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Motivated Cognition: Neural and Computational Mechanisms of Curiosity, Attention, and Intrinsic Motivation
Motivated Cognition: Neural and Computational Mechanisms of Curiosity, Attention, and Intrinsic Motivation
International audience; Based on a synthesis of findings from psychology, neuroscience, and machine learning, we propose a unified theory of curiosity as a form of motivated cognition. Curiosity, we propose, is comprised of a family of mechanisms that range in complexity from simple heuristics based on novelty, salience, or surprise, to drives based on reward and uncertainty reduction and finally, to self-directed metacognitive processes. These mechanisms, we propose, have evolved to allow agents to discover useful regularities in the world ! steering them toward niches of maximal learning progress and away from both random and highly familiar tasks. We emphasize that curiosity arises organically in conjunction with cogni- tion and motivation, being generated by cognitive processes and in turn, motivating them. We hope that this view will spur the systematic study of curiosity as an integral aspect of cognition and decision making during development and adulthood.
- Université Paris Diderot France
- French Institute for Research in Computer Science and Automation France
- King’s University United States
Microsoft Academic Graph classification: media_common.quotation_subject Metacognition Salience (neuroscience) Uncertainty reduction theory media_common Novelty Cognition Surprise Curiosity Psychology Heuristics Cognitive psychology
exploration, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], memory, [SCCO]Cognitive science, active learning, development, [SCCO.NEUR]Cognitive science/Neuroscience, learning progress, artificial intelligence, attention, machine learning, Intrinsic motivation, metacognition
exploration, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], memory, [SCCO]Cognitive science, active learning, development, [SCCO.NEUR]Cognitive science/Neuroscience, learning progress, artificial intelligence, attention, machine learning, Intrinsic motivation, metacognition
Microsoft Academic Graph classification: media_common.quotation_subject Metacognition Salience (neuroscience) Uncertainty reduction theory media_common Novelty Cognition Surprise Curiosity Psychology Heuristics Cognitive psychology
4915
Baldi, P., & Itti, L. Of bits and wows: A Bayesian theory of surprise with applications to attention. Neural Netw, 23(5), 649-666.
Baranès, A., & Oudeyer, P.-Y. (2009). R-IAC: Robust intrinsically motivated exploration and active learning. Autonomous Mental Development, IEEE Transactions on, 1(3), 155-169. [OpenAIRE]
Baranes, A., & Oudeyer, P. Y. (2013). Active learning of inverse models with intrinsically motivated goal exploration in robots. Robotics and Autonomous Systems, 61(1), 49-73. [OpenAIRE]
Baranes, A. F., Oudeyer, P. Y., & Gottlieb, J. (2014). The effects of task difficulty, novelty and the size of the search space on intrinsically motivated exploration. Frontiers in Neuroscience(Oct. 14). [OpenAIRE]
Baranes, A. F., Oudeyer, P. Y., & Gottlieb, J. (2015). Eye movements encode epistemic curiosity in human observers. Vis Res, in press. [OpenAIRE]
Barto, A., Mirolli, M., & Baldassare, G. (2013). Novelty or surprise? Frontiers in Psychology, 11 December.
Bisley, J., & Goldberg, M. (2010). Attention, intention, and priority in the parietal lobe. Annual Review of Neuroscience, 33, 1-21. [OpenAIRE]
Blanchard, T. C., Hayden, B. Y., & Bromberg-Martin, E. S. (2015). Orbitofrontal cortex uses distinct codes for different choice attributes in decisions motivated by curiosity. Neuron, 85(3), 602- 614. [OpenAIRE]
Brafman, R. I., & Tennenholtz, M. (2003). R-max-a general polynomial time algorithm for near-optimal reinforcement learning. The Journal of Machine Learning Research, 3, 213-231.
Bromberg-Martin, E. S., & Hikosaka, O. (2009). Midbrain dopamine neurons signal preference for advance information about upcoming rewards. Neuron, 63(1), 119-126. [OpenAIRE]
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).19 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).19 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!- Université Paris Diderot France
- French Institute for Research in Computer Science and Automation France
- King’s University United States
International audience; Based on a synthesis of findings from psychology, neuroscience, and machine learning, we propose a unified theory of curiosity as a form of motivated cognition. Curiosity, we propose, is comprised of a family of mechanisms that range in complexity from simple heuristics based on novelty, salience, or surprise, to drives based on reward and uncertainty reduction and finally, to self-directed metacognitive processes. These mechanisms, we propose, have evolved to allow agents to discover useful regularities in the world ! steering them toward niches of maximal learning progress and away from both random and highly familiar tasks. We emphasize that curiosity arises organically in conjunction with cogni- tion and motivation, being generated by cognitive processes and in turn, motivating them. We hope that this view will spur the systematic study of curiosity as an integral aspect of cognition and decision making during development and adulthood.