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82 results for “Attentional Control”
Dataset from: "Voluntary Control of Task Selection Does Not Eliminate the Impact of Selection History on Attention"
<p>Dataset for Henare, D. T., Kadel, H., & Schubö, A. (2020). Voluntary Control of Task Selection Does Not Eliminate the Impact of Selection History on Attention. <em>Journal of Cognitive Neuroscience</em>, <em>32</em>(11), 2159-2177. <a href="https://doi.org/10.1162/jocn_a_01609">https://doi.org/10.1162/jocn_a_01609</a></p>
Datasets to article "Selection history alters attentional filter settings persistently and beyond top-down control"
<p>Single-Subject Behavioral and ERP mean amplitude data for Experiments 1 to 3.</p>
Audiovisual, Gaze-controlled Auditory Attention Decoding Dataset KU Leuven (AV-GC-AAD)
<p>This dataset is described in detail in the following journal paper [1]:<br>Rotaru, I., Geirnaert, S., Heintz, N., Van de Ryck, I., Bertrand, A., & Francart, T. (2024). What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention. Journal of Neural Engineering, 21(1), 016017.<br><a href="https://iopscience.iop.org/article/10.1088/1741-2552/ad2214/meta">https://iopscience.iop.org/article/10.1088/1741-2552/ad2214/meta</a></p> <p><em><strong> If using this dataset, please cite the original paper above and the current Zenodo repository. </strong></em></p> <p><strong>Note from the authors: </strong>Recent evaluations reveal that various published AAD (Auditory Attention Decoding) algorithms do not achieve significant above-chance performance on this AV-GC-AAD dataset, and in particular on the two gaze-incongruent conditions 'MovingVideo' and 'MovingTargetNoise'). This suggests that previously reported successes may have been largely influenced by eye gaze confounds present in other datasets, which can be exploited as shortcuts by machine learning algorithms. Despite these findings, poor performance on the AV-GC-AAD dataset is often dismissed, with reasons cited such as insufficient training data, high heterogeneity in audiovisual conditions, or the claim that participants were unable to focus their auditory attention due to the complexity of the instructions.</p> <p>To address these concerns, we provide a supplementary technical report (and accompanying code), showcasing results from a simple linear stimulus reconstruction AAD algorithm applied to this dataset. Our findings demonstrate that high AAD accuracy can be achieved within individual conditions, and that the model generalizes across conditions, new subjects, and even across different datasets.</p> <p><a title="https://doi.org/10.48550/arxiv.2412.01401" href="https://doi.org/10.48550/arXiv.2412.01401" target="_blank" rel="noreferrer noopener">Report</a> | <a title="https://github.com/alexanderbertrandlab/linear-stimulus-reconstruction-aad-av-gc-aad-dataset" href="https://github.com/AlexanderBertrandLab/linear-stimulus-reconstruction-AAD-AV-GC-AAD-dataset" target="_blank" rel="noreferrer noopener">Matlab Code</a></p> <p>Through this report, we aim to remove any doubts that the AV-GC-AAD dataset's limitations are the primary cause of AAD algorithms failing to exceed chance-level performance. Additionally, this report and its accompanying code offer a simple baseline evaluation procedure, which can serve as a minimal benchmark for testing more advanced AAD algorithms on this dataset.</p> <p><em>When reporting results on this data set, it is good practice to show performance for each condition separately, since 2 of the 4 conditions still contain gaze shortcuts, which could be exploited by machine learning algorithms. </em></p> <p>________________________________________________________________________________</p> <p><strong>Dataset description</strong></p> <p>This work was performed at ExpORL, Dept. Neurosciences, KU Leuven and Dept. Electrical Engineering (ESAT), KU Leuven (Belgium), with the goal of investigating and controlling for the effect of gaze during a competing listening task.</p> <p>The full dataset contains EEG and EOG data collected from 16 normal-hearing subjects, during a competing listening task, where the subjects were instructed to focus on one of two competing speech signals. However, subjects 2, 5 and 6 were excluded from the online repository due to not consenting to sharing their data in a public database (cf. signed informed consents approved by KU Leuven Ethical Committee). EEG recordings were conducted in a soundproof, electromagnetically shielded room at ExpORL, KU Leuven. The BioSemi ActiveTwo system was used to record 64-channel EEG signals at 8196 Hz sample rate. Additionally, the participants' gaze movements were measured via 4 EOG (electrooculography) electrodes placed symmetrically around the eyes. </p> <p>The audio signals were administered to each subject at 65 dB SPL through a pair of insert phones (Etymotic ER10). In some experimental trials, the video depicting the attended talker was also presented on the screen. The original presented speech and video stimuli (.wav and .mp4 files) are excluded from the dataset due to copyrights. However, the acoustic envelopes of the attended and unattended audio stimuli are calculated and included in the dataset (see below). <br>The experiments were conducted using custom-made Python scripts.</p> <p>The experimental trials were split into 2 blocks. Each block consisted of the following sequence of conditions: MovingVideo, MovingTargetNoise, NoVisuals, StaticVideo. The auditory task was the same for all conditions: the subjects had to attend to one of the two presented talkers, as indicated by an arrow on the screen. The visual task differed across conditions:</p> <ul> <li>MovingVideo: the subjects had to follow the moving video of the to-be-attended speaker presented on a randomized horizontal trajectory on the screen.</li> <li>MovingTargetNoise: the subjects had to follow a moving cross-hair presented on a randomized horizontal trajectory on the screen.</li> <li>NoVisuals: a black screen was presented and the subjects had to fixate on an imaginary point in the center of the screen while minimizing the eye movements.</li> <li>StaticVideo: the subjects had to fixate the static video of the to-be-attended speaker presented on the same side with the audio stimulus of the attended speaker.</li> </ul> <p>The full description of all experimental conditions can be consulted in [1].</p> <p>Each trial/condition lasted for 10 minutes, with a <strong><em>spatial switch</em></strong> in attention after 5 minutes (i.e., the presented speech stimuli were programmed to swap sides - from L to R or vice versa, such that after the switch the subjects kept listening to the same speaker, but coming from the opposite spatial location). This means that the participant kept attending to the same speaker throughout an entire trial. To keep the subjects motivated, they had to answer one comprehension question related to the attended acoustic stimulus after each trial.</p> <p>For each subject, there is a<strong> .mat file</strong> containing the following variables:<br><strong>conditionID:</strong> the condition ID for each trial <br><strong>data</strong>: the preprocessed EEG and EOG data for each trial (first 64 channels are EEG, last 4 are EOG)<br><strong>fs:</strong> the sampling rate of the EEG, EOG and stimuli envelopes<br><strong>initAttention</strong>: the initial spatial location of the attended stimulus for each trial<br><strong>metadata</strong>: the original metadata (e.g. channel names, triggers) saved in the raw .bdf files for each trial<br><strong>params</strong>: the filtering parameters used for each trial<br><strong>randomization</strong>: the randomization parameters (e.g. presented stimuli, attention switch times etc.) for each trial<br><strong>stimulus</strong>: the precalculated envelopes for the attended and unattended stimuli for each trial<br><strong>subjID</strong>: the anonymised ID of the current subject</p> <p><strong>Preprocessing EEG and EOG</strong></p> <p>All the following preprocessing steps were applied per trial. The EEG was initially downsampled using an antialiasing filter from 8192 Hz to 256 Hz. The data was then filtered between 1–40 Hz using a zero-phase Chebyshev filter (type II, with 80 dB attenuation at 10% outside the passband). Finally, downsampling to 128 Hz was performed to speed up computation.</p> <p><strong>Speech envelopes extraction</strong></p> <p>The original speech signals at 44100 Hz were downsampled to 8192 Hz (to match the EEG sampling rate). They were then passed through a gammatone filterbank, which roughly approximates the spectral decomposition as performed by the human auditory system. Per subband, the audio envelopes were extracted, and their dynamic range was compressed using a power-law operation with exponent 0.6 (as proposed in [2]). Each subband was then bandpass-filtered with the same filter used for the EEG data. The resulting subband envelopes were then summed to construct a single broadband envelope. Finally, the envelope signals were downsampled to 128 Hz to match the sampling rate of the preprocessed EEG.</p> <p><strong>Notes</strong></p> <ol> <li>For subjects 1-3, 6 trials corresponding to 3 conditions (MovingVideo, NoVisuals, StaticVideo) were measured.</li> <li>For subjects 4-16, 8 trials corresponding to 4 conditions (MovingVideo, MovingTargetNoise, NoVisuals, StaticVideo) were measured.</li> <li>For subject 14, trial 2 from the StaticVideo condition was not recorded due to some technical problems.</li> <li>In the dataset, 'FixedVideo' is the alias name for the 'StaticVideo' condition described in [1].</li> <li>The EEG/EOG data was not referenced. Before further analysis, rereferencing the data (e.g., to an arbitrary EEG channel, or the common-average of all channels) is necessary to achieve a better common-mode rejection and thus increase the SNR of recorded data. (for details, see https://www.biosemi.com/faq/cms&drl.htm)</li> </ol> <p><strong>References</strong></p> <p>[1] Rotaru, Iustina, et al. "What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention." <em>Journal of Neural Engineering</em> 21.1 (2024): 016017.</p> <p>[2] Biesmans, Wouter, et al. "Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario." <em>IEEE Transactions on neural systems and rehabilitation engineering</em> 25.5 (2016): 402-412.</p>
Dataset supplementing Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Annals of the New York Academy of Sciences. 1339, 138-153.
<p>Data supplementing the paper Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. <em>Annals of the New York Academy of Sciences. 1339, </em>138-153. doi: 10.1111/nyas.12575 The files can be freely used for scientific purposes, provided this reference is appropriately cited.</p> <p>Files contain the behavioral data, the model can be found at https://doi.org/10.5281/zenodo.573026</p> <p> </p> <p>The following files are contained in this folder:</p> <p>dataExp1.mat contains the data of experiment 1</p> <p>The variables durationLeft and durationRight contain 5 x 6 x 6 cell arrays with the dominance durations for the left and right grating, respectively. Dimensions are subject x contrast level left x contrast level right.</p> <p><br> dataExp2.mat contains the data of experiment 2</p> <p>Variables buttonStart, buttonEnd and whichButton contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain the start time and end time of each button press, and which button (1/2) was pressed, respectively.</p> <p>Variables presStart and presEnd contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain start and end of each blank period. All time stamps refer to the onset of the first blanking trial (end of continuous presentation)</p> <p>Variable prevPerz contains the percept (button) that was pressed at the end of the continuous presentation period.</p> <p><br> figure3_human.m, figure4_human.m and figure6_human.m exemplify the usage of the data by re-plotting the figures containing human data of the aforementioned paper</p>
Tramadol effects on physical performance and sustained attention during a 20-min indoor cycling time-trial: A randomised controlled trial
<p>Objectives: To investigate the effect of tramadol on performance during a 20-min cycling time-trial (Exper- iment 1), and to test whether sustained attention would be impaired during cycling after tramadol intake (Experiment 2). Design: Randomized, double-blind, placebo controlled trial. Methods: In Experiment 1, participants completed a cycling time-trial, 120-min after they ingested either tramadol or placebo. In Experiment 2, participants performed a visual oddball task during the time-trial. Electroencephalography measures (EEG) were recorded throughout the session. Results: In Experiment 1, average time-trial power output was higher in the tramadol vs. placebo condition (tramadol: 220 W vs. placebo: 209 W; p < 0.01). In Experiment 2, no differences between conditions were observed in the average power output (tramadol: 234 W vs. placebo: 230 W; p > 0.05). No behavioural differences were found between conditions in the oddball task. Crucially, the time frequency analysis in Experiment 2 revealed an overall lower target-locked power in the beta-band (p < 0.01), and higher alpha suppression (p < 0.01) in the tramadol vs. placebo condition. At baseline, EEG power spectrum was higher under tramadol than under placebo in Experiment 1 while the reverse was true for Experiment 2. Conclusions: Tramadol improved cycling power output in Experiment 1, but not in Experiment 2, which may be due to the simultaneous performance of a cognitive task. Interestingly enough, the EEG data in Experiment 2 pointed to an impact of tramadol on stimulus processing related to sustained attention. Trial registration: EudraCT number: 2015-005056-96.</p>
Effects of Attentional Focus on Motor Performance and Physiology in a Slow-Motion Violin Bow-Control Task: Evidence for the Constrained Action Hypothesis in Bowed String Technique
<p>The constrained action hypothesis states that focusing attention on action outcomes rather than body movement improves motor performance. Dexterity of motor control is key to successful music performance, making this a highly relevant topic to music education. We investigated effects of focus of attention (FOA) on motor skill performance and EMG muscle activity in a violin bowing task among experienced and novice upper strings players. Following a pedagogically informed exercise, participants attempted to produce single oscillations of the string at a time under three FOA: internal (on arm movement), external (on sound produced), and somatic (on string resistance). Experienced players’ number of bow slips was significantly reduced under somatic focus relative to internal, although number of successful oscillations was not affected. Triceps electromyographic activity was also significantly lower in somatic compared to internal foci for both expertise groups, consistent with physiological understandings of FOA effects. Participants’ reported thoughts during the experiment provided insight into whether aspects of constrained action may be evident in performers’ conscious thinking. These results provide novel support for the constrained action hypothesis in violin bow control, suggesting a somatic FOA as a promising performance-enhancing strategy for bowed string technique.</p>
Contributions of Open-loop and Closed-loop Control in a Continuous Tracking Task Differ Depending on Attentional Demands during Practice
<p>We provide tracking data of the analysed segments after filtering, cognitive performance data and indivdual force levels. To this end, the data set contains three zip-files of numbers of csv-files and an additional csv-file. In each csv-file, comma delimits information within the rows. Participant numbers range between 1 and 21 where the first 11 numbers always refer to the participants of the single-task training group and the final 10 numbers refer to the participants of the dual-task training group.</p> <p>The <em>IndividualForceMaximum.csv</em> includes separate rows of participant’s information about training group, individual force maximum, and the 10% value of the force maximum, which was important for adapting the required force to the individual applicable leg force.</p> <p>The <em>CognitiveTaskData.zip </em>contains four csv-files. Two files are for the <em>TEST</em> sessions, which include either the <em>total</em> number of responses or the number of <em>correct</em> responses per trial for each participant within a row. The other two files are for the <em>TRAINING</em> data (either <em>total</em> number of responses or number of <em>correct</em> responses) of the cognitive task for the participants of the dual-task training group. The file includes 15 trials of the first and the last five training days (i.e., days 1 to 5, and days 18 to 22). For each participant the training data is presented in a row.</p> <p>The tracking data is split with respect to the training groups in either the single-task training group, i.e., <em>MotorTaskData_SingleTaskTrainingGroup.zip</em>, or the dual-task training group, i.e., <em>MotorTaskData_DualTaskTrainingGroup.zip</em>. Each of the zip-files includes <em>TESTING</em> and <em>TRAINING</em> data in csv-files for each participant <em>P</em> separately. We further separated the <em>TEMPLATE</em> data and the <em>PERFORMANCE</em> data, i.e., for instance template test file of P1 corresponds with performance test file of P1. Each trial is presented in a row, where the header includes the segment information (i.e., first segment = 1, second segment = 2, and third segment = 3) for each data point. <em>TEST </em>data include trials with the practiced second segment and the catch trials with another second segment. <em>TRAINING</em> data include the 15 trials of the first and the last five training days (i.e., days 1 to 5, and days 18 to 22).</p>
Data from: Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks
<p><span>External tasks evoke characteristic fMRI BOLD signal deactivations in the default mode network (DMN). However, for the corresponding metabolic glucose demands both decreases and increases have been reported. To resolve this discrepancy, functional PET/MRI data from 50 healthy subjects performing Tetris® were combined with previously published data sets of working memory, visual and motor stimulation. We show that the glucose metabolism of the posteromedial DMN is dependent on the metabolic demands of the correspondingly engaged task-positive networks. Specifically, the dorsal attention and frontoparietal network shape the glucose metabolism of the posteromedial DMN in opposing directions. While tasks that mainly require an external focus of attention lead to a consistent downregulation of both metabolism and the BOLD signal in the posteromedial DMN, cognitive control during working memory requires a metabolically expensive BOLD suppression. This indicates that two types of BOLD deactivations with different oxygen-to-glucose index may occur in this region. We further speculate that consistent downregulation of the two signals is mediated by decreased glutamate signaling, while divergence may be subject to active GABAergic inhibition. The results demonstrate that the DMN relates to cognitive processing in a flexible manner and does not always act as a cohesive task-negative network in isolation.</span></p>
Modulation of Cortical Brain Activity During Attentional Control
ClinicalTrials.gov study NCT06748976. IPD Sharing: YES. Countries: 1. Publications: 3.
A Guided Self-determination Intervention Versus Attention Control for People With Type 2 Diabetes in Outpatient Clinics
ClinicalTrials.gov study NCT04601311. IPD Sharing: YES. Countries: 1. Publications: 2.
Attention Control Training (ACT) and Very Preterm Infants
ClinicalTrials.gov study NCT03896490. IPD Sharing: NO. Countries: 1. Publications: 3.
The Impact of the Attention Training Technique on Attention Control and High Worry
ClinicalTrials.gov study NCT03216382. IPD Sharing: NO. Countries: 1. Publications: 2.
Brain Stimulation and Attention Control in Children With ADHD
ClinicalTrials.gov study NCT07182344. IPD Sharing: YES. Countries: 1. Publications: 17.
A Controlled Study of Solriamfetol for Attention Deficit Hyperactivity Disorder (ADHD) in Adults
ClinicalTrials.gov study NCT04839562. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks
Open the record for dataset details and reuse information.
The Effects of Tramadol and Paracetamol on Physical Performance, Brain Dynamics and Sustained Attention During Cycling Exercise: A Double Blind Randomized Placebo-Controlled Clinical Trial
<p>The purposes of the current project are: 1) to investigate further the effect of Tramadol on cycling (at the physical, cognitive and brain levels) on the basis of the preliminary results of the TRAWADA2015 study (<a href="https://zenodo.org/deposit/1308615">https://zenodo.org/deposit/1308615</a>); 2) to study the effect of paracetamol at the physical, cognitive and brain levels during an aerobic and anaerobic exercise on cycle-ergometer, and compare it to that of tramadol</p>
Behavioural Weight Loss Treatment Plus Motivational Interviewing versus Attention Control: A Randomized Controlled Trial
<p>Studies evaluating the benefit of adding motivational interviewing (MI) to behavioural weight loss programs (BWLPs) have yielded mixed findings. The aims of this randomized controlled trial were to: (1) assess the efficacy of adding MI to a BWLP on weight loss and adherence among 135 overweight and obese individuals (77.8% female; mean BMI = 33.6 kg/m<sup>2</sup>) enrolled in a 12-week BWLP, and (2) explore levels of importance, confidence, and readiness for change ratings. Participants, who were randomized to receive 2 MI sessions or 2 attention control sessions, were assessed at baseline, end of BWLP, and 6 months post-BWLP. Both groups decreased their weight from baseline to end of the BWLP; however, there was no weight change from baseline to 6 months post-BWLP in either group. We observed no group differences in importance, confidence, and readiness for change after each session. Participants may not have benefited from MI because they were already highly motivated to change. These findings suggest that pre-treatment assessment and treatment monitoring may help enhance MI+BWLP efficacy by guiding a stepped-care approach that identifies individuals for whom additional MI sessions are needed, and when. A focus on refining elements of treatment remains an important direction for effective obesity treatment.</p>
Effect of Stimulant Medication on Loss of Control Eating in Youth With Attention Deficit/Hyperactivity Disorder
ClinicalTrials.gov study NCT05592119. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ACtive Children Enhance LEaRning and AttenTION: A Randomized Controlled Trial (RCT)
ClinicalTrials.gov study NCT05794360. IPD Sharing: NO. Countries: 1. Publications: 4.
Phase I Single Dose, Open-Label Pharmacokinetic Study and Single-Blind, Placebo-Controlled Dose Escalation Study of NFC-1 in Adolescents With Attention-Deficit Hyperactivity Disorder
ClinicalTrials.gov study NCT02286817. IPD Sharing: NO. Countries: 1. Publications: 3.
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OpenNeuro
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