Tacit sensitivity to structure and judgements of style in everyday behaviour

人们通过偶然接触个别例子，对日常经验中的结构性变化变得敏感。 因此，我们对其他人的个人风格以及个别艺术家和作曲家作品中的风格产生了敏感性，对什么属于和不属于特定写作风格的感觉，最喜欢的电视系列的可能结论和适当序列，左车道的司机可能会做什么而右车道的司机不会，以及与我们银行出纳员的正常互动是什么样的。 通常，这种敏感性的发展是毫不费力的，我们没有任何学习这些知识的意图，甚至没有任何意识到有任何这样的知识需要学习。 然而，共同的经验和实验室研究都表明，这种隐性的敏感性影响着我们在几乎每一项任务中的表现和期望。 许多非人类动物，如黑猩猩、老鼠、鸽子，甚至鱼类，都能对艺术、音乐甚至笔迹的风格表现出类似的敏感性。 拟议的研究探讨了这种隐性的结构性知识的来源，以及它如何影响我们在分类，识别和回忆方面的表现，以及它如何影响我们和其他动物的偏见和偏好。 先前的研究表明，这些影响的来源不是以规则或原型等高级抽象的形式存在的知识，而是潜伏在我们的记忆中，并分布在我们对特定经验的记忆中。 这些记忆在对类似的当前事件的反应中被集体招募，以提供一种熟悉感或“正确性”或流动性，使我们对事件的反应与我们在类似情况下经历的事件的结构一致。 这种提取也提供了检测事件与结构不一致的能力，即使我们无法清楚地表达我们是如何“知道”它的。拟议中的研究探索了这种记忆和提取过程的模型，该模型将允许人和动物对结构的内隐学习和使用。该模型采用人工神经网络的形式，应用于单个事件的低级别表示，例如图像或数字化声音的像素图。

People become sensitive to the structural regularities in everyday experiences through incidental exposure to individual examples. Thus, we develop sensitivity to other people's personal styles and the styles in the works of individual artists and composers, a sense of what does and does not belong to a particular genre of writing, the likely conclusion to and appropriate sequences of a favorite televison series, what drivers in then left lane are likely to do that drivers in the right are not, and what a normal interaction with a teller at our bank is like. Often this sensitivity develops effortlessly and without any intention on our part to learn these regularities, and even without any awareness that there are any such regularities to learn. Yet, both common experience and laboratory research alike demonstrate that this tacit sensitivity influences our performance and expectations in virtually every task we undertake. Many non-human animals, such as chimpanzees, rats, pigeons, and even fish can demonstrate similar sensitivities to style in art, music, and even handwriting. The proposed research explores the source of this tacit, structural knowledge and how it comes to influence our performance in classification, recognition, and recall, and how it influences our and other animal's biases and preferences. Previous research suggests that the knowledge that is the source of these influences is not in the form of high-level abstractions such as rules or prototypes, but rather remains latent in and distributed over our memory for specific episodes of experience. These memories are recruited collectively in response to a similar current event to provide a feeling of familiarity or "rightness" or a fluidity to our responding to events consistent with the structure of those we've experienced in similar situations. This retrieval also provides for the ability to detect when an event is inconsistent with structure, even if we are unable to articulate how we "know" it. The proposed research explores a model of such memory and retrieval processes that would allow for this implicit learning and use of structure in both people and animals. The model is in the form of artificial neural network applied to low-level representations of the individual events, such as pixel-maps of images or digitised sounds.