Studies examining the effects of age on the neural correlates of recognition memory have yielded mixed results. In the present study, we employed a modified remember-know paradigm to compare the fMRI correlates of recollection and familiarity in samples of healthy young and older adults. After studying a series of words, participants underwent fMRI scanning during a test phase in which they responded "remember" to a test word if any qualitative information could be recollected about the study event. When recollection failed, participants signaled how confident they were that the test item had been studied. Young and older adults demonstrated statistically equivalent estimates of recollection and familiarity strength, while recognition memory accuracy was significantly lower in the older adults. Robust, age-invariant fMRI effects were evident in two sets of a priori defined brain regions consistently reported in prior studies to be sensitive to recollection and familiarity respectively. In addition, the magnitudes of 'familiarity-attenuation effects' in perirhinal cortex demonstrated age-invariant correlations with estimates of familiarity strength and memory accuracy, replicating prior findings. Together, the present findings add to the evidence that the neural correlates of recognition memory are largely stable across much of the healthy human adult lifespan.
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http://dx.doi.org/10.1016/j.bandc.2021.105785 | DOI Listing |
J Integr Neurosci
January 2025
Department of Psychology, The Affiliated Hospital of Jiangnan University, 214151 Wuxi, Jiangsu, China.
Background: Deficits in emotion recognition have been shown to be closely related to social-cognitive functioning in schizophrenic. This study aimed to investigate the event-related potential (ERP) characteristics of social perception in schizophrenia patients and to explore the neural mechanisms underlying these abnormal cognitive processes related to social perception.
Methods: Participants included 33 schizophrenia patients and 35 healthy controls (HCs).
Sensors (Basel)
January 2025
Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China.
The objective identification of depression using physiological data has emerged as a significant research focus within the field of psychiatry. The advancement of wearable physiological measurement devices has opened new avenues for the identification of individuals with depression in everyday-life contexts. Compared to other objective measurement methods, wearables offer the potential for continuous, unobtrusive monitoring, which can capture subtle physiological changes indicative of depressive states.
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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
Department of Artifcial Intelligence, Chung-Ang University, Heukseok-dong, Dongjak-gu, Seoul 06974, Republic of Korea.
Sensor-based gesture recognition on mobile devices is critical to human-computer interaction, enabling intuitive user input for various applications. However, current approaches often rely on server-based retraining whenever new gestures are introduced, incurring substantial energy consumption and latency due to frequent data transmission. To address these limitations, we present the first on-device continual learning framework for gesture recognition.
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January 2025
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Behavioral computing based on visual cues has become increasingly important, as it can capture and annotate teachers' and students' classroom states on a large scale and in real time. However, there is a lack of consensus on the research status and future trends of computer vision-based classroom behavior recognition. The present study conducted a systematic literature review of 80 peer-reviewed journal articles following the Preferred Reporting Items for Systematic Assessment and Meta-Analysis (PRISMA) guidelines.
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