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

探索人类认知的起源：整合物理知识和向他人学习

• 批准号: RGPIN-2016-05552
• 负责人: Buchsbaum, Daphna
• 金额: $ 2.04万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=646465
• 关键词: Exploring; origins; human; cognition; Integrating

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. **

人类思维的独特之处是什么？它为什么会进化？从弄清楚哪个按钮能让你从自动售货机里得到糖果，到决定沿着两个不稳定的树枝中的哪一个爬沿着去够到美味的水果，人类和非人类动物每天都面临着具有挑战性的因果和物理推理问题。物理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，特别是在我们收集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。不同物种如何整合这些线索？* 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理解因果关系的研究计划扩展到包括狗--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，而这些技能甚至是我们最亲近的灵长类动物亲戚所不具备的。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为的影响。我还将扩展这个研究主题，以研究狗和不同年龄的儿童如何整合他们的身体信仰（例如，当物体掉落时会向下掉落）与某人传达给他们的信息--特别是当这些信息来源相互冲突时。* 除了从狗和儿童那里收集经验数据外，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，可以精确地描述儿童和非人类动物的想法和知识，并根据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。* 我的研究计划结合了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。在我的实验室的培训将为HQP提供知识，经验和技术技能的组合，在加拿大是独一无二的，在国际上也是罕见的。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有关，以及加强宠物和服务犬的训练。**