El videojuego League of legends y su efecto en memoria de trabajo visual y solución de problemas

El propósito de este estudio es medir los efectos que tiene el videojuego League of Legends en los procesos cognitivos de memoria de trabajo visual (MVT) y solución de problemas (SP). Para medir dichos efectos se implementó un diseño pre test-post con un grupo experimental y uno control, compuestos cada uno por siete participantes, en donde se evaluaron los procesos previamente mencionados utilizando los cubos de Corsi para MVT y las matrices del WAIS III para SP. Después de realizar los respectivos entrenamientos se encontraron resultados significativos en los diferentes momentos de aplicación. En el grupo experimental se encontraron diferencias en la variable dependiente SP, mientras que en el grupo control en MVT, pero no en la interacción entre grupos ni diferencias entre grupos, lo que sugiere un efecto de familiarización a la prueba.The purpose of this study was to measure the effects of the videogame League Of Legends on the higher cognitive processes of visual working memory (VWM) and problem solving (PS). For this purpose, a pretest-postest design was implemented with an experimental and control group composed of seven participants in each one of these groups. The previously mentioned processes were tested using the Corsi block-tapping task (VWM) and matrix reasoning of WAIS III (PS). After completing the respective training, significant results were found at the different measure points. For the experimental group, significant differences were found in PS, and for the control group significant differences were found for VWM. However no significant results were found for the interaction with group or between the groups. This suggests that a familiarization effect in the application of the tests

Spiking Analog VLSI Neuron Assemblies as Constraint Satisfaction Problem Solvers

Solving constraint satisfaction problems (CSPs) is a notoriously expensive computational task. Recently, it has been proposed that efficient stochastic solvers can be obtained via appropriately configured spiking neural networks performing Markov Chain Monte Carlo (MCMC) sampling. The possibility to run such models on massively parallel, low-power, neuromorphic hardware holds great promise; however, previously proposed networks are based on probabilistic spiking neurons, and thus rely on random number generators or external sources of noise to achieve the necessary stochasticity, leading to significant overhead in the implementation. Here we show how stochasticity can be achieved by implementing deterministic models of integrate and fire neurons using subthreshold analog circuits that are affected by thermal noise. We present an efficient implementation of spike-based CSP solvers implemented on a reconfigurable neural network VLSI device, which exploits the device's intrinsic noise sources. We apply the overall concept to the solution of generic Sudoku problems, and present experimental results obtained from the neuromorphic device generating solutions to the problem at high rates