Remembering when events took place is a key component of episodic memory. Using a sensitive behavioral measure, the present study investigates whether spontaneous event segmentation and script-based prior knowledge affect memory for the time of movie scenes. In three experiments, different groups of participants were asked to indicate when short video clips extracted from a previously encoded movie occurred on a horizontal timeline that represented the video duration. When participants encoded the entire movie, they were more precise at judging the temporal occurrence of clips extracted from the beginning and the end of the film compared to its middle part, but also at judging clips that were closer to event boundaries. Removing the final part of the movie from the encoding session resulted in a systematic bias in memory for time. Specifically, participants increasingly underestimated the time of occurrence of the video clips as a function of their proximity to the missing part of the movie. An additional experiment indicated that such an underestimation effect generalizes to different audio-visual material and does not necessarily reflect poor temporal memory. By showing that memories are moved in time to make room for missing information, the present study demonstrates that narrative time can be adapted to fit a standard template regardless of what has been effectively encoded, in line with reconstructive theories of memory.
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http://dx.doi.org/10.1016/j.cognition.2020.104557 | DOI Listing |
Sensors (Basel)
January 2025
Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia.
The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges.
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January 2025
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China.
Real-time and accurate traffic forecasting aids in traffic planning and design and helps to alleviate congestion. Addressing the negative impacts of partial data loss in traffic forecasting, and the challenge of simultaneously capturing short-term fluctuations and long-term trends, this paper presents a traffic forecasting model, D-MGDCN-CLSTM, based on Multi-Graph Gated Dilated Convolution and Conv-LSTM. The model uses the DTWN algorithm to fill in missing data.
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January 2025
School of Mechanical and Vehicle Engineering, Changchun University, Changchun 130022, China.
Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks (CNNs) do not effectively leverage data channel features and spatial features in residual life prediction. Firstly, Fast Fourier Transform (FFT) is applied to convert the data into the frequency domain.
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January 2025
College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Gesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on esNet ong Short-Term Memory with an ttention Mechanism (RLA). In the aspect of signal processing in RLA, a range-Doppler map is obtained through the extraction of the range and velocity features in the original mmWave radar signal.
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January 2025
College of Geoexploration Science and Technology, Jilin University, Changchun 130012, China.
As gravity exploration technology advances, gravity gradient measurement is becoming an increasingly important method for gravity detection. Airborne gravity gradient measurement is widely used in fields such as resource exploration, mineral detection, and oil and gas exploration. However, the motion and attitude changes of the aircraft can significantly affect the measurement results.
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