Information seeking, curiosity and attention: computational and neural mechanisms

By Jacqueline Gottlieb, Pierre-Yves Oudeyer, Manuel Lopes, Adrien Baranes. 2013

115 citations recorded by the source corpus.

Summary Intelligent animals devote much time and energy to exploring and obtaining information, but the underlying mechanisms are poorly understood. We review recent developments on this topic that have emerged from the traditionally separate fields of machine learning, eye movements in natural behavior, and studies of curiosity in psychology and neuroscience. These studies show that exploration may be guided by a family of mechanisms that range from automatic biases toward novelty or surprise, to systematic search for learning progress and information gain in curiosity-driven behavior. In addition, eye movements reflect visual information search in multiple conditions and are amenable for cellular-level investigations, suggesting that the oculomotor system is an excellent model system for understanding information sampling mechanisms.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Molecular Biology, Neuroscience, Cognitive Science & Psychology

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Citations in the corpus

Continual Lifelong Learning with Neural Networks: A Review (2018, positive): “ taken inspiration from how human children choose their goals and progressively acquire skills for deﬁning developmental structures in lifelong learning frameworks (Baldassarre and Mirolli, 2013; see Gottlieb et al., 2013 for a review). Infants seem to select experiences that maximize an intrinsic l”