In praise of "false" models and rich data

The authors argue that "true" models that aim at faithfully mimicking or reproducing every property of the sensorimotor system cannot be compact as they need many free parameters. Consequently, most scientists in motor control use what are called "false" models--models that derive from well-defined approximations. The authors conceptualize these models as a priori limited in scope and approximate. As such, they argue that a quantitative characterization of the deviations between the system and the model, more than the mere act of falsifying, allows scientists to make progress in understanding the sensorimotor system. Ultimately, this process should result in models that explain as much data variance as possible. The authors conclude by arguing that progress in that direction could strongly benefit from databases of experimental results and collections of models.     

Time knowledge difficulties following treatment for malignant cerebellar tumors

In children treated for malignant cerebellar tumors, there are only a few studies investigating temporal skills, despite the role of the cerebellum in time processing being generally acknowledged. Children’s time knowledge has been defined as the correct representation and use of familiar time units. The present study compares time knowledge in 38 children treated for malignant cerebellar tumors (mean age 11.6 years) with 105 typically-developing (TD) children. The performances on all time knowledge subtests were significantly lower in the tumor group. The results also confirm a lower mean IQ in the children treated for cerebellar tumors, related to slower processing speed and poorer performance on working memory and non-verbal tasks. However, the lower IQ does not explain the considerable difficulties in the acquisition of time knowledge. These results are discussed in the light of the role of the cerebellum in time processing and in the context of two different models of temporal processing: the internal clock model and the neural network state model.