Taylor Alex H, Bastos Amalia P M, Brown Rachael L, Allen Colin
School of Psychology, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand.
School of Psychology, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand; Department of Cognitive Science, University of California, San Diego, CA, USA.
Trends Cogn Sci. 2022 Sep;26(9):738-750. doi: 10.1016/j.tics.2022.06.002. Epub 2022 Jun 27.
Making inferences from behaviour to cognition is problematic due to a many-to-one mapping problem, in which any one behaviour can be generated by multiple possible cognitive processes. Attempts to cross this inferential gap when comparing human intelligence to that of animals or machines can generate great debate. Here, we discuss the challenges of making comparisons using 'success-testing' approaches and call attention to an alternate experimental framework, the 'signature-testing' approach. Signature testing places the search for information-processing errors, biases, and other patterns centre stage, rather than focussing predominantly on problem-solving success. We highlight current research on both biological and artificial intelligence that fits within this framework and is creating proactive research programs that make strong inferences about the similarities and differences between the content of human, animal, and machine minds.
由于存在多对一的映射问题，即任何一种行为都可能由多种可能的认知过程产生，因此从行为推断认知存在问题。在将人类智力与动物或机器的智力进行比较时，试图跨越这一推理鸿沟可能会引发激烈的争论。在这里，我们讨论使用“成功测试”方法进行比较所面临的挑战，并提请注意另一种实验框架，即“特征测试”方法。特征测试将寻找信息处理错误、偏差和其他模式置于核心位置，而不是主要关注解决问题的成功与否。我们重点介绍了当前符合这一框架的关于生物和人工智能的研究，这些研究正在创建积极主动的研究项目，以对人类、动物和机器思维内容之间的异同做出有力推断。
