Much research indicate that language processing is expectation-based, drawing on statistical patterns in the input (MacDonald 2013). In this talk, I present evidence for this idea from experimental and corpus-based studies on the comprehension and production of grammatical functions (GFs) in Swedish transitive sentences. The preferred word order in such sentences is SVO. However, Swedish also allows for OVS word ordering, with the object placed sentence-initially and the subject post-verbally. Since the NP argument GFs of such sentences may not be correctly determined from the sentence constituent order (i.e., NPs and verb), they are potentially ambiguous. They can therefore be costly to comprehend when the initial NP lacks case marking. In such cases, comprehenders need to revise their initial sentence interpretation as subject-initial upon encountering the disambiguating post-verbal subject NP (Hörberg et al. 2013).

However, corpus-based and typological research shows that GFs correlate with prominence-based (e.g., animacy and definiteness) and verb-semantic (e.g., volitionality) information, both in the frequency distributions in language use within individual languages (e.g., Bouma 2008), and the grammatical encoding of GFs across languages (e.g., Aissen 2003), creating complex statistical regularities in the distribution of  prominence-based, morphosyntactic and verb-semantic properties. These properties and their interplay may be utilized during encoding and decoding of GFs in production and comprehension in order to overcome potential ambiguity problems.

I will present results from a corpus study of written Swedish investigating the distribution of these properties in subject-initial, object-initial and passive sentences. I will argue that writers tend to balance their use of these properties in order to avoid GF ambiguities. In particular, writers less frequently use OVS sentences when other morphosyntactic or animacy-based information about GFs are unavailible (Hörberg 2018). In such cases, writers more frequently use the unambiguous passive construction.

I will then present an expectation-based model of processing difficulty during incremental GF assignment in Swedish transitive sentences, based upon the statistical regularities observed in the corpus data (Hörberg 2016). Processing difficulty is quantified as the on-line change in the expectation of a particular GF assignment (subject- or object-initial) upon encountering the properties of a constituent (e.g., NP2) with respect to the previously encountered properties (e.g., NP1 and verb(s)) in terms of Bayesian surprise.

I will finally provide empirical evidence for this expectation-based model on the basis of a self-paced reading experiment, testing some of the most prominent model predictions. Here, by-region reading times converged with the region-specific Bayesian surprise predicted by the model. For example, NP2 reading times in ambiguous OVS sentences were mitigated when NP1 animacy and its interaction with verb class bias towards an object-initial word order.

These findings provide evidence for the expectation-based account in that they indicate that language users are sensitive to statistical regularities in their language during both production and comprehension of GFs. During production, writers seem to balance their use of morphosyntactic and prominence-based cues to GFs in a manner that accommodates comprehension. During comprehension, incremental GF assignment draws upon statistical regularities in the distribution of morphosyntactic, prominence-based and verb-semantic properties.