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Information theory answers two fundamental questions in communication theory: What is the ultimate data compression (answer: the entropy H ), and what is the ultimate transmission rate of communication (answer: the channel capacity C). For this reason some consider information theory to be a subset of communication theory. We argue that it is much more. Indeed, it has fundamental contributions to make in statistical physics (thermodynamics), computer science (Kolmogorov complexity or algorithmic complexity), statistical inference (Occam s Razor: The simplest explanation is best ), and to probability and statistics (error exponents for optimal hypothesis testing and estimation). This rst lecture chapter goes backward and forward through information theory and its naturally related ideas. The full de nitions and study of the subject begin in 2. Figure 1.1 illustrates the relationship of information theory to other elds. As the gure suggests, information theory intersects physics (statistical mechanics), mathematics (probability theory), electrical engineering (communication theory), and computer science (algorithmic complexity). We now describe the areas of intersection in greater detail. Electrical Engineering (Communication Theory). In the early 1940s it was thought to be impossible to send information at a positive rate with negligible probability of error. Shannon surprised the communication theory community by proving that the probability of error could be made nearly zero for all communication rates below channel capacity. The capacity can be computed simply from the noise characteristics of the channel. Shannon further argued that random processes such as music and speech have an irreducible complexity below which the signal cannot be compressed. This he named the entropy, in deference to the parallel use of this word in thermodynamics, and argued that if the entropy of the

Vallerand, 1997) Finally, motivation at the situational level refers to the motivation individuals experience when engaging in a specific activity at a given moment in time Situational motivation refers to a motivational state It is important to distinguish among these three levels as such a conceptualization provides a more refined understanding of motivational processes involved in human behavior Assessing Motivation It is important to have a brief look at methodological advances that have taken place to assess the different types of motivation across the three levels of generality This will facilitate the understanding of the review presented in the later sections At the situational level, researchers have developed the Situational Motivation Scale (SIMS; Guay, Vallerand, & Blanchard, 2000), which measures intrinsic motivation (without distinguishing the three types), identified and external types of extrinsic motivation, and amotivation.

That s just the way I was brought up That s the way golf teaches you to be When I got home, there was a lot of media attention, but after it was over, I felt ne It s kind of like confessing your sins, you know You just feel better after it s all said and done Is there something about the clarity you feel that you think impacts your game Or perhaps the better way to ask the question is the opposite: Is there something about that con icted feeling that you think would impact your game DAVID: It s very hard to perform without a clear head or a clear conscience You have to be mentally and physically ready and prepared to play..

The choice to measure only four motivational types was dictated by the need to keep the scale as brief as possible (16 items, although an introjected regulation scale is also available) to capture situational motivation in many lab and field situations without overloading participants with a long questionnaire The results of several studies (Edwards, Portman, & Bethea, 2002; Guay et al, 2000; L vesque & Pelletier, 2003; Standage & Treasure, 2002; Standage, Treasure, Duda, & Prusak, 2003) have shown that the scale displays adequate factorial structure and internal consistency and leads to theory-informed predictions Although several studies typically yielded support for the factorial structure of the scale (see Guay et al, 2000), some authors (Standage, Treasure, et al, 2003) suggested that the factorial structure of a 14-item version of the scale might be more appropriate than the 16-item original version.

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identi ed between Type A personality characteristics and heart disease (see chapter by O Callahan, Andrews, and Krantz in this volume), has not been fruitful. Speci cally, a Type C personality has not been proven to be premorbidly predictive of cancer incidence.