Executive function involves the efficient and adaptive engagement of the control processes of updating, shifting, and inhibition (Miyake, 2000) to guide behavior toward a goal. It is associated with decrements in many other cognitive functions due to aging (West, 1996; Raz, 2000) with itself particularly vulnerable to the effect of aging (Treitz et al., 2007). Cognitive training in the form of structural experience with executive coordination demands exhibited effective enhancement in the elderly (Hertzog et al., 2008). The current study was thus aimed at the development and evaluation of a training regime for executive function in the elderly. The breakfast cooking task of Craik and Bialystok (2006) was adapted into a multitasking training task in a session (pre-test vs. post-test) by group (control vs. training). In the training condition, participants constantly switched, updated, and planned in order to control the cooking of several foods and concurrently performed a table setting secondary task. Training gains were exhibited on task related measures. Transfer effect was selectively observed on the letter–number sequencing and digit symbol coding test. The cooking training produced short term increase in the efficiency of executive control processing. These effects were interpreted in terms of the process overlap between the training and the transfer tasks.

Can a robot think like a human being? Scientists in recent years have been trying to achieve this dream, and we are also committed to this same goal. In this paper, we use an example of throwing the ball into the basket to make the robots process with human-like thinking behavior. Such thinking behavior adopted in this paper is divided into two modes: fast and slow. The fast mode belongs to the intuitional reaction, and the slow mode represents the complicated cogitation in human brain. This fascinating human thinking concept is inspired by the book, Thinking, Fast and Slow, which explains the process of the human brain. In addition, the psychology theories proposed in this book are also adopted to realize the thinking algorithms, and our experiments verify that the thinking mode of human beings is reasonable and effective in robots. INDEX TERMSAnchoring effect, fast and slow systems, FIRA, humanoid robot, learning algorithm, peak-end rule, psychology.

A cognition learning algorithm based on a deep belief network and inertia weight Particle Swarm Optimization (PSO) is presented and examined in a humanoid robot. The psychology concepts were adopted from Thinking, Fast and Slow by Daniel Kahneman. The human brain comprises two systems, System 1 and System 2. Based on their characteristics, System 1 and System 2 handle different tasks during cerebration. In this study, Deep Belief Network (DBN) is trained to construct the function of System 1 for the rapid reaction. On the other hand, PSO is applied to build System 2 for the slow and complicated brain behavior. Through the cooperation of System 1 and System 2, the proposed cognition learning algorithm can apply the psychology theories to allow the humanoid robot for learning the suitable pitching postures autonomously. In the experiments conducted in this study, the robot was trained for only five selected points and was then asked to throw precisely to nine points. The proposed algorithm provided 100% accuracy in the robot pitching game. The feasibility of the proposed algorithm was thus verified. INDEX TERMS Deep belief network, humanoid robot, machine learning, particle swarm optimization.

No abstract