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Table of Contents

Volume: 1  Issue: 2   Date: August 2009

Link: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isYear=2009&isnumber=5290358 

(Previous issue: Vol. 1, No. 1, May 2009)

Default Network and Intelligence Difference
Ming Song; Yong Liu; Yuan Zhou; Kun Wang; Chunshui Yu; Tianzi Jiang
Page(s): 101-109
Digital Object Identifier 10.1109/TAMD.2009.2029312

Abstract: In the last few years, many studies in the cognitive and system neuroscience found that a consistent network of brain regions, referred to as the default network, showed high levels of activity when no explicit task was performed. Some scientists believed that the resting state activity might reflect some neural functions that consolidate the past, stabilize brain ensembles, and prepare us for the future. Here, we modeled the default network as undirected weighted graph, and then used graph theory to investigate the topological properties of the default network of the two groups of people with different intelligence levels. We found that, in both groups, the posterior cingulate cortex showed the greatest degree in comparison to the other brain regions in the default network, and that the medial temporal lobes and cerebellar tonsils were topologically separations from the other brain regions in the default network. More importantly, we found that the strength of some functional connectivities and the global efficiency of the default network were significantly different between the superior intelligence group and the average intelligence group, which indicates that the functional integration of the default network might be related to the individual intelligent performance.

Full Text from IEEE: PDF (466 KB) ; Contact the author by email

Connectionist Models of Reinforcement, Imitation, and Instruction in Learning to Solve Complex Problems
Dandurand, F.; Shultz, T.R.
Page(s): 110-121
Digital Object Identifier 10.1109/TAMD.2009.2031234

Abstract: We compared computational models and human performance on learning to solve a high-level, planning-intensive problem. Humans and models were subjected to three learning regimes: reinforcement, imitation, and instruction. We modeled learning by reinforcement (rewards) using SARSA, a softmax selection criterion and a neural network function approximator; learning by imitation using supervised learning in a neural network; and learning by instructions using a knowledge-based neural network. We had previously found that human participants who were told if their answers were correct or not (a reinforcement group) were less accurate than participants who watched demonstrations of successful solutions of the task (an imitation group) and participants who read instructions explaining how to solve the task. Furthermore, we had found that humans who learn by imitation and instructions performed more complex solution steps than those trained by reinforcement. Our models reproduced this pattern of results.

Full Text from IEEE: PDF (1469 KB) ; Contact the author by email

Some Basic Principles of Developmental Robotics
Stoytchev, A.
Page(s): 122-130
Digital Object Identifier 10.1109/TAMD.2009.2029989

Abstract: This paper formulates five basic principles of developmental robotics. These principles are formulated based on some of the recurring themes in the developmental learning literature and in the author's own research. The five principles follow logically from the verification principle (postulated by Richard Sutton) which is assumed to be self-evident. This paper also gives an example of how these principles can be applied to the problem of autonomous tool use in robots.

Full Text from IEEE: PDF (198 KB); Contact the author by email 

Acquisition of the Head-Centered Peri-Personal Spatial Representation Found in VIP Neuron
Fuke, S.; Ogino, M.; Asada, M.
Page(s): 131-140
Digital Object Identifier 10.1109/TAMD.2009.2031013

Abstract: Both body and visuo-spatial representations are supposed to be gradually acquired during the developmental process as described in cognitive and brain sciences. A typical example is face representation in a neuron (found in the ventral intraparietal (VIP) area) of which the function is not only to code for the location of visual stimuli in the head-centered reference frame, but also to connect visual sensation with tactile sensation. This paper presents a model that enables a robot to acquire such representation. The proprioception of arm posture is utilized as reference data through the ldquohand regard behavior,rdquo that is, the robot moves its hand in front of its face, and the self-organizing map (SOM) and Hebbian learning methods are applied. The simulation results are shown and discussions on the limitation of the current model and future issues are given.

Full Text from IEEE: PDF (1419 KB); Contact the author by email

Active Information Selection: Visual Attention Through the Hands
Chen Yu; Smith, L.B.; Hongwei Shen; Pereira, A.F.; Smith, T.
Page(s): 141-151
Digital Object Identifier 10.1109/TAMD.2009.2031513

Abstract: Development imposes great challenges. Internal “cortical” representations must be autonomously generated from interactive experiences. The eventual quality of these developed representations is of course important. Additionally, learning must be as fast as possible—to quickly derive better representation from limited experiences. Those who achieve both of these will have competitive advantages. We present a cortex-inspired theory called lobe component analysis (LCA) guided by the aforementioned dual criteria. A lobe component represents a high concentration of probability density of the neuronal input space. We explain how lobe components can achieve a dual—spatiotemporal (“best” and “fastest”)—optimality, through mathematical analysis, in which we describe how lobe components' plasticity can be temporally scheduled to take into account the history of observations in the best possible way. This contrasts with using only the last observation in gradient-based adaptive learning algorithms. Since they are based on two cell-centered mechanisms—Hebbian learning and lateral inhibition—lobe components develop in-place, meaning every networked neuron is individually responsible for the learning of its signal-processing characteristics within its connected network environment. There is no need for a separate learning network. We argue that in-place learning algorithms will be crucial for real-world large-size developmental applications due to their simplicity, low computational complexity, and generality. Our experimental results show that the learning speed of the LCA algorithm is drastically faster than other Hebbian-based updating methods and independent component analysis algorithms, thanks to its dual optimality, and it does not need to use any second- or higher order statistics. We also- - introduce the new principle of fast learning from stable representation.

Full Text from IEEE: PDF (1066 KB) ; Contact the author by email