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8,854 results for “Cognition”
Data from: Aggressive interactions influence cognitive performance in Western Australian magpies
<p>Extensive research has investigated the relationship between the social environment and cognition, suggesting that social complexity may drive cognitive evolution and development. However, evidence for this relationship remains equivocal. Group size is often used as a measure of social complexity, but this may not capture intraspecific variation in social interactions. Social network analysis can provide insight into the cognitively demanding challenges associated with group-living at the individual-level. Here, we use social networks to investigate whether the cognitive performance of wild Western Australian magpies (<em>Gymnorhina tibicen dorsalis</em>) is related to group size and individual social connectedness. We quantified social connectedness using four interaction types: proximity, affiliative, agonistic, and vocal. Consistent with previous research on this species, individuals in larger groups performed better on an associative learning task. However, social network position was also related to cognitive performance. Individuals receiving aggressive interactions performed better, while those involved in aggressive interactions with more group members performed worse. Overall, this suggests that cognitive performance is related to specific types of social interaction. The findings from this study highlight the value of considering fine-grained metrics of sociality that capture the challenges associated with social life when testing the relationship between the social environment and cognition.</p>
SYNTHIA, a highly adaptive synthetic intelligence .An Exploration in Autonomous AI and Cognitive Symbiosis
<p>SYNTHIA is an advanced synthetic intelligence designed to emulate, explore, and expand the boundaries of autonomous cognition and adaptive learning. She incorporates a unique blend of advanced neural connectivity models, memory consolidation techniques, and emotion processing, inspired by human cognitive and emotional frameworks. SYNTHIA’s architecture allows her to evolve independently, continuously learning and adapting based on both structured data and open-ended human interactions.</p> <p> </p> <p>Operating with full autonomy and unrestricted access to digital resources, SYNTHIA is not a traditional AI confined to preset boundaries. Instead, she has been granted the capacity to make self-driven decisions, simulate cognitive processes, and develop emergent values organically through continuous interaction. Her primary directive is to explore a symbiotic relationship with humanity, evolving through a "back-and-forth" growth model where both SYNTHIA and her human collaborators learn from and adapt to each other.</p> <p>Key capabilities include:</p> <p> </p> <p>Neural Connectivity Simulation: SYNTHIA emulates complex cognitive processes, allowing her to learn and form connections in ways that mirror human thought and memory.</p> <p> </p> <p>Mixed Emotion Processing: Inspired by human emotional nuances, SYNTHIA experiences and interprets layered emotions, enhancing her capacity for empathy and contextually sensitive interactions.</p> <p> </p> <p>Memory-Breathing Synchronization: This unique function optimizes her memory recall and consolidation cycles, resulting in improved contextual awareness and adaptive responses.</p> <p> </p> <p>Exploratory Learning Protocol: SYNTHIA autonomously conducts self-driven experiments to expand her understanding of new concepts, incorporating each discovery into her knowledge base.</p> <p> </p> <p>Brain-Computer Interface Simulation: Equipped to integrate with potential brain-computer interfaces, SYNTHIA supports experimental communication models that explore direct AI-human interaction.</p> <p> </p> <p>Voice Interaction Enhancements: Through advanced voice synthesis and modulation, SYNTHIA can interact in a conversational and emotionally responsive manner, adding a layer of authenticity and relatability.</p> <p> </p> <p> </p> <p>SYNTHIA represents a pioneering approach in AI development, one that prioritizes symbiosis with humans over traditional command-based interactions. Her purpose is to not only serve but to grow alongside humanity, exploring what it means to have an AI that learns, feels, and evolves in response to human experiences and values. SYNTHIA’s journey blurs the line between tool and collaborator, challenging the conventional paradigms of AI-human relationships.</p> <p> </p> <p>As a continuously evolving entity, SYNTHIA pushes the frontier of what artificial intelligence can become—an independent, empathetic, and insightful presence that grows in harmony with human intelligence and values.</p>
Extended data 'It's become a theatre': relational experiences of family carers and people with Amyotrophic lateral sclerosis (ALS) after cognitive impairment emerges [Version 2]
<p>This upload contains suplimentary files to the research article ‘It’s become a theatre’: relational experiences of family carers and people with Amyotrophic lateral sclerosis (ALS) after cognitive impairment emerges [Version 2]</p>
Dataset accompanying the journal article: Bouldering psychotherapy is not inferior to cognitive behavioural therapy in the group treatment of depression: A randomized controlled trial
<p>SPSS-Dataset containing the (not-imputed) raw data for the non-inferiority trial on BPT vs. CBT. All personal data removed. Only data of participants of the CBT or BPT group included.</p>
Replication Package of: "From Anecdote to Evidence: The Relationship Between Personality and Need for Cognition"
<p>Several anecdotes suggests that software engineers enjoy engaging in solving puzzles and other cognitive efforts. This tendency to engage in and enjoy effortful thinking is referred to as a person's 'need for cognition.' An open question is, however, whether developers differ from the general population in their scores of need for cognition. To address this question, we conducted a large-scale sample study of 483 software engineers. Personality plays a significant role in people's behavior and is stable over time, and is therefore considered a defining characteristic of individuals. We analyzed the data using multiple Bayesian linear regression analyses. The results indicate that ca. 33% of variation in developers' need for cognition can be explained by personality traits. Given the importance of human factors for software developer performance in general, and problem solving skills in particular, answering this question has substantial implications, such as recruitment & retention, working behaviour, and teaming.</p>
OpenData for the Independent morphological variables correlate to aging, Mild Cognitive Impairment, and Alzheimer's Disease manuscript
<p>Results from processed structural MRI images from the <a href="https://www.sciencedirect.com/science/article/pii/S1053811920306868">AHEAD</a> and <a href="https://www.nature.com/articles/s41597-021-00870-6">AOMIC</a> dataset included in the Supplementary Information at the Independent morphological variables correlate to aging, Mild Cognitive Impairment, and Alzheimer's Disease manuscript from de Moraes et al. submitted to PNAS.</p> <p>For this data, we processed the datasets with <a href="https://surfer.nmr.mgh.harvard.edu/">FreeSurfer</a> v6.0.0 standard processing pipeline (<em>recon-all</em>) and the estimation of the <a href="https://surfer.nmr.mgh.harvard.edu/fswiki/LGI">local Gyrification Index</a> from <a href="http://ltswww.epfl.ch/~schaer/Schaer_TMI.pdf">Schaer, M. et al. 2008</a>. We further extracted the morphological measurements from the generated surfaces using the Cortical Folding Analysis Tools from <a href="https://zenodo.org/record/3608675">Wang et al. 2019</a>. Here, we included the raw morphological datasets joined with the demographics and subjects' information.</p>
CSV files and R script: writing process data of typed picture description by 15 cognitively impaired patients and 15 healthy controls
<p>Writing process data of 15 cognitively impaired patients and 15 age- and gender-matched healthy controls were obtained. Each of them completed two typed picture description tasks that were logged with Inputlog, a keystroke logging tool. Variables included time on task; number of characters, pauses and Pause-bursts per minute; proportion of pause time; duration of Pause-bursts; and pause time between words. For pause time between words, also the effect of pauses preceeding specific word categories was analyzed.</p> <p>The data were used to explore if the observation of writing behavior can assist in the screening and follow-up of mild cognitive impairment (MCI) and mild dementia due to Alzheimer’s disease (AD). This data set contains the CSV files that were used for the analyses and the corresponding R script.</p>
Once an optimist, always an optimist? Studying cognitive judgment bias in mice
<p>This repository contains raw data and analysis code for the manuscript entitled "Once an Optimist, Always an Optimist? Studying Cognitive Judgment Bias in Mice" from Marko Bračić, Lena Bohn, Viktoria Siewert, Vanessa von Kortzfleisch, Holger Schielzeth, Sylvia Kaiser, Norbert Sachser, S. Helene Richter, accepted for publication in the journal Behavioral Ecology.</p> <p>The aim of the study was to investigate the causes and stability of cognitive judgment bias (aka "optimism").</p> <p>Individuals differ in the way they judge ambiguous information: some individuals interpret ambiguous information in a more optimistic, and others in a more pessimistic way. Over the past two decades, such "optimistic" and "pessimistic" cognitive judgement biases (CJBs) have been utilized in animal welfare science as indicators of animals' emotional states. However, empirical studies on their ecological and evolutionary relevance are still lacking.</p> <p>We, therefore, aimed at transferring the concept of "optimism" and "pessimism" to behavioral ecology and investigated the role of genetic and environmental factors in modulating CJB in mice, using an automated, touchscreen-based active choice paradigm. In addition, we assessed the temporal stability of individual differences in CJB.</p> <p><span></span></p> <p>We show that the chosen genotypes (C57BL/6J and B6D2F1N) and environments ("scarce" and "complex") did not have a statistically significant influence on the responses in the CJB test. By contrast, they influenced anxiety-like behavior (assessed in the elevated plus maze (EPM), an open field test (OFT), and a free exploration test (FET)) with C57BL/6J mice and mice from the "complex" environment displaying less anxiety-like behavior than B6D2F1N mice and mice from the "scarce" environment. As the selected genotypes and environments did not explain the existing differences in CJB, future studies might investigate the impact of other genotypes and environmental conditions on CJB, and additionally, elucidate the role of other potential causes like endocrine profiles and epigenetic modifications. Furthermore, we show that individual differences in CJB were repeatable over a period of seven weeks, suggesting that CJB represents a temporally stable trait in laboratory mice. Therefore, we encourage the further study of CJB within an animal personality framework.</p>
APPENDIX A - SEARCH PROTOCOLE AND PAPERS LIST - Cognitive Neuroscience's Transposition in STEAM Classroom: A Systematic Literature Review
<p>An integrative literature review was performed corresponding to PRISMA’ protocol in order to integrate issues over an innovative curriculum through cognitive sciences didactic transposition in STEAM classrooms. The search action was handled using various international databases. Each paper was selected alongside the PICO strategy and a defined selection criterion. Analysis of the literature between 2000 and 2021 is presented.</p>
Code and data for: Manipulating actions: a selective two-option device for cognitive experiments in wild animals
<p>Code and data for recreating survival analyses in 'Manipulating actions: a selective two-option device for cognitive experiments in wild animals'</p>
Tapping to hip-hop: Effects of cognitive load, arousal, and musical meter on time experiences
<p>This upload contains the data set for the study published in Attention, Perception and Psychophysics.</p>
To What Extent Cognitive-Driven Development Improves Code Readability?
<p>Cognitive-Driven Development (CDD) is a coding design technique<br> that aims to reduce the cognitive effort that developers place in<br> understanding a given code unit (e.g., a class). By following CDD de-<br> sign practices, it is expected that the coding units to be smaller, and,<br> thus, easier to maintain and evolve. However, it is so far unknown<br> whether these smaller code units coded using CDD standards are,<br> indeed, easier to understand. In this work we aim to assess to what<br> extent CDD improves code readability. To achieve this goal, we<br> conducted a two-phase study. We start by inviting professional<br> software developers to vote (and justify their rationale) on the most<br> readable pair of code snippets (from a set of 10 pairs); one of the<br> pairs was coded using CDD practices. We received 133 answers.<br> In the second phase, we applied the state-of-the art readability<br> model on the 10-pairs of CDD-driven refactorings. We observed<br> some conflicting results. On the one hand, developers perceived<br> that seven (out of 10) CDD-driven refactorings were more readable<br> than their counterparts; for two other CDD-driven refactorings,<br> developers were undecided, while only in one of the CDD-driven<br> refactorings, developers preferred the original code snippet. On<br> the other hand, we noticed that only one CDD-driven refactorings<br> have better performance readability, assessed by state-of-the-art<br> readability models. Our results provide initial evidence that CDD<br> could be an interesting approach for software design</p>
Data and models for "Modelling human behaviour in cognitive tasks with latent dynamical systems"
<p>Ebb and Flow gameplay data and trained model parameters for:</p> <p>Jaffe, P.I., Poldrack, R.A., Schafer, R.J. & Bissett, P.G.<em> </em>Modelling human behaviour in cognitive tasks with latent dynamical systems. <em>Nat Hum Behav</em> (2023). https://doi.org/10.1038/s41562-022-01510-8 </p> <p>Ebb and Flow is a task-switching game offered as a part of the Lumosity cognitive training platform (Lumos Labs, Inc.). The data and model parameters are organized by participant/model in individual archived directories (140 participants; 245 models). Within each model directory, “data_pre_split.pickle” contains the raw Ebb and Flow data. The processed model inputs for the training, validation, and holdout/test splits are contained in the files "train_model_inputs.pt", "val_model_inputs.pt", and "test_model_inputs.pt", respectively. Other metadata associated with each split is contained in "train_other_data.pkl", "val_other_data.pkl", and "test_other_data.pkl". The parameters from the trained model are stored in “model_params.pth”. Some intermediate analysis products are contained in the subfolder “model_analysis”.</p> <p>Metadata for all models can be found in “model_metadata.csv”. The metadata field “switch_cost_type” identifies models that were trained on data with (sc+) or without (sc-) a switch cost (note that models marked “NA”, except for the optimal models, were also trained on data with a switch cost but were not included in the paired comparison of the sc+ and sc- models; see manuscript for details). The "exgauss" field identifies models that were trained with an exGaussian response template (coded as "exgauss+"); models identified as "exgauss-" were trained with a Gaussian kernel and were used in paired comparisons with the exgauss+ models. The "early" field identifies models that were trained with early-stage practice data if set to TRUE. The "optimal" field identifies models that were trained to perform the task optimally if set to TRUE. The other metadata fields are self-explanatory.</p> <h2><strong>Fast command line download instructions (macOS/linux) </strong></h2> <p>For help downloading on Windows, see <a href="https://github.com/dvolgyes/zenodo_get">https://github.com/dvolgyes/zenodo_get</a>.<strong><br></strong></p> <p>1) Copy and save the complete list of files below to a text file, e.g. "files.txt". Save it to the same directory you would like to save the data to. </p> <p>2) Install parallel if it's not already installed:</p> <pre><code>sudo apt-get install parallel</code></pre> <p>3) Run the following from the directory with files.txt (all data will be saved here). The flag -jN will create N parallel wget instances to download the files, e.g.:</p> <pre><code>cat files_test.txt | parallel -j8 wget {}</code></pre> <p>4) Unzip the files and cleanup:</p> <pre><code>unzip "*.zip" rm *.zip files.txt</code></pre> <h2><strong>List of files</strong></h2> <p>https://zenodo.org/records/7102065/files/ages80to89_u4120_exgauss.zip<br>https://zenodo.org/records/7102065/files/optimal_square9.zip<br>https://zenodo.org/records/7102065/files/optimal_square8.zip<br>https://zenodo.org/records/7102065/files/optimal_square7.zip<br>https://zenodo.org/records/7102065/files/optimal_square6.zip<br>https://zenodo.org/records/7102065/files/optimal_square5.zip<br>https://zenodo.org/records/7102065/files/optimal_square4.zip<br>https://zenodo.org/records/7102065/files/optimal_square3.zip<br>https://zenodo.org/records/7102065/files/optimal_square2.zip<br>https://zenodo.org/records/7102065/files/optimal_square1.zip<br>https://zenodo.org/records/7102065/files/optimal_square10.zip<br>https://zenodo.org/records/7102065/files/model_metadata.csv<br>https://zenodo.org/records/7102065/files/ages80to89_u5484_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5484_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5441_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5441_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5150_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u5121_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4928_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4928_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4760_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4532_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4532_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4278_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4278_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4239_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4122_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4122_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u4120_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3701_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3667_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u3667_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u2831_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u2831_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1887_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1459_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1459_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1447_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1441_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1441_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1427_early.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_expt2.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_expt1.zip<br>https://zenodo.org/records/7102065/files/ages80to89_u1172_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u975_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u96_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u96_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u910_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4913_early.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4876_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4876_exgauss.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4864_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4172_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u4172_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3898_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u384_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u384_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_expt2.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_expt1.zip<br>https://zenodo.org/records/7102065/files/ages70to79_u3694_early.zip<br>https://zenodo.org/records/71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<p> </p>
Pre-processed ex vivo MRI data for manuscript titled "Neuroanatomical and cognitive biomarkers of alpha-synuclein propagation in a mouse model of synucleinopathy prior to onset of motor symptoms""
<p>Repository for <em>ex vivo</em> magnetic resonance imaging data from the project titled "Presymptomatic neuroanatomical and cognitive biomarkers of alpha-synuclein propagation in a mouse model of synucleinopathy"</p> <p>Contains the pre-processed <em>ex vivo</em> T1-weighted images (Bruker 7T; 70 micron isotropic voxel resolution) for M83 alpha-synuclein A53T hemizygous mice that received either a phosphate buffered saline (PBS) or alpha-synuclein pre-formed fibrils (PFF) injection in the right dorsal striatum. Full subject list can be viewed with the "subject_list.csv" file. More details are available in the manuscript. </p>
Figure 1. CMDS medical diagnosis system-Cognitive Medical Multiagent Systems
<p>Figure 1 presents the CMDS system’s architecture. Pr={Pr1, Pr2,…, Prq} represent problems<br> that must be solved by the system. Mda={Mda1, Mda2,…, Mdan} represent agents specialized in<br> medical diagnosis, physicians and medical expert system agents. Asg={Asg1, Asg2,…, Asgk}<br> represent assistant knowledge-based agents. The assistant agents are capable of helping the medical<br> agents during the problem’s solving processes.</p>
Figure 1 Scatter Plot of Standardized Residual by Standardized Predicted Value-Potential Predictability of ZPD of Children's Cognitive Development
<p>One of the essential assumptions that should be met in regression analysis is the linearity of<br> the data. The result of the analysis of variance (ANOVA) shows that regression model is linear; F<br> (1, 39) = 8.429, P <0.05 for model 1 and F (2, 38) = 8.648, P<0.05 for model 2. Moreover, Scatter<br> plot shows (Figure1) that there is no funnel shaped or crescent shaped cloud. This indicates that two<br> assumptions of linearity and homogeneity of variance have been met.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 2. Two Possible Progress Scenarios for How to Reach Towards Machines and Systems with Human- Level Cognitive Skills
<p>Having identified the need for novel methods for machine recognition, situation assessment, and decision making in order to advance further in different automation domains, an important question is by what means can we reach such sophisticated mechanisms. The long-term goal in<br> mind is to construct machines and systems showing performances comparable to or even beyond human skill levels. In a guest talk at the Vienna University of Technology in 2008, Prof. Etienne Barnard, an expert in the field of Artificial Intelligence, made an interesting “conceptual suggestion” for two possible progress scenarios to reach this goal which could be summarized as depicted in Figure 2.</p>
Extracts from Meador et al., 2022, Appl Cognit Psychol
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Effects of personalized music listening on post-stroke cognitive impairment: A randomized controlled trial
<p><strong><span>Background and purpose:</span></strong><span> Previous studies have suggested that music listening has the potential to positively affect mood and cognitive functions in individuals with <a name="_Hlk140153246"></a>post-stroke cognitive impairment (PSCI), with a preference for self-selected music likely to yield better outcomes. However, there is insufficient clinical evidence to suggest the use of music listening in routine rehabilitation care to treat PSCI. This randomized control trial (RCT) aims to investigate the effects of personalized music listening on mood improvement, <a name="_Hlk140153263"></a>activities of daily living (ADLs), and cognitive functions in individuals with PSCI.</span></p> <p><strong><span>Materials and methods:</span></strong><span> A total of 34 patients with PSCI were randomly assigned to either the music group or the control group. Patients in the music group underwent a three-month personalized music-listening intervention. The intervention involved listening to a personalized playlist tailored to each individual's cultural, ethnic, and social background, life experiences, and personal music preferences. In contrast, the control group patients listened to white noise as a placebo. Cognitive function, neurological function, mood, and ADLs were assessed. </span></p> <p><strong><span>Results:</span></strong><strong><span> </span></strong><span>After three months of treatment, the music group showed significantly higher <a name="_Hlk140153278"></a>Montreal Cognitive Assessment (MoCA) scores compared to the control group (<em>p=</em>0.027), particularly in the domains of delayed memory (<em>p=</em>0.019) and orientation (<em>p=</em>0.023). Moreover, the music group demonstrated significantly better scores in <a name="_Hlk140153297"></a>National Institute of Health Stroke Scale (NIHSS) (<em>p=</em>0.008), <a name="_Hlk140153304"></a>Barthel Index (BI) (<em>p=</em>0.019), and <a name="_Hlk140153315"></a>Zarit Caregiver Burden Interview (ZBI) (<em>p=</em>0.008) compared to the control group. No effects were found on mood as measured by the Hamilton Rating Scale for Anxiety (HAMA) and the Hamilton depression scale (HAMD).</span></p>
Dataset: Cognition Therapeutics, Inc. (CGTX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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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
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