Prompting teaching modulates children's encoding of novel information by facilitating higher-level structure learning and hindering lower-level statistical learning.

Young children are not only prepared to learn from teaching, but they also start to spontaneously teach others, indicating that teaching is a natural instinct of the humankind. During childhood, teaching seems to precede the emergence of several cognitive abilities, so the question arises: how does teaching affect the development of later emerging cognitive skills? Since teaching requires explicit, accessible representations of the knowledge of the teacher, we hypothesized that the motivation to teach might impact the way children encode novel information, by biasing them towards a model-based encoding, which can help them to structure the incoming information in a more abstract and explicitly accessible way. In our study, 7-10-year-old children were presented with a well-established probabilistic sequence learning task on two consecutive days, after receiving an instruction that on the second day, they would have to teach a peer about the task. During the task, we could simultaneously measure two different types of learning: model-free learning of local (lower-level) statistical correlations and model-based learning of the global (higher-level) statistical structures of the sequences. We predicted that in case the motivation to teach facilitates model-based encoding, children who received the instruction to teach would perform better in learning the higher-level statistical structures than children in the control group, who did not receive an instruction to teach. Furthermore, since previous studies showed competition between the two types of encoding processes during development, we also predicted that facilitating children's model-based learning will impair their model-free learning of the lower-level statistical correlations. Our results confirmed both predictions, showing an improved model-based higher-level structure learning and an impaired model-free lower-level statistical correlation learning in the Teaching Group, compared to the controls. Thus, prompting teaching affects children's encoding of the novel information, by biasing them to learn in a model-based way, which can help to build more abstract and explicitly accessible representations that could be shared with others.

Distortional effect of beam-hardening artefacts on microCT: a simulation study based on an in vitro caries model.

OBJECTIVE: The aim of this study was to assess quantitatively the degrading effect of artefacts caused by beam hardening on the microscopic computerized tomography (microCT) measurements of an in vitro caries model. STUDY DESIGN: A simulation-based method was described, with which the degrading effect of microCT artefacts on certain parameters of the observed structure could be determined. Simulations were carried out with polychromatic and monochromatic X-ray source, and a linearization method with a second-order polynomial fit algorithm was used in specific cases to correct the beam hardening artefact. The virtual test object was a half-crown of a tooth with an artificial caries lesion. RESULTS: For simulation with monochromatic X-ray source, the relative error of lesion depth and thickness measurements of the remineralized layer was found to be 1%-2%. For polychromatic X-ray source, and omitting beam hardening correction, the relative error exceeded 6%. After appropriate beam-hardening correction, the relative error of the measurement could be reduced to 1%-2%. CONCLUSION: With the adjustment simulated in this study, microCT having polychromatic X-ray source resulted in the same level of error as with monochromatic source if the linearization method to correct the beam hardening was used. The presented simulation-based method is a useful way to determine artefact-caused distortions for other studies testing objects with different material and geometry.