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？从计算哪一个按钮可以从自动售货机上拿到糖果，到决定沿着两根摇摇欲坠的树枝中的哪一根爬到美味的水果上，人类和非人类动物每天都面临着具有挑战性的因果和物理推理问题。与现实世界的先前经验可能会影响我们的决策，但社会信息也是有价值的，特别是在我们收集自己证据的能力有限的情况下。社会学习和物理推理能力都被认为是人类独特认知的来源。这些线索是如何被不同物种整合在一起的？*在接下来的五年里，我将扩展我正在进行的关于儿童和非人类灵长类动物对因果理解的研究项目，将狗包括在内。从进化的角度来看，狗是一个非常有趣的物种，因为它们具有与人类相似的社交技能，而我们最亲密的灵长类亲属甚至没有分享这些技能。有趣的是，这些不同的物种如何解释人类示威者的意图，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儿童如何将他们的身体信念(例如，物体在跌落时向下坠落)与某人传达给他们的信息整合在一起，特别是在这些信息来源发生冲突的时候。*除了收集狗和儿童的经验数据外，我还将使用计算模型来理清个人用来解决问题的不同信息来源之间的复杂相互作用。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所想和所知的假设，并根据实验表现对它们进行测试。这种跨学科的方法以前曾成功地捕捉到儿童在各种问题解决任务中的表现，包括在我自己的工作中，但很少被应用于动物认知。*我的研究项目将计算机科学和统计学中的数学工具与发展心理学和比较认知的经验方法相结合。我实验室的培训将为HQP提供加拿大独有的、在国际上罕见的知识、经验和技术技能的结合。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。从实践的角度来看，结果可能与幼儿教育以及加强宠物和服务犬训练有关。**

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. **