Cognitive models in psychology and neuroscience widely assume that the human brain maintains an abstract representation of tasks. This assumption is fundamental to theories explaining how we learn quickly, think creatively, and act flexibly. However, neural evidence for a verifiably generative abstract task representation has been lacking. Here, we report an experimental paradigm that requires forming such a representation to act adaptively in novel conditions without feedback. Using functional magnetic resonance imaging, we observed that abstract task structure was represented within left mid-lateral prefrontal cortex, bilateral precuneus and inferior parietal cortex. These results provide support for the neural instantiation of the long-supposed abstract task representation in a setting where we can verify its influence. Such a representation can afford massive expansions of behavioral flexibility without additional experience, a vital characteristic of human cognition.

Artificial Neural Networks (ANNs) have demonstrated to be a good tool to characterise, model and predict a great quantity of non-linear processes. In this article, we have used ANNs in the classification of different wine-making processes of the variety Vinha˜o (Vitis vinifera) for crops between the years 2000 and 2004. After being trained employing the data corresponding to years from 2000 to 2004, the ANNs demonstrated a root mean square error (RMSE) index between the real data and the calculated ones always lower than 0.14. Furthermore, their operation has been verified by using the previously reserved data of 10 famous wines. As a result, a RMSE index between observed and calculated data always lower than 0.17 was obtained for all of them, confirming the capacity of the ANN as a model of prediction of wine processes for this variety.Keywords: artificial neural networks; multilayer perceptron; DOC wine; sensory and physico-chemical parameters Las redes neuronales (ANNs) han demostrado ser una buena herramienta para caracterizar, modelar y predecir una gran cantidad de procesos no lineales. En este artı´culo hemos usado una ANN para la clasificacio´n de diferentes procesos de vinificacio´n de la variedad Vinha˜o (Vitis vinifera) para vendimias entre los an˜os 2000 y 2004. Despue´s del entrenamiento con los datos correspondientes a los an˜os 2000 a 2004, la ANN demostro´un ı´ndice raı´z del error cuadra´tico (RMSE) entre los datos reales y los calculados siempre inferior a 0,14. Adema´s, su funcionamiento ha sido verificado mediante datos reservados anteriormente de 10 vinos. Como resultado se obtuvo un RMSE entre los datos observados y calculados siempre inferior al 0,17 lo que confirma la capacidad de la ANN como modelo de prediccio´n de los procesos de vino para esta variedad.Palabras clave: redes neuronales artificiales; perceptro´n multicapa; denominacio´n de origen controlada; para´metros sensoriales y fı´sico-quı´micos