期刊
ANNUAL REVIEW OF PSYCHOLOGY, VOL 64
卷 64, 期 -, 页码 169-+出版社
ANNUAL REVIEWS
DOI: 10.1146/annurev-psych-113011-143807
关键词
LTP; spatial learning; Bayesian inference; information theory; cognitive map; geometric module
资金
- NIA NIH HHS [R01AG029289] Funding Source: Medline
- NIMH NIH HHS [R01MH077027] Funding Source: Medline
From the traditional perspective of associative learning theory, the hypothesis linking modifications of synaptic transmission to learning and memory is plausible. It is less so from an information-processing perspective, in which learning is mediated by computations that make implicit commitments to physical and mathematical principles governing the domains where domain-specific cognitive mechanisms operate. We compare the properties of associative learning and memory to the properties of long-term potentiation, concluding that the properties of the latter do not explain the fundamental properties of the former. We briefly review the neuroscience of reinforcement learning, emphasizing the representational implications of the neuroscientific findings. We then review more extensively findings that confirm the existence of complex computations in three information-processing domains: probabilistic inference, the representation of uncertainty, and the representation of space. We argue for a change in the conceptual framework within which neuroscientists approach the study of learning mechanisms in the brain.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据