Working Memory Training in Children with Mild Intellectual Disability, Through Designed Computerized Program

Objectives: The aim of this research is designing a computerized program, in game format, for working memory training in mild intellectual disabled children.

Methods: 24 students participated as test and control groups. The auditory and visual-spatial WM were assessed by primary test, which included computerized Wechsler numerical forward and backward subtests, and secondary tests, which contained three parts: dual visual-spatial test, auditory test, and a onesyllable word recalling test.

Results: The results showed significant differnces between working memory capacity in the intellectually disabled children and normal ones [p-value <0.00001]. After using the computerized working memory training, Visual-spatial WM, auditory WM, and speaking were improved in the trained group. The mentioned four tests showed significant differences between pre-test and post-test. The trained group showed more improvements in forward tasks. The trained participant's processing speed increased with training.

Discussion: According to the results, comprehensive human-computer interfaces and the aplication of computer in children training, especially in traing of intellectual disabled children with impairements in visual and auditory perceptions, could be more effective and vaulable.

Prediction in human decision making: a modeling approach

Human beings can determine optimal behaviors, which depends on the ability to make planned and adaptive decisions. Decision making is defined as the ability to choose between different alternatives. Purpose: this study, we have addressed the prediction aspect of human decision making from neurological, experimental and modeling points of view. Methods: We used a predictive reinforcement learning framework to simulate the human decision making behavior, concentrating on the role of frontal brain regions which are responsible for predictive control of human behavior. The model was tested in a maze task and the human subjects were asked to do the same task. A group of six volunteers including three men and three women at the age of 23-26 participated in this experiment. Results: The similarity between responses of the model and the human behavior was observed after varying the prediction horizons. We found that subjects with less risky choices usually decide based on considering long term advantages of their action selections, which is equal to the longer prediction horizon. However, they are more susceptible to reach suboptimal solutions if their predictions become wrong due to some reasons like changing environment or inaccurate models. Conclusion: The concept of prediction result in faster learning and minimizing future losses in decision making problems. Since the problem solving in human beings is very faster than a trial and error system, considering this ability will help to describe the human behavior more desirably. This observation is compatible to the recent findings about the role of Dorsolateral Prefrontal Cortex in prediction and its relations to Anterior Cingulate Cortex with the ability of conflict monitoring and action selection.

gray-box neural network model of parkinson's disease using gait signal

In this study, we focused on the gait of Parkinson's disease [PD] and presented a gray box model for it. We tried to present a model for basal ganglia structure in order to generate stride time interval signal in model output for healthy and PD states. Because of feedback role of dopamine neurotransmitter in basal ganglia, this part is modelled by "Elman Network", which is a neural network structure based on a feedback relation between each layer. Remaining parts of the basal ganglia are modelled with feed-forward neural networks. We first trained the model with a healthy person and a PD patient separately. Then, in order to extend the model generality, we tried to generate the behaviour of all subjects of our database in the model. Hence, we extracted some features of stride signal including mean, variance, fractal dimension and five coefficients from spectral domain. With adding 10% tolerance to above mentioned neural network weights and using genetic algorithm, we found proper parameters to model every person in the used database. The following points may be regarded as clues for the acceptability of our model in simulating the stride signal: the high power of the network for simulating normal and patient states, high ability of the model in producing the behaviour of different persons in normal and patient cases, and the similarities between the model and physiological structure of basal ganglia