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The role of efficacy beliefs in human behavior spans many areas of psychology, with sport psychology being only one area (Bandura, 1997). In this chapter, our primary interest was collective efficacy in sport psychology and not other related subareas, such as collective efficacy in industrial and organizational psychology. Therefore, our review of published work (unpublished manuscripts, theses and dissertations, and presentations and abstracts were cited sparingly) should be viewed as relatively exhaustive within sport psychology only. We included literature outside the sport domain only when it was deemed appropriate to make a point within sport psychology; the coverage of relevant literature outside sport psychology was not exhaustive. The role of efficacy beliefs in sport has proven to be a rich focus area in sport psychology. Because so much related work has been done, a book has been commissioned to chronicle the current knowledge base (see Feltz, Short, & Sullivan, in press). s from other books, including a previous edition of the Handbook of Sport Psychology (Feltz & Lirgg, 2001), also provide quality reviews of subareas within this field of study. The focus of this chapter, transitional methodological issues in collective efficacy research, is unique and has yet to receive We would like to acknowledge Ahnalee Brincks, MA, for her work extensive attention. Specific issues that are intended to be on editing this chapter. the primary contribution of this chapter include:

Within this approach we must point out the work of Wilson et al [10], where the evolution in simulation and the real world are interleaved Initially, the authors produced primitive behaviors in simulation that were tested on the physical robot In later stages, these basic behaviors were randomly concatenated to create sequences of behaviors Another relevant approach is the application of anytime learning [11] to hexapod robots, developed by Parker et al [12], where evolution took place in simulation and the best solution was sent to the physical robot periodically The information provided by the real robot application was used to modify the genetic algorithm itself Finally, Walker [13] et al have developed an approach to investigate the relative contributions to robot performance of the training stage and lifelong learning.

In this case, the authors make use of a standard genetic algorithm for the training phase and an evolutionary strategy for the real-time application stage In their work, they conclude that the real application stage improves the performance of a trained robot controller in dynamic environments, and that the initial knowledge is very important for the performance of the lifelong phase In this line, in a recent review paper [14], Walker has noted the observed advantages of using a combination of an initial training phase where a knowledge base is created and a lifelong learning phase where the robot can adapt this knowledge to the dynamics of real operation.

All of the aforementioned research, however, is based on consideration of the autonomous robot as a system to be controlled and not really a cognitive system in terms of having an internal structure that allows it to autonomously train and retrain itself a structure that allows the robot to have some type of modi able internal representation of its world, itself, and its objectives so that it can really apply cognitive strategies to solving the problems it encounters throughout its lifetime as opposed to just following a set of cleverly obtained recipes for particular situations in which it has been trained In this chapter, we provide an overview of what would be required as a rst step toward a cognitive robot and, in this line, one that can learn throughout its lifetime in an adaptive manner.

In particular, we will describe an approach that considers evolution an intrinsic part of the cognitive system and that allows a robot to be able to learn different tasks and objectives Taking the process one step further, we will incorporate the concept of controller and controller architecture into cognition as a way to implement actions and behaviors on the part of the robot; in particular, behaviors that are repeatedly and successfully used will become re ex in terms of not having to go through the whole cognitive process in order to activate them under particular circumstances It is important.