Words of a Feather Flock Together: The Role of Morphology in Human Auditory Word Recognition Models

When we listen to spoken language, we recognise words extremely quickly, even though speech is a continuous and constantly changing sound signal. This thesis investigates how people achieve this, with a particular focus on words that consist of multiple meaningful parts, such as un-happi-ness or re-view. While it is well established that such internal word structure plays a role in reading, it has long remained unclear whether and how it also influences the recognition of spoken words. Using large-scale experimental data and computational models, this thesis examines how listeners make use of the internal structure of spoken words. The results suggest that word recognition is neither driven solely by whole words nor exclusively by their component parts. Instead, recognition reflects a gradual interaction in which both the full word and its parts can contribute. In addition, words are recognised more quickly when they belong to a larger “word family” of related words, especially when these related words are similar in both sound and meaning. These findings suggest that spoken word recognition is a dynamic and probabilistic process, in which word structure subtly shapes how evidence accumulates. The thesis also demonstrates how the method of computational modelling can help clarify and test theories about how the human brain recognises spoken language.