The Experts below are selected from a list of 11706 Experts worldwide ranked by ideXlab platform
Jeffrey M. Zacks - One of the best experts on this subject based on the ideXlab platform.
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Event boundaries in perception affect memory encoding and updating
Journal of Experimental Psychology: General, 2009Co-Authors: Khena M Swallow, Jeffrey M. Zacks, Richard A AbramsAbstract:Memory for naturalistic Events over short delays is important for visual scene processing, reading comprehension, and social interaction. The research presented here examined relations between how an ongoing activity is Perceptually segmented into Events and how those Events are remembered a few seconds later. In several studies, participants watched movie clips that presented objects in the context of goal-directed activities. Five seconds after an object was presented, the clip paused for a recognition test. Performance on the recognition test depended on the occurrence of Perceptual Event boundaries. Objects that were present when an Event boundary occurred were better recognized than other objects, suggesting that Event boundaries structure the contents of memory. This effect was strongest when an object's type was tested but was also observed for objects' Perceptual features. Memory also depended on whether an Event boundary occurred between presentation and test; this variable produced complex interactive effects that suggested that the contents of memory are updated at Event boundaries. These data indicate that Perceptual Event boundaries have immediate consequences for what, when, and how easily information can be remembered.
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Human brain activity time-locked to Perceptual Event boundaries
Nature Neuroscience, 2001Co-Authors: Jeffrey M. Zacks, Todd S. Braver, Margaret A. Sheridan, David I. Donaldson, Abraham Z. Snyder, John M. Ollinger, Randy L. Buckner, Marcus E. RaichleAbstract:Temporal structure has a major role in human understanding of everyday Events. Observers are able to segment ongoing activity into temporal parts and sub-parts that are reliable, meaningful and correlated with ecologically relevant features of the action. Here we present evidence that a network of brain regions is tuned to Perceptually salient Event boundaries, both during intentional Event segmentation and during naive passive viewing of Events. Activity within this network may provide a basis for parsing the temporally evolving environment into meaningful units.
Noboru Babaguichi - One of the best experts on this subject based on the ideXlab platform.
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a new linguistic Perceptual Event model for spatio temporal Event detection and personalized retrieval of sports video
International Conference on Image Analysis and Processing, 2009Co-Authors: Minhson Dao, Sharma Ishan Nath, Noboru BabaguichiAbstract:This paper proposes a new linguistic-Perceptual Event model tailoring to spatio-temporal Event detection and conceptual-visual personalized retrieval of sports video sequences. The major contributions of the proposed model are hierarchical structure, independence between linguistic and Perceptual part, and ability of capturing temporal information of sports Events. Thanks to these advanced contributions, it is very easy to upgrade model Events from simple to complex levels either by self-studying from inner knowledge or by being taught from plug-in additional knowledge. Thus, the proposed model not only can work well in unwell structured environments but also is able to adapt itself to new domains without the need (or with a few modification) for external re-programming, re-configuring and re-adjusting. Thorough experimental results demonstrate that Events are modeled and detected with high accuracy and automation, and users' expectation of personalized retrieval is highly satisfied.
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ICIAP - A New Linguistic-Perceptual Event Model for Spatio-Temporal Event Detection and Personalized Retrieval of Sports Video
Image Analysis and Processing – ICIAP 2009, 2009Co-Authors: Minhson Dao, Sharma Ishan Nath, Noboru BabaguichiAbstract:This paper proposes a new linguistic-Perceptual Event model tailoring to spatio-temporal Event detection and conceptual-visual personalized retrieval of sports video sequences. The major contributions of the proposed model are hierarchical structure, independence between linguistic and Perceptual part, and ability of capturing temporal information of sports Events. Thanks to these advanced contributions, it is very easy to upgrade model Events from simple to complex levels either by self-studying from inner knowledge or by being taught from plug-in additional knowledge. Thus, the proposed model not only can work well in unwell structured environments but also is able to adapt itself to new domains without the need (or with a few modification) for external re-programming, re-configuring and re-adjusting. Thorough experimental results demonstrate that Events are modeled and detected with high accuracy and automation, and users' expectation of personalized retrieval is highly satisfied.
Marcus E. Raichle - One of the best experts on this subject based on the ideXlab platform.
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Human brain activity time-locked to Perceptual Event boundaries
Nature Neuroscience, 2001Co-Authors: Jeffrey M. Zacks, Todd S. Braver, Margaret A. Sheridan, David I. Donaldson, Abraham Z. Snyder, John M. Ollinger, Randy L. Buckner, Marcus E. RaichleAbstract:Temporal structure has a major role in human understanding of everyday Events. Observers are able to segment ongoing activity into temporal parts and sub-parts that are reliable, meaningful and correlated with ecologically relevant features of the action. Here we present evidence that a network of brain regions is tuned to Perceptually salient Event boundaries, both during intentional Event segmentation and during naive passive viewing of Events. Activity within this network may provide a basis for parsing the temporally evolving environment into meaningful units.
Pawan Sinha - One of the best experts on this subject based on the ideXlab platform.
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towards perception awareness Perceptual Event detection for brain computer interfaces
International Conference of the IEEE Engineering in Medicine and Biology Society, 2015Co-Authors: Hossein Nejati, Kleovoulos Tsourides, Victor Pomponiu, Evan Ehrenberg, Ngaiman Cheung, Pawan SinhaAbstract:Brain computer interface (BCI) technology is becoming increasingly popular in many domains such as entertainment, mental state analysis, and rehabilitation. For robust performance in these domains, detecting Perceptual Events would be a vital ability, enabling adaptation to and act on the basis of user's perception of the environment. Here we present a framework to automatically mine spatiotemporal characteristics of a given Perceptual Event. As this “signature” is derived directly from subject's neural behavior, it can serve as a representation of the subject's perception of the targeted scenario, which in turn allows a BCI system to gain a new level of context awareness: perception awareness. As a proof of concept, we show the application of the proposed framework on MEG signal recordings from a face perception study, and the resulting temporal and spatial characteristics of the derived neural signature, as well as it's compatibility with the neuroscientific literature on face perception.
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EMBC - Towards perception awareness: Perceptual Event detection for Brain computer interfaces
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2015Co-Authors: Hossein Nejati, Kleovoulos Tsourides, Victor Pomponiu, Evan Ehrenberg, Ngaiman Cheung, Pawan SinhaAbstract:Brain computer interface (BCI) technology is becoming increasingly popular in many domains such as entertainment, mental state analysis, and rehabilitation. For robust performance in these domains, detecting Perceptual Events would be a vital ability, enabling adaptation to and act on the basis of user's perception of the environment. Here we present a framework to automatically mine spatiotemporal characteristics of a given Perceptual Event. As this “signature” is derived directly from subject's neural behavior, it can serve as a representation of the subject's perception of the targeted scenario, which in turn allows a BCI system to gain a new level of context awareness: perception awareness. As a proof of concept, we show the application of the proposed framework on MEG signal recordings from a face perception study, and the resulting temporal and spatial characteristics of the derived neural signature, as well as it's compatibility with the neuroscientific literature on face perception.
Minhson Dao - One of the best experts on this subject based on the ideXlab platform.
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a new linguistic Perceptual Event model for spatio temporal Event detection and personalized retrieval of sports video
International Conference on Image Analysis and Processing, 2009Co-Authors: Minhson Dao, Sharma Ishan Nath, Noboru BabaguichiAbstract:This paper proposes a new linguistic-Perceptual Event model tailoring to spatio-temporal Event detection and conceptual-visual personalized retrieval of sports video sequences. The major contributions of the proposed model are hierarchical structure, independence between linguistic and Perceptual part, and ability of capturing temporal information of sports Events. Thanks to these advanced contributions, it is very easy to upgrade model Events from simple to complex levels either by self-studying from inner knowledge or by being taught from plug-in additional knowledge. Thus, the proposed model not only can work well in unwell structured environments but also is able to adapt itself to new domains without the need (or with a few modification) for external re-programming, re-configuring and re-adjusting. Thorough experimental results demonstrate that Events are modeled and detected with high accuracy and automation, and users' expectation of personalized retrieval is highly satisfied.
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ICIAP - A New Linguistic-Perceptual Event Model for Spatio-Temporal Event Detection and Personalized Retrieval of Sports Video
Image Analysis and Processing – ICIAP 2009, 2009Co-Authors: Minhson Dao, Sharma Ishan Nath, Noboru BabaguichiAbstract:This paper proposes a new linguistic-Perceptual Event model tailoring to spatio-temporal Event detection and conceptual-visual personalized retrieval of sports video sequences. The major contributions of the proposed model are hierarchical structure, independence between linguistic and Perceptual part, and ability of capturing temporal information of sports Events. Thanks to these advanced contributions, it is very easy to upgrade model Events from simple to complex levels either by self-studying from inner knowledge or by being taught from plug-in additional knowledge. Thus, the proposed model not only can work well in unwell structured environments but also is able to adapt itself to new domains without the need (or with a few modification) for external re-programming, re-configuring and re-adjusting. Thorough experimental results demonstrate that Events are modeled and detected with high accuracy and automation, and users' expectation of personalized retrieval is highly satisfied.