Tricking our brains to learn and remember; is all learning incidental?

IF 4.8 2区 医学 Q1 NEUROSCIENCES
Aaron R. Seitz
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引用次数: 0

Abstract

Do we choose what we learn? On the contrary, research suggests that much of learning is incidental. The present article reviews frameworks of incidental statistical and perceptual learning and discusses implications of these frameworks to memory. This research supports the premise that much of what we know is shaped by statistical regularities in the environment, how our attention is directed, and what reinforcement we receive from successes and failures. This incidental learning shapes what we perceive and what we remember. This idea that we don’t control when and what we learn, instead we at best trick our brain into states that will lead to desired learning outcomes, has important implications both to individuals and society.
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来源期刊
Current Opinion in Neurobiology
Current Opinion in Neurobiology 医学-神经科学
CiteScore
11.10
自引率
1.80%
发文量
130
审稿时长
4-8 weeks
期刊介绍: Current Opinion in Neurobiology publishes short annotated reviews by leading experts on recent developments in the field of neurobiology. These experts write short reviews describing recent discoveries in this field (in the past 2-5 years), as well as highlighting select individual papers of particular significance. The journal is thus an important resource allowing researchers and educators to quickly gain an overview and rich understanding of complex and current issues in the field of Neurobiology. The journal takes a unique and valuable approach in focusing each special issue around a topic of scientific and/or societal interest, and then bringing together leading international experts studying that topic, embracing diverse methodologies and perspectives. Journal Content: The journal consists of 6 issues per year, covering 8 recurring topics every other year in the following categories: -Neurobiology of Disease- Neurobiology of Behavior- Cellular Neuroscience- Systems Neuroscience- Developmental Neuroscience- Neurobiology of Learning and Plasticity- Molecular Neuroscience- Computational Neuroscience
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