不同智力水平儿童的自我调控特点

Self-regulation has recently become an important topic in cognitive and developmental domain. According to previous theories and experimental studies, it is shown that self-regulation consist of both a personality (or social) aspect and a behavioral cognitive aspect of psychology. Self-regulation can be divided into self-regulation personality and self-regulation ability. In the present study researches have been carried out from two perspectives: child development and individual differences. We are eager to explore the characteristics of self-regulation in terms of human cognitive development. In the present study, we chose two groups of early adolescences one with high intelligence and the other with normal intelligence. In Study One Questionnaires were used to compare whether the highly intelligent group had had better self-regulation personality than the normal group. In Study Two experimental psychology tasks were used to compare whether highly intelligent children had had better self-regulation cognitive abilities than their normal peers. Finally, in Study Three we combined the results of Study One and Study Two to further explore the neural mechanisms for highly intelligent children with respect to their good self-regulation abilities. Some main results and conclusions are as follows: (1) Questionnaire results showed that highly intelligent children had better self-regulation personalities, and they got higher scores on the personalities related to self-regulation such as, self-reliance, stability, rule-consciousness. They also got higher scores on self-consciousness which meant that they could know their own self better than the normal children. (2) Among the three levels of cognitive difficulties in self-regulation abilities, the highly intelligent children had faster reaction speed than normal children in the primary self-regulation tasks. In the intermediate self-regulation tasks, highly intelligent children’s inhibition processing and executive processing were both better than their normal peers. In the advanced self-regulation tasks, highly intelligent children again had faster reaction speed and more reaction accuracy than their normal peers when facing with conflict and inconsistency experimental conditions,. Regression model’s results showed that primary and advanced self-regulation abilites had larger predictive power than intermediate self-regualation ability. (3) Our neural experiments showed that highly intelligent children had more efficient neural automatic processing ability than normal children. They also had better, faster and larger neural reaction to novel stimuli under pre-attentional condition which made good and firm neural basis for self-regualation. Highly intelligent children had more mature frontal lobe and pariental functions for inhibition processing and executive processing. P3 component in ERP was closely related to executive processing which mainly activated pariental function. There were two time-periods for inhibition processing—first it was the pariental function and later it was the coordination function of frontal and pariental lobes. While conflict control task had pariental N2 and frontal-pariental P3 neural sources, highly intelligent children had much smaller N2 and shorter P3 latency than normal children. Inconsistency conditions induced larger N2 than conditions without inconsistency, and conditions without inconsistency (or Conflict) induced higher P3 amplitudes than with Inconsistency (or Conflict) conditions. In conclusion, the healthy development of self-regulation was very important for children’s personality and cognition maturity, and self-regulation had its own specific characteristics in ways of presentation and ways of development. Better understanding of self-regulation can further help the exploration of the nature of human intelligence and consciousness.

Descriptive Simulation: Combining Symbolic and Numerical Methods in the Analysis of Chemical Reaction Mechanisms

The Kineticist's Workbench is a computer program currently under development whose purpose is to help chemists understand, analyze, and simplify complex chemical reaction mechanisms. This paper discusses one module of the program that numerically simulates mechanisms and constructs qualitative descriptions of the simulation results. These descriptions are given in terms that are meaningful to the working chemist (e.g., steady states, stable oscillations, and so on); and the descriptions (as well as the data structures used to construct them) are accessible as input to other programs.

Different mechanisms for the formation of acetaldehyde and ethanol on the Rh-based catalysts

Different mechanisms for the formation of acetaldehyde and ethanol on the Rh-based catalysts were investigated by the TPR (temperature programmed reaction) method, and the active sites were studied by CO-TPD, TPSR (temperature programmed surface reaction of preadsorbed CO by H-2) and XPS techniques. The TPR results indicated that ethanol and acetaldehyde might be formed through different intermediates, whereas ethanol and methanol might result from the same intermediate. Results of CO-TPD, TPSR, and XPS showed that on the Rh-based catalyst, the structure of the active sites for the formation of C-2-oxygenates is ((RhxRhy+)-Rh-0)-O-Mn+ (M=Mn or Zr, x>>y, 2 less than or equal ton less than or equal to4). The tilt-adsorbed CO species is the main precursor for CO dissociation and the precursor for the formation of ethanol and methanol. Most of the linear and geminal adsorbed CO species desorbed below 500 K. Based on the suggested model of the active sites, detailed mechanisms for the formation of acetaldehyde and ethanol are proposed. Ethanol is formed by direct hydrogenation of the tilt-adsorbed CO molecules, followed by CH2 insertion into the surface CH2-O species and the succeeding hydrogenation step. Acetaldehyde is formed through CO insertion into the surface CH3-Rh species followed by hydrogenation, and the role of the promoters was to stabilize the intermediate of the surface acetyl species. (C) 2000 Academic Press.

The Role of Chemical Mechanisms in Neural Computation and Learning

Most computational models of neurons assume that their electrical characteristics are of paramount importance. However, all long-term changes in synaptic efficacy, as well as many short-term effects, are mediated by chemical mechanisms. This technical report explores the interaction between electrical and chemical mechanisms in neural learning and development. Two neural systems that exemplify this interaction are described and modelled. The first is the mechanisms underlying habituation, sensitization, and associative learning in the gill withdrawal reflex circuit in Aplysia, a marine snail. The second is the formation of retinotopic projections in the early visual pathway during embryonic development.

Hypothesizing Device Mechanisms: Opening Up the Black Box

I describe an approach to forming hypotheses about hidden mechanism configurations within devices given external observations and a vocabulary of primitive mechanisms. An implemented causal modelling system called JACK constructs explanations for why a second piece of toast comes out lighter, why the slide in a tire gauge does not slip back inside when the gauge is removed from the tire, and how in a refrigerator a single substance can serve as a heat sink for the interior and a heat source for the exterior. I report the number of hypotheses admitted for each device example, and provide empirical results which isolate the pruning power due to different constraint sources.

Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning

Q. Meng and M. H. Lee, 'Construction of Robot Intra-modal and Inter-modal Coordination Skills by Developmental Learning', Journal of Intelligent and Robotic Systems, 48(1), pp 97-114, 2007.

Staged development of Robot Motor Coordination

M.H. Lee and Q. Meng, 'Staged development of Robot Motor Coordination', IEEE International Conference on Systems, Man and Cybernetics, (IEEE SMC 05), Hawaii, USA, v3, 2917-2922, 2005.

Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior

Huelse, M., Wischmann, S., Manoonpong, P., Twickel, A.v., Pasemann, F.: Dynamical Systems in the Sensorimotor Loop: On the Interrelation Between Internal and External Mechanisms of Evolved Robot Behavior. In: M. Lungarella, F. Iida, J. Bongard, R. Pfeifer (Eds.) 50 Years of Artificial Intelligence, LNCS 4850, Springer, 186 - 195, 2007.