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The foundations of behaviorism are perhaps more associated with J B Watson than with any other psychologist, though Watson was preceded by other important figures in the history of learning theory, notably Thorndike and Pavlov Although a variety of learning theories eventually developed, behaviorism as a formal dogma is most associated with the views of B F Skinner According to Skinner s strict behaviorism, it is unnecessary to posit the existence of unobservable emotional states or cognitive expectancies to account for behavior and its pathologies Hypothetical inner states are discarded and explanations are formulated solely in terms of external sources of stimulation and reinforcement Thus, all disorders become the simple product of environmentally based reinforcing experiences These shape the behavioral repertoire of the individual, and differences between adaptive and maladaptive behaviors can be traced entirely to differences in the reinforcement patterns to which individuals are exposed.

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Inner states, such as traits or schemata, are considered throwbacks to primitive animism Instead, the understanding of a behavior can be complete only when the contextual factors in which the event is embedded are illuminated The logic is relatively simple: If there are no innate ideas, sensation or stimuli are by definition all that exist Because sensation originates in the environment, the environment must ultimately control all behavior, however complex The mind becomes an empty vessel, or tabula rasa, that contains only what the environment puts there All behavior is said to be under stimulus control For this reason, the relationship between personality and behaviorism has been mainly antagonistic, and understandably so, because behavioral psychology exclusively focuses on observable surface behavior rather than on inferred entities, such as personality traits, cognitive schemata, instinctual drives, or interpersonal dispositions, all essential units in the study of personality.

if(High > intraHigh) intraHigh = High if(Low < intraLow) intraLow = Low {must keep high of current date manually} {must keep low of current date manually}

Leamer, E E (1973) Multicollinearity: A Bayesian Interpretation Review of Economics and Statistics 55 (3), 371 80 Leamer, E E (1978) Speci cation Searches: Ad Hoc Inference with Nonexperimental Data New York: John Wiley & Sons Leamer, E E (1983) Lets Take the Con Out of Econometrics American Economic Review 73, 31 43 Leamer, E E (1984) Global Sensitivity Results for Generalized Least Squares Estimates Journal of the American Statistical Association 79, 867 70 Leamer, E E (1985) Sensitivity Analysis Would Help American Economic Review 75, 308 13 Leamer, E E and Leonard, H (1983) Reporting the Fragility of Regression Estimates Review of Economics and Statistics 65, 306 17 Learmonth, G P and Lewis, P A W (1973) Some Widely Used and Recently Proposed Uniform Random Number Generators Proceedings of Computer Science and Statistics: 7th Annual Symposium on the Interface, W J Kennedy (ed).

By the mid-1980s, a number of crucial reinterpretations of traditional assessment had been made that allowed clinically applied behavioral approaches to become successively broader and more moderate Most notably, the diagnoses of Axis I, regarded in psychiatry.

6.4.2 Focus on the Number of Use Cases per Module The number of use cases per module is an important measure because it indicates design ef ciency: in general, more use cases per module means greater design ef ciency. Once all the modules are tracked by the infrastructure, each use case that employs the module should also be tracked. This information should then be available as a panel through the reporting system. This allows project members to view the number of use cases per module, as shown in Figure 6.8. A similar panel showing number of modules used for each use case could be used to show design completeness (i.e., that all use cases are being implemented), as illustrated in Figure 6.9.

Ames, IA: Iowa State University, pp 163 71 L Ecuyer, P (1990) Random Numbers for Simulation Communications of the ACM 33, 85 97 L Ecuyer, P (1992) Testing Random Number Generators In Proceedings of the 1992 Winter Simulation Conference Piscataway, NJ: IEEE Press L Ecuyer, P (1994) Uniform Random Number Generation Annals of Operations Research 53, 77 120 L Ecuyer, P and Hellekalek, P (1998) Random Number Generators: Selection Criteria and Testing In Random and Quasi-random Point Sets, P Hellekalek (ed) New York: Springer-Verlag L Ecuyer, P and Panneton, F (2000) A New Class of Linear Feedback Shift Register Generators In Proceedings of the 2000 Winter Simulation Conference, J A Joines, R R Barton, K Kang, and P A Fishwick (eds) Piscataway, NJ: IEEE Press, pp 690 96 L Ecuyer, P and Simard, R (2003).

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