Next Generation Psychological Embeddings

人们拥有庞大的知识库，这使他们能够表示有关世界的相关信息并据此采取行动。虽然有一些专业的计算机系统可以在特定的任务上胜过人类，比如下棋，但人类在通才方面仍然是冠军。人类如何表达他们丰富的知识，以便他们能够欣赏物体之间的相似之处，无论这些相似之处是基于表面的特性还是基于深层的联系，比如属于共同的生物类别？这是一个很难回答的问题，既有理论又有实际的影响。了解人们如何感知世界是预测和改善人类行为的关键。同样，构建这种代表性或嵌入空间将为使AI系统更像人类提供强大的工具。自20世纪50年代以来，推断心理表征的标准技术已被广泛使用，但在重要方面受到限制。标准技术需要大量数据，而且计算速度很慢。因此，这些技术不能很好地处理通常包含超过一百万项的实际问题。我们的目标是帮助心理学向大规模建模过渡，我们希望这能引发一场革命，就像十年前机器学习和人工智能领域转向大规模数据集那样。标准技术的另一个限制是，它们不能检测或利用表征空间中项目之间的关系。例如，如果人们知道两个品种的狗都是狗，即使它们的大小不同，他们在进行推理时也会使用这种结构知识。我们的建模方法可以发现和使用这样的概念关系。同样，我们可以捕捉到不同的群体，他们的生活经历不同，可能以略微不同的方式代表世界。通过这样做，我们可以捕捉到每个人经验的独特性，而不是对数据采用一种通用的方法，这对数据科学和心理学来说是一种大多数人的暴政。最后，我们的方法可以适应不同的方法来测量相似性。总的来说，标准方法中的这些限制阻碍了实验室见解到现实世界环境的转移。虽然已经做了一些工作来解决其中的一些限制，但没有一项工作能够一次完全解决所有这些限制。我们的目标是考虑两个自然图像（即照片）数据库，每个数据库包含超过一百万张图像。我们的目标不是提供渐进式的进步，而是将最先进的空间表示技术提高一个数量级以上，并通过捕捉图像和如上所述的人群之间的关系来提高解决方案的质量。我们将公开和免费提供这些资源和工具，并指导如何扩展它们以支持其他人的工作，无论是在心理学，教育，人机交互，人工智能还是其他领域。推断这两个大型数据集的心理表征将消除研究界长期存在的障碍，这将有助于机器学习和认知科学研究人员创建更好的人类认知模型。这个新的框架和资源将使个体之间的差异建模成为可能，使我们能够更好地理解不同的生活经历（如以年龄、性别和地理位置衡量）如何影响我们对世界的看法。

People have vast knowledge bases that allow them to represent relevant information about the world and act upon it. While there are specialist computer systems that best people in specific tasks, such as playing chess, humans are still the champions at being generalists. How do humans represent their rich knowledge so that they can appreciate similarities between objects, whether those similarities rest on superficial properties or deep connections, such as belonging to a shared biological category? It is a difficult question to answer that has both theoretical and practical ramifications. Understanding how people perceive the world is key to predicting and improving human behaviour. Likewise, building such representational or embedding spaces would provide a powerful tool for making AI systems more human-like.Standard techniques for inferring psychological representations have been in wide use since the 1950s, but are limited in important ways. Standard techniques are data hungry and computationally slow. As a consequence, these techniques do not work well with real-world problems that often contain more than a million items. We aim to help Psychology transition to large-scale modelling, which we hope leads to a revolution like that experienced a decade ago in machine learning and AI when those fields moved to large-scale datasets. Another limitation of standard techniques is that they can't detect or take advantage of the relationships between items in the representational space. For example, if people know that two breeds of dogs are both dogs, even if they differ in size, they use that structural knowledge when making inferences. Our modelling approaches can discover and use such conceptual relationships. Likewise, we can capture how different groups, who vary in their life experiences, may represent the world in slightly different ways. In doing so, we can capture the uniqueness of each human experience rather than force a one-size-fits-all approach on the data, which would be a kind of tyranny of the majority for data science and Psychology. Finally, our methods can be adapted to take advantage of different approaches to measuring similarity. Collectively, these limitations in standard approaches block the transfer of laboratory insights into real-world settings. While work has been done to address some of these limitations, no work has addressed all these limitations fully at once. We aim to do so at scale considering two databases of natural images (i.e., photographs) that each contain over a million images. Rather than offer an incremental advance, we aim to advance the state-of-the-art for representing spaces by more than an order of magnitude in size and improve the quality of the solution by capturing relations between images and groups of people as discussed above. We will make these resources and the tools publicly and freely available with guidance on how they can be extended to support others' work, whether it be in Psychology, Education, Human-Computer Interaction, AI, or other fields. Inferring psychological representations for these two large datasets will remove a long-standing hurdle in the research community, which should help machine learning and cognitive science researchers create better models of human cognition. This new framework and resource will make it possible to model differences between individuals, allowing us to better understand how different life experiences, such as measured by age, gender, and geographical location, impact how we think about the world.