Competing Biases in Real-Time Sentence Processing

In sentences such as “John remembered the boy took some time to rest”, the locally ambiguous noun phrase (NP) “the boy” is initially parsed as the direct object of the matrix verb “remembered” (the object analysis). When the embedded verb “took” is encountered, the NP is revised as the subject of the embedded clause (the subject analysis). Open questions are how the real-time resolution of this complement ambiguity is influenced by semantic/categorial constraints (i.e., whether the locally ambiguous NP is a semantically/categorially appropriate object of the matrix verb) and selectional frequency (i.e., the frequency with which the matrix verb takes a direct object NP). The present study addressed these questions and also examined whether temporal adjuncts, which can bias parsing towards the object analysis (e.g., “John remembered the boy after…”), influence real-time ambiguity resolution. The results showed that semantic/categorial constraints and selectional frequency drive the processor towards the subject analysis before embedded-verb disambiguation. However, temporal adjuncts gradually increased in influence and ultimately overrode these biases during processing. The observed parsing process was also simulated within an interactive constraint-based framework. Together, the experimental and simulation results suggest that real-time sentence processing is dynamically shaped by multiple competing biases.