Learning attribute relevance in context in instance-based learning algorithms

Aha, D. W., and Goldstone, R. L. (1990). Learning attribute relevance in context in instance-based learning algorithms. Proceedings of the Twelfth Annual Conference of the Cognitive Science Society. (pp. 141-148). Hillsdale, New Jersey: Lawrence Erlbaum Associates.

There has been an upsurge of interest, in both artificial intelligence and cognitive psychology, in exemplar-based process models of categorization, which preserve specific instances instead of maintaining abstractions derived from them. Recent exemplar-based models provided accurate fits for subject results in a variety of experiments because, in accordance with Shepard`s (1987) observations, they define similarity to degrade exponentially with the distance between instances in psychological space. Although several researchers have shown that an attribute`s relevance in similarity calculations varies according to its context (i.e., the values of the other attributes in the instance and the target concept,” previous exemplar models define attribute relevance to be invariant across all instances. This paper introduces the GCM-ISW model, an extension of Nosofsky`s GCM model that uses context-specific attribute weights for categorization tasks. Since several researchers have reported that humans make context-sensitive classification decision, our model will fit subject data more accurately when attribute relevance is context-sensitive. We also introduce a process component for GCM-ISW and show that its learning rate is significantly faster than the rates of both previous exemplar-based process models when attribute relevance varies among instances. GCM-ISM is both computationally more efficient and more psychologically plausible than previous exemplar-based models.