Retroactive interference in neural networks and in humans: The effect of pattern-based learning

Abstract

Catastrophic interference is addressed as a problem that arises from pattern-based learning algorithms. As such, it is not limited to artificial neural networks but can be demonstrated in human subjects in so far as they use a pattern-based learning strategy. The experiment tests retroactive interference in humans learning lists of consonant-vowel-consonant nonsense syllable pairs. Results show significantly more interference for subjects learning patterned lists than subjects learning arbitrarily paired lists. To examine how different learning strategies depend on the structure of the learning task, a mixture-of-experts neural network model is presented. The results show how these strategies may interact to give rise to the results seen in the human data.