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Self-interpretability of human cognition: How reportable knowledge emerges in learning

CORDIS · observation · Publication date unknown

Self-interpretability of human cognition: How reportable knowledge emerges in learning Current artificial intelligence (AI) surpasses human-level performance in a vast range of tasks. However, its decisional processes are opaque, referred to as the AI interpretability problem. Humans, on the other hand, can verbally describe their decisional processes and strategies. The accuracy of these reports varies, especially in complex environments. Yet, people often come up with reasonably accurate explanations for their decisions, thereby allowing knowledge transfer in society. However, the mechanisms of accurate verbal report generation remain unclear. Therefore, the main research objective of the REPORT-IT project is to study how humans generate adequate reportable knowledge during learning through experience. Inspired by the recent findings from research on metacognition (i.e., insight into one's own cognition) and cognition-emotion interaction, I will test the novel hypothesis that me

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recordType

award

status

SIGNED

region

EU

value

203464.32

unit

EUR