Exploring the origins of human cognition: Integrating physical knowledge and learning from others

人类思维的独特之处是什么？它为什么会进化？从找出哪个按钮可以从自动售货机中取出糖果，到决定沿着两根不稳定的树枝爬上哪根去摘美味的水果，人类和非人类动物每天都面临着具有挑战性的因果关系和物理推理问题。先前在现实世界的经验可能会影响我们的决策，但社会信息也很有价值，特别是在我们自己收集证据的能力有限的情况下。社会学习能力和身体推理能力都被认为是人类独特认知的来源。不同物种是如何整合这些线索的？***在接下来的五年里，我将把我正在进行的关于儿童和非人类灵长类动物对因果关系理解的研究项目扩展到狗——从进化的角度来看，狗是一个非常有趣的物种，因为它们具有“类似人类”的社交技能，而我们最亲近的灵长类动物亲戚甚至没有这种技能。有趣的是，这些不同的物种如何解释人类演示者的意图，以及它们是否受到与理想结果相关的机械必要行动的影响。我还将扩展这一研究主题，以调查狗和不同年龄的孩子如何将他们的物理信念（例如，物体掉落时向下）与某人传达给他们的信息结合起来——特别是当这些信息来源相互冲突时。***除了从狗和孩子身上收集经验数据外，我还将使用计算建模来解开个人用来解决问题的不同信息来源之间复杂的相互作用。贝叶斯推理模型为我们提供了一种方法，可以精确地描述关于儿童和非人类动物的想法和认知的假设，并通过实验表现对其进行测试。这种跨学科的方法以前在捕捉儿童在各种解决问题的任务中的表现方面取得了成功，包括在我自己的工作中，但很少应用于动物认知。***我的研究项目将计算机科学和统计学的数学工具与发展心理学和比较认知的实证方法相结合。在我的实验室进行的培训将为HQP提供加拿大独有的、国际罕见的知识、经验和技术技能。我们的发现可能具有广泛的跨学科吸引力，并将与发展和比较心理学、动物行为和计算机科学的研究人员相关。从实际的角度来看，结果可能与幼儿教育有关，也可能与加强宠物和服务犬的培训有关。**

What is unique about the human mind and why did it evolve? From figuring out which buttons will get you candy from a vending machine to deciding which of two precarious branches to climb along to reach a tasty fruit, human and non-human animals are confronted with challenging causal and physical reasoning problems on a daily basis. Prior experience with the physical world is likely to influence our decision-making, but social information is also valuable, particularly in cases where we have limited ability to collect our own evidence. Social learning and physical reasoning abilities have both been proposed as the source of uniquely human cognition. How are these cues integrated by different species? ***In the next five years I will extend my ongoing research program with children and non-human primates on understanding of cause and effect to include dogs – a species that is extremely interesting from an evolutionary perspective given their “human-like” social skills that are not even shared by our closest primate relatives. Of interest is how these different species interpret the intentions of a human demonstrator, and whether they are influenced by how mechanically necessary actions seem in relation to a desirable outcome. I will also expand this research theme to investigate how dogs, and children of different ages, integrate their physical beliefs (e.g., that objects fall downwards when dropped) with information communicated to them by someone – particularly when these sources of information are in conflict. ***In addition to collecting empirical data from dogs and children, I will use computational modelling to disentangle the complex interactions between different sources of information that individuals use to solve problems. Bayesian inference models give us a way of precisely characterizing hypotheses about what both children and non-human animals think and know and testing them against experimental performance. This interdisciplinary approach has been previously successful in capturing children's performance on a variety of problem-solving tasks, including in my own work, but has rarely been applied to animal cognition. ***My research program combines mathematical tools from computer science and statistics with the empirical methods of developmental psychology and comparative cognition. Training in my lab will provide HQP with a combination of knowledge, experience and technical skills unique within Canada, and rare internationally. Our findings are likely to have broad interdisciplinary appeal and will be relevant to researchers in developmental and comparative psychology, animal behaviour, and computer science. From a practical standpoint, results are likely to be relevant to early childhood education, as well as enhancing pet and service-dog training. **