Chapter 4: Global responses to the integration challenge

Chapter 4:Global responses to the integration challenge

Cognitive Science José Luis Bermúdez / Cambridge University Press 2010

Overview• Explore 2 global responses to the integration challenge

• Model of intertheoretic reduction from philosophy of science• Marr’s tri-level hypothesis

• Sketch out alternative approach

• mental architecture approach


The integration challenge

The challenge of providing an unified account of cognition that draws upon and integrates the whole space

• Many regions within the “space” of cognitive science remain little studied

• The “space” is not organized by discipline


3 approaches to IC

Local integrations• Examples of specific cases where cognitive scientists have built bridges across levels of explanation and between disciplines

Global models of integration• Blueprints for solving the integration challenge

The mental architectures approach


Models of global integration

•Two main candidates:

• Models of inter-theoretic reduction derived from philosophy of science

– analogy with unity of science hypothesis in the physical sciences

• Marr’s tri-level hypothesis - explicitly proposed as a way of bridging different levels of explanation


Intertheoretic reduction

• Relation between theories • Model for showing how one theory can be understood in terms of another• Standard examples are all in physics

• Two components• Principles for connecting vocabularies• Derivations of laws


Applicability to cognitive science?• Very few laws in the cognitive sciences

• The laws that there are function very differently from laws in physics

• predictive without being explanatory• effects that themselves need to be explained

• Basic problem – knowledge in cognitive science is not organized in the right sort of way for intertheoretic reduction to be a good model


A Marrian model of unity

•The computational level is the privileged level of explanation

•The tri-level hypothesis gives two top-down relations between levels

• Algorithm at level n+1 computing information-processing problem at level n

• Implementation at level m+1 of algorithm running at level m


Problems for the Marr approach It cannot work as a general model of cognition. Marr’s model is only

applicable to modular systems

• It requires an information-processing task sufficiently circumscribed to be solvable algorithmically

domain-specificity

• Algorithms must be computationally tractable – there can only be a limited number of representational primitives and parameters (on pain of frame problem)

informational encapsulation


Computational analysis and modularity

Basic idea – modular systems are specialized for carrying out very specific information-processing tasks

Two versions:

• Fodor modules

• Darwinian modules

Differ over the extent to which the systems are informationally encapsulated


Examples of modules

Fodor modules:

• Marr’s early visual system

• Face recognition

• Syntactic parsing of heard utterances

• Detecting rhythmic structure of acoustic arrays

Darwinian modules

• cheater detection

• mate selection

• social understanding


Modularity and computational analysis

Computational analysis can only work for systems performing functions that can be algorithmically solved

• Clear specification of what form the output needs to take

• E.g. Marr’s analysis of early visual system

Fodor distinguishes central processing from modular processing

• Central processing can draw on any type of information

• Darwinian modules seem closer to central processing


Mental architectures approach

Starts off from the basic assumption that cognition is a form of information-processing

• Assumption governs all levels of organization (from neurons upwards) and almost all explanatory models/hypotheses within the

individual cognitive sciences

But there is relatively little discussion w/in those disciplines of how information and information-processing are to be understood


Mental architecture

A mental architecture is a model of how the mind is organized and how it works to process information

1) In what format does a cognitive system carry information?

2) How does that system transform and process information?

3) How is the mind as a whole organized into information-processing sub-systems?


Two models of information-processing

The physical symbol system hypothesis• e.g. Turing machine model of information-

processing• associated with classical, symbolic AI

Connectionism/artificial neural networks• neurally-inspired models of information-processing• used to model cognitive/perceptual abilities that have posed problems for classical AI

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Chapter 4: Global responses to the integration challenge