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connectionism1

Computational Theory of Mind

  • Information processing involves symbol manipulation via rules.

  • Distinction made between:

    • Structure (symbols)

    • Process (rules, algorithms)

  • Types of processing:

    • Sentential

    • Propositional

    • Sequential

  • Language of thought concept.

Connectionist Theory of Mind

  • Alternative view of information processing, also known as:

    • Parallel Distributed Processing (PDP)

    • Neural Networks

  • Historical contributions:

    • McCulloch & Pitts (1940s)

    • Rosenblatt (1960s) - perceptron (2-layer network)

Applications of Connectionist Networks

  • Commonly utilized to classify patterns by defining decision regions in pattern space.

  • Examples from Churchland’s "The Engine of Reason, the Seat of the Soul" include:

    • Color Space: Classification of colors based on opponent cells.

    • Taste Space: Categorization of tastes with respect to different activation levels for various substances.

    • Face Space: Parameters like nose width, eye separation, and mouth fullness classified in three dimensions.

Connectionist Networks Overview

  • Definition: Networks of simple units (processors) operating simultaneously (in parallel) and inspired by biological processes.

Processing Stages

  1. Input Phase: Input information analogous to perception.

  2. Computation Phase: Transformation of input analogous to thinking.

  3. Output Phase: Generates output analogous to action.

Structure of Connectionist Networks

  • Each processing unit behaves as a simplified neuron:

    • Computes total incoming signals from previous layers.

    • Adopts level of internal activation based on these signals.

    • Generates outgoing signals modulated by connection weights.

Connection Weights

  • Function as communication channels between processing units (similar to synapses).

  • Can be:

    • Inhibitory (negative weights)

    • Excitatory (positive weights)

    • Strength of connection varies based on absolute values (range: -1 to +1).

Training Connectionist Networks

  • Initial connection weights assigned randomly.

  • Network trained via presenting patterns associated with known responses.

  • Error term calculated as the difference between desired and actual outputs:

    • Used to adjust connection weights via backpropagation.

    • Trained until error cannot be further reduced.