论文标题
代表意味着什么？心理表示作为可伪造的记忆模式
What does it mean to represent? Mental representations as falsifiable memory patterns
论文作者
论文摘要
表示是神经科学和人工智能（AI）的关键概念。但是，一场长期以来的哲学辩论突出了指定的代表性比看起来更棘手的。在这篇简短的意见论文中，我们希望将代表性的哲学问题引起人们的注意，并提供可实施的解决方案。我们注意到，神经科学家和工程师经常采用的因果和目的论方法无法提供令人满意的代表性描述。我们根据该替代方案来绘制一个替代方案，该替代形式对应于世界上推断的潜在结构，该结构根据有条件的激活模式确定。假定这些结构客观地具有某些属性，从而可以计划，预测和检测意外事件。我们通过模拟简单的神经网络模型来说明我们的建议。我们认为，这种更牢固的代表概念可以为神经科学和AI的未来研究提供信息。
Representation is a key notion in neuroscience and artificial intelligence (AI). However, a longstanding philosophical debate highlights that specifying what counts as representation is trickier than it seems. With this brief opinion paper we would like to bring the philosophical problem of representation into attention and provide an implementable solution. We note that causal and teleological approaches often assumed by neuroscientists and engineers fail to provide a satisfactory account of representation. We sketch an alternative according to which representations correspond to inferred latent structures in the world, identified on the basis of conditional patterns of activation. These structures are assumed to have certain properties objectively, which allows for planning, prediction, and detection of unexpected events. We illustrate our proposal with the simulation of a simple neural network model. We believe this stronger notion of representation could inform future research in neuroscience and AI.
