Laboratory experiments have shown that the first causal explanation people hear “imprints” on the mind. New causal explanations become combined with the first, rather than replace it. The COVID-19 pandemic presented a real-world version of these experiments. The initial causal model communicated by scientific publications focused on how the virus spread via contact with surfaces. However, by July 2020 the science was clearly showing that aerosol transmission was more important, yet there has still been a greater focus on cleaning surfaces than ventilation since then. The current studies show how the surface model and aerosol model coexist in people’s minds, are associated with risk perceptions, and drives preferences for pandemic control strategies. In addition, the studies shows that a tutorial based on a deeper explanation of aerosol transmission focused on the density of virus particles in the air helps causal model updating.

Metacognitive monitoring of uncertainty is critical for the development of self-regulated learning because recognition of uncertainty triggers information-seeking or a strategy change. Uncertainty monitoring is assessed with the calibration of explicit self-reports of certainty with objective levels of certainty. Typically, this is done with cognitive tasks where each trial is independent from the last, such as with perceptual judgments of noisy images. However, uncertainty monitoring is perhaps most important when there are multiple consecutive decisions to be made that are contingent on each other, such as problems requiring multiple steps to solve. Reasoners have to reflect on each step and consider if they are getting closer or further from a solution. In the current experiment, both children aged 5-10 and adults calibrated their initial certainty judgments similarly, showing sensitivity to differing initial levels of certainty. However, only adults updated their judgments as they progressed through consecutive decision steps.

Recent research has examined whether children can modify tools to solve novel problems. For example, when childrenare given a pipe cleaner with the goal to retrieve a little bucket at the bottom of a tube, will they realize that bending thepipe cleaner into a hook will solve the problem? Children younger than 7 almost all fail at this task, and children under10 are far from ceiling. Because problem solving is often helped via generalization from analogous problems, the currentstudy examined whether children in this task could take advantage of being read a story (with pictures) about fishing,emphasising the importance of hooks. Interesting we found an interaction wherein preschool children were helped bythe analogy, while school-aged children were not, who also solved the task at much higher rates overall (but still belowceiling).

Language and music are structured at multiple temporal scalesand have been characterized as having meter: a hierarchical andperiodic alternation of the prominence of syllables/beats. Meter isthought to emerge from the entrainment of neural oscillators,affording temporal expectations and selective attention. Higher-levels of a metric hierarchy also tend to track syntactic phrasestructure, however, it is not clear within the framework oftemporal attending why this would be advantageous. Neuraloscillations have recently been shown to also track syntacticphrases. We propose that meter aligns to phrase structure so as tomake syntactic processing more efficient. In two experiments(both visual and auditory language), we show that certainalignments of meter to syntax influence sentence comprehensionand we suggest potential mechanisms for why certain alignmentstend to be preferred. Our results underline the rhythmicity of notonly low-level perception but also of higher-level cognitiveprocessing of syntactic sequences.

Children are capable of using the causal properties of objects to determine category membership. Can they also recognize causal system categories, or abstract patterns of causation that apply across phenomena (Rottman et al., 2012)? For example, do children recognize that one causal chain (e.g. the transmission of salmonella across species) shares the same underlying structure as a chain in another domain (e.g. heat transfer between objects)? If so, recognizing that superficially distinct events share the same underlying causal system may facilitate knowledge transfer. In Study 1, 6- to 7-year-olds and adults, but not 4 to 5-year-olds, are capable of identifying simple machines that share the same causal structure. In ongoing work, we control for low-level perceptual similarities and explore whether adults and children recognize similar causal structures among unique mechanical systems. We also consider whether their recognition of causal systems can be used to support knowledge transfer.

We report two within-subjects experiments exploring the relationship between causal structure and causal attribution judgments. Both experiments comprised scenarios instantiating five unique causal structures: (a) causal chain, (b) positive feedback loop, (c) negative feedback loop, (d) common cause, and (e) common effect. Each scenario involved various causal and outcome events occurring in a sequence (e.g., Events 1 and 2 caused Event 3) and subjects were asked to make judgments about the impact that each causal event had on the outcome event. Experiment 1 lacked domain information, whereas this information was included in Experiment 2. We find evidence of recency and primacy effects, but these effects differed based on the scenarios’ causal structure. Recency effects occurred for scenarios with causal chains and positive and negative feedback loops, whereas primacy effects were found for common cause scenarios. We thus demonstrate that causal attribution judgments indeed vary as a function of causal structure.