Attachment security priming effects therapeutic change in people with depression and anxiety. Preliminary studies indicate that visualising secure attachment memories also reduces paranoia in non-clinical and clinical groups, probably due to a decrease in cognitive fusion. Benefits to clinical populations depend on the sustainability of these effects and the impact on help-seeking behaviours. The combination of paranoia and an insecure-avoidant attachment style is likely to be a particular barrier to help seeking. We used a longitudinal experimental design to test the impact of repeated attachment priming on paranoia, mood and help-seeking intentions and whether cognitive fusion mediates these effects. Seventy-nine people with high levels of non-clinical paranoia, aged 18-50 years (M = 20.53, SD = 4.57), were randomly assigned to a secure or insecure-avoidant priming condition. Participants rehearsed the visualisation prime on four consecutive days and were assessed on standardised measures of paranoia, positive and negative affect, help-seeking intentions and cognitive fusion. A series of mixed-model analyses of variance showed that security priming decreases paranoia, negative affect and cognitive fusion and increases positive affect and help seeking, compared to insecure-avoidant priming. Examining the impact of primed attachment (rather than measured attachment style) allows us to draw conclusions about the causal processes involved; mediation analyses showed indirect effects of the primes on paranoia and negative affect through cognitive fusion. With a growing understanding of (1) the impact of security priming on paranoia, affect and help-seeking behaviours, (2) causal mechanisms and (3) sustainability of effects, security priming may be developed into a viable intervention for clinical populations.
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http://dx.doi.org/10.3390/brainsci11101257 | DOI Listing |
Sensors (Basel)
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Cognitive Systems Lab, University of Bremen, 28359 Bremen, Germany.
This paper presents an approach for event recognition in sequential images using human body part features and their surrounding context. Key body points were approximated to track and monitor their presence in complex scenarios. Various feature descriptors, including MSER (Maximally Stable Extremal Regions), SURF (Speeded-Up Robust Features), distance transform, and DOF (Degrees of Freedom), were applied to skeleton points, while BRIEF (Binary Robust Independent Elementary Features), HOG (Histogram of Oriented Gradients), FAST (Features from Accelerated Segment Test), and Optical Flow were used on silhouettes or full-body points to capture both geometric and motion-based features.
View Article and Find Full Text PDFJ Clin Med
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Department of Neurosurgery, "Carol Davila" University of Medicine and Pharmacy, 020021 Bucharest, Romania.
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI's cutting-edge algorithms-ranging from deep learning to neuromorphic computing-are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain-computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments.
View Article and Find Full Text PDFJ Appl Gerontol
January 2025
University of Alabama at Birmingham, Birmingham, AL, USA.
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View Article and Find Full Text PDFFront Neurosci
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The Basic Department, The Tourism College of Changchun University, Changchun, China.
Introduction: In the field of medical listening assessments,accurate transcription and effective cognitive load management are critical for enhancing healthcare delivery. Traditional speech recognition systems, while successful in general applications often struggle in medical contexts where the cognitive state of the listener plays a significant role. These conventional methods typically rely on audio-only inputs and lack the ability to account for the listener's cognitive load, leading to reduced accuracy and effectiveness in complex medical environments.
View Article and Find Full Text PDFEur J Neurosci
January 2025
Department of Psychology and Cognitive Sciences (DiPSCo), University of Trento, Trento, Italy.
The Dark Triad (DT), encompassing narcissism, Machiavellianism and psychopathy traits, poses significant societal challenges. Understanding the neural underpinnings of these traits is crucial for developing effective interventions and preventive strategies. Our study aimed to unveil the neural substrates of the DT by examining brain scans from 201 individuals (mean age: 32.
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