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Dataset results
61 results for “individual learning”
Raw data of individuals with Down syndromre, individuals with Williams syndrome, healthy children and adults in a visual learning task, a conditional learning task and a transitive inference task.
<p>Raw data of 17 individuals with Down syndrome (8 girls/women; average age: 17.8 years; range: 7.2-30.8 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 27 individuals with Williams syndrome (16 girls/women; average age: 23.7; range: 9.4-43.8 at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 71<strong> </strong>healthy children (31 girls; average age: 6.42 years; range: 2.95-11.64 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p> <p>Raw data of 22 healthy adults (11 femaleswomen; average age: 26.05 years; range: 20.32-29.76 years at the beginning of the study) in a visual learning task, a 3-item conditional learning task, and a 5-item conditional learning and transitive inference task.</p>
Computational Chromatography: A Machine Learning Strategy for Demixing Individual Chemical Components in Complex Mixtures
<p>This repository contains data for "Computational Chromatography: A Machine Learning Strategy for Demixing Individual Chemical Components in Complex Mixtures". </p>
Learning from dynamic traits: Seasonal shifts yield insights into ecophysiological tradeoffs across scales from macroevolutionary to intra-individual
<p><strong>Premise of the Research.</strong> Phylogenetic comparative methods provide a powerful approach for exploring the macroevolution of plant functional traits. Such approaches can uncover trait-trait correlations through evolutionary time, as well as provide evidence of the role of traits in adaptation across environmental gradients. For continuous traits, most phylogenetic comparative approaches to date employ a single trait value per species, often a mean of sampled individuals, or alternatively incorporate intraspecific variation as a distribution around such a mean. It has been known for quite some time that many of the most physiologically and ecologically important plant traits are actually highly plastic, changing dynamically across a growing season, with whole-plant development, or in response to environmental conditions. Here we demonstrate one possible approach to assessing the evolution of such dynamic traits, the use of function-valued phylogenetic comparative methods.<br> <strong>Methodology.</strong> Leaf traits were sampled across 25 taxa in the genus <em>Cornus</em> at six time points throughout the growing season in a common garden context, followed by contrasting sets of alternative analyses to demonstrate the consequences of researcher decisions on study conclusions.<br> <strong>Pivotal Results. </strong>The vast majority of assessed traits exhibit substantial seasonal shifts. These shifts cause traditional macroevolutionary correlations assessed at different sampling dates to yield conflicting results. Function-valued approaches indicate that seasonal shifts in many traits are evolutionarily correlated, with implications for the origin of trait-trait tradeoffs. Seasonal trait plasticity is also evolutionarily correlated with native habitat environmental gradients across <em>Cornus</em>.<br> <strong>Conclusions.</strong> Because a very large number of plant functional traits are not fixed, but vary dynamically over time or with environmental conditions, stronger insights into the evolution of plant functional traits can emerge when this dynamism is explicitly incorporated into phylogenetic comparative approaches. We encourage the adoption of such approaches, as well as the development of better tools for doing so.</p>
Data from: Statistically testing the role of individual learning and decision-making in trapline foraging
Trapline foraging, a behavior consisting of repeated visitation to spatially fixed resources in a predictable sequence, has been observed over diverse taxa and is important ecologically for efficient resource gathering. Despite this, few null models exist to test the significance of suspected traplines, particularly for studies interested in the role of individual decision-making in the formation of traplines versus the role of resource layouts and random movement patterns. Here we present a spatially explicit, individual-based null model, which may be used to test whether resource layout and realistic forager movement may account for sequence repeats in suspected traplines. In our model, we generate resource visitation sequences by modeling a forager without spatial memory using a random walk to discover and visit spatially-fixed resources. We quantify traplining using Determinism, a metric derived from recurrence quantification analysis. Using both simulated and empirical bee foraging data, we compared our model with two existing null models—a completely random model and a sample randomization model. The former creates null sequences by randomly selecting available resources, while the latter randomizes the order of visits in observed sequences. We found that our model has a higher propensity of being (correctly) rejected than a sample randomization model for trapliners, and a lower propensity of being (incorrectly) rejected for non-trapliners compared to a completely random model. The use of a spatially explicit individual-based null model to test the statistical significance of patterns in empirical data is a novel approach that may be useful for other spatial and individual-based processes.
Switch-like and persistent learning in individual Drosophila larvae
Associative learning allows animals to use past experience to predict future events. The circuits underlying memory formation support immediate and sustained changes in function, often in response to a single example. Larval Drosophila is a genetic model for memory formation that can be accessed at molecular, synaptic, cellular, and circuit levels, often simultaneously, but existing behavioral assays for larval learning and memory do not address individual animals, and it has been dicult to form long lasting memories, especially those requiring synaptic reorganization. We demonstrate a new assay for learning and memory capable of tracking the changing preferences of individual larvae. We use this assay to explore how activation of a pair of reward neurons changes the response to the innately aversive gas Carbon Dioxide (CO2). We conrm that when coupled to odor presentation in appropriate temporal sequence, optogenetic reward reduces avoidance of CO2. We nd that learning is switch-like: all-or-none and quantized in two states. Memories can be extinguished by repeated unrewarded exposure to CO2 but are stabilized against extinction by repeated training or overnight consolidation. Finally, we demonstrate long-lasting protein synthesis dependent and independent memory formation.
Transition from transperitoneal to retroperitoneal approach in laparoscopic living donor nephrectomy: Team-based and individual learning curve: A cross-sectional study
<p><strong><span>Background</span></strong><span>: </span><span>Laparoscopic living donor nephrectomy (LLDN) is a standard practice for kidney donor transplantation due to fewer complications associated with the treatment. Retroperitoneal approach to LLDN is thought to be more advantageous in preventing injuries to various abdominal organs. However, the transition process from transperitoneal to retroperitoneal LLDN is important to ensure the best outcome for the patients</span><span>. The aim of this study was to investigate the learning curve for the retroperitoneal approach in LLDN among urologists in an individual and team-based approach</span></p> <p><span><strong>Methods</strong>: </span><span>A retrospective analysis</span> <span>of retroperitoneal LLDN was performed on procedures performed by a kidney donor team consisting of four (4) urologists from January 2019 to January 2022 at Cipto Mangunkusumo National General Hospital, Indonesia. The data were taken from pre-existing medical records. The learning curve for the operation time, warm ischemic time, and estimated blood loss were analyzed using cumulative sum (CUSUM) analysis. Phase 1 represents the initial learning curve, the phase 2 plateau represents the period of proficiency, and phase 3 represents the mastery period.</span></p> <p><span><strong>Results</strong>: </span><span>A total of 127 retroperitoneal LLDN procedures were done during the study period by four operators with various experience. The average procedure needed to achieve proficiency was 16.5 procedures. Meanwhile, the average procedure needed to achieve mastery was 28 procedures. A shorter learning curve was achieved by operators with more experience in other laparoscopic procedures.</span></p> <p><strong><span>Conclusions</span></strong><span>: </span><span>The learning curve for transition from transperitoneal to retroperitoneal approach to LLDN is relatively short and feasible for both individuals and teams of urologists. Therefore, the transition to a retroperitoneal approach is a feasible option to reduce the complication rate of LLDN.</span></p>
Tailored machine learning for evaluating the long-term diabetes risk in older individuals: Findings from The Irish Longitudinal Study on Ageing (TILDA)
<p>The original data for "Tailored machine learning for evaluating the long-term diabetes risk in older individuals: Findings from The Irish Longitudinal Study on Ageing (TILDA)" that published in BMJ Open.</p>
A Study to Learn About New COVD-19 RNA Vaccine Candidates for New Variants in Healthy Individuals
ClinicalTrials.gov study NCT05997290. IPD Sharing: NO. Countries: 1. Publications: 4.
Motor Learning in Individuals With Lower Limb Loss and Chronic Diabetes
ClinicalTrials.gov study NCT03989063. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study to Learn About New COVID-19 RNA Vaccine Candidates in COVID-19 Vaccine-Experienced Healthy Individuals
ClinicalTrials.gov study NCT05472038. IPD Sharing: NO. Countries: 1. Publications: 3.
Transition from transperitoneal to retroperitoneal approach in laparoscopic living donor nephrectomy: Team-based and individual learning curve: A cross-sectional study
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Data from: Individual-specific strategies inform category learning
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Data from: Statistically testing the role of individual learning and decision-making in trapline foraging
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Switch-like and persistent learning in individual Drosophila larvae
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Learning from dynamic traits: Seasonal shifts yield insights into ecophysiological tradeoffs across scales from macroevolutionary to intra-individual
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Data from: The Achilles' heel hypothesis: misinformed keystone individuals impair collective learning and reduce group success
Many animal societies rely on highly influential keystone individuals for proper functioning. When information quality is important for group success, such keystone individuals have the potential to diminish group performance if they possess inaccurate information. Here we test whether information quality (accurate or inaccurate) influences collective outcomes when keystone individuals are the first to acquire it. We trained keystone or generic individuals to attack or avoid novel stimuli and implanted these seed individuals within groups of naïve colony-mates. We subsequently tracked how quickly groups learned about their environment in situations that matched (accurate information) or mismatched (inaccurate information) the training of the seed individual. We found that colonies with just one accurately informed individual were quicker to learn to attack a novel prey stimulus than colonies with no informed individuals. However, this effect was no more pronounced when the informed individual was a keystone individual. In contrast, keystones with inaccurate information had larger effects than generic individuals with identical information: groups containing keystones with inaccurate information took longer to learn to attack/avoid prey/predator stimuli and gained less weight than groups harboring generic individuals with identical information. Our results convey that misinformed keystone individuals can become points of vulnerability for their societies.
Neuromodulation of premotor and posterior parietal cortices for enhancing explicit motor sequence learning in healthy individuals: a randomized, sham-controlled crossover trial
<p>Data collected and analysed in the manuscript '<strong>Neuromodulation of premotor and posterior parietal cortices for enhancing explicit motor sequence learning in healthy individuals: a randomized, sham-controlled crossover trial', </strong>by Russo et al. PPCR. The Principles and Practice of Clinical Research, 2021</p>
Neural Arbitration between Social and Individual Learning Systems
<p><span><span><span><span><span><span><span><span><span><span><span>Decision making requires integrating self-gathered information with advice from others. However, the arbitration process by which one source of information is selected over the other has not been fully elucidated. In this study, we formalised arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modelling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our relative precision definition of arbitration. Functional neuroimaging demonstrated arbitration signals that were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favour of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favour of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioural and neural levels.</span></span></span></span></span></span></span></span></span></span></span></p>
Biogenic amines for individual and social learning
<p>Individuals learn about their environment through personal observation or by observing their social conspecifics. The speed-accuracy trade-off associated with these two strategies results in two possible learning phenotypes – slow but more accurate individual learners and fast but more inaccurate social learners. While the costs and benefits of these different learning strategies are well known, we know much less about the possible neurophysiological mechanisms that underlie such cognitive variation. Neurotransmitters, based on their important role in driving cognitive differences in all taxa, are the most likely candidates that underlie the neurophysiological differences between these learning phenotypes. We measured individual honeybees for their performance on an individual and a social learning task followed by quantifying the concentrations of various neurotransmitters in their brains. Our results show that the two learning phenotypes and their accompanying differences in the speed-accuracy trade-off are consistently characterized by the opposite effects of an interaction between octopamine and serotonin and another between octopamine and glutamate. These findings challenge the general notion that social learning is simply an extension of individual learning and provide an important insight into the mechanistic underpinnings of the two different learning phenotypes and the neurophysiological mechanism that underlies slow-fast differences across taxa.</p>
Bimanual Motor Skill Learning Through Robotics in Chronic Cerebellar Stroke Survivors and Healthy Individuals
ClinicalTrials.gov study NCT04642599. IPD Sharing: YES. Countries: 1. Publications: 4.
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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.