AIMC Topic: Choice Behavior
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Patients' Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment.
Journal of medical Internet research
Feb 23, 2021
BACKGROUND: Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians...
Middle Aged
China
Surveys and Questionnaires
Latent Class Analysis
Aged, 80 and over
Young Adult
Adult
Male
Diagnosis
Patient Preference
Choice Behavior
Diagnostic Techniques and Procedures
Adolescent
Time Factors
Logistic Models
Health Expenditures
Humans
Artificial Intelligence
Pandemics
COVID-19
Physicians
Female
SARS-CoV-2
Aged
Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm.
PloS one
Dec 16, 2020
Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. S...
Experimentally revealed stochastic preferences for multicomponent choice options.
Journal of experimental psychology. Animal learning and cognition
Jul 27, 2020
Realistic, everyday rewards contain multiple components. An apple has taste and size. However, we choose in single dimensions, simply preferring some apples to others. How can such single-dimensional preference relationships refer to multicomponent c...
A simple three layer excitatory-inhibitory neuronal network for temporal decision-making.
Behavioural brain research
Jan 20, 2020
Humans and animals do not only keep track of time intervals but they can also make decisions about durations. Temporal bisection is a psychophysical task that is widely used to assess the latter ability via categorization of durations as short or lon...
A Hierarchical Recurrent Neural Network for Symbolic Melody Generation.
IEEE transactions on cybernetics
Dec 2, 2019
In recent years, neural networks have been used to generate symbolic melodies. However, the long-term structure in the melody has posed great difficulty to design a good model. In this article, we present a hierarchical recurrent neural network (HRNN...
Neural Mechanisms for Accepting and Rejecting Artificial Social Partners in the Uncanny Valley.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Jul 1, 2019
Artificial agents are becoming prevalent across human life domains. However, the neural mechanisms underlying human responses to these new, artificial social partners remain unclear. The uncanny valley (UV) hypothesis predicts that humans prefer anth...
Confidence resets reveal hierarchical adaptive learning in humans.
PLoS computational biology
Apr 9, 2019
Hierarchical processing is pervasive in the brain, but its computational significance for learning under uncertainty is disputed. On the one hand, hierarchical models provide an optimal framework and are becoming increasingly popular to study cogniti...
Change in hippocampal theta oscillation associated with multiple lever presses in a bimanual two-lever choice task for robot control in rats.
PloS one
Feb 12, 2018
Hippocampal theta oscillations have been implicated in working memory and attentional process, which might be useful for the brain-machine interface (BMI). To further elucidate the properties of the hippocampal theta oscillations that can be used in ...
Statistical learning of parts and wholes: A neural network approach.
Journal of experimental psychology. General
Jan 12, 2017
Statistical learning is often considered to be a means of discovering the units of perception, such as words and objects, and representing them as explicit "chunks." However, entities are not undifferentiated wholes but often contain parts that contr...
Predict or classify: The deceptive role of time-locking in brain signal classification.
Scientific reports
Jun 20, 2016
Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying as...
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