Though generalization of conditioned fear has been implicated as a central feature of pathological anxiety, surprisingly little is known about the psychobiology of this learning phenomenon in humans. Whereas animal work has frequently applied methods to examine generalization gradients to study the gradual weakening of the conditioned-fear response as the test stimulus increasingly differs from the conditioned stimulus (CS), to our knowledge no psychobiological studies of such gradients have been conducted in humans over the last 40 years. The current effort validates an updated generalization paradigm incorporating more recent methods for the objective measurement of anxiety (fear-potentiated startle). The paradigm employs 10, quasi-randomly presented, rings of gradually increasing size with extremes serving as CS+ and CS-. The eight rings of intermediary size serve as generalization stimuli (GSs) and create a continuum-of-similarity from CS+ to CS-. Both startle data and online self-report ratings demonstrate continuous decreases in generalization as the presented stimulus becomes less similar to the CS+. The current paradigm represents an updated and efficacious tool with which to study fear generalization--a central, yet understudied conditioning-correlate of pathologic anxiety.
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http://dx.doi.org/10.1016/j.brat.2008.02.005 | DOI Listing |
Elife
January 2025
School of Psychology, Korea University, Seoul, Republic of Korea.
Recent studies suggest that calcitonin gene-related peptide (CGRP) neurons in the parabrachial nucleus (PBN) represent aversive information and signal a general alarm to the forebrain. If CGRP neurons serve as a true general alarm, their activation would modulate both passive nad active defensive behaviors depending on the magnitude and context of the threat. However, most prior research has focused on the role of CGRP neurons in passive freezing responses, with limited exploration of their involvement in active defensive behaviors.
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January 2025
Facultad de Ciencias Agropecuarias, Escuela Profesional de Ingeniería Pesquera, Universidad Nacional Jorge Basadre Grohmann, Tacna, Tacna, Peru.
The Peruvian grunt, , is beginning its domestication as a candidate species for marine aquaculture. The optimal management of fingerling production requires precise knowledge on early development. Herein, we report the methodology for capturing and conditioning wild specimens to find a viable broodstock.
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January 2025
Social Determinants of Health Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
This study investigates factors influencing physical activity based on the Transtheoretical model (TTM) among adolescents. This study was conducted on 745 individuals between the ages of 12 and 16 years and was analyzed using a generalized linear model (GLM) approach with appropriate link functions using both classical and Bayesian frameworks. The results show that in model 1, the probit link function is a more appropriate approach to determine the risk factors for physical activity.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, Scotland, UK.
Background: To date, all computerised perivascular spaces (PVS) quantification methods require case-wise, imaging modality, or study-specific parameter adjustments, and suffer from generalisability problems in clinical settings, and misdetection of other cerebral small vessel disease (CSVD) markers. We propose a deep learning-based PVS detection method to overcome these issues. We compare our proposal on magnetic resonance imaging data of CSVD participants against the performance of the Frangi filter.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Background: Brain age (BA) prediction models have emerged as valuable tools for understanding individual differences in trajectories of brain aging. These models aim to estimate overall brain health by predicting BA based on structural MRI data. To enhance the specificity of existing BA models, we introduce a deep learning-based BA prediction model.
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