Developmental stuttering is a widely discussed speech fluency disorder. Research on its mechanism has focused on an atypical interface between the planning and execution processes, known collectively as the EXPLAN model. However, it remains unclear how this atypical interface influences people who stutter. A straightforward assumption is that stuttering speakers adopt a smaller scope of speech planning, whereas a defect in word retrieval can be confounding. To shed light on this issue, we took the semantic blocking effect as an index (...) to examine lexical planning in word and phrase production. In Experiment 1, for word production, pictures from the same semantic category were combined to form homogeneous blocks, and pictures from different categories were combined to form heterogeneous blocks. A typical effect of semantic blocking showing longer naming latencies for homogeneous blocks than heterogeneous ones was observed for both stuttering and fluent speakers. However, this effect was smaller for stuttering speakers, when it was subject to lexical defects in stuttering. In Experiment 2, for a conjoined noun phrase production task, the pictures referring to the first noun were manipulated into homogeneous and heterogeneous conditions. The semantic blocking effect was also much smaller for stuttering speakers, indicating a smaller scope of lexical planning. Therefore, the results provided more evidence in support of the EXPLAN model and indicated that a smaller scope of lexical planning rather than lexical defects causes the atypical interface for stuttering. Moreover, a comparison between these two tasks showed that the study findings have implications for syntactic defects in stuttering. (shrink)

Previous studies have shown that multiple reference frames are available and compete for selection during the use of spatial terms such as “above.” However, the mechanisms that underlie the selection process are poorly understood. In the current paper we present two experiments and a comparison of three computational models of selection to shed further light on the nature of reference frame selection. The three models are drawn from different areas of human cognition, and we assess whether they may be applied (...) to a reference frame selection by examining their ability to account for both existing and new empirical data comprising acceptance rates, response times, and response time distributions. These three models are the competitive shunting model, the leaky competing accumulator model, and a lexical selection model. Model simulations show that only the LCA model satisfactorily accounts for the empirical observations. The key properties of this model that seem to drive its success are its bounded linear activation function, its number and type of processing stages, and its use of decay. Uncovering these critical properties has important implications for our understanding not only of spatial term use, in particular, but also of conflict and selection in human cognition more generally. (shrink)

Syntactic priming in language production is the increased likelihood of using a recently encountered syntactic structure. In this paper, we examine two theories of why speakers can be primed: error‐driven learning accounts (Bock, Dell, Chang, & Onishi, 2007; Chang, Dell, & Bock, 2006) and activation‐based accounts (Pickering & Branigan, 1999; Reitter, Keller, & Moore, 2011). Both theories predict that speakers should be primed by the syntactic choices of others, but only activation‐based accounts predict that speakers should be able to prime (...) themselves. Here we test whether speakers can be primed by their own productions in three behavioral experiments and find evidence of structural persistence following both comprehension and speakers’ own productions. We also find that comprehension‐based priming effects are larger for rarer syntactic structures than for more common ones, which is most consistent with error‐driven accounts. Because neither error‐driven accounts nor activation‐based accounts fully explain the data, we propose a hybrid model. (shrink)