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Attention and attentional processes pervade virtually all aspects of perception, cognition, and action indeed, it is difficult to conceive of any aspect of human skill that is not, in some way, either dependent on or influenced by attention. Equally, it is difficult to conceive of any aspect of psychology that may be more central to the enhancement of skill learning and expert performance than attention (Rogers, Rousseau, & Fisk, 1999). However, despite its ubiquitous nature and its long history of philosophical and experimental scrutiny, attention remains a poorly defined and (mis)understood concept. In large part, this is because attention as a term is used to refer to a variety of different processes, many of which are only quite loosely related (Nougier, Stein, & Bonnel, 1991). Attention is not a unitary construct but a construct composed of a range of different, diverse, and contextually sensitive processes (Parasuraman & Davies, 1984). In spite of its diversity, it is nevertheless possible to identify some common roles that attention plays in skill learning and expert performance (Posner & Boies, 1971). One critical role of attention is to preferentially select only particular information for detailed processing. This process of selective attention is one that permits relevant or essential information to gain access to the nervous system s limited processing resources while at the same time providing a means to effectively gate out irrelevant or potentially distracting or misleading sources of information, the processing of which may impede performance. Effective and efficient operation of selective attentional processes is essential for skilled performance in sports tasks where critical cues may be available only momentarily and sources of distraction abound. Selective attention is frequently examined experimentally using tasks in which focusing of attention ( concentration ) to information

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|A(n) |2 n(H (X1 ) ) 2 n(H (X2 ,Y ) 2 n(H (X1 )+H (X2 ,Y ) H (X1 ,X2 ,Y ) 3 = 2 n(I (X1 ;X2 ,Y ) 3

section (see Algorithm 1). The basic characteristics of FLEXFIS-Class MM can be summarized in the following (for further details please refer to [42]):

10 2

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Here b 2 [0,1] is a learning rate. As pointed out in [14], b is generally less than a. We see that ak is essentially an estimate of the negation, one minus, the compatibility. Here we note that a low arousal rate, small values for ak, is an indication of a good correspondence between the agent s belief and the external environment. In this case, the agent has con dence in the correctness of its belief system. On the other hand, a value for ak closer to one arouses the agent to the fact that there appears to be some inconsistency between what it believes and the external environment. We see from equation (Ib) that when ak % 0, the learning from the current observation Dk is strongly modulated by rk, the compatibility of Dk with the current belief Vk 1. If the compatibility rk is high, we learn at a rate close to a; if the compatibility rk is low, we do not learn from the current observation. On the other hand, when ak % 1, and therefore the agent is concerned about the correctness of its belief, the term rk 1 ak %1 is independent of the value rk and hence the agent is not restrained by its current belief from looking at all observations. We note that the updation algorithm (I) is closely related to the classic Widrow-Hoff learning rule [16]. Vk i Vk 1 i a dk i Vk 1 i W-H

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69 IP Address Assignment (The Old Method)

1 (8.56) I (X; Y ) = h(X) + h(Y ) h(X, Y ) = log(1 2 ). 2 If = 0, X and Y are independent and the mutual information is 0. If = 1, X and Y are perfectly correlated and the mutual information is in nite. 8.6 PROPERTIES OF DIFFERENTIAL ENTROPY, RELATIVE ENTROPY, AND MUTUAL INFORMATION Theorem 8.6.1 D(f ||g) 0 with equality iff f = g almost everywhere (a.e.). Proof: Let S be the support set of f . Then D(f ||g) = g f S g log f S f f log (8.58) (by Jensen s inequality) (8.59) (8.57)

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The second component is based on the IEEE 802.11e QoS facilities. Speci cally, four access categories (AC) (in IEEE 802.11e) are de ned, in decreasing priority:

203 204 204 204 205 221 223 225

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