Costs and benefits of orthographic inconsistency in reading:evidence from a cross-linguistic comparison

We compared reading acquisition in English and Italian children up to late primary school analyzing RTs and errors as a function of various psycholinguistic variables and changes due to experience. Our results show that reading becomes progressively more reliant on larger processing units with age, but that this is modulated by consistency of the language. In English, an inconsistent orthography, reliance on larger units occurs earlier on and it is demonstrated by faster RTs, a stronger effect of lexical variables and lack of length effect (by fifth grade). However, not all English children are able to master this mode of processing yielding larger inter-individual variability. In Italian, a consistent orthography, reliance on larger units occurs later and it is less pronounced. This is demonstrated by larger length effects which remain significant even in older children and by larger effects of a global factor (related to speed of orthographic decoding) explaining changes of performance across ages. Our results show the importance of considering not only overall performance, but inter-individual variability and variability between conditions when interpreting cross-linguistic differences

Automatic Creation of Environment Models via Training

Abstract. Model checking suffers not only from the state-space explosion problem, but also from the environment modeling problem: how can one create an accurate enough model of the environment to enable precise yet efficient model checking? We present a novel approach to the automatic creation of environment models via training. The idea of training is to take several programs that use a common API and apply model checking to create abstractions of the API procedures. These abstractions then are reused on subsequent verification runs to model-check different programs (which utilize the same API). This approach has been realized in SLAM, a software model checker for C programs, and applied to the domain of Windows device drivers that utilize the Windows Driver Model API (a set of entry points into the Windows kernel). We show how the boolean abstractions of the kernel routines accessed from a device driver are extracted and merged into a boolean library that can be reused by subsequent model checking runs on new drivers. We show that the merged abstraction is a conservative extension of the boolean abstractions created by training.