Metacognitive computations for information search: Confidence in control

The metacognitive sense of confidence can play a critical role in regulating decision making. In particular, a lack of confidence can justify the explicit, potentially costly, instrumental acquisition of extra information that might resolve uncertainty. Human confidence is highly complex, and recent computational work has suggested a statistically sophisticated tapestry behind the information that governs both the making and monitoring of choices. However, the consequences of the form of such confidence computations for search have yet to be understood. Here, we reveal extra richness in the use of confidence for information seeking by formulating joint models of action, confidence, and information search within a Bayesian and reinforcement learning framework. Through detailed theoretical analysis of these models, we show the intricate normative downstream consequences for search arising from more complex forms of metacognition. For example, our results highlight how the ability to monitor errors or general metacognitive sensitivity impact seeking decisions and can generate diverse relationships between action, confidence, and the optimal search for information. We also explore whether empirical search behavior enjoys any of the characteristics of normatively derived prescriptions. More broadly, our work demonstrates that it is crucial to treat metacognitive monitoring and control as closely linked processes

Poly(I:C) Enhances the Susceptibility of Leukemic Cells to NK Cell Cytotoxicity and Phagocytosis by DC

α Active specific immunotherapy aims at stimulating the host's immune system to recognize and eradicate malignant cells. The concomitant activation of dendritic cells (DC) and natural killer (NK) cells is an attractive modality for immune-based therapies. Inducing immunogenic cell death to facilitate tumor cell recognition and phagocytosis by neighbouring immune cells is of utmost importance for guiding the outcome of the immune response. We previously reported that acute myeloid leukemic (AML) cells in response to electroporation with the synthetic dsRNA analogue poly(I:C) exert improved immunogenicity, demonstrated by enhanced DC-activating and NK cell interferon-γ-inducing capacities. To further invigorate the potential of these immunogenic tumor cells, we explored their effect on the phagocytic and cytotoxic capacity of DC and NK cells, respectively. Using single-cell analysis, we assessed these functionalities in two- and three-party cocultures. Following poly(I:C) electroporation AML cells become highly susceptible to NK cell-mediated killing and phagocytosis by DC. Moreover, the enhanced killing and the improved uptake are strongly correlated. Interestingly, tumor cell killing, but not phagocytosis, is further enhanced in three-party cocultures provided that these tumor cells were upfront electroporated with poly(I:C). Altogether, poly(I:C)-electroporated AML cells potently activate DC and NK cell functions and stimulate NK-DC cross-talk in terms of tumor cell killing. These data strongly support the use of poly(I:C) as a cancer vaccine component, providing a way to overcome immune evasion by leukemic cells

Mechanisms of Mistrust: A Bayesian Account of Misinformation Learning

From the intimate realm of personal interactions to the sprawling arena of political discourse, discerning the trustworthy from the dubious is crucial. Here, we present a novel behavioral task and accompanying Bayesian models that allow us to study key aspects of this learning process in a tightly controlled setting. In our task, participants are confronted with several different types of (mis-)information sources, ranging from ones that lie to ones with biased reporting, and have to learn these attributes under varying degrees of feedback. We formalize inference in this setting as a doubly Bayesian learning process where agents simultaneously learn about the ground truth as well as the qualities of an information source reporting on this ground truth. Our model and detailed analyses reveal how participants can generally follow Bayesian learning dynamics, highlighting a basic human ability to learn about diverse information sources. This learning is also reflected in explicit trust reports about the sources. We additionally show how participants approached the inference problem with priors that held sources to be helpful. Finally, when outside feedback was noisier, participants still learned along Bayesian lines but struggled to pick up on biases in information. Our work pins down computationally the generally impressive human ability to learn the trustworthiness of information sources while revealing minor fault lines when it comes to noisier environments and news sources with a slant

Confidence in control: Metacognitive computations for information search

Having low confidence in a decision can justify the costly search for extra information. Rich literatures have separately modelled the metacognitive monitoring processes involved in confidence formation and the control processes guiding search, but these two processes have yet to be treated in unison. Here, we model the two as inference and action in a unified partially-observable Markov decision problem where decision confidence is generated by more sophisticated postdecisional or second-order models. Our work highlights how different metacognitive monitoring architectures generate diverse relationships between object- and meta-level accuracy as well as normative information collection in the face of costs. In particular, we demonstrate that decreased metacognitive efficiency prescribes both increased and decreased search, depending on the underlying model of metacognitive confidence. More broadly, our work shows how it is crucial to model interactions between metacognitive monitoring and control, whether in information search or beyond