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8,854 results for “Cognition”
Background music and cognitive task performance: systematic review dataset
<p>This repository contains the raw data used for a systematic review of the impact of background music on cognitive task performance (Cheah et al., 2022). Our intention is to facilitate future updates to this work.</p><p><strong>Contents description</strong></p><p>This repository contains eight Microsoft Excel files, each containing the synthesised data pertaining to each of the six cognitive domains analysed in the review, as well as task difficulty, and population characteristics:</p><ul><li><i>raw-data-attention</i></li><li><i>raw-data-inhibition</i></li><li><i>raw-data-language</i></li><li><i>raw-data-memory</i></li><li><i>raw-data-thinking</i></li><li><i>raw-data-processing-speed</i></li><li><i>raw-data-task-difficulty</i></li><li><i>raw-data--population</i></li></ul><p><strong>Files description</strong></p><p><i>Tabs organisation</i></p><p>The files pertaining to each cognitive domain include individual tabs for each cognitive task analysed (c.f. Figure 2 in the original paper for the list of cognitive tasks). The file with the population characteristics data also contains separate tabs for each characteristic (extraversion, music training, gender, and working memory capacity).</p><p><i>Tabs contents</i></p><p>In all files and tabs, each row corresponds to the data of a test. The same article can have more than one row if it reports multiple tests. For instance, the study by Cassidy and MacDonald (2007; cf. <i>Memory.xlsx</i>, tab: <i>Memory-all</i>) contains two experiments (immediate and delayed free recall) each with multiple test (immediate free recall: tests 25 – 32; delayed free recall: tests 58 – 61). Each test (one per row), in this experiment, pertains to comparisons between conditions where the background music has different levels of arousal, between groups of participants with different extraversion levels, between different tasks material (words or paragraphs) and different combinations of the previous (e.g., high arousing music vs silence test among extraverts whilst completing an immediate free recall task involving paragraphs; cf. test 30).</p><p>The columns are organised as follows:</p><ul><li>"TESTS": the index of the test in a particular tab (for easy reference);</li><li>"ID": abbreviation of the cognitive tasks involved in a specific experiment (see glossary for meaning);</li><li>"REFERENCE": the article where the data was taken from (see main publications for list of articles);</li><li>"CONDITIONS": an abbreviated description of the music condition of a given test;</li><li>"MEANS (music)": the average performance across all participants in a given experiment with background music;</li><li>"MEANS (silence)": the average performance across all participants in a given experiment without background music.</li></ul><p>Then, in horizontal arrangement, we also include groups of two columns that breakdown specific comparisons related to each test (i.e., all tests comparing the same two types of condition, e.g., L-BgM vs I-BgM, will appear under the same set of columns). For each one, we indicate mean difference between the respective conditions ("MD" column) and the direction of effect ("Standard Metric" column). Each file also contains a "Glossary" tab that explains all the abbreviations used in each document.</p><p><strong>Bibliography</strong></p><p>Cheah, Y., Wong, H. K., Spitzer, M., & Coutinho, E. (2022). Background music and cognitive task performance: A systematic review of task, music and population impact. <i>Music & Science, 5</i>(1), 1-38. <a href="https://doi.org/10.1177/0305735699272005">https://doi.org/10.1177/20592043221134392</a></p>
An Empirical Evaluation of the "Cognitive Complexity" Measure as a Predictor of Code Understandability
<p>The paper provides an evaluation of the “Cognitive Complexity” measure as an indicator of code understandability.<br> The evaluation is performed via an empirical study.<br> ""Cognitive Complexity" is compared with traditional code measure, like LoC and McCabe's complexity.</p> <p>The data used in the empirical study are provided.</p>
Atrophy Pattern Maps of Alzheimer's Disease, Mild Cognitive Impairment, Parkinson's Disease, and Frontotemporal Dementia
<p>The files contain voxel-wise t-statistics maps contrasting deformation based morphometry (DBM) measurements of Alzheimer's disease (AD), Parkinson's disease (PD), mild cognitive impairment (MCI), and fronto-temporal dementia (FTD) patients against matched normal controls.</p> <p>AD and MCI maps are based on ADNI data, available at:</p> <p>PD map is based on PPMI data, available at:</p> <p>FTD map is based on NIFD data, available at:</p> <p>For more information regarding the participants and method details, see:</p> <p>Dadar, Mahsa, et al. "White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer's disease and frontotemporal dementia." <em>Neurobiology of aging</em> 111 (2022): 54-63.</p>
COG-BCI database: A multi-session and multi-task EEG cognitive dataset for passive brain-computer interfaces
<p>Brain-Computer Interfaces, and especially passive Brain-Computer Interfaces (pBCI), with their ability to estimate and detect mental states, are receiving increasing attention from both the scientific and the research and development communities. Many pBCIs aim to increase the safety of complex work environments such as in the aeronautical domain. Therefore, mental workload, vigilance and decision-making are some of the most commonly examined aspects of cognition within this field of research. A large proportion of pBCIs involve a component of machine learning and signal processing as the data that are collected need to be transformed into a reliable estimate of the users’ current mental state (e.g. mental workload). Improving this component is a major challenge for researchers, requiring large quantities of data. While data sharing is common for the active BCI community, open pBCI datasets are scarcer and generally incomplete with regards to the information they report. This is particularly true for datasets encompassing several tasks or sessions, which are of importance for tackling the challenges of transfer learning. Testing new pipelines, feature extraction algorithms and classifiers are central issues for future advances in research within this domain, as well as for algorithm benchmark and research reproducibility.The COG-BCI database presented here is comprised of the recordings of 29 participants over 3 individual sessions with 4 different tasks designed to elicit different cognitive states. This results in a total of over 100 hours of open electrophysiological (EEG) and electrocardiogram (ECG) data. The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342). The dataset was validated on a subjective, behavioral and physiological level (i.e. cardiac and cerebral activity), to ensure its usefulness to the pBCI community. This body of work represents a large effort to promote the use of pBCIs, as well as the use of open science.</p> <p> </p> <p><strong>The data are in the Brain Imaging Data Structure (BIDS) format. For more information, please read the COG-BCI_info.pdf file.</strong></p> <p><strong>Please note that version 4 corrected an electrode name mismatch, for which we sincerely apologize. The answers to the RSME and KSS questionnaires are provided in two separate .txt files.</strong></p>
Dataset for "Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey"
<p>Data and R code used for the analysis of data for the publication: Coumoundouros et al., Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey. BMC Nephrology</p> <p><strong>Summary of study</strong></p> <p>An online cross-sectional survey for informal caregivers (e.g. family and friends) of people living with chronic kidney disease in the United Kingdom. Study aimed to examine informal caregivers' cognitive behavioural therapy self-help intervention preferences, and describe the caregiving situation (e.g. types of care activities) and informal caregiver's mental health (depression, anxiety and stress symptoms).</p> <p>Participants were eligible to participate if they were at least 18 years old, lived in the United Kingdom, and provided unpaid care to someone living with chronic kidney disease who was at least 18 years old.</p> <p>The online survey included questions regarding (1) informal caregiver's characteristics; (2) care recipient's characteristics; (3) intervention preferences (e.g. content, delivery format); and (4) informal caregiver's mental health. Informal caregiver's mental health was assessed using the 21 item Depression, Anxiety, and Stress Scale (DASS-21), which is composed of three subscales measuring depression, anxiety, and stress, respectively.</p> <p>Sixty-five individuals participated in the survey.</p> <p>See the published article for full study details.</p> <p><strong>Description of uploaded files</strong></p> <p>1. ENTWINE_ESR14_Kidney Carer Survey Data_FULL_2022-08-30: Excel file with the complete, raw survey data. Note: the first half of participant's postal codes was collected, however this data was removed from the uploaded dataset to ensure participant anonymity.</p> <p>2. ENTWINE_ESR14_Kidney Carer Survey Data_Clean DASS-21 Data_2022-08-30: Excel file with cleaned data for the DASS-21 scale. Data cleaning involved imputation of missing data if participants were missing data for one item within a subscale of the DASS-21. Missing values were imputed by finding the mean of all other items within the relevant subscale. </p> <p>3. ENTWINE_ESR14_Kidney Carer Survey_KEY_2022-08-30: Excel file with key linking item labels in uploaded datasets with the corresponding survey question.</p> <p>4. R Code for Kidney Carer Survey_2022-08-30: R file of R code used to analyse survey data.</p> <p>5. R code for Kidney Carer Survey_PDF_2022-08-30: PDF file of R code used to analyse survey data.</p>
Dataset: "Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?"
<p>This repository contains the dataset presented in "Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?" (https://doi.org/10.3390/ijerph20053798) as well as the SPSS syntax used for the statistical evaluation. Additionally, calibrated binaural recordings of the evaluated stimuli are provided as 32 bit .wav files, the values stored in those files correspond to pascals.</p>
Data of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".
<p>This repository contains data files of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".</p>
Cognition in Social Engineering Empirical Research: a Systematic Literature Review
<p># Description of contents</p> <p>This repository contains the codebook, dataset and analysis scripts in R used for the following publication: Pavlo Burda, Luca Allodi, Nicola Zannone. "Cognition in Social Engineering Empirical Research: a Systematic Literature Review". ACM Transactions on Computer-Human Interaction (TOCHI).<br> The repository consists of the following files:</p> <p>## dataset_and_codebook.xlsx contains the dataset, the codebook and a detailed description of contents.</p> <p>## scripts/ contains the R scripts used for the analysis</p> <p>## readme.txt contains this readme</p> <p><br> # Dataset and codebook</p> <p>The dataset_and_codebook.xlsx contains the following sheets:</p> <p>## Codebook<br> Contains a detailed description of the dataset (tables, columns, fields, etc.).<br> The codebook describes the concepts and variables that are present in the dataset. This includes explanations on meaning, numerical values, classification schemes and labels.</p> <p>## Hypotheses table <br> Contains the hypotheses for all analyzed papers and is used in the results of the paper. <br> Each row is a hypothesis of an included paper, cells contain one or more values (e.g., value1,value2,...) with or without sub values (e.g., value1,value2(sub-value1, ...)). Any content that is in square brackets [] is ignored in the analysis. Empty cells mean that there is no applicable value for that column.</p> <p>## Papers table<br> Contains the analyzed papers and is used in the results of the paper. It is also used in the overview table (Table 6) in Appendix C.<br> Each row is an included paper, cells contain up to two values (e.g., value1, value2) or the word 'multiple' in case of more than two values. Empty cells mean that there is no applicable value for that column.</p> <p>## Values table <br> Contains the description of cell values of 'Hypotheses' and 'Papers' and sub-values (specific variables in a study) that belong to a value. <br> It has a hierarchical structure from left to right where each field on the right column falls under the first non-empty field on the immediate left-top.</p> <p><br> # Reproducing results with scripts/ (generate figures)<br> To run the R scripts included in the 'scripts' directory it is sufficient to follow the instructions in and run the 'RUN_ALL_SCRIPTS.R' in 'scripts' directory.<br> The scripts use the TSV (Tab Separated Values) format of the dataset, which is the exact copy of the 'Papers' and 'Hypotheses' tables in the 'dataset_and_codebook.xlsx' file.<br> The resulting figures are stored in the 'scripts/results' directory.</p>
Prescheduled Interleaving of Processing Reduces Interference in Motor-Cognitive Dual Tasks
<p>All performance-data sets are provided in three seperate .txt-files for each subject.</p> <p>Subject_##_CognitiveTask.txt consists of a chronology table of 8 columns and subject depent number of rows for the 2-back task.</p> <p>col. 1: "Stimulus number" = running number of n stimuli ("0001,0002, ..., n").<br> col. 2: "Day" = Number of the testing day, which is:<br> "1" for day 1, <br> "2" for day 2, or<br> "3" for day 3.<br> col. 3: "Walking Speed [km/h]" = Speed condition of the testing day, which is:<br> "3" for 3 km/h,<br> "4" for 4 km/h, or<br> "5" for 5 km/h.<br> col. 4: "Test block" = Each day includes seven test blocks, where:<br> "1" is the Sitting condition conducted twice, in the beginning and the end of each testing day.<br> "2" is the 0% condition,<br> "3" is the 50% condition,<br> "4" is the 75% condition,<br> "5" is the Earlier condition, and<br> "6" is the Later condition.<br> col. 5: "Trial" = "1" is the first trial,<br> "2" is the second trial, and<br> "3" is the third trial.<br> col. 6: "Reaction time [ms]" = Reaction time data in milliseconds as floating-point numbers. Missing values are substituted by a "NaN" signature.<br> col. 7: "Required decision" = This column shows, whether there was a matching ("1") or a non-matching ("0") required in the 2-back task. Missing values are substituted by a "NaN" signature.<br> col. 8: "Performed decision" = The column shows, whether the subject responded indicating a matching ("1") or a non-matching ("0") in the 2-back task. Missing values are substituted by a "NaN" signature.</p> <p><br> Subject_##_MotorTask_StrideDuration.txt consists of a chronology table of 7 columns and subject depent number of rows for walking strides under single-task and dual-task condition.</p> <p>col. 1: Stride number" = running number of n strides ("0001,0002, ..., n").<br> col. 2: "Day" = Number of the testing day, which is:<br> "1" for day 1, <br> "2" for day 2, or<br> "3" for day 3.<br> col. 3: "Walking Speed [km/h]" = Speed condition of the testing day, which is:<br> "3" for 3 km/h,<br> "4" for 4 km/h, or<br> "5" for 5 km/h.<br> col. 4: "Test block" = Each day includes seven test blocks, where:<br> "2" is the 0% condition,<br> "3" is the 50% condition,<br> "4" is the 75% condition,<br> "5" is the Earlier condition, and<br> "6" is the Later condition.<br> col. 5: "Trial" = Each block consisted of six trials ("1, 2,..., or 6").<br> col. 6: "Task condition" = shows whether the trial was instructed as a single-task ("1") or as a motor-cognitive dualtask ("2").<br> col. 7: "Stride duration [s]" = The duration of each detected stride is written in seconds. Missing values are substituted by a "NaN" signature.</p> <p><br> Subject_##_MotorTask_StrideLength.txt consists of a chronology table of 7 columns and subject depent number of rows for walking strides under single-task and dual-task condition.</p> <p>col. 1: Stride number" = running number of n strides ("0001,0002, ..., n").<br> col. 2: "Day" = Number of the testing day, which is:<br> "1" for day 1, <br> "2" for day 2, or<br> "3" for day 3.<br> col. 3: "Walking Speed [km/h]" = Speed condition of the testing day, which is:<br> "3" for 3 km/h,<br> "4" for 4 km/h, or<br> "5" for 5 km/h.<br> col. 4: "Test block" = Each day includes seven test blocks, where:<br> "2" is the 0% condition,<br> "3" is the 50% condition,<br> "4" is the 75% condition,<br> "5" is the Earlier condition, and<br> "6" is the Later condition.<br> col. 5: "Trial" = Each block consisted of six trials ("1, 2,..., or 6").<br> col. 6: "Task condition" = shows whether the trial was instructed as a single-task ("1") or as a motor-cognitive dual task ("2").<br> col. 7: "Stride duration [mm]" = The length of each detected stride is written in millimeter. Missing values are substituted by a "NaN" signature.</p>
Resting-State High-Density EEG using EGI GES 300 with 256 Channels of Healthy Elders, People with Subjective and Mild Cognitive Impairment and Alzheimer's Disease
<p>This repository contains Matlab files including 4 samples of resting-state EEG recording for Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Subjective Cognitive Decline (SCD), and Healthy Controls (HC) using the HD-EEG EGI GES 300.</p> <p><strong>[AD: i108, MCI: i100, SCD: i090, HC: s055]</strong></p> <p> </p> <p><strong>Participants & Settings</strong></p> <p>In total 230 participants have been recruited from the memory and dementia clinic of the Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD) and the 1st Department of Neurology, U.H. AHEPA, Aristotle University of Thessaloniki, Greece.</p> <p>The full dataset includes:</p> <p><strong>Healthy Controls Elders (60+ years old)</strong>: 33 participants</p> <p><strong>Subjective Cognitive Decline:</strong> 34 participants</p> <p><strong>Mild Cognitive Impairment</strong>: 79 participants</p> <p><strong>Alzheimer's Disease</strong>: 48 participants</p> <p><strong>Healthy Young (25-40 years old):</strong> 36 participants</p> <p>The study was carried out in accordance with the Declaration of Helsinki and received approval by the Scientific and Ethics Committee of GAADRD (No56_27/11/2016), and written informed consent was obtained from all participants prior to their participation in the study. The diagnosis of AD was conducted by a neuropsychiatrist according to their medical history, neuropsychological performance, structural magnetic resonance imaging (MRI), and clinical and neurological examinations.</p> <p>Participants with AD fulfilled the National Institute of Neurological and Communication Disorders and Stroke/Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable AD, as well as the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criteria for dementia of Alzheimer’s type (American Psychological Association, 1994). On the other hand, the MCI participants fulfilled the Petersen criteria, while the SCD group met International Working Group-2 guidelines and the recent National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease (NI-AA), as well as the SCD-I Working Group instructions. </p> <p><strong>Resting-State EEG Recording</strong></p> <p>Fifteen-minute resting EEG activity was recorded for all the participants. For the whole duration of the resting state EEG recording, participants were advised to keep themselves relaxed as much as possible, close their eyes and open them after the researcher’s demand, sit still, minimize blinking or mouth movements and let their mind wander. The experimental procedure was monitored by a research assistant aiming to identify cases of horizontal eye movements, continued blinking, or excessive movement by visually inspecting the EEG traces during the experiment. More specifically, an EEG was registered for both resting conditions (eyes open, EO and eyes closed, EC) for at least 2–3 min for each period.</p> <p><strong>EEG Data Acquisition</strong></p> <p>The EEG data were collected by using the EGI 300 Geodesic EEG system (GES 300, CERTH-ITI, Thessaloniki, Greece) with a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz (EGI Eugene, OR). Moreover, the researcher placed the electrodes in accordance with the 256 HCGSN adult 1.0 montage system, while the signals were recorded relative to a vertex reference electrode (Cz), with AFz as the ground electrode with the electrodes’ impedance below 50 kΩ throughout the experimental procedure, as recommended for the high-input impedance amplifier. In detail, the HD-EEG data were analyzed offline in order to detect any artifact, as well as to conduct pre-processing (filtering, segmentation, bad channel replacement) using Net Station 4.3 software (EGI). HD-EEG data were initially filtered with a 5th-order bandpass Butterworth IIR filter of 0.3–30 Hz. Once the segmentation was completed, the detection of artifacts was performed by using the Net Station artifact detection tool for the automatic detection of excessive eye blinking and movement. Afterward, the signals were baseline corrected using 200 msec before the start of the experiment period and average re-referenced to transform them into reference-independent values.</p> <p> </p> <p><strong>Full Dataset Access</strong></p> <p>More information about the sample dataset and access to the full dataset can be available after request via e-mail:</p> <p><strong>Ioulietta Lazarou</strong> BSc, MSc, PhD candidate</p> <p>Neuropsychologist - Clinical Research Associate </p> <p>Centre for Research and Technology Hellas (CERTH), Information Technologies Institute (ITI)</p> <p>6th km Charilaou-Thermi Road, P.O. Box 60361, 57001 Thermi-Thessaloniki, Greece</p> <p>E-mail: <a href="mailto:iouliettalaz@iti.gr">iouliettalaz@iti.gr</a></p>
Code and data for: Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?
<p>This repository is a companion to the manuscript "<em>Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy?</em>" and is linked to <a href="https://github.com/CBeardsworth/Pheasant_OrientStrat_Habitat">Github</a>.</p> <p>For any questions about the code please contact Christine at <a href="mailto:c.e.beardsworth@gmail.com">c.e.beardsworth@gmail.com</a></p> <p>To use any data contained in this repository contact Joah at <a href="mailto:j.r.madden@exeter.ac.uk">j.r.madden@exeter.ac.uk</a> for permission.</p> <p>In this repository, we have included a run-through of the R analysis <a href="https://cbeardsworth.github.io/Pheasant_OrientStrat_Habitat/">here</a> to show the outputs of the analysis without the need to run the code. For those that might want to run the code themselves, we have included three R scripts (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/R">/R</a>) and their accompanying datasets (<a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a>). A description of the code and the data needed to run them is below:</p> <p><em>Cognition analysis and figs.R</em> = Run the cognition analysis for the first section of the manuscript and create the figures. For this, the datasets mazeData.csv (the learning trials) and mazeRotationResults.csv (the probe trial) are required. </p> <p><em>iSSA analysis and bootstrapping.R</em> = Run iSSA models and bootstrapping. This produces the datasets required for the next stage of analysis. For this code, the datasets habitat.grd (habitat information), atlas2018-strategy.csv (atlas data + id and strategy data for each bird) and FeederCoords2017_27700.csv (coordinates of feeder locations from 2017-2018) are required. The produced datasets are included in <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">/Data</a> therefore to run subsequent analyses, this code does not need to be run. To develop this code we relied heavily on the code included in the supplementary material of <a href="https://doi.org/10.1002/ece3.4823">Signer et al. (2019)</a> as well as an <a href="https://bsmity13.github.io/log_rss">online tutorial</a> from Brian J. Smith for calculating log-RSS.</p> <p><em>Habitat analysis and Figs.R</em> = Run the statistical models for the final section of the manuscript and create the figures. For this code, the datasets produced in the previous R script are required (habitatOrientation_coefs.csv and habitatOrientation_avail.csv). We have included <a href="https://github.com/CBeardsworth/NavigationHabitat/blob/master/Data">these datasets</a> so users do not need to run the iSSA analysis and bootstrapping.R script themselves. </p>
Cognition-mediated Evolution of Low-Quality Floral Nectars
<p>Data from virtual selection experiments, in which real or virtual nectar-feeding bats visited artificial or virtual flowers, exerting selection on their nectar traits.</p> <p> </p>
Bodily and Visual-Cognitive Navigation Aids to Enhance Spatial Recall in Mild Cognitive Impairment
<p>Individuals with mild cognitive impairment (MCI) syndrome often report navigation difficulties, accompanied by impairments in egocentric and allocentric spatial memory. However, studies have shown that both bodily cues (e.g., motor commands, proprioception, vestibular information) and visual-cognitive cues (e.g., maps, directional arrows, attentional markers) can support spatial memory in MCI. These aids offer valuable insights for designing navigation training programs in aging. Fifteen MCI patients were recruited for this study. Their egocentric and allocentric memory recall performances were tested through a navigation task with five different virtual reality (VR) assistive encoding procedures (bodily, vision only, interactive allocentric map, reduced executive load, free navigation without cues). Bodily condition consisted of an immersive VR setup to engage self-motion cues, vision only condition consisted of passive navigation without interaction, in the interactive allocentric map condition patients could use a bird-view map, in the reduced executive load condition directional cues and attentional markers were employed, and during free navigation no aid was implemented. Bodily condition improved spatial memory compared to vision only and free navigation without cues. In addition, the interactive allocentric map was superior to the free navigation without cues. Surprisingly, the reduced executive load was comparable to vison only condition. Moreover, a detrimental impact of free navigation was observed on allocentric memory across testing trials. These findings challenge the notion of an amodal representation of space in aging, suggesting that spatial maps can be affected by the modality in which the environment was originally encoded.</p>
Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen'
<p>Data for Project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' consisting of (1) the complete data set of all data analyzed for the project 'Diagnostic Accuracy, Reliability, and Construct Validity of the German Quick Mild Cognitive Impairment Screen' ('Data_Brain-IT-Validation-Qmci_for-publication.xlsx'; and (2) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments
<h1>Abstract</h1> <p>As artificial intelligence (AI) systems have already proven useful in human lives generally, there is an opportunity for specialized human-AI interaction (HAI) systems to support and provide care for older adults with mild cognitive impairment (MCI). However, the integration of this technology in this population must be thoughtfully designed to accommodate specific needs and limitations. This includes careful measurement of both humans and systems. We developed an evolving dataset categorizing relevant measurement tools into five groups: cognitive ability, demographics & personality, activity level, state of mind, and perceptions of the AI system. Each instance of the tool being used in the literature cataloged in the dataset is qualified in terms of how likely we would recommend using it in the domain of HAI for older adults with MCI based on contextual factors and internal reliability measures. This dataset will serve as a valuable resource for future research, aiding in the identification of promising areas and trends in AI systems for older adults with MCI as well as providing essential tools for future studies.</p> <h1>Methodology</h1> <p>This dataset was not derived through a typical literature review or survey process, but rather followed a more flexible research method. To collect resources for the dataset, we searched numerous databases to identify studies and review types of publications in journals and conferences between the dates of 2000 to 2022. For the papers that contained extensive reviews of literature or cited original tools, we would further look into the citations of those papers, taking us beyond our limited date range. The tools used were categorized into five groups to broadly distinguish their usage in a study, measuring:</p> <ol> <li>Cognitive ability</li> <li>Demographics, personality, and experiences</li> <li>Activity level</li> <li>State of mind</li> <li>Perceptions of the AI system</li> </ol> <p>Subsequently, we conducted an examination of their Cronbach’s 𝛼 scores to assess internal reliability. We created tiers based on how likely we would be to recommend using each tool in the domain of human-AI (HAI) with older adults with MCI, as follows:</p> <ul> <li>Tier 1 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults with MCI in experimental settings interacting with AI</li> <li>Tier 2 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults, with or without MCI, in experimental settings with or without AI interaction</li> <li>Tier * included tools that satisfy the criteria for Tier 1, but, to the best of our knowledge, lack reported Cronbach’s 𝛼 scores</li> <li>Tier 3 included all remaining tools that do not meet the criteria for Tier 1, 2, or *</li> </ul> <p>It should be emphasized that a tool may be found in one or more tiers because multiple studies used the same tool yet resulted in varying reliability scores, contexts, etc.</p> <h1>Contribute</h1> <p>Readers are encouraged to reach out to Adam Norton (adam[underscore]norton[at]uml.edu) to recommend additional tools and entries to the dataset.</p> <h1>Publication</h1> <p>This dataset is published as a short contribution to the Human-Robot Interaction (HRI) 2024 conference. The corresponding paper citation is below:</p> <p>Daisy M. Kiyemba, Jasmin Marwad, Elizabeth J. Carter, and Adam Norton. <strong>Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments</strong>. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24), March 11–14, 2024, Boulder, CO, USA. ACM, New York, NY, USA, 4 pages. <a href="https://doi.org/10.1145/3610977.3637474" target="_blank" rel="noopener">https://doi.org/10.1145/3610977.3637474</a></p> <h1>Acknowledgements</h1> <p>This work was supported by the National Science Foundation (IIS-2112633) as part of the AI-CARING Institute: <a href="https://ai-caring.org/" target="_blank" rel="noopener">https://ai-caring.org/</a></p>
Different components of cognitive-behavioural therapy affect specific cognitive mechanisms: supporting data
<p>This repository holds data associated with the manuscript "Different components of cognitive-behavioural therapy affect specific cognitive mechanisms" (<a href="https://osf.io/preprints/psyarxiv/ydct5">https://osf.io/preprints/psyarxiv/ydct5</a>).</p><ul><li>rew-eff-planningX .csv files represent data from experiments testing effects of a planning/goal-setting intervention on reward-effort decision-making (X=1, initial discovery sample, X=2, replication sample)</li><li>causal-attr-X .csv files represent data from experiments testing the effects of a cognitive restructuring intervention on causal attribution tendencies (X=1, initial discovery sample, X=2, replication sample)</li><li>crossover-study-X .csv files represent data from the crossover-design study (data saved separately for participants randomly allocated to complete the reward-effort vs causal attribution tasks).</li></ul><p>Data-generating task code and full analysis code can be found at <a href="https://github.com/agnesnorbury/cognitive-mechanisms-psychotherapy">https://github.com/agnesnorbury/cognitive-mechanisms-psychotherapy</a>.</p>
Heritability of cognitive performance in wild Western Australian magpies
<p>Individual differences in cognitive performance can have genetic, social and environmental components. Most research on the heritability of cognitive traits comes from humans or captive non-human animals, whilst less attention has been given to wild populations. Western Australian magpies (<em>Gymnorhina tibicen dorsalis</em>, hereafter magpies) show phenotypic variation in cognitive performance, which affects reproductive success. Despite high levels of individual repeatability, we do not know whether cognitive performance is heritable in this species. Here, we quantify broad-sense heritability of associative learning ability in a wild population of Western Australian magpies. Specifically, we explore whether offspring associative learning performance is predicted by maternal associative learning performance, or by the social environment (group size) when tested at three time points during the first year of life. We found no significant relationship between maternal and offspring associative learning performance, with an estimated broad-sense heritability of just -0.004 ± 0.024 (CI: -0.050/0.044). However, complementing previous findings, we find that at 300 days post-fledging, individuals raised in larger groups passed the test in fewer trials compared to individuals from small groups. Our results highlight the pivotal influence of the social environment on cognitive development.</p>
Cerebral microstructural alterations in Post-COVID-condition are related to cognitive impairment, olfactory dysfunction, and fatigue
<p>After contracting COVID-19, a substantial number of individuals develop a Post-COVID-Condition (PCC), marked by neurologic symptoms such as cognitive deficits, olfactory dysfunction, and fatigue, which can have detrimental socioeconomic consequences. Despite this, biomarkers and pathophysiological understandings of this condition remain limited. Employing magnetic resonance imaging, we conduct a comparative analysis of cerebral microstructure among patients with post-COVID condition, healthy controls, and individuals who contracted COVID-19 without long-term symptoms. This reveals widespread alterations in cerebral microstructure, attributed to a shift in volume from neuronal compartments to free fluid, associated with the severity of the initial infection. Correlating these alterations with cognition, olfaction, and fatigue unveils distinct affected networks, which are in a close anatomical-functional relationship with the respective symptoms. This plausibility of symptom-specific networks not only provides insights into the disease's pathophysiological foundations, which align well with an accelerated aging process but also underscores the significance of microstructure as an imaging biomarker.</p>
Data from: Social complexity affects cognitive abilities but not brain structure in a Poecilid fish
<p>Some cognitive abilities are suggested to be the result of a complex social life, allowing individuals to achieve higher fitness through advanced strategies. However, most evidence is correlative. Here, we provide an experimental investigation of how group size and composition affect brain and cognitive development in the guppy (<em>Poecilia reticulata</em>). For six months, we reared sexually mature females in one of three social treatments: a small conspecific group of three guppies, a large heterospecific group of three guppies and three splash tetras (<em>Copella arnoldi</em>) – a species that co-occurs with the guppy in the wild, and a large conspecific group of six guppies. We then tested the guppies' performance in self-control (inhibitory control), operant conditioning (associative learning), and cognitive flexibility (reversal learning) tasks. Using X-ray imaging, we measured their brain size and major brain regions. Larger groups of six individuals, both conspecific and heterospecific groups, showed better cognitive flexibility than smaller groups, but no difference in self-control and operant conditioning tests. Interestingly, while social manipulation had no significant effect on brain morphology, relatively larger telencephalons were associated with better cognitive flexibility. This suggests alternative mechanisms beyond brain region size enabled greater cognitive flexibility in individuals from larger groups. Although there is no clear evidence for the impact on brain morphology, our research shows that living in larger social groups can enhance cognitive flexibility. This indicates that the social environment plays a role in the cognitive development of guppies.</p>
Data and ARRIVE 2.0 checklist for the original article "Lockbox enrichment facilitates manipulative and cognitive activities for mice"
<p>This repository contains data (XLSX file) related to the original article "Lockbox enrichment facilitates manipulative and cognitive activities for mice", which was submitted for publication to Open Research Europe. Moreover, the ARRIVE checklist including the ARRIVE Essential 10 and the Recommended Set is provided in Version v2.</p>
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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.