Rutgers Center for Cognitive Science, Rutgers University, Piscataway, New Jersey 08854-8020, USA.
Annu Rev Psychol. 2013;64:169-200. doi: 10.1146/annurev-psych-113011-143807. Epub 2012 Jul 12.
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.
从联想学习理论的传统角度来看，将突触传递的改变与学习和记忆联系起来的假设是合理的。但从信息处理的角度来看，情况就不那么简单了，因为学习是由计算介导的，这些计算对支配特定领域认知机制运作的物理和数学原则做出了隐含的承诺。我们将联想学习和记忆的性质与长时程增强的性质进行了比较，得出的结论是，后者的性质并不能解释前者的基本性质。我们简要回顾了强化学习的神经科学，强调了神经科学发现的表示含义。然后，我们更广泛地回顾了证实复杂计算存在于三个信息处理领域的发现：概率推理、不确定性表示和空间表示。我们认为，神经科学家在研究大脑中的学习机制时，应该改变他们的概念框架。
