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Research into pleasant and unpleasant idiosyncratic emotions has made it increasingly clear that in real-life situations, athletes experiences are better described by mixed rather than pure selected emotions (Diener & Emmons, 1985; Gould & Tuffey, 1996; Hanin, 1997, 2000, 2003; Hanin & Syrj , 1995; Jones & Hanton, 2001; Morgan, 1984; Plutchik, 1980; Schimmack, 2001) To illustrate this notion, idiosyncratic emotion labels generated by a junior international-level tennis player describing his emotional experiences prior to, during, and after his best and worst games are presented in Figure 22a and 22b Prior to his best-ever game (Figure 22a), the player felt high intensity of pleasant optimal emotions (P+): He felt highly determined, confident, excited, dynamic, and comfortable He also felt moderately aggressive, alarmed, and somewhat uncertain (N+) at the same time Moreover, his unpleasant dysfunctional emotions (N ; nervous, afraid, worried, and intense) were of low intensity.

This pattern was similar during that game, except that he felt alert and quick but not too excited and had no premature satisfaction In contrast, prior to his worst-ever game (Figure 22b), this player felt highly nervous and worried (N ), and these experiences were even more intense during the game Interestingly, at the same time, his optimal pleasant emotions prior to and during the game were of moderate and low intensity, respectively If only the anxiety level in this player in his best and worst games were measured, the entire profile of his emotional experiences and their impact on his performance would be missed..

Elner and Hughes have used optimal foraging theory to explain the prey choice made by shore crabs Carcinus maenas foraging on mussels Mytilus edulis They presented hungry crabs with equal numbers of mussels, of a range of sizes, and recorded which ones the crabs chose to eat Bigger mussels provide more calories per meal and so we might expect that the crabs would have chosen the larger bivalves in preference to the smaller ones However, the results obtained showed that they did not (Fig 66) The crabs were clearly able to open and consume mussels from the full size range, but why did they specialize in those between 2 and 25 centimeters long Optimal foraging theory makes the assumption that animals make choices that will maximize their net energy gain and in doing so take in more energy than they expend feeding.

Fig. 7.4 Step-test: data collection and use. The raw data shown in the table produce the typically stepped pro le when plotted on the raw data graph (top left) of time versus drawdown. A plot of Q versus drawdown (bottom plot) will yield a linear relationship as long as the well is operating within its mechanical limitations. The pumping rate at which this plot departs from linearity de nes the maximum yield for this particular well.

So Elner and Hughes took into account not only the calori c value of the food, but also the energy cost involved in processing and eating it To do this they recorded the time taken by a feeding crab to open the mussel shell (an expression of the energy expenditure) in relation to the crab s calori c intake Bigger mussels take longer to open and so the crabs may spend more energy to reach the food they contain This can be expressed as an optimality model (the mathematical expression: energy divided by time or E/T ), which allows the prediction of the pro tablity of a mussel of a given size Figure 66 shows the distribution of pro tability of the range of mussels offered to the crabs in the experiment.

Small mussels are easily opened but yield little, and very big mussels offer a higher yield but are dif cult to crack The match between the most pro table mussel size predicted by the optimality model and that chosen by the crabs (compare Fig 66 (a) and (b)) is a good one, and so we would say that these crabs are optimal foragers in this situation..

15.3.2 Basic Scheme In the basic scheme, two-hop neighbor information, which includes time slot assignment data, is rst exchanged among nodes. Then each node determines if it has the highest priority among all its neighbors that are within two hops (referred to as two-hop neighbors here) and that have not chosen a time slot. To do this, a node starts a timer if it nds it has the highest priority. If the node receives a message from any two-hop neighbor with a higher priority than itself before the timer expires, it stops the timer. When the timer expires, it selects the smallest time slot that has not been used by any its two-hop neighbor.1 Figure 15.2 shows two examples of time slot assignments using the basic scheme. In both examples, priority is simply determined according to node ID,

Major Professional Associations in Clinical Psychology American Academy of Clinical Psychology P.O. Box 1082 Niwot, CO 80544-1082 (303) 652-9154 www.aclinp.org Canadian Psychological Association 151 Rue Slater Street, Suite 205 Ottawa, Ontario K1P 5H3 Canada (888) 472-0657 www.cpa.ca British Psychological Society St. Andrews House 48 Princess Road East Leicester LE1 7DR England Tel: 0116 254 9568 www.bps.org.uk International Society of Clinical Psychology (ISCP) Contact: Susan F. Swierc, ISCP Secretary 900 N. Orange Street, Suite 301 Missoula, MT 59803 USA (406) 327-3350 http://www.iscp.org