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With the rising significance of visual marketing, differences in how tourists from various age groups visually engage with tourism promotional materials remain insufficiently studied. This study recruited 48 participants and used a quasi-experimental design combined with eye-tracking technology to examine visual attention, scan path patterns, and their relationship to reading performance among different age groups. Independent t-tests, correlation analyses, and Lag Sequential Analysis were conducted to compare the differences between the two groups. Results indicated that elder participants had significantly higher fixation counts and longer fixation durations in text regions than younger participants, as well as higher perceived novelty scores. A positive correlation emerged between text fixation duration and perceived novelty. Additionally, elder participants showed greater interaction between text and images, while younger participants exhibited a more linear reading pattern. This study offers empirical insights to optimize tourism promotional materials, highlighting the need for age-specific communication strategies.
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http://dx.doi.org/10.3390/jemr18030016 | DOI Listing |
Exp Brain Res
September 2025
Siena Brain Investigation and Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, University of Siena, Siena, Italy.
Postdiction is a perceptual phenomenon where the perception of an earlier stimulus is influenced by a later one. This effect is commonly studied using the 'rabbit illusion', in which temporally regular, but spatially irregular, stimuli are perceived as equidistant. While previous research has focused on short inter-stimulus intervals (100-200 ms), the role of longer intervals, which may engage late attentional processes, remains unexplored.
View Article and Find Full Text PDFCogn Neuropsychiatry
September 2025
Department of Psychology, Faculty of Arts, Comenius University in Bratislava, Bratislava, Slovakia.
Introduction: Schizophrenia (SCZ) spectrum is characterised by aberrant processing of social cues. However, little is known about the specific stages of visual attention and their connection to subclinical and clinical symptoms in psychosis. This study aimed to investigate the visual processing of social and non-social parts of naturalistic scenes, and its link to positive and negative symptoms.
View Article and Find Full Text PDFAm Psychol
September 2025
State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences.
In cluttered and complex natural scenes, selective attention enables the visual system to prioritize relevant information. This process is guided not only by perceptual cues but also by imagined ones. The current research extends the imagery-induced attentional bias to the unconscious level and reveals its cross-category applicability between different social cues (e.
View Article and Find Full Text PDFElife
September 2025
Center for Mind and Brain, University of California, Davis, Davis, United States.
Visual search relies on the ability to use information about the target in working memory to guide attention and make target-match decisions. The 'attentional' or 'target' template is thought to be encoded within an inferior frontal junction (IFJ)-visual attentional network. While this template typically contains veridical target features, behavioral studies have shown that target-associated information, such as statistically co-occurring object pairs, can also guide attention.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
School of Information Science and Automation, Northeastern University, Shenyang, 110819 China.
Accurate prediction of drug-target interactions (DTIs) is crucial for improving the efficiency and success rate of drug development. Despite recent advancements, existing methods often fail to leverage interaction features at multiple granular levels, resulting in suboptimal data utilization and limited predictive performance. To address these challenges, we propose CF-DTI, a coarse-to-fine drug-target interaction model that integrates both coarse-grained and fine-grained features to enhance predictive accuracy.
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