Parallel Distributed Processing

Abstract

What makes people smarter than machines? They certainly are not quicker or more precise. Yet people are far better at perceiving objects in natural scenes and noting their relations , at understanding language and retrieving contextually appropriate information from memory, at making plans and carrying out contextually appropriate actions, and at a wide range of other natural cognitive .tasks. People are also far better at learning to do these things more accurately and fluently through processing experience. What is the basis for these differences? One answer, perhaps the classic one we might expect from artificial intelligence, is " software." If we only had the right computer program, the argument goes , we might be able to capture the fluidity and adaptability of human information proceSSIng. Certainly this answer is partially correct. There have been great breakthroughs in our understanding of cognition as a result of the development of expressive high-level computer languages and powerful algorithms. No doubt there will be more such breakthroughs in the future. However , we do not think that software is the whole story. In our view, people are smarter than today s computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at. In this chapter, we will show through examples that these tasks generally require the simultaneous consideration of many pieces of information or constraints. Each constraint may be imperfectly specified and ambiguous, yet each can playa potentially .-J THE POP PERSPECTIVE decisive role in determining the outcome of processing. After examining these points , we will introduce a computational framework for modeling cognitive processes that seems well suited to exploiting these constaints and that seems closer than other frameworks to the style of computation as it might be done by the brain. We will review several early examples of models developed in this framework, and we will ! show that the mechanisms these models employ can give rise to power-\ ful emergent properties that begin to suggest attractive alternatives to ! traditional accounts of various aspects of cognition. We will also show that models of this class provide a basis for understanding how learning \ can occur spontaneously, as a by-product of processing activity. Hundreds of times each day we reach for things. We nearly never think about these acts of reaching. And …

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