Review
Psychology, Mathematical
Sahil Luthra
Summary: Although listeners can recognize speech from different talkers, such variability can lead to slower word recognition. This review discusses two possible theoretical mechanisms for this processing cost. One view suggests that talker accommodation, a resource-demanding process that involves comparing sensory representations and adjusting acoustic-to-phonetic mapping, contributes to the cost. Another proposal suggests that the cost arises from salient stimulus-level discontinuities that disrupt auditory attention. Recent data indicate that both mechanisms may play a role. This article provides a primer on talker accommodation and auditory streaming, reviews studies on multitalker processing costs, and emphasizes the need for comprehensive theories that integrate auditory attention.
PSYCHONOMIC BULLETIN & REVIEW
(2023)
Article
Psychology, Mathematical
Ja Young Choi, Rita S. N. Kou, Tyler K. Perrachione
Summary: The efficiency of speech processing in mixed-talker settings improves when listeners are given time to reorient their attention to each new talker. However, the study found that it takes time to fully reorient attention to a new talker in order to achieve the same level of efficiency as when listening to a single talker, and this adaptation process does not continue to improve with longer durations of speech from a talker.
PSYCHONOMIC BULLETIN & REVIEW
(2022)
Article
Multidisciplinary Sciences
Viola Mocz, Su Keun Jeong, Marvin Chun, Yaoda Xu
Summary: The human brain represents objects by averaging the responses to paired objects, while convolutional neural networks (CNNs) show deviations from this pattern. These differences could limit CNNs' ability to generalize object representations formed in different contexts.
SCIENTIFIC REPORTS
(2023)
Article
Neurosciences
Shiyu Wang, Ling Huang, Qinglin Chen, Jingyi Wang, Siting Xu, Xilin Zhang
Summary: This study found that the awareness-dependent attention field can be changed by manipulating the gain of attentional selection for visual stimuli. The impact of visible and invisible cues on the spatial cueing effect is consistent with changes in contrast gain and response gain. Analysis supports the idea that subjects' awareness-dependent attention fields can be simulated using the normalization model of attention.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Chemistry, Multidisciplinary
Hongwei Zhang, Zhaohui Wu, Yuxuan Qiu, Xiangcheng Zhai, Zichen Wang, Peng Xu, Zhenzheng Liu, Xiantong Li, Na Jiang
Summary: Automated detection of road damage is a challenging topic in road maintenance, and this study addresses it by using deep learning to automatically detect and assess the severity of road damage. A new road damage dataset called CNRDD is introduced, which is labeled according to the latest evaluation standard for highway technical conditions in China. Additionally, a novel baseline model with attention fusion and normalization is proposed, achieving significantly better results than existing methods.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Ruiqi Liu, Jing Tian, Yuemei Li, Na Chen, Jianshe Yan, Taihao Li, Shupeng Liu
Summary: Nailfold capillaroscopy is a safe, simple, noninvasive, and inexpensive method for detecting and analyzing microvascular abnormalities. Segmentation of nailfold microhemorrhages provides valuable pathological information for further investigations.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Juntao Hu, You Yang, Lu Yao, Yongzhi An, Longyue Pan
Summary: This paper proposes an improvement to the Transformer framework for image captioning by introducing a Bi-Positional Attention module and Group Normalization method. By precisely exploring the internal relations and geometric information between objects and leveraging the channel dependence of visual features, this method achieves competitive performance on the MSCOCO dataset.
IMAGE AND VISION COMPUTING
(2022)
Article
Neurosciences
Michaela Klimova, Ilona M. Bloem, Sam Ling
Summary: Attention and divisive normalization both contribute to making visual processing more efficient. Attention selectively increases neural gain of relevant information, while divisive normalization improves efficiency by suppressing responses to homogeneous inputs and highlighting salient boundaries. Research suggests that attention does not alter the qualitative properties of normalization.
JOURNAL OF NEUROPHYSIOLOGY
(2023)
Article
Biology
Narges Doostani, Gholam-Ali Hossein-Zadeh, Maryam Vaziri-Pashkam
Summary: This study examined the role of normalization in the human visual cortex using functional MRI. The results showed that a normalization model can better predict neural responses to multiple stimuli and can explain the effects of attention on cortical responses. These findings provide evidence for normalization as a fundamental operation in the human brain.
Article
Chemistry, Multidisciplinary
Xing Yi, Hao Pan, Huaici Zhao, Pengfei Liu, Canyu Zhang, Junpeng Wang, Hao Wang
Summary: This study proposed a cycle generative adversarial network method based on gradient normalization to address the problems of poor infrared image generation, lack of texture detail, and unstable models. The method used a residual network with better feature extraction capability as the generator and introduced channel attention and spatial attention mechanisms to enhance feature perception and generate image details. A gradient normalization module was also introduced to stabilize the model. The experimental results showed satisfactory performance on multiple datasets.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
You Yang, Yongzhi An, Juntao Hu, Longyue Pan
Summary: Attention mechanisms and grid features are widely used in visual language tasks. However, the connection between attention scores in different layers is weak in hierarchical structures like Transformer. Additionally, geometric information is lost when grid features are flattened. To address these issues, residual attention paths and residual attention with relative position module are proposed.
INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL
(2022)
Article
Computer Science, Artificial Intelligence
Xi Chen, Hongdong Zhao, Dongxu Yang, Yueyuan Li, Qing Kang, Haiyan Lu
Summary: In this paper, a single-image generation adversarial network based on the self-attention mechanism is proposed, which stabilizes the training process and outperforms single-sample generative adversarial networks, showing better performance on multiple datasets and deserving further investigation.
MACHINE VISION AND APPLICATIONS
(2021)
Article
Psychology, Multidisciplinary
Joshua M. Carlson, Lin Fang, Caleb Coughtry-Carpenter, John Foley
Summary: Climate change is a pressing issue that captures attention, but assessing individual differences in attention processing of climate change information using reaction time-based measures is unreliable. Measures of attentional bias based on reaction time and variability have low reliability and poor predictive validity, making them unsuitable for capturing individual differences in attention to climate change information.
FRONTIERS IN PSYCHOLOGY
(2023)
Article
Biochemistry & Molecular Biology
Vivian Viallon, Mathilde His, Sabina Rinaldi, Marie Breeur, Audrey Gicquiau, Bertrand Hemon, Kim Overvad, Anne Tjonneland, Agnetha Linn Rostgaard-Hansen, Joseph A. Rothwell, Lucie Lecuyer, Gianluca Severi, Rudolf Kaaks, Theron Johnson, Matthias B. Schulze, Domenico Palli, Claudia Agnoli, Salvatore Panico, Rosario Tumino, Fulvio Ricceri, W. M. Monique Verschuren, Peter Engelfriet, Charlotte Onland-Moret, Roel Vermeulen, Therese Haugdahl Nost, Ilona Urbarova, Raul Zamora-Ros, Miguel Rodriguez-Barranco, Pilar Amiano, Jose Maria Huerta, Eva Ardanaz, Olle Melander, Filip Ottoson, Linda Vidman, Matilda Rentoft, Julie A. Schmidt, Ruth C. Travis, Elisabete Weiderpass, Mattias Johansson, Laure Dossus, Mazda Jenab, Marc J. Gunter, Justo Lorenzo Bermejo, Dominique Scherer, Reza M. Salek, Pekka Keski-Rahkonen, Pietro Ferrari
Summary: Pooling metabolomics data across studies poses challenges due to methodological differences, but a new pipeline has been developed to improve data comparability through normalization and sequential steps. The pipeline successfully enhances the statistical power of analysis and can be adapted for other molecular data, making it valuable for international consortia and molecular epidemiologists.
Article
Biochemical Research Methods
Jiayi Liu, Anat Kreimer, Wei Vivian Li
Summary: The emergence of single-cell RNA sequencing (scRNA-seq) technologies allows for gene expression analysis at the single-cell level, enabling quantification and comparison of transcriptional variability among individual cells. However, there is a lack of statistical methods for quantifying and testing differential variability between groups of cells. To address this, we propose and compare 12 statistical pipelines for analyzing single-cell gene expression data, using different combinations of normalization, feature selection, dimensionality reduction, and variability calculation methods. Using synthetic datasets, we found that a pipeline with simple library size normalization, inclusion of all genes, and denSNE-based distances for clustering medoids performed the best. Applying this pipeline to scRNA-seq datasets of COVID-19 and autism patients, we identified cellular variability changes between patients with different severity status or between patients and healthy controls.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Neurosciences
Douglas A. Ruff, Joshua J. Alberts, Marlene R. Cohen
JOURNAL OF NEUROPHYSIOLOGY
(2016)
Article
Neurosciences
Douglas A. Ruff, Marlene R. Cohen
JOURNAL OF NEUROSCIENCE
(2016)
Article
Neurosciences
Douglas A. Ruff, Marlene R. Cohen
JOURNAL OF NEUROSCIENCE
(2016)
Article
Multidisciplinary Sciences
A. M. Ni, D. A. Ruff, J. J. Alberts, J. Symmonds, M. R. Cohen
Article
Neurosciences
Marieke Mur, Douglas A. Ruff, Jerzy Bodurka, Peter A. Bandettini, Nikolaus Kriegeskorte
Article
Neurosciences
Alexandra Smolyanskaya, Douglas A. Ruff, Richard T. Born
JOURNAL OF NEUROPHYSIOLOGY
(2013)
Article
Neurosciences
Douglas A. Ruff, David H. Brainard, Marlene R. Cohen
JOURNAL OF NEUROPHYSIOLOGY
(2018)
Article
Neurosciences
Marieke Mur, Douglas A. Ruff, Jerzy Bodurka, Peter De Weerd, Peter A. Bandettini, Nikolaus Kriegeskorte
JOURNAL OF NEUROSCIENCE
(2012)
Article
Neurosciences
Douglas A. Ruff, Marlene R. Cohen
JOURNAL OF NEUROSCIENCE
(2014)
Article
Neurosciences
Douglas A. Ruff, Marlene R. Cohen
NATURE NEUROSCIENCE
(2014)
Editorial Material
Neurosciences
Douglas A. Ruff, Marlene R. Cohen
Article
Neurosciences
Douglas A. Ruff, Marlene R. Cohen
NATURE NEUROSCIENCE
(2019)
Article
Multidisciplinary Sciences
Douglas A. Ruff, Cheng Xue, Lily E. Kramer, Faisal Baqai, Marlene R. Cohen
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2020)
Article
Multidisciplinary Sciences
Amy M. Ni, Brittany S. Bowes, Douglas A. Ruff, Marlene R. Cohen
Summary: Most systems neuroscience studies can be categorized into basic science work and translational work. This study combines these two approaches and reveals that orally administered methylphenidate enhances spatially selective visual attention and improves visual performance. Furthermore, it suggests that decreased correlated variability of neurons may be a general mechanism for treating neuropsychiatric disorders.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)