Background: Naming and lexical retrieval difficulties are common symptoms of aphasia. Naming abilities are usually evaluated by means of real objects or pictures or line drawings that are printed.
Objective: The aim of this study was to investigate whether the ability to name objects among individuals with aphasia is influenced by the dimensions of the visual stimuli and to understand whether the order of presentation of the stimuli, number of years of education and length of time post-onset are involved in the success of naming.
Methods: The naming abilities of healthy controls and patients with acute or chronic aphasia due to stroke were assessed through presentation of two sets of 24 stimuli consisting of real objects and color photographs of the same objects presented on a screen. The results obtained under these two conditions were compared by means of within-subject paired ANOVA, controlling for presentation order.
Results: 40 patients (62.4 ± 17.3 years old; 70% females; mean education level of 8.5 ± 5.3 years; and 60% evaluated within the first eight days after stroke) and 50 controls that were age, gender and education-matched were included. Object naming was significantly better than naming color photographs among the patients (p = 0.001), but no differences were observed among the controls. Age, education, length of time post-onset and presentation sequence did not have any impact on naming performance.
Conclusion: These results suggest that use of digital color photographs for naming objects in assessment of aphasia may be associated with lower naming performance, compared with use of real objects. This needs to be taken into account when different forms of stimuli are presented in sequential aphasia evaluations.
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http://dx.doi.org/10.1590/0004-282X-ANP-2020-0361 | DOI Listing |
Sci Rep
January 2025
China Institute of Water Resources and Hydropower Research, Beijing, 100048, China.
In the underwater domain, small object detection plays a crucial role in the protection, management, and monitoring of the environment and marine life. Advancements in deep learning have led to the development of many efficient detection techniques. However, the complexity of the underwater environment, limited information available from small objects, and constrained computational resources make small object detection challenging.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430070, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430070, China. Electronic address:
Artificial intelligence-assisted imaging biosensors have attracted increasing attention due to their flexibility, allowing for the digital image analysis and quantification of biomarkers. While deep learning methods have led to advancements in biomarker identification, the diversity in the density and adherence of targets still poses a serious challenge. In this regard, we propose CellNet, a neural network model specifically designed for detecting dense targets.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electrical and Control Engineering, North China University of Technology, Beijing, China.
This paper proposes a new strategy for analysing and detecting abnormal passenger behavior and abnormal objects on buses. First, a library of abnormal passenger behaviors and objects on buses is established. Then, a new mask detection and abnormal object detection and analysis (MD-AODA) algorithm is proposed.
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January 2025
Experimental Psychology, University College London, London, United Kingdom.
Communication comprises a wealth of multimodal signals (e.g., gestures, eye gaze, intonation) in addition to speech and there is a growing interest in the study of multimodal language by psychologists, linguists, neuroscientists and computer scientists.
View Article and Find Full Text PDFPerception
January 2025
Polymer and Color Engineering Department, Amirkabir University of Technology, Tehran, Iran.
This study investigated the performance of various spectrophotometric methods in predicting visually perceived translucency and evaluated the efficiency of imaging techniques in quantifying translucency. We conducted the visual assessment in two stages using the pair comparison method. In the first stage, pairs of samples with identical colors but differing levels of translucency were compared.
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