Doubt as a mechanism of the change of epistemic states (on the materials of the philosophy of pragmatism and neuropsychology)

Science has not yet found a solution to the question of how cognitive mechanisms, which are crucial for the subjective processes of evaluating the reliability of information and doubt, are arranged. This ability is usually associated with both the pragmatic consequences of accepting a belief and the material substance of consciousness. In this article, the author proposes to compare one of the largest conceptions of doubt in philosophy, the pragmatic conception, with the theory of false tags, which was presented in the last decade by the neuropsychologist E. Asp in order to explain the phenomena of doubt. The article presents the theoretical aspects of both conceptions, which allows to derive the properties of doubt as an epistemic state, that is, the state of the subject’s cognitive reality, formed under the influence of external (situational, pragmatic) and internal (neuropsychological) factors. The results of the study presented in the article allow us to conclude the possibility of an interdisciplinary approach in further studies of human cognitive activity as a mechanism of various epistemic states. It is concluded that doubt itself is not one of these states, but is a secondary psychological act that ensures the transition from one epistemic state to another. This offers an alternative view to the approach established in philosophy, in which doubt precedes the fact of accepting knowledge and is an essential stage of the cognitive process.

Embodied Irrationality? Knowledge Avoidance, Willful Ignorance, and the Paradox of Autonomy

In the current philosophical and psychological literature, knowledge avoidance and willful ignorance seem to be almost identical conditions involved in irrational patterns of reasoning. In this paper, we will argue that not only these two phenomena should be distinguished, but that they also fall into different parts of the epistemic rationality-irrationality spectrum. We will adopt an epistemological and embodied perspective to propose a definition for both terms. Then, we will maintain that, while willful ignorance is involved in irrational patterns of reasoning and beliefs, knowledge avoidance should be considered epistemically rational under particular circumstances. We will begin our analysis by considering which of the two phenomena is involved in patterns of reasoning that are still amply recognized as irrational—as wishful thinking, self-deception, and akrasia. We will then discuss the impact of epistemic feelings—which are emotional events that depend on epistemic states—on agents' decision-making. Then, we will consider the impact of willful ignorance and knowledge avoidance on agents' autonomy. By considering these issues, we will argue that when agents are aware that they are avoiding certain information (and aware of what kind of feelings acquiring the information would trigger), knowledge avoidance should be considered a rational, autonomy-increasing, hope-depended selection of information.

Nonentities

A diverse range of ‘things’ can be viewed as nonentities; that is, negative particulars. Some of the main candidates are considered to see whether anything about them threatens the soft Parmenidean project. Some think of holes as absences, for instance, since they are where something else isn’t. We can think of a hole instead as immaterial and dependent on their hosts, drawing a distinction between real holes and negative, non-existent holes. Similarly, negative facts, limits and boundaries, privations, shadows, omissions, negative norms, negative epistemic states, and logico-mathematical entities such as zero and the empty set are considered. None of them appears to pose a serious threat to soft Parmenideanism.

Changing Minds ― Epistemic Interventions in Causal Reasoning

Did Tom’s use of nuts in the dish cause Billy’s allergic reaction? According to counterfactual theories of causation, an agent is judged a cause to the extent that their action made a difference to the outcome (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2020; Gerstenberg, Halpern, & Tenenbaum, 2015; Halpern, 2016; Hitchcock & Knobe, 2009). In this paper, we argue for the integration of epistemic states into current counterfactual accounts of causation. In the case of ignorant causal agents, we demonstrate that people’s counterfactual reasoning primarily targets the agent’s epistemic state – what the agent doesn’t know –, and their epistemic actions – what they could have done to know – rather than the agent’s actual causal action. In four experiments, we show that people’s causal judgment as well as their reasoning about alternatives is sensitive to the epistemic conditions of a causal agent: Knowledge vs. ignorance (Experiment 1), self-caused vs. externally caused ignorance (Experiment 2), the number of epistemic actions (Experiment 3), and the epistemic context (Experiment 4). We see two advantages in integrating epistemic states into causal models and counterfactual frameworks. First, assuming the intervention on indirect, epistemic causes might allow us to explain why people attribute decreased causality to ignorant vs. knowing causal agents. Moreover, causal agents’ epistemic states pick out those factors that can be controlled or manipulated in order to achieve desirable future outcomes, reflecting the forward-looking dimension of causality. We discuss our findings in the broader context of moral and causal cognition.

DEL-based Epistemic Planning for Human-Robot Collaboration: Theory and Implementation

Epistemic planning based on Dynamic Epistemic Logic (DEL) allows agents to reason and plan from the perspective of other agents. The framework of DEL-based epistemic planning thereby has the potential to represent significant aspects of Theory of Mind in autonomous robots, and to provide a foundation for human-robot collaboration in which coordination is achieved implicitly through perspective shifts. In this paper, we build on previous work in epistemic planning with implicit coordination. We introduce a new notion of indistinguishability between epistemic states based on bisimulation, and provide a novel partition refinement algorithm for computing unique representatives of sets of indistinguishable states. We provide an algorithm for computing implicitly coordinated plans using these new constructs, embed it in a perceive-plan-act agent loop, and implement it on a robot. The planning algorithm is benchmarked against an existing epistemic planning algorithm, and the robotic implementation is demonstrated on human-robot collaboration scenarios requiring implicit coordination.

Knowledge is a mental state (at least sometimes)

It is widely held in philosophy that knowing is not a state of mind. On this view, rather than knowledge itself constituting a mental state, when we know, we occupy a belief state that exhibits some additional non-mental characteristics. Fascinatingly, however, new empirical findings from cognitive neuroscience and experimental philosophy now offer direct, converging evidence that the brain can—and often does—treat knowledge as if it is a mental state in its own right. While some might be tempted to keep the metaphysics of epistemic states separate from the neurocognitive mechanics of our judgements about them, here I will argue that these empirical findings give us sufficient reason to conclude that knowledge is at least sometimes a mental state. The basis of this argument is the epistemological principle of neurocognitive parity—roughly, if the contents of a given judgement reflect the structure of knowledge, so do the neurocognitive mechanics that produced them. This principle, which I defend here, straightforwardly supports the inference from the empirical observation that the brain sometimes treats knowledge like a mental state to the epistemological conclusion that knowledge is at least sometimes a mental state. All told, the composite, belief-centric metaphysics of knowledge widely assumed in epistemology is almost certainly mistaken.

Uncertainty, equality, fraternity

Epistemic states of uncertainty play important roles in ethical and political theorizing. Theories that appeal to a “veil of ignorance,” for example, analyze fairness or impartiality in terms of certain states of ignorance. It is important, then, to scrutinize proposed conceptions of ignorance and explore promising alternatives in such contexts. Here, I study Lerner’s probabilistic egalitarian theorem in the setting of imprecise probabilities. Lerner’s theorem assumes that a social planner tasked with distributing income to individuals in a population is “completely ignorant” about which utility functions belong to which individuals. Lerner models this ignorance with a certain uniform probability distribution, and shows that, under certain further assumptions, income should be equally distributed. Much of the criticism of the relevance of Lerner’s result centers on the representation of ignorance involved. Imprecise probabilities provide a general framework for reasoning about various forms of uncertainty including, in particular, ignorance. To what extent can Lerner’s conclusion be maintained in this setting?

I can tell you know a lot, although I'm not sure what: Modeling broad epistemic inference from minimal action

Inferences about other people's knowledge and beliefs are central to social interaction. In many situations, however, it's not possible to be sure what other people know because their behavior is consistent with a range of potential epistemic states. Nonetheless, this behavior can give us coarse intuitions about how much someone might know, even if we cannot pinpoint the exact nature of this knowledge. We present a computational model of this kind of broad epistemic-state inference, centered on the expectation that agents maximize epistemic utilities. We evaluate our model in a graded inference task where people had to infer how much an agent knew based on the actions they chose. Critically, the agent's behavior was always under-determined, but nonetheless contained information about how much knowledge they possessed. Our model captures nuanced patterns in participant judgments, revealing a quantitative capacity to infer amorphous knowledge from minimal behavioral evidence.