Chapter 9 begins with a review of the contribution of talk to maintaining homeostasis through “small talk” and other forms of “grooming,” both as a response to complex social structure and in facilitating social cooperation.It discusses the role of talk in forward projection of group-level representations, and the role of forward projection of a conversation topic in sustaining and managing conversations.It discusses the role of both physical constraints and social constraints such as politeness norms and facework in shaping the structure of conversation practices.It discusses the fine structure of conversation, in which the interpretations of utterances are confirmed or revised by subsequent utterances and the macro-structure of conversation (beginnings, turn-taking, topics, and closure).

Chapter 8 demonstrates in detail the simulation of data acquisition and analysis in a large experiment. The case chosen is simulation of event detection in a clinical PET scanner and the subsequent reconstruction of activity distribution in the subject. This is directly relevant to people working in medical imaging but also more generally it is an example of how to approach a large simulation.We present examples illustrating random event generation, ray tracing in both simple and more complex geometries and the subsequent analysis to find the detector response in the form of a system matrix. Reconstruction of simulated patient data is then performed using the MLEM algorithm. Numerous optimisation details are discussed including the use of polar coordinates to fully exploit the symmetry of the detector system. The calculations involved are substantial and the GPU is very effective with speed-ups of over 1000 for simulation and the MLEM, iteration time is reduced to a few seconds. At the end of the chapter, Richardson–Lucy deconvolution of some blurred text is demonstrated as a different application of the MLEM method. The method converges slowly and we find that deblurring continues to improve even after 1000000 iterations.