Several methods that are promising for studying the neurophysiology of fear conditioning (e.g., EEG, MEG) require a high number of trials to achieve an adequate signal-to-noise ratio. While electric shock and white noise burst are among the most commonly used unconditioned stimuli (US) in conventional fear conditioning studies with few trials, it is unknown whether these stimuli are equally well suited for paradigms with many trials. Here, N = 32 participants underwent a 260-trial differential fear conditioning and extinction paradigm with a 240-trial recall test 24 h later and neutral faces as conditioned stimuli. In a between-subjects design, either white noise bursts (n = 16) or electric shocks (n = 16) served as US, and intensities were determined using the most common procedure for each US (i.e., a fixed 95 dB noise burst and a work-up procedure for electric shocks, respectively). In addition to differing US types, groups also differed in closely linked US-associated characteristics (e.g., calibration methods, stimulus intensities, timing). Subjective ratings (arousal/valence), skin conductance, and evoked heart period changes (i.e., fear bradycardia) indicated more reliable, extinction-resistant, and stable conditioning in the white noise burst versus electric shock group. In fear conditioning experiments where many trials are presented, white noise burst should serve as US.
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http://dx.doi.org/10.1111/psyp.12677 | DOI Listing |
Int J Pediatr Otorhinolaryngol
January 2025
Department of Physical Therapy, Speech-Language Pathology and Occupational Therapy, Medical School, University of São Paulo, São Paulo, 05360-160, Brazil.
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Department of Otolaryngology, Head and Neck Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health;
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December 2024
Deep Mining and Rock Burst Research Branch, Chinese Institute of Coal Science, Qingniangou Road No. 5, Beijing, 100013, China.
The essential of semi-supervised semantic segmentation (SSSS) is to learn more helpful information from unlabeled data, which can be achieved by assigning adequate quality pseudo-labels or managing noisy pseudo-labels during training. However, most relevant state-of-the-art (SOTA) methods are mainly devoted to improving one aspect. By revisiting the representative SSSS methods from a robust learning view, this paper discovers that the appropriate combination of multiple noise-robust methods contributes both to assigning sufficient quality pseudo labels and managing noisy labels.
View Article and Find Full Text PDFFront Neurosci
December 2024
Department of Psychology, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
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December 2024
School of Psychological Science, University of Bristol, Bristol, South West England, United Kingdom.
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