Temporal orienting of attention can affect multiple stages of processing to guide adaptive behaviour. We tested whether temporal expectation in different task contexts is compromised in individuals with Parkinson's disease (PD). In Experiment 1 two temporal-orienting tasks were used: a speeded task emphasizing motor preparation and a non-speeded task emphasizing perceptual discrimination using rapid serial visual presentation. In both tasks, auditory cues indicated the likelihood of a target appearing after a short or long interval. In the speeded-response task, participants used the cues to anticipate an easily detectable target stimulus. In the non-speeded perceptual-discrimination task, participants used the cues to help discriminate a target letter embedded in a stream of letters. Relative to healthy participants, participants with PD did not show altered temporal orienting effects in the speeded-response task. However, they were impaired in using temporal cues to improve perceptual discrimination. In Experiment 2, we tested whether the temporal-orienting deficits in the perceptual-discrimination task depended on the requirement to ignore temporally distracting stimuli. We replicated the impaired temporal orienting for perceptual discrimination in an independent group of individuals with PD, and showed the impairment was abolished when individuals were on their dopaminergic medication. In a task without any distracting letters, however, patients off or on medication benefited normally from temporal orienting cues. Our findings suggest that deficits in temporal orienting in individuals with PD interact with specific task demands, such as the requirement to select target from temporally competing distractors.
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http://dx.doi.org/10.1111/ejn.15114 | DOI Listing |
Sensors (Basel)
January 2025
School of Automation, Beijing Institute of Technology, Beijing 100081, China.
Existing autonomous driving systems face challenges in accurately capturing drivers' cognitive states, often resulting in decisions misaligned with drivers' intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers' electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers' spatial cognition across two dimensions: relative distance and relative orientation.
View Article and Find Full Text PDFToxics
January 2025
Beijing Key Laboratory of Resource-Oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Road dust carries various contaminants and causes urban non-point source pollution in waterbodies through runoff. Road dust samples were collected in each month in two years and then sieved into five particle size fractions. The concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in each fraction were measured.
View Article and Find Full Text PDFHeliyon
January 2025
Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022, València, Spain.
Resting state electroencephalography (EEG) has proved useful in studying electrophysiological changes in neurodegenerative diseases. In many neuropathologies, microstate analysis of the eyes-closed (EC) scalp EEG is a robust and highly reproducible technique for assessing topological changes with high temporal resolution. However, scalp EEG microstate maps tend to underestimate the non-occipital or non-alpha-band networks, which can also be used to detect neuropathological changes.
View Article and Find Full Text PDFJ Vis
January 2025
Department of Psychology, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Beyond the light reflex, the pupil responds to various high-level cognitive processes. Multiple statistical regularities of stimuli have been found to modulate the pupillary response. However, most studies have used auditory or visual temporal sequences as stimuli, and it is unknown whether the pupil size is modulated by statistical regularity in the spatial arrangement of stimuli.
View Article and Find Full Text PDFFront Neurorobot
January 2025
School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
Significant strides have been made in emotion recognition from Electroencephalography (EEG) signals. However, effectively modeling the diverse spatial, spectral, and temporal features of multi-channel brain signals remains a challenge. This paper proposes a novel framework, the Directional Spatial and Spectral Attention Network (DSSA Net), which enhances emotion recognition accuracy by capturing critical spatial-spectral-temporal features from EEG signals.
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