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256 results for “Working memory”
Working Memory and Reward in Children with and without Attention Deficit Hyperactivity Disorder (ADHD)
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Differential brain mechanisms of selection and maintenance of information during working memory (MEG data)
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Working Memory and Reward in Adults
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EEG Data for: "Cortical oscillations and entrainment in speech processing during working memory load"
<p>This repository contains EEG and audio data used and described in:</p> <p><strong>Hjortkjær, J, Märcher-Rørsted, J, Fuglsang, SA, Dau, T (2018). Cortical oscillations and entrainment in speech processing during working memory load. European Journal of Neuroscience. </strong><strong>doi</strong><strong>:10.1111/ejn.13855</strong></p> <p>Please cite this article when using the data</p> <p> </p> <p>The MAT-files contain the aligned EEG and audio data for each subject (N=22). The envelopes of the speech audio (without noise) have been extracted as described in the paper. Each file (data_N.mat) contains a Matlab struct in the format of the Fieldtrip toolbox containing the following fields:</p> <p> </p> <p>data.trial: EEG and audio data for all 40 trials [channels x timepoints]</p> <ul> <li>channels 1-64: scalp EEG</li> <li>channel 65: left mastoid electrode</li> <li>channel 66: right mastoid electrode</li> <li>channel 67: horizontal EOG</li> <li>channel 68: vertical EOG for left eye</li> <li>channel 69: vertical EOG for right eye</li> <li>channel 70: audio envelopes</li> </ul> <p>data.trialinfo: Experimental condition in each trial</p> <ul> <li>1 = low noise, 1-back</li> <li>2 = low noise, 2-back</li> <li>3 = high noise, 1-back</li> <li>4 = high noise, 2-back</li> </ul> <p>data.time: Sample indices for each trial in seconds</p> <p>data.label: Name of each channel in data.trial</p> <p>data.fsample: EEG/audio sampling rate in Hz (128)</p>
Visual working memory: Study one Task fMRI and Behavioural response
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Visual working memory: Study two Task fMRI and Behavioural response
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Prioritization of semantic over visuo- perceptual aspects in multi-item working memory
<p>All data and code supporting Prioritization of semantic over visuo- perceptual aspects in multi-item working memory</p>
Data from: Capacity and selection in immersive visual working memory following naturalistic object disappearance
<p>Trial datasets and timeseries datasets associated with the experiment reported in the manuscript "Capacity and selection in immersive visual working memory following naturalistic object disappearance", by Babak Chawoush, Dejan Draschkow & Freek van Ede</p>
Neural Evidence of the Strategic Choice Between Working Memory and Episodic Memory in Prospective Remembering.
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EEG: Visual Working Memory + Cabergoline Challenge
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EEG: Visual Working Memory in Acute TBI
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Working memory capacity of crows and monkeys arises from similar neuronal computations
<p>Complex cognition relies on flexible working memory, which is severely limited in its capacity. The neuronal computations underlying these capacity limits have been extensively studied in humans and in monkeys, resulting in competing theoretical models. We probed the working memory capacity of crows (<em>Corvus corone</em>) in a change detection task, developed for monkeys (<em>Macaca mulatta</em>), while we performed extracellular recordings of the prefrontal-like area nidopallium caudolaterale. We found that neuronal encoding and maintenance of information were affected by item load, in a way that is virtually identical to results obtained from monkey prefrontal cortex. Contemporary neurophysiological models of working memory employ divisive normalization as an important mechanism that may result in the capacity limitation. As these models are usually conceptualized and tested in an exclusively mammalian context, it remains unclear if they fully capture a general concept of working memory or if they are restricted to the mammalian neocortex. Here we report that carrion crows and macaque monkeys share divisive normalization as a neuronal computation that is in line with mammalian models. This indicates that computational models of working memory developed in the mammalian cortex can also apply to non-cortical associative brain regions of birds.</p>
Data from two studies of learning in visual span working memory tasks.
<p>Data from two working memory span tasks. The span set size could be up to six, and each row in each .csv is one response (i.e., one "click" on an item). Thus, each trial's data is spread out on multiple rows, with accuracy being a binary variable.</p>
EEG recordings during resting-state and the maintenance periods of a spatial working memory task in humans
<p>Scripts used to analyze data for the manuscript submitted for publication in EJN</p> <p><strong>Script_Curve_Fitting_HBM.rtf</strong></p> <p>Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com </p> <p><span>We therefore considered this continuous change in power as an extraneous variable </span><em><span>y<sub>k</sub>(x)</span></em><span> impacting the measured power spectrum </span><em><span>Pow(E<sub>k</sub>)</span></em><span>, and modeled it with a binomial equation that best fit the data, where the coefficients in <em>p<sub>i</sub></em> are in descending powers, and the length of <em>p</em> is <em>(n+1), k </em>is trial number (<em>k = 1 to 10</em>):</span></p> <p><strong><em><span>y<sub>k</sub>(x) = p<sub><span>1 </span></sub>. x<sup><span>2</span></sup><span><span> </span></span>+ p<sub><span>2 </span></sub>. x<span> </span>+ p<sub><span>3</span></sub></span></em></strong></p> <p><span>In order to statistically compare the topographies between the trials with perfect recall and the trials with failed recall, we subtracted this variable from the mean spectral topographies of each subject and for each electrode by first producing the mean spectral curves of each maintenance trial in the theta and alpha frequency bands, taking into account the IAF, and then calculating the coefficients (</span><em><span>p<sub>1</sub></span></em><span>, </span><em><span>p<sub>2</sub></span></em><span> and </span><em><span>p<sub>3</sub></span></em><span>) of the binomial equation using the Matlab function <em>polyfit.m.</em> Once the coefficients were determined, this estimate was subtracted from each power spectrum matrix using the following formula:</span></p> <p><strong><em><span>PowFit(E<sub><span>k</span></sub>) = Pow (E<sub><span>k</span></sub>) – </span></em></strong><strong><em><span>y<sub>k</sub>(x)</span></em></strong></p> <p> </p> <p><strong>Script_Perf_Fail_EEG_Power_Spec_HBM.rtf</strong></p> <p>Dr. Hadj Meziane: hbmeziane@gmail.com<br>This script calculates EEG power spectra then compares perf and fail conditions, then plots brain topographies with statical results</p> <p> </p> <p><strong>Script_Perf_Fail_EEG_Sources_Spec_HBM.rtf</strong></p> <p>Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com<br>This script compares EEG source spectra then compares Perf vs. Fail conditions then plot statistical results (significant voxels) on MRI volume</p>
Data_MathyChekafCowan_JOC2018_Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression
<p>Original data files for the article Mathy, Fabien, Chekaf, Mustapha, & Cowan Nelson (2018). Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression. Journal of Cognition.</p> <p>Abstract : Complex working memory span tasks were designed to engage multiple aspects of working memory and impose interleaved processing demands that limit the use of mnemonic strategies, such as chunking. Consequently, the average span is usually lower (4 ± 1 items) than in simple span tasks (7 ± 2 items). One possible reason for the higher span of simple span tasks is that participants can take advantage of the spare time to chunk multiple items together to form fewer independent units, approximating 4 ± 1 chunks. It follows that the respective spans of these two types of tasks could be equal (at around 4 ± 1) if stimulus lists exclusively used nonchunkable stimulus items. To manipulate the chunkability of the stimulus lists, our method involved a measure of their compressibility, i.e., the extent to which a pattern exists that can be detected and used as a basis of chunk formation. We predicted an interaction between the types of tasks and chunkability/compressibility, supporting a single higher span for the condition in which a simple span task was combined with chunkable items. The three other conditions were predicted to prevent chunking processes, either because the interleaved processing task did not allow any chunking process to occur or because the noncompressible material inherently limited the chunkability of information. The prediction that chunking is important solely in simple spans was not confirmed: Effects of information compression contributed to performance levels to a similar extent in both tasks according to a theoretically-based metric. This result suggests that i) complex span tasks might overestimate storage capacity in general, and ii) the difference between simple and complex span performance levels must rest in some mechanism other than prevention of a chunking strategy by the interleaved processing task in complex span tasks.</p>
Data from: Prospection of potential actions during visual working memory starts early, is flexible, and predicts behavior.
<p>Raw data (EEG and behavior) reported in the manuscript "Prospection of potential actions in visual working memory starts early, is flexible, and predicts behavior" by Rose Nasrawi, Sage E.P. Boettcher, and Freek van Ede</p>
Working memory capacity of crows and monkeys arises from similar neuronal computations
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Data from: Neural mechanisms of resource allocation in working memory
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A human working memory advantage for social network information
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Data from: Hippocampal sequences represent working memory and implicit timing
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.