Research on the acquisition of morphology commonly predicts
that agglutinating systems should be easier to learn than
fusional systems. This is argued to be due to compositional
transparency: the mapping between morphemes and meanings
is one-to-one in agglutinating systems, but not in fusional
systems. This is supported by findings in first and second
language learning (Goldschneider & DeKeyser 2001, Slobin
1973), typology (Dressler 2003, Haspelmath & Michaelis
2017), and language evolution (Brighton 2002). We present
findings from a series of artificial language learning
experiments which complicate this picture. First, we show that
when only two features (e.g., NOUN CLASS and NUMBER) are
morphologically encoded, the learnability of fusional and
agglutinating systems does not differ significantly. This
finding holds when learners are given an additional cue to
morpheme segmentation–which in principle should make the
agglutinating system easier. However, the error patterns of the
two groups provide some evidence that learners might have a
bias for transparent structures. Our results suggest that the
advantages of agglutinating over fusional systems may be
overstated, particularly when a small number of features are
encoded. Since agglutinating systems likely bear additional
costs (e.g., segmentation, longer word length, and the online
cost of mapping between morphemes and meanings), such
systems do not guarantee learning ease under all
circumstances
