A Metacognitive Approach to Trust and a Case Study: Artificial Agency

Trust is defined as a belief of a human H (‘the trustor’) about the ability of an agent A (the ‘trustee’) to perform future action(s). We adopt here dispositionalism and internalism about trust: H trusts A iff A has some internal dispositions as competences. The dispositional competences of A are high-level metacognitive requirements, in the line of a naturalized virtue epistemology. (Sosa, Carter) We advance a Bayesian model of two (i) confidence in the decision and (ii) model uncertainty. To trust A, H demands A to be self-assertive about confidence and able to self-correct its own models. In the Bayesian approach trust can be applied not only to humans, but to artificial agents (e.g. Machine Learning algorithms). We explain the advantage the metacognitive trust when compared to mainstream approaches and how it relates to virtue epistemology. The metacognitive ethics of trust is swiftly discussed

The influence of the noradrenergic system on optimal control of neural plasticity

Decision making under uncertainty is challenging for any autonomous agent. The challenge increases when the environment’s stochastic properties change over time, i.e., when the environment is volatile. In order to efficiently adapt to volatile environments, agents must primarily rely on recent outcomes to quickly change their decision strategies; in other words, they need to increase their knowledge plasticity. On the contrary, in stable environments, knowledge stability must be preferred to preserve useful information against noise. Here we propose that in mammalian brain, the locus coeruleus (LC) is one of the nuclei involved in volatility estimation and in the subsequent control of neural plasticity. During a reinforcement learning task, LC activation, measured by means of pupil diameter, coded both for environmental volatility and learning rate. We hypothesize that LC could be responsible, through norepinephrinic modulation, for adaptations to optimize decision making in volatile environments. We also suggest a computational model on the interaction between the anterior cingulate cortex (ACC) and LC for volatility estimation