The motivation behind the workshop is twofold. In terms of theory, research demonstrates that humans extract information from various sources directly or indirectly to infer and reason about cause-effect relations in both everyday life and in more formal contexts such as science. Work on causal cognition focuses on mapping and understanding the cognitive processes that are involved in making these judgements, particularly with 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; how the ability to make causal inferences develops.

 

Over the past years research in computer science and AI has moved much closer to modelling human cognition, aiming to capture a variety of cognitive modes by taking inspiration from some leading psychologists. There is indeed a great drive for making machines mirror human reasoning and make their behaviours more human-like.  At the core of these formulations is the role of causality, which take inspiration both from human-perceived causality and causality in the physical world. 

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Although work in both disciplines 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 therefore a particularly apposite moment for researchers from these different backgrounds to share their different theoretical frameworks and methodologies. For the consistency, we intend to organise this event biennially.