AIMC Topic: Choice Behavior
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Neural and computational mechanisms of momentary fatigue and persistence in effort-based choice.
Nature communications
Jul 28, 2021
From a gym workout, to deciding whether to persevere at work, many activities require us to persist in deciding that rewards are 'worth the effort' even as we become fatigued. However, studies examining effort-based decisions typically assume that th...
Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features.
Nature human behaviour
May 20, 2021
It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Here, we developed and tested a computational framework to investigate how aesthetic values are formed. We show...
A dataset of human and robot approach behaviors into small free-standing conversational groups.
PloS one
Feb 25, 2021
The analysis and simulation of the interactions that occur in group situations is important when humans and artificial agents, physical or virtual, must coordinate when inhabiting similar spaces or even collaborate, as in the case of human-robot team...
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...
Aged
Aged, 80 and over
Young Adult
Adult
COVID-19
Physicians
Female
SARS-CoV-2
Latent Class Analysis
Adolescent
Time Factors
Logistic Models
Health Expenditures
Male
Middle Aged
China
Surveys and Questionnaires
Humans
Artificial Intelligence
Pandemics
Diagnosis
Patient Preference
Choice Behavior
Diagnostic Techniques and Procedures
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...
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