Do Humans Think Causally, and How?

This comprehensive chapter explores the intricate realm of human causal thinking, highlighting its pivotal role in decision-making, problem-solving, and scientific inquiry. The chapter not only emphasizes the strengths of causal thinking but also probes the vulnerabilities inherent in human cognition, including cognitive biases and deviations from rationality. The dynamic nature of epistemic causality is scrutinized through historical examples, emphasizing the evolving understanding of causality in the context of scientific progress. The exploration extends to the integration of bioinspired cognition into artificial intelligence (AI) systems, recognizing the limitations of current deep learning models. The discussion navigates through the complexities of emulating human cognition, stressing the significance of bioinspiration in addressing challenges such as cognitive biases, inefficient information processing, and the necessity for context integration. Bioinspiration emerges as a key element in developing AI systems that not only solve problems but also exhibit creativity, draw inspiration from diverse sources, and generate original ideas. Thirteen successful strategies for implementing bioinspired designs are presented, encompassing biomimetic design, swarm intelligence, neuromorphic computing, and emotional processing. These strategies offer practical pathways for enhancing AI capabilities, including pattern recognition, adaptive learning, and innovative problem-solving.