Which of the following is NOT used to capture tacit knowledge?

The option indicating that neural networks are not used to capture tacit knowledge is correct because neural networks primarily focus on identifying patterns and relationships within large datasets rather than directly capturing the nuanced, experiential insights that characterize tacit knowledge. Tacit knowledge is often unspoken and deeply rooted, making it challenging to codify or express in strict data formats.

In contrast, expert systems, case-based reasoning, and fuzzy logic are all techniques that can assist in capturing and processing tacit knowledge. Expert systems rely on heuristics and rule-based approaches to emulate human decision-making, which can incorporate tacit knowledge from human experts. Case-based reasoning utilizes past experiences to inform current decisions, directly tapping into the essence of tacit knowledge through practical examples. Fuzzy logic brings in the ability to handle uncertainty and imprecision, which is crucial when dealing with the subjective nature of tacit knowledge.

Thus, while neural networks excel at tasks involving clear numerical inputs and outputs, they do not emphasize the capture of tacit knowledge in the same way that the other methods do.