scholarly journals Situational expectancy or association? The influence of event knowledge on the N400

Author(s):  
Elisabeth Rabs ◽  
Francesca Delogu ◽  
Heiner Drenhaus ◽  
Matthew W. Crocker
Keyword(s):  
2009 ◽  
Author(s):  
Susanne Raisig ◽  
Herbert Hagendorf ◽  
Elke E. van der Meer
Keyword(s):  

2021 ◽  
pp. 026565902199554
Author(s):  
Lynn Dempsey

Planning intervention for narrative comprehension deficits requires a thorough understanding of a child’s skill in all component domains. The purpose of this study was to examine the validity of three methods of measuring pre-readers’ event knowledge, an important predictor of story comprehension. Thirty-eight typically developing children (12 males; 26 females) between the ages of 30–59 months ( M = 42.05 SD = 7.62) completed three measures – verbal account, enactment, picture-sequencing – that tapped their knowledge of two different events before listening to stories based on each of those events and completing story comprehension tasks. Scores for verbal account and enactment, but not for picture sequencing, (1) were moderately correlated with comprehension scores for the corresponding story; (2) reflected differential knowledge of the two events, though not in the expected direction; (3) were moderately correlated with one another in the case of each story. In general measures for the same event were more highly correlated with one another than with measures of the other event. Overall, results suggest that verbal account and enactment may yield information useful for clinicians planning intervention for children with narrative comprehension deficits.


2016 ◽  
Vol 7 ◽  
Author(s):  
Dickey Michael ◽  
Holcomb Michelle ◽  
Warren Tessa
Keyword(s):  

Author(s):  
Tingting Tang ◽  
Wei Liu ◽  
Weimin Li ◽  
Jinliang Wu ◽  
Haiyang Ren

2010 ◽  
Vol 63 (4) ◽  
pp. 489-505 ◽  
Author(s):  
Klinton Bicknell ◽  
Jeffrey L. Elman ◽  
Mary Hare ◽  
Ken McRae ◽  
Marta Kutas

Author(s):  
Richard Clewley ◽  
Jim Nixon

Objective We extend the theory of conceptual categories to flight safety events, to understand variations in pilot event knowledge. Background Experienced, highly trained pilots sometimes fail to recognize events, resulting in procedures not being followed, damaging safety. Recognition is supported by typical, representative members of a concept. Variations in typicality (“gradients”) could explain variations in pilot knowledge, and hence recognition. The role of simulations and everyday flight operations in the acquisition of useful, flexible concepts is poorly understood. We illustrate uses of the theory in understanding the industry-wide problem of nontypical events. Method One hundred and eighteen airline pilots responded to scenario descriptions, rating them for typicality and indicating the source of their knowledge about each scenario. Results Significant variations in typicality in flight safety event concepts were found, along with key gradients that may influence pilot behavior. Some concepts were linked to knowledge gained in simulator encounters, while others were linked to real flight experience. Conclusion Explicit training of safety event concepts may be an important adjunct to what pilots may variably glean from simulator or operational flying experiences, and may result in more flexible recognition and improved response. Application Regulators, manufacturers, and training providers can apply these principles to develop new approaches to pilot training that better prepare pilots for event diversity.


2019 ◽  
Vol 25 (4) ◽  
pp. 483-502 ◽  
Author(s):  
E. Chersoni ◽  
E. Santus ◽  
L. Pannitto ◽  
A. Lenci ◽  
P. Blache ◽  
...  

AbstractMost compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a structured distributional model (SDM) that combines word embeddings with formal semantics and is based on the assumption that sentences represent events and situations. The semantic representation of a sentence is a formal structure derived from discourse representation theory and containing distributional vectors. This structure is dynamically and incrementally built by integrating knowledge about events and their typical participants, as they are activated by lexical items. Event knowledge is modelled as a graph extracted from parsed corpora and encoding roles and relationships between participants that are represented as distributional vectors. SDM is grounded on extensive psycholinguistic research showing that generalized knowledge about events stored in semantic memory plays a key role in sentence comprehension.We evaluate SDMon two recently introduced compositionality data sets, and our results show that combining a simple compositionalmodel with event knowledge constantly improves performances, even with dif ferent types of word embeddings.


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