2020
DOI: 10.1016/j.actpsy.2020.103162
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The activation of object-state representations during online language comprehension

Abstract: Understanding the time-course of event knowledge activation is crucial for theories of language comprehension. We report two experiments using the 'visual world paradigm' (VWP) that investigated the dynamic mapping between object-state representations and real-time language processing. In Experiment 1, participants heard sentences that described events resulting in either a substantial change of state (e.g. The chef will chop the onion) or a minimal change of state (e.g. The chef will weigh the onion). Concurr… Show more

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Cited by 4 publications
(5 citation statements)
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“…The participants were not asked to give any behavioral responses such as pressing keys or clicking a mouse. The "look and listen" version of the visual world paradigm has been widely used in previous studies (e.g., Altmann & Kamide, 1999Kang et al, 2020;Salverda et al, 2011). In a task-oriented visual world paradigm, participants are instructed to move or point at the target entity.…”
Section: Task and Materialsmentioning
confidence: 99%
“…The participants were not asked to give any behavioral responses such as pressing keys or clicking a mouse. The "look and listen" version of the visual world paradigm has been widely used in previous studies (e.g., Altmann & Kamide, 1999Kang et al, 2020;Salverda et al, 2011). In a task-oriented visual world paradigm, participants are instructed to move or point at the target entity.…”
Section: Task and Materialsmentioning
confidence: 99%
“…The sample size was determined based on a previous study (Kang et al, 2020). Compared to the previous study, the present study has fewer conditions (2 vs. 4) but more trials per condition than the previous study (12 vs. 9).…”
Section: Methods Participantsmentioning
confidence: 99%
“…In the 2nd-9th time windows, we included Degree of Change, Object-state, their interaction as fixed effects, participants as random effects in bysubject models, and items as random effects in by-item models. 1 See example models below: By-subject<-lmer(Trans_Prop∼Degree-of-Change * Object -state + (1| Subject), data = T2) 1 We also analyzed the data using linear mixed models by including Object-state, Degree of Change, and their interactions as fixed effects, and both participants and items as random effects by following Kang et al (2020). During the 3rd time window, there was a significant interaction among native speakers (χ 2 = 5.70, p = 0.017), but not among non-native speakers (χ 2 = 0.037, p = 0.848).…”
Section: Data Processingmentioning
confidence: 99%
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