Adaptive behavior requires the ability to orient attention to the moment in time at which a relevant event is likely to occur. Temporal orienting of attention has been consistently associated with activation of the left intraparietal sulcus (IPS) in prior fMRI studies. However, a direct test of its causal involvement in temporal orienting is still lacking. The present study tackled this issue by transiently perturbing left IPS activity with either online (Experiment 1) or offline (Experiment 2) transcranial magnetic stimulation (TMS). In both experiments, participants performed a temporal orienting task, alternating between blocks in which a temporal cue predicted when a subsequent target would appear and blocks in which a neutral cue provided no information about target timing. In Experiment 1 we used an online TMS protocol, aiming to interfere specifically with cue-related temporal processes, whereas in Experiment 2 we employed an offline protocol whereby participants performed the temporal orienting task before and after receiving TMS. The right IPS and/or the vertex were stimulated as active control regions. While results replicated the canonical pattern of temporal orienting effects on reaction time, with faster responses for temporal than neutral trials, these effects were not modulated by TMS over the left IPS (as compared to the right IPS and/or vertex regions) regardless of the online or offline protocol used. Overall, these findings challenge the causal role of the left IPS in temporal orienting of attention inviting further research on its underlying neural substrates.
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http://dx.doi.org/10.1016/j.neuropsychologia.2023.108561 | 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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