Recent infant cognition research suggests that core knowledge involves event-type representations: During perception, the mind automatically categorizes physical events into broad types (e.g., occlusion and containment), which then guide attention to different properties (e.g., with width processed at a younger age than height in containment events but not occlusion events). We tested whether this aspect of infant cognition also structures adults' visual processing. In 6 experiments, adults had to detect occasional changes in ongoing dynamic displays that depicted repeating occlusion or containment events. Mirroring the developmental progression, change detection was better for width versus height changes in containment events, but no such difference was found for otherwise equivalent occlusion events, even though most observers were not even aware of the subtle occlusion-containment difference. These results suggest for the first time that event-type representations exist and operate automatically and unconsciously as part of the underlying currency of adult visual cognition.
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http://dx.doi.org/10.1037/a0037750 | DOI Listing |
Cognition
January 2025
Central European University, Vienna, Austria. Electronic address:
How events are ordered in time is one of the most fundamental pieces of information guiding our understanding of the world. Linguistically, this order is often not mentioned explicitly. Here, we propose that the mental construal of temporal order in language comprehension is based on event-structural properties.
View Article and Find Full Text PDFJ Exp Psychol Gen
October 2024
Department of Psychology, Yale University.
During visual processing, input that is continuous in space and time is segmented, resulting in the representation of discrete tokens-objects or events. And there has been a great deal of research about how object representations are generalized into types-as when we see an object as an instance of a broader category (e.g.
View Article and Find Full Text PDFBMC Bioinformatics
July 2024
Aural & Language Intelligence, Institute for Infocomm Research, Agency for Science, Technology and Research, 1 Fusionopolis Way, Singapore, Singapore.
Background: Detecting event triggers in biomedical texts, which contain domain knowledge and context-dependent terms, is more challenging than in general-domain texts. Most state-of-the-art models rely mainly on external resources such as linguistic tools and knowledge bases to improve system performance. However, they lack effective mechanisms to obtain semantic clues from label specification and sentence context.
View Article and Find Full Text PDFJ Comput Biol
July 2024
Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Research on drug-drug interaction (DDI) prediction, particularly in identifying DDI event types, is crucial for understanding adverse drug reactions and drug combinations. This work introduces a Bidirectional Recurrent Neural Network model for DDI event type prediction (BiRNN-DDI), which simultaneously considers structural relationships and contextual information. Our BiRNN-DDI model constructs drug feature graphs to mine structural relationships.
View Article and Find Full Text PDFBioinformatics
July 2021
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China.
Motivation: Multiple events extraction from biomedical literature is a challenging task for biomedical community. Usually, biomedical event extraction is modeled as two sub-tasks, trigger identification and argument detection. Most existing methods perform these two sub-tasks sequentially, and fail to make full use of the interaction between them, leading to suboptimal results for multiple biomedical events extraction.
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