While cognitive changes in aging and neurodegenerative disease have been widely studied, language changes in these populations are less well understood. Inflecting novel words in a language with complex inflectional paradigms provides a good opportunity to observe how language processes change in normal and abnormal aging. Studies of language acquisition suggest that children inflect novel words based on their phonological similarity to real words they already know. It is unclear whether speakers continue to use the same strategy when encountering novel (...) words throughout the lifespan or whether adult speakers apply symbolic rules. We administered a simple speech elicitation task involving Finnish‐conforming pseudo‐words and real Finnish words to healthy older adults, individuals with mild cognitive impairment, and individuals with Alzheimer's disease (AD) to investigate inflectional choices in these groups and how linguistic variables and disease severity predict inflection patterns. Phonological resemblance of novel words to both a regular and an irregular inflectional type, as well as bigram frequency of the novel words, significantly influenced participants' inflectional choices for novel words among the healthy elderly group and people with AD. The results support theories of inflection by phonological analogy (single‐route models) and contradict theories advocating for formal symbolic rules (dual‐route models). (shrink)

Determining optimal units of representing morphologically complex words in the mental lexicon is a central question in psycholinguistics. Here, we utilize advances in computational sciences to study human morphological processing using statistical models of morphology, particularly the unsupervised Morfessor model that works on the principle of optimization. The aim was to see what kind of model structure corresponds best to human word recognition costs for multimorphemic Finnish nouns: a model incorporating units resembling linguistically defined morphemes, a whole-word model, or a (...) model that seeks for an optimal balance between these two extremes. Our results showed that human word recognition was predicted best by a combination of two models: a model that decomposes words at some morpheme boundaries while keeping others unsegmented and a whole-word model. The results support dual-route models that assume that both decomposed and full-form representations are utilized to optimally process complex words within the mental lexicon. (shrink)