From measurements to objects: multidimensional generalisation and categorisation in chicks

虽然看起来毫不费力，但我们视觉识别和分类物体的能力令人印象深刻。我们离理解大脑中实现的物体识别原理，或者在机器视觉中匹配它们还有很长的路要走。从本质上讲，眼睛和所有的感觉器官一样，对刺激进行物理测量，我们识别这些刺激为颜色、图案、形状等等。对于给定类别的对象，任何给定度量或维度的差异可能相关，也可能不相关。例如，狗在大小上的差异比猫大，草莓的颜色比苹果的颜色更能说明它的可食用性。动物必须学会哪些方面的变化是重要的，哪些是无关紧要的，以及不同方面的变化--如颜色和大小--是如何联系在一起的。基本的问题是，新遇到的Object-Z是否与以前遇到的Objects-X，Objects-Y相同，或者完全是新的。动物如何学习X和Y，然后将这些知识应用于Z？有两个问题使这个问题的研究变得复杂：第一，动物的决定不仅基于其受控和已知的实验经验，而且还基于其余生中不受控制和未知的经验。其次，我们不知道动物用什么尺度（或感知维度）来表征自然物体。为了解决这些问题，我们训练小鸡在印有彩色图案的纸容器中寻找食物。小鸡自然会啄那些容器。它们能快速准确地知道哪些颜色能预测食物，哪些是不赚钱的。如果给它们新的颜色，它们的啄食率会显示出它们认为哪些颜色最像以前训练过的食物包。最重要的是，我们可以控制小鸡之前的所有经验，我们可以精确地定义颜色，这是其他刺激无法做到的。大脑处理大量的感觉数据，任何现实计算策略的最佳解决方案都是未知的。该项目将测试动物或任何系统如何对复杂信号进行分类的三个主要理论。其中两个是在行为和心理学文献中建立的。第三个是基于我们自己以前的工作，并将作为项目的一部分开发。第一类模型（包括Delta Rule和Rescorla-Wagner模型）广泛应用于动物学习，也广泛应用于从工程到信用评级的神经网络应用中。第二，有“范例”模型，它存储了每一个先前的经验，并广泛应用于人类心理学。第三类是一个简单的“生成”模型的对象识别，我们已经开发。生成模型解决了从图像到对象的困难问题，从对象到图像的简单问题开始：他们问“如果有一个苹果，我会看到什么？”“简单问题的解决方案-从对象到测量-通过称为贝叶斯规则的数学恒等式变成了我们想要的解决方案-从测量到对象。贝叶斯模型在概念上是优雅的，易于使用，并且非常有效。它们在现代科学中有许多应用，但到目前为止还没有在视觉物体识别方面的工作。这些模型对如何对在多个维度上变化的刺激进行分类做出了明确而独特的预测（比如色调和饱和度），我们将通过观察小鸡对新颖颜色食物容器的偏好来测试。

Although seemingly effortless, our ability to visually recognise and classify objects is impressive. We are a long way from understanding the principles of object recognition implemented in the brain, or from matching them in machine vision. In essence the eye, like all sense organs, makes physical measurements of stimuli, which we recognise as colour, pattern, shape and so-forth. For a given class of object, a difference in any given measure, or dimension, may or may not be relevant. For example, dogs vary more in size than cats, and the colour of a strawberry is more informative about its edibility than the colour of an apple. Animals have to learn which dimensions of variation are significant, which are irrelevant, and how variations on different dimensions - such as colour and size - are related. The basic question is whether a newly encountered Object-Z is of the same kind as previously encountered Objects-X, Objects-Y or completely novel. How do animals learn abut X's and Y's, and then apply this knowledge to Z? Two problems complicate research on this question: firstly, the animal's decision will be based not only on its controlled and known experimental experience, but also on uncontrolled and unknown experience from the rest of its life. Secondly, we do not know what measures (or perceptual dimensions) animals use to represent natural objects. To solve these problems we train young chicks to find food in paper containers, which are printed with colour patterns. Chicks naturally peck at the containers. They learn quickly and accurately which colours predict food, and which are unprofitable. Given new colours their pecking rate shows which colours the birds believe are most like those of previously trained food parcels. Crucially, we can control all of the chicks' previous experience, and we can define colour precisely in a way that is not feasible with other stimuli.The problem of classifying natural objects has no straightforward solution. The brain processes vast quantities of sense data, and the best solution for any realistic computational strategy is unknown. This project will test the three main theoretical accounts of how animals - or any system - should classify complex signals. Two of these are established in the behavioural and psychological literature. The third is based our own previous work, and will be developed as part of the project. The first class of model (including the Delta Rule and Rescorla-Wagner models) is widely applied in animal-learning, and also in neural network applications that used in applications from engineering to credit rating. Secondly, there are "exemplar" models, which store every previous experience, and are widely applied in human psychology. The third class is a simple "generative" model of object recognition, which we have developed. Generative models solve the difficult problem of going from images to objects, by starting from simple problem of going from objects to images: they ask "if there was an apple, what would I see?" The solution to the easy problem - from objects to measurements -, is turned into the solution we want - from measurements to objects -, by the mathematical identity known as Bayes' rule. Bayesian models are conceptually elegant, simple to use, and highly effective. They have many applications in modern science, but have not so far in work on visual object recognition. The models make clear and distinct predictions about how to classify stimuli that vary on multiple dimensions (say hue' and 'saturation'), which we will test by observing chicks' preferences for novel coloured food containers.