John Dorsch

One routinely undergoes a noetic feeling (also called “metacognitive feeling” or “epistemic feeling”), the so-called “feeling of knowing”, whenever trying to recall a person’s name. One feels the name is known despite being unable to recall it. Other experiences also fall under this category, e.g., the tip-of-the-tongue experience, the feeling of confidence. A distinguishing characteristic of noetic feelings is how they are crucially related to the facts we know, so much so that the activation of semantic memory can easily result in the production of noetic feelings – a regularity that memory research has often exploited. And yet little is known about the mechanism that produces noetic feelings. Is it solely brain-based or does it depend upon the extracerebral body for its production of feelings? To arrive at an answer, various studies in metamemory research will be analyzed to determine what ought to be made of the mechanism responsible for noetic feelings. I argue that evidence suggests that it relies upon extracerebral processes, in particular cardiovascular processes, the result being support for an embodied view of metacognition.

Many policies and ethical guidelines recommend developing “trustworthy AI”. We argue that developing morally trustworthy AI is not only unethical, as it promotes trust in an entity that cannot be trustworthy, but it is also unnecessary for optimal calibration. Instead, we show that reliability, exclusive of moral trust, entails the appropriate normative constraints that enable optimal calibration and mitigate the vulnerability that arises in high-stakes hybrid decision-making environments, without also demanding, as moral trust would, the anthropomorphization of AI and thus epistemically dubious behavior. The normative demands of reliability for inter-agential action are argued to be met by an analogue to procedural metacognitive competence (i.e., the ability to evaluate the quality of one’s own informational states to regulate subsequent action). Drawing on recent empirical findings that suggest providing reliability scores (e.g., F1-scores) to human decision-makers improves calibration in the AI system, we argue that reliability scores provide a good index of competence and enable humans to determine how much they wish to rely on the system.