The Spacing Effect on Nettalk, a Massively-parallel Network

NETtalk is a massively-parallel network that learns to convert English text to phonemes. In NETtalk, the memory representations are shared among many processing units, and these representations are learned by practice. In humans, distributed practice is more effective for longterm retention than massed practice, and we wondered whether learning in NETtalk had similar properties. NETtalk was tested on cued paired-associate recall using nonwords as stimuli. Retention of these target items was measured as a function of spacing, or the number of interspersed items between successive repetitions of the target. A significant advantage for spaced or distributed items was found for spacings of up to forty intervening items when tested at a retention interval of 64 items. Conversely, a significant advantage for massed items was found if testing immediately followed study. These results'are strikingly similar to the results of many experiments using human subjects and suggest an explanation based on distributed representations in massively-parallel network architectures.