a new framework in which interactions, occurring at all levels, give rise to emergent forms and behaviors. These outcomes often may be highly constrained and universal, yet are not themselves directly contained in the genes in any domain-specific way. One of the key contributions of Rethinking Innatenessis a taxonomy of ways in which a behavior can be innate. These include constraints at the level of representation, architecture, and timing; typically, behaviors arise through the interaction of constraints at several of these levels. The ideas are explored through dynamic models inspired by a new kind of "developmental connectionism," a marriage of connectionist models and developmental neurobiology, forming a new theoretical framework for the study of behavioral development. While relying heavily on the conceptual and computational tools provided by connectionism, Rethinking Innateness also identifies ways in which these tools need to be enriched by closer attention to biology.

Preface
New perspectives on development
The problem of change
Advances in neuroscience
Neural computation: the connectionist revolution
Our perspective
From genes to behavior
How genes do their work
How cells come to be
The problem of interaction
Taking a biological perspective
What does it mean to be innate?
Ways to be innate: A framework
(1) Representational constraints
(2) Architectural constraints
(3) Chronotopic constraints
On domain specificity
The shape of change
Partial knowledge
The value of simulations
Why connectionism?
A conversation
Nuts and bolts (or nodes and weights)
Basic concepts
Learning
Hebbian learning
The Perceptron Convergence Procedure
Similarity in neural networks--a strength and a weakness
Solving the problem: Allowing internal representations
Backpropagation of error (informal account)
Formal account
Learning as gradient descent in weight space
Other architectures and learning algorithms
Issues in connectionist models
Representing time
Scaling and modularity
Where does the teacher come from? Supervised vs. unsupervised learning
Finding first principles
Who's in charge? Eliminating the homunculus
Connectionist representations
Connectionist processing: The importance of nonlinearity
Final words: What connectionism is, and is not
Just how rasa is the tabula, anyway?
Modularity
Do connectionist models have rules?
Is connectionist neo-Behaviorism?
The importance of biology
Ontogenetic development: A connectionist synthesis
Introduction
Development in terms of emergent properties
The child's sensitivity to faces
The child's sensitivity to speech and language
Predicting the next sound
Vocabulary development
Learning the past tense
The child's sensitivity to events in the physical world
Fast Learning
Readiness
Simulating Cognitive Development
The shape of change
Dynamical systems
Dynamics and nonlinearity in neural networks
Nonlinearity in networks
Interactions: A case study in nonlinearity
Dynamics
Dynamics in learning
Readiness, stages of learning, and bifurcations
Brain development
Basic questions
What and where: The issue of localization
How and when do things develop in the brain?
The question of "who": Is it the same for everyone?
Building a vertebrate brain
Embryological development of the brain
The basic vertebrate brain plan
Cerebral cortex and functional areas: Protomap or protocortex?
Plasticity
Early plasticity in vertebrates and infrahuman primates
Varying extent of sensory input
Redirecting input
Transplanting cortex
Lesion studies of infant monkeys
Plasticity in adult organisms
Sensitive periods
And finally. . . the human brain
Human brain structure: Differences and similarities to other mammals
Development of the human brain4
Table 5.2 Plasticity and (re)organization in the human brain
Conclusion
A shared definition of knowledge and change
Representational plasticity
The importance of noise
Interactions, all the way down
Brain systems level interaction
Chick imprinting
A connectionist model of imprinting and object recognition
The importance of time
Tieing it all together
Interactions occur at all levels
Interactions increase the complexity possible in development
Why development takes time
Rethinking innateness
Where does knowledge come from ?
A crucial point: Mechanism and content are not the same thing
Dramatic effects can be produced by small changes
Multiplicity underlying unity: A single event has many causes, and the same event can come about in ...
On knowledge
Why development?
What we are not
Emergent form
Models: brain or behavior
Does anyone disagree?
Twelve arguments about innate representations, with special reference to language
Species specificity
Genetically based language disorders Localization
Localization I: Lesion studies
Localization II: Activation studies of grammar in the normal brain
Structural eccentricity
Poverty of the stimulus
Universals
Modularity of processing
Dissociations
Maturational course
Critical periods
Robustness
Where do we go from here?
Multi-tasking in complex environments
Active and goal-oriented models
Social models
Higher-level cognition
More realistic brain models
A final note
References
Subject index
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Rethinking Innateness

9780262550307

026255030X

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