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70 results for “Human Behaviour”
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> 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<p> </p>
SSRA and Human Behaviour Study - Associated Raw Data and Scripts
<p>We are releasing the final version of this dataset containing source data and scripts for a paper concerning an SSRA drug and human behaviour.</p> <p>V4 -> V5 - Fixed minor issues following editorial feedback.</p> <p>For any questions, please contain Michael Colwell (michaelcolwell92@gmail.com / michael.colwell@psych.ox.ac.uk).</p>
Figure4 in Analysis of Behavioural Patterns of the Amazonian Manatee, Trichechus manatus manatus, under Human Care (Mammalia:Sirenia)
Figure4. Space use data of the manatee individuals linked to the referring numbers on the map; 1 - 10 deepwater zone (Z); 11/12 semi-deep-water zone (Y); 13/14 shallow-water zone (X)
Figure3 in Analysis of Behavioural Patterns of the Amazonian Manatee, Trichechus manatus manatus, under Human Care (Mammalia:Sirenia)
Figure3. Map of the manatee tank divided into grid squares, each filled with similar volume of water; 1 - 10 deep-water zone (Z); 11/12 semi-deep-water zone (Y); 13/14 shallow-water zone (X)
Fig. 2 in The Effects Of Human-Dolphin Interaction Programmes On The Behaviour Of Three Captive Indo-Pacific Humpback Dolphins (Sousa Chinensis)
Fig. 2. Proportion of behaviours exhibited by the individual dolphins before and after the SWD programme. *P<0.05; **P<0.01; ***P<0.001.
Fig. 1 in The Effects Of Human-Dolphin Interaction Programmes On The Behaviour Of Three Captive Indo-Pacific Humpback Dolphins (Sousa Chinensis)
Fig. 1. Diagram of the dolphin lagoon consisting of the main pool (Length: 37 m, Width: 20 m, Depth: 3.5 m) alongside three holding pools. Locations that were utilised during scan sampling are marked out (1–3).
Fig. 3 in The Effects Of Human-Dolphin Interaction Programmes On The Behaviour Of Three Captive Indo-Pacific Humpback Dolphins (Sousa Chinensis)
Fig. 3. Proportion of behaviours exhibited by the individual dolphins before and after the MWD programme. *P<0.05; **P<0.01; ***P<0.001.
Understanding behavioural responses to human-induced rapid environmental change: A meta-analysis
<p>Behavioural responses are often the first reaction of an organism to human induced rapid environmental change (HIREC), yet current empirical evidence provides no consensus about the main environmental features that animals respond to behaviourally or which behaviours are responsive to HIREC. To understand how changes in behaviour can be predicted by different forms of HIREC, we conducted a meta-analysis of the existing empirical literature focusing on behavioural responses to five axes of environmental change (climate change, changes in CO<sub>2</sub>, direct human impact, changes in nutrients and biotic exchanges) in five behavioural domains (aggression, exploration, activity, boldness and sociability) across a range of taxa but with a focus on fish and bird species. Our meta-analysis revealed a general absence of directional behavioural responses to HIREC. However, the absolute magnitude of the effect sizes was large. This means that animals have strong behavioural responses to HIREC, but the responses are not clearly in any particular direction. Moreover, absolute magnitude of the effect sizes differed between different behaviours and different forms of HIREC: Exploration responded more strongly than activity, and climate change induced the strongest behavioural responses. Model heterogeneities identified that effect sizes varied primarily because of study design, and the specific sample of individuals used in a study; phylogeny also explains significant variation in our bird model. Based on these results, we make four recommendations to further our understanding: 1) a more balanced representation of laboratory and field studies, 2) consideration of context dependency, 3) standardisation of the methods and definitions used to quantify and study behaviours, and 4) consideration of the role for individual differences in behaviour.</p>
Understanding behavioural responses to human-induced rapid environmental change: A meta-analysis
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Data for: What you have, not who you know: food-enhanced social capital and changes in social behavioural relationships in a non-human primate
<p>Social network position in non-human primates has far-reaching fitness consequences. Critically, social networks are both heterogenous and dynamic, meaning an individual's current network position is likely to change due to both intrinsic and extrinsic factors. However, our understanding of the drivers of changes in social network position is largely confined to opportunistic studies. Experimental research on the consequences of <em>in situ, </em>controlled<em> </em>network perturbations is limited. Here we conducted a food-based experiment in rhesus macaques to assess whether allowing an individual the ability to provide high-quality food to her group changed her social behavioural relationships. We considered both her social network position across five behavioural networks, as well as her dominance and kin interactions. We found that gaining control over a preferential food resource had far-reaching social consequences. There was an increase in both submission and aggression centrality and changes in the socio-demographic characteristics of her agonistic interaction partners. Further, we found that her grooming balance shifted in her favour as she received more grooming than she gave. Together, these results provide a novel, preliminary insight into how <em>in situ</em>, experimental manipulations can modify social network position and point to broader network-level shifts in both social capital and social power.</p>
Data and Scripts associated with "Fisher activity patterns show potential for behavioural adaptations to human modified landscapes"
<p>Script and data for running both models from manuscript. Data contains daily and nightly mean activity levels (ODBA) for fisher captured from 2021-2023 in Rhode Island, USA. </p>
Figure2 in Analysis of Behavioural Patterns of the Amazonian Manatee, Trichechus manatus manatus, under Human Care (Mammalia:Sirenia)
Figure2. Distribution of respiratory frequencies (diving phases) for each manatee
Figure1 in Analysis of Behavioural Patterns of the Amazonian Manatee, Trichechus manatus manatus, under Human Care (Mammalia:Sirenia)
Figure1. Map of the manatee tank with current and spatial zones (edited © Nuremberg Zoo 2012)
Fig. 6 in The Effects Of Human-Dolphin Interaction Programmes On The Behaviour Of Three Captive Indo-Pacific Humpback Dolphins (Sousa Chinensis)
Fig. 6. Association index of each dyad before and after each programme.
Fig. 5 in The Effects Of Human-Dolphin Interaction Programmes On The Behaviour Of Three Captive Indo-Pacific Humpback Dolphins (Sousa Chinensis)
Fig. 5. Average SPI before and after the MWD programme. *P<0.05; **P<0.01; ***P<0.001.
Fig. 4 in The Effects Of Human-Dolphin Interaction Programmes On The Behaviour Of Three Captive Indo-Pacific Humpback Dolphins (Sousa Chinensis)
Fig. 4. Average SPI before and after the SWD programme. *P<0.05; **P<0.01; ***P<0.001.
Antelope space-use and behaviour indicate multi-level responses to varying anthropogenic influences in a highly human-dominated landscape
<p><span></span></p> <p>A primary means of conserving a species or a habitat in a human-dominated landscape is through promoting coexistence with humans while minimizing conflict. For this, we should understand how wildlife are impacted by direct and indirect human activities. Such information is rare from areas with high human densities. To investigate how animals respond to altered ecological conditions in human-dominated landscapes, we focussed on a wild herbivore of conservation concern in Krishnasaar Conservation Area (KrCA) in Nepal. Here, blackbuck, <em>Antilope cervicapra</em>, a generalist grazer, lives in refugia located in a growing human population. We studied the impacts of humans on habitat use and behaviour of blackbuck. We laid 250 x 250 m grid cells in the entire KrCA and carried out indirect sign surveys with three replications for habitat use assessment. We observed herds of blackbuck for 89 hours in different habitat types using scan sampling methods. Our habitat-use survey showed that habitats under intensive human use were hardly used by blackbuck, even when high-quality forage was available. Habitat openness was the major predictor of habitat-use inside the core area, where levels of human activities were low. We also found a positive correlation between habitat use by blackbuck and livestock. Blackbuck were substantially more vigilant when they were in forest than in grassland, again indicating an influence of risk. Overall, blackbuck appear to be sensitive to risk associated with both natural and anthropogenic factors. Our findings have direct implications for managing human-wildlife interactions in this landscape, specifically regarding strategies for livestock grazing in habitats highly used by blackbuck and concerning predictions of how changing land use will impact long-term persistence of blackbuck. Our work suggests that wild herbivores may be able to persist in landscapes with high human densities so long as there are refuges where human activities are relatively low.</p>
NexusStreets: a dataset combining human and autonomous driving behaviours
<p>The NexusStreets dataset contains human and autonomous driving scenes. They are collected by monitoring a target vehicle that can be either autonomous or controlled by a human driver. Data is presented in the shape of:</p> <ul> <li>sequences of JPEG images, one image per timestamp</li> <li>target vehicle state information for each timestamp</li> </ul> <p>The dataset has been built on the CARLA simulator, thanks to Baidu Apollo and a Logitech G29 steering wheel for the autonomous and human drivings, respectively.<br>The dataset consists of 520 scenes (260 pairs of mirrored scenarios) of 60 seconds each.<br>The folders are organized as follows:</p> <pre><code>. ├── ... ├── <driving mode> │ ├── <town> │ │ ├── <trial> │ │ │ └── ... │ │ └── ... │ └── ... └── ...</code></pre> <ul> <li>driving mode: corresponds to the control modality of the target vehicle under test and can be either Baidu Apollo or manual driving;</li> <li>town: one of the five default maps in CARLA (e.g., Town01, Town02, etc);</li> <li>trial: 60 different trials per map, they differ in traffic and weather conditions (except Town04). Each trial records 60 seconds of simulation, logging 120 frames per video and an equal number of rows per CSV. In particular, each trial includes: <ul> <li>video: this folder groups the JPEG images;</li> <li>state_features.csv: reports the state information of the target vehicle for each frame;</li> <li>detection_features.csv: reports the 2D bounding box detections obtained from a pre-trained YOLOv7 detector.</li> </ul> </li> </ul>
Human socialization influences point following behaviour of free-ranging dogs
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Videos related to: Noise matters: Elephants show risk-avoidance behaviour in response to human-generated seismic cues
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.