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1,032 results for “Decision making”
Data from: Dynamics of mesoscale brain network during decision-making learning revealed by chronic, large-scale single-unit recording
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Data from: Effect of information about the benefits and harms of mammography on women's decision making: the InforMa randomised controlled trial
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Data for: Structured decision-making in suburban and semi-rural community deer management
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Data from: Statistically testing the role of individual learning and decision-making in trapline foraging
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Data from: Impact of explainable artificial intelligence assistance on clinical decision making of novice dental clinicians
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Visual processing and collective motion-related decision-making in desert locusts
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Reducing racial ingroup biases in empathy and altruistic decision-making by shifting racial identification
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Data from: Social complexity during early development has long-term effects on neuroplasticity in the social decision-making network
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Understanding smallholder decision-making to increase farm tree diversity: Enablers and barriers for forest landscape restoration in Western Kenya
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Recorded neural data from: Nonlinear feedback modulation contributes to the optimization of flexible decision-making
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Data from: Transitions in dynamical regime and neural mode underlie perceptual decision-making
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Qualitative raw data and behavioral analysis for understanding VMMC policy decision-making
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WaveMAP analysis of extracellular waveforms from monkey premotor cortex during decision-making
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Collective decision-making when quantity is more important than quality: Lessons from a kidnapping social parasite
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Consistent patterns of distractor effects during decision making
The value of a third potential option or distractor can alter the way in which decisions are made between two other options. Two hypotheses have received empirical support: that a high value distractor improves the accuracy with which decisions between two other options are made and that it impairs accuracy. Recently, however, it has been argued that neither observation is replicable. Inspired by neuroimaging data showing that high value distractors have different impacts on prefrontal and parietal regions, we designed a dual route decision-making model that mimics the neural signals of these regions. Here we show in the dual route model and empirical data that both enhancement and impairment effects are robust phenomena but predominate in different parts of the decision space defined by the options' and the distractor's values. However, beyond these constraints, both effects co-exist under similar conditions. Moreover, both effects are robust and observable in six experiments.
The impact of learning opportunities on the development of learning and decision making: an experiment with passerine birds
<p>Learning abilities have been shown to be influenced by the developmental context, namely through experiments that imposed severe nutritional and/or environmental constraints (i.e. lack of enrichment). In contrast, we know little on the impact of opportunities for learning on the development of cognition in animals, despite that such opportunities are known to influence human cognitive development. We exposed young zebra finches (<i>Taenopygia guttata</i>) (n=26) to one of three experimental conditions, i.e. an environment where (i) the presence of food was predicted by one colour cue (associative learning), (ii) a combination of two-colour cues (conditional learning), or (iii) colour cues were non-informative (control). After conducting two different discrimination tasks, our results show that experience with predictive cues can cause increased choice accuracy and decision-making speed. Our first learning task showed that<span> individuals in the associative treatment outperformed the control treatment, while task 2 showed that individuals in the conditional treatment shad shorter latencies when making choices compared to the control treatment.</span> We found no support for a speed-accuracy trade-off. This dataset provides a rare longitudinal and experimental examination of the effect of predictive vs non-predictive cues during development on the cognition of adult animals.</p>
Data from: A circuit mechanism for decision making biases and NMDA receptor hypofunction
<table> <tbody> <tr> <td>Decision-making biases can be features of normal behaviour, or deficits underlying neuropsychiatric symptoms. We used behavioural psychophysics, spiking-circuit modelling and pharmacological manipulations to explore decision-making biases during evidence integration. Monkeys showed a pro-variance bias (PVB): a preference to choose options with more variable evidence. The PVB was also present in a spiking circuit model, revealing a potential neural mechanism for this behaviour. To model possible effects of NMDA receptor (NMDA-R) antagonism on this behaviour, we simulated the effects of NMDA-R hypofunction onto either excitatory or inhibitory neurons in the model. These were then tested experimentally using the NMDA-R antagonist ketamine, a pharmacological model of schizophrenia. Ketamine yielded an increase in subjects' PVB consistent with lowered cortical excitation/inhibition balance from NMDA-R hypofunction predominantly onto excitatory neurons. These results provide a circuit-level mechanism that bridges across explanatory scales, from the synaptic to the behavioural, in neuropsychiatric disorders where decision-making biases are prominent.</td> </tr> <tr> </tr> </tbody> </table>
Lee and Daunizeau choice data from: Trading mental effort for confidence in the metacognitive control of value-based decision-making
<p>Why do we carefully ponder some decisions, but not others? In this work, we derive a computational model of the metacognitive control of decisions or MCD. In brief, we assume that fast and automatic processes first provide an initial (and largely uncertain) representation of options' values, yielding prior estimates of decision difficulty. This uncertain value representation is then refined by deploying cognitive (e.g., attentional, mnesic) resources, the allocation of which is controlled by an effort-confidence tradeoff. Importantly, the anticipated benefit of allocating resources varies in a decision-by-decision manner according to the prior estimate of decision difficulty. The ensuing MCD model predicts choices, response time, subjective feeling of effort, choice confidence, and choice-induced preference change. We test these predictions in a systematic manner, using a dedicated behavioral paradigm. Our results provide a quantitative link between mental effort, choice confidence, and preference reversals, which could inform interpretations of related neuroimaging findings.</p>
Dataset of Correlation of clinical decision-making with probability of disease: A web-based study among general practitioners
<p>In databases we find the following information:</p> <p>- the true probability of the disease in the clinical vignette<br> - the probability of the disease estimated by the general practitioner (GP) in the clinical vignette<br> - the clinical decision of the general practitioner<br> - the baseline characteristics of the participants</p>
Data from: How to make methodological decisions when inferring social networks.
<p>Social network analyses allow studying the processes underlying the associations between individuals and the consequences of those associations. Constructing and analysing social networks can be challenging, especially when designing new studies as researchers are confronted with decisions about how to collect data and construct networks, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods, and risk generating false results arising from multiple hypotheses testing. Here, we suggest an approach for making decisions when starting social network research in a new study system that avoids the pitfall of multiple hypotheses testing. We argue that best edge definition for a network is a decision that can be made using <i>a priori</i> knowledge about the species, and that is independent from the hypotheses that the network will ultimately be used to evaluate. We illustrate this approach with a study conducted on a colonial cooperatively breeding bird, the sociable weaver. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then evaluated which combination of data collection and association definition maximised (i) the assortment of individuals into previously known 'breeding groups' (birds that contribute towards the same nest and maintain cohesion when foraging), and (ii) socially differentiated relationships (more strong and weak relationships than expected by chance). This evaluation of different methods based on <i>a priori</i> knowledge of the study species can be implemented in a diverse array of study systems and makes the case for using existing, biologically meaningful knowledge about a system to help navigate the myriad of methodological decisions about data collection and network inference.</p>
ScienceDex guides
Understand access before you commit
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.