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Despite the importance of the agent role for language grammar and processing, its definition and features are still controversially discussed in the literature on semantic roles. Moreover, diagnostic tests to dissociate agentive from non-agentive roles are typically applied with qualitative introspection data. We investigated whether quantitative acceptability ratings obtained with a well-established agentivity test, the DO-cleft, provide evidence for the feature-based prototype account of (Dowty, David R. 1991. Thematic protoroles and argument selction. Language 67(3). 547-619) postulating that agentivity increases with the number of agentive features that a role subsumes. We used four different intransitive verb classes in German and collected acceptability judgements from non-expert native speakers of German. Our results show that sentence acceptability increases linearly with the number of agentive features and, hence, agentivity. Moreover, our findings confirm that sentience belongs to the group of proto-agent features. In summary, this suggests that a multidimensional account including a specific mechanism for role prototypicality (feature accumulation) successfully captures gradient acceptability clines. Quantitative acceptability estimates are a meaningful addition to linguistic theorizing.
Although the N400 was originally discovered in a paradigm designed to elicit a P300 (Kutas and Hillyard, 1980), its relationship with the P300 and how both overlapping event-related potentials (ERPs) determine behavioral profiles is still elusive. Here we conducted an ERP (N = 20) and a multiple-response speed-accuracy tradeoff (SAT) experiment (N = 16) on distinct participant samples using an antonym paradigm (The opposite of black is white/nice/yellow with acceptability judgment). We hypothesized that SAT profiles incorporate processes of task-related decision-making (P300) and stimulus-related expectation violation (N400). We replicated previous ERP results (Roehm et al., 2007): in the correct condition (white), the expected target elicits a P300, while both expectation violations engender an N400 [reduced for related (yellow) vs. unrelated targets (nice)]. Using multivariate Bayesian mixed-effects models, we modeled the P300 and N400 responses simultaneously and found that correlation between residuals and subject-level random effects of each response window was minimal, suggesting that the components are largely independent. For the SAT data, we found that antonyms and unrelated targets had a similar slope (rate of increase in accuracy over time) and an asymptote at ceiling, while related targets showed both a lower slope and a lower asymptote, reaching only approximately 80% accuracy. Using a GLMM-based approach (Davidson and Martin, 2013), we modeled these dynamics using response time and condition as predictors. Replacing the predictor for condition with the averaged P300 and N400 amplitudes from the ERP experiment, we achieved identical model performance. We then examined the piecewise contribution of the P300 and N400 amplitudes with partial effects (see Hohenstein and Kliegl, 2015). Unsurprisingly, the P300 amplitude was the strongest contributor to the SAT-curve in the antonym condition and the N400 was the strongest contributor in the unrelated condition. In brief, this is the first demonstration of how overlapping ERP responses in one sample of participants predict behavioral SAT profiles of another sample. The P300 and N400 reflect two independent but interacting processes and the competition between these processes is reflected differently in behavioral parameters of speed and accuracy.