No guts, no glory: underestimating the benefits of providing children with mechanistic details

Previous research shows that children effectively extract and utilize causal information, yet we find that adults doubt children’s ability to understand complex mechanisms. Since adults themselves struggle to explain how everyday objects work, why expect more from children? Although remembering details may prove difficult, we argue that exposure to mechanism benefits children via the formation of abstract causal knowledge that supports epistemic evaluation. We tested 240 6–9 year-olds’ memory for concrete details and the ability to distinguish expertise before, immediately after, or a week after viewing a video about how combustion engines work. By around age 8, children who saw the video remembered mechanistic details and were better able to detect car-engine experts. Beyond detailed knowledge, the current results suggest that children also acquired an abstracted sense of how systems work that can facilitate epistemic reasoning.

The Cost of the Epistemic Step: Investigating Scalar Implicatures in Full and Partial Information Contexts

We present the first ERP experiments that test the online processing of the scalar implicature some ⇝ not all in contexts where the speaker competence assumption is violated. Participants observe game scenarios with four open cards on the table and two closed cards outside of the table, while listening to statements made by a virtual player. In the full access context, the player makes a fully informed statement by referring only to the open cards, as cards on the table; in the partial access context, she makes a partially informed statement by referring to the whole set of cards, as cards in the game. If all of the open cards contain a given object X (Fullset condition), then some cards on the table contain Xs is inconsistent with the not all reading, whereas it is unknown whether some cards in the game contain X is consistent with this reading. If only a subset of the open cards contains X (Subset condition), then both utterances are known to be consistent with the not all implicature. Differential effects are observed depending on the quantifier reading adopted by the participant: For those participants who adopt the not all reading in the full access context, but not in the partial access context (weak pragmatic reading), a late posterior negativity effect is observed in the partial access context for the Fullset relative to the Subset condition. This effect is argued to reflect inference-driven context retrieval and monitoring processes related to epistemic reasoning involved in evaluating the competence assumption. By contrast, for participants who adopt the logical interpretation of some (some and possibly all), an N400 effect is observed in the partial access context, when comparing the Subset against the Fullset condition, which is argued to result from the competition between the two quantifying expressions some cards on the table and some cards in the game functioning in the experiment as scalar alternatives.

A Conflict Tolerant Logic of Explicit Evidence

Standard epistemic modal logic is unable to adequately deal with the FrauchigerRenner paradox in quantum physics. We introduce a novel justification logic CTJ, in which the paradox can be formalized without leading to an inconsistency. Still CTJ is strong enough to model traditional epistemic reasoning. Our logic tolerates two different pieces of evidence such that one piece justifies a proposition and the other piece justifies the negation of that proposition. However, our logic disallows one piece of evidence to justify both a proposition and its negation. We present syntax and semantics for CTJ and discuss its basic properties. Then we give an example of epistemic reasoning in CTJ that illustrates how the different principles of CTJ interact. We continue with the formalization of the Frauchiger–Renner thought experiment and discuss it in detail. Further, we add a trust axiom to CTJ and again discuss epistemic reasoning and the paradox in this extended setting.

Does Amy Know Ben Knows You Know Your Cards? A Computational Model of Higher-Order Epistemic Reasoning

Reasoning about what other people know is an important cognitive ability, known as epistemic reasoning, which has fascinated psychologists, economists, and logicians. In this paper, we propose a computational model of humans’ epistemic reasoning, including higher-order epistemic reasoning—reasoning about what one person knows about another person’s knowledge—that we test in an experiment using a deductive card game called “Aces and Eights”. Our starting point is the model of perfect higher-order epistemic reasoners given by the framework of dynamic epistemic logic. We modify this idealized model with bounds on the level of feasible epistemic reasoning and stochastic update of a player’s space of possibilities in response to new information. These modifications are crucial for explaining the variation in human performance across different participants and different games in the experiment. Our results demonstrate how research on epistemic logic and cognitive models can inform each other.