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Washington, DC: American Psychological Association Hutchins, E (1995) Cognition in the wild Cambridge, MA: MIT Press Jones, M B (1974) Regressing group on individual effectiveness Organizational Behavior and Human Performance, 11, 426 451 Kanki, B G, Folk, V G, & Irwin, C M (1991) Communication variations and aircrew performance International Journal of Aviation Psychology, 1, 149 162 Kanki, B G, Lozito, S, & Foushee, H (1989) Communication indexes of crew coordination Aviation, Space, and Environmental Medicine, 60, 56 60 Kerr, N L, & Tindale, R S (2004) Group performance and decision making Annual Review of Psychology, 55, 623 655 Kidd, J S (1961) A comparison of one-, two-, and three-man work units under various conditions of workload Journal of Applied Psychology, 45, 195 200 Kraiger, K, Ford, J K, & Salas, E (1993).

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+ 2[(1.0417)( .2083)(.1230)] = .2414,

Application of cognitive, skill-based, and affective theories of learning outcomes to new methods of training evaluation Journal of Applied Psychology, 78, 311 328 Kraiger, K, & Wenzel, L H (1997) Conceptual development and empirical evaluation of measures of shared mental models as indicators of team effectiveness In M T Brannick, E Salas, & C Prince (Eds), Team performance assessment and measurement: Theories, methods, and applications (pp 63 84) Mahwah, NJ: Erlbaum Kravitz, D A, & Martin, B (1986) Ringlemann rediscovered: The original article Journal of Personality and Social Psychology, 50, 936 941 Lausic, D, Eccles, D W, Jeong, A, Johnson, T, & Tenenbaum, G (2005) Explicit and implicit types of communication: A.

From expression (12.23), it is easy to see that GK-like clusters are identical to the clusters generated by the similarity measure (12.22). Therefore, we can use either (12.22) or (12.24) to determine the clusters. In the next section, we will introduce the similarity measure (12.22) as a criterion for deciding whether a data vector belongs to a cluster in the process of creating the eGKL clusters. Further, we will employ the distance measure (12.25) in order to calculate the degrees of membership of the individual data vectors similarly to the GK clustering algorithm. Nevertheless, condition (12.22) provides a natural and objective way to de ne the cluster radii. Its application is connected with restrictions for a normal distribution. If this assumption is not valid, the vectors yk can be wrongly associated with process control situations or can be identi ed as outliers. Several authors, for example, Thissen et al. [29] and Fraley and Raftery [31], have explored the idea of using the multivariate process

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functional brain imaging at all are dependent upon many factors, including cost and availability of appropriate equipment, spatial and temporal resolution, intrusiveness of the scanning environment and repeatability of the scanning operation, and artifact susceptibility (Nadeau & Crosson, 1995). Regardless of the speci c choices that are made, neuropsychologists are particularly well positioned to contribute such functional imaging studies because of their expertise in cognitive task design and behavioral measurement. In a typical functional imaging study, metabolic aspects of brain activity (e.g., glucose utilization) are measured indirectly while the subject performs a cognitive task. The resulting pattern of activity is then registered and reconstructed anatomically based either on the use of a stereotactic approach (Talairach & Tournoux, 1988) or by an individual approach based on the establishment of skull-based markers or ducials (Mazziotta et al., 1982; Shukla, Honeyman, Crosson, Williams, & Nadeau, 1992). One key aspect of functional imaging studies is the manner by which results are visually displayed and interpreted. For those unaccustomed to viewing such images, it is critical to be mindful of the distinction between images that represent some direct or indirect readout of metabolism or CBF and images that represent a statistical map of differences in activity between two conditions. In many instances, what is displayed in an image does not bear a simple or direct relationship to actual metabolic activity or CBF occurring at a speci ed location; instead, what is displayed are probability values that describe, on a voxel-by-voxel basis, the likelihood that differences in activity between, say, a cognitive activation condition and a control or resting state occurred by chance. An example of this can be seen in Figure 12.3. This image was taken from a recent

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