One central problem in perception research is to understand how internal experiences are linked to physical variables. Most commonly, this relationship is measured using the method of adjustment, but this has two shortcomings: The perceptual scales that relate physical and perceptual variables are not measured directly, and the method often requires perceptual comparisons between viewing conditions. To overcome these problems, we measured perceptual scales of surface lightness using maximum likelihood difference scaling, asking observers only to compare the lightness of surfaces presented in the same context. Observers were lightness constant, and the perceptual scales qualitatively and quantitatively predicted perceptual matches obtained in a conventional adjustment experiment. Additionally, we show that a contrast-based model of lightness perception predicted 98% of the variance in the scaling and 88% in the matching data. We suggest that the predictive power was higher for scales because they are closer to the true variables of interest.
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http://dx.doi.org/10.1167/17.4.1 | DOI Listing |
J Psychiatr Res
January 2025
School of Social Work, Ariel University, Ariel, Israel. Electronic address:
Objectives: This study examines the association between the Subjective Traumatic Outlook (STO), somatization, and physical and mental aspects of disability during wartime in Ukraine. It highlights the STO's role in emphasizing the perceptual component of trauma as a screening tool, distinguishing somatic symptoms alongside physical and psychological disability.
Method: A national sample of 1895 Ukrainians affected by the Russian invasion completed the International Trauma Questionnaire (ITQ), the Somatic Symptom Scale-8 (SSS-8), the World Health Organization Disability Assessment Schedule (WHODAS), and the STO.
Sci Rep
January 2025
School of Electronics and Information, Xijing University, Xi'an, 710123, China.
To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resolution, substantially enhancing reconstruction quality and detail recovery.
View Article and Find Full Text PDFPLoS Biol
January 2025
Neuroscience Institute, New York University Grossman School of Medicine, New York, New York, United States of America.
Perceptual awareness results from an intricate interaction between external sensory input and the brain's spontaneous activity. Pre-stimulus ongoing activity influencing conscious perception includes both brain oscillations in the alpha (7 to 14 Hz) and beta (14 to 30 Hz) frequency ranges and aperiodic activity in the slow cortical potential (SCP, <5 Hz) range. However, whether brain oscillations and SCPs independently influence conscious perception or do so through shared mechanisms remains unknown.
View Article and Find Full Text PDFFront Child Adolesc Psychiatry
June 2024
Centre for Psychedelic Research, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom.
This study aimed to investigate differences in long-term psychological effects, acute subjective effects, and side effects associated with psychedelic use in adolescents (aged 16-24), compared with adults (aged 25+). Data from two observational online survey cohorts was pooled, involving adolescents (average age 20.4 ± 2.
View Article and Find Full Text PDFSci Rep
January 2025
Zhongyu (Fujian) Digital Technology Co., Ltd, Fuzhou, 350108, China.
Attention mechanisms have been introduced to exploit deep-level information for image restoration by capturing feature dependencies. However, existing attention mechanisms often have limited perceptual capabilities and are incompatible with low-power devices due to computational resource constraints. Therefore, we propose a feature enhanced cascading attention network (FECAN) that introduces a novel feature enhanced cascading attention (FECA) mechanism, consisting of enhanced shuffle attention (ESA) and multi-scale large separable kernel attention (MLSKA).
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