Research shows that humans extract information from various sources, directly or indirectly, to perceive and reason about the cause-effect relations in both everyday lives and in more formal contexts such as science.
Work on causal cognition focuses on mapping and understanding the cognitive processes that are involved in formulating these judgments, with particular regard to thinking about evidence; when and how causal relations are identified; how different sources and types of information are construed and communicated, and with what degree of accuracy; and how the ability to make causal inferences develops.
Over the past years research in computer science and AI has moved much closer to the modeling of human cognition, aiming to capture a variety of cognitive modes by taking inspiration from some leading psychologists. For example, work has been done on a compositional model of Gardenfors' conceptual spaces, and its relation to Smolensky's architectures.
There is indeed a great drive for making machines mirror human reasoning and make their behaviors more human-like. At the core of these formulations lies the role of causality, inspired both by human-perceived causality and causality in the physical world.
Although investigations in both disciplines have focused on similar territory, the links regarding causal cognition have received less attention, and the implications and potential of the two fields to inform each other is largely unexplored.
This is a particularly relevant moment for researchers coming from these different backgrounds to share their different theoretical frameworks and methodologies.
This Research Topic will provide a record of contributions to a workshop Causal Cognition in Humans and Machines , held in Oxford in May 2019, which was designed to promote this dialogue and provide a point of departure for future work (for further information please see https://www.causalcognitioninhumansandmachines.com).
Workshop presenters and researchers working in these areas who would like to contribute to the Research Topic are welcome to contact the workshop organizers (Selma Dündar-Coecke and Andrew Tolmie) at CCHMworkshop@gmail.com with their suggested title and abstract (350 words).
We welcome original Research manuscripts, reviews, hypothesis/theory manuscripts, mini-reviews, perspectives, and Brief Research reports.
Research shows that humans extract information from various sources, directly or indirectly, to perceive and reason about the cause-effect relations in both everyday lives and in more formal contexts such as science.
Work on causal cognition focuses on mapping and understanding the cognitive processes that are involved in formulating these judgments, with particular regard to thinking about evidence; when and how causal relations are identified; how different sources and types of information are construed and communicated, and with what degree of accuracy; and how the ability to make causal inferences develops.
Over the past years research in computer science and AI has moved much closer to the modeling of human cognition, aiming to capture a variety of cognitive modes by taking inspiration from some leading psychologists. For example, work has been done on a compositional model of Gardenfors' conceptual spaces, and its relation to Smolensky's architectures.
There is indeed a great drive for making machines mirror human reasoning and make their behaviors more human-like. At the core of these formulations lies the role of causality, inspired both by human-perceived causality and causality in the physical world.
Although investigations in both disciplines have focused on similar territory, the links regarding causal cognition have received less attention, and the implications and potential of the two fields to inform each other is largely unexplored.
This is a particularly relevant moment for researchers coming from these different backgrounds to share their different theoretical frameworks and methodologies.
This Research Topic will provide a record of contributions to a workshop Causal Cognition in Humans and Machines , held in Oxford in May 2019, which was designed to promote this dialogue and provide a point of departure for future work (for further information please see https://www.causalcognitioninhumansandmachines.com).
Workshop presenters and researchers working in these areas who would like to contribute to the Research Topic are welcome to contact the workshop organizers (Selma Dündar-Coecke and Andrew Tolmie) at CCHMworkshop@gmail.com with their suggested title and abstract (350 words).
We welcome original Research manuscripts, reviews, hypothesis/theory manuscripts, mini-reviews, perspectives, and Brief Research reports.