Learning in complex, multi-component cognitive systems: Different learning challenges within the same system

Using word learning as an example of a complex system, we investigated how differences in the structure of the subcomponents in which learning occurs can have significant consequences for the challenge of integrating new information within such systems. Learning a new word involves integrating information into the two key stages/subcomponents of processing within the word production system. In the first stage, multiple semantic features are mapped onto a single word. Conversely, in the second stage, a single word is mapped onto multiple segmental features. We tested whether the unitary goal of word learning leads to different local outcomes in these two stages because of their reversed mapping patterns. Neurotypical individuals (N = 17) learned names and semantic features for pictures of unfamiliar objects presented in semantically related, segmentally related and unrelated blocks. Both similarity types interfered with word learning. However, feature learning was differentially affected within the two subcomponents of word production. Semantic similarity facilitated learning distinctive semantic features (i.e., features unique to each item), whereas segmental similarity facilitated learning shared segmental features (i.e., features common to several items in a block). These results are compatible with an incremental learning model in which learning not only strengthens certain associations but also weakens others according to the local goals of each subcomponent. More generally, they demonstrate that the same overall learning goal can lead to opposite learning outcomes in the subcomponents of a complex system. The general principles uncovered may extend beyond word learning to other complex systems with multiple subcomponents.