Language of Thought Hypothesis - Relation To Connectionism

Relation To Connectionism

Connectionism is a recent applied approach to artificial intelligence that often accepts a lot of the same theoretical framework that LOTH accepts; that mental states are computational and causally efficacious and very often that they are representational. However, connectionism stresses the possibility of thinking machines, most often realized as neural networks, an inter-connectional set of nodes, and describes mental states as able to create memory by modifying the strength of these connections over time. Some popular types of neural networks are interpretations of units, and learning algorithm. "Units" can be interpreted as neurons or groups of neurons. A learning algorithm is such that, over time, a change in connection weight is possible, allowing networks to modify their connections. Connectionist neural networks are able to change over time via their activation. An activation is a numerical value that represents any aspect of a unit that a neural network has at any time. Activation spreading is the spreading or taking over of other over time of the activation to all other units connected to the activated unit.

Since connectionist models can change over time, supporters of connectionism claim that it can solve the problems that LOTH brings to classical AI. These problems are those that show that machines with a LOT syntactical framework very often are much better at solving problems and storing data than human minds, yet much worse at things that the human mind is quite adept at such as recognizing facial expressions, objects in photographs and understanding nuanced gestures. Fodor defends LOTH by arguing that a connectionist model is just some realization or implementation of the classical computational theory of mind and therein necessarily employs a symbol-manipulating LOT.

Fodor and Zenon Pylyshyn use the notion of cognitive architecture in their defense. Cognitive architecture is the set of basic functions of an organism with representational input and output. They argue that it is a law of nature that cognitive capacities are productive, systematic and inferentially coherent - they have the ability to produce and understand sentences of a certain structure if they can understand one sentence of that structure. A cognitive model must have a cognitive architecture that explains these laws and properties in some way that is compatible with the scientific method. Fodor and Pylyshyn say that cognitive architecture can only explain the property of systematicity by appealing to a system of representations and that connectionism either employs a cognitive architecture of representations or else does not. If it does, then connectionism uses LOT. If it does not then it is empirically false.

Connectionists have responded to Fodor and Pylyshyn by denying that connectionism uses LOT, by denying that cognition is essentially a function that uses representational input and output or denying that systematicity is a law of nature that rests on representation.