The current study introduces the Multi-Motive Grid Mobility in an age-stratified sample that aims to disentangle six motive components – hope of success, hope of affiliation, hope of power, fear of failure, fear of rejection, and fear of power – in mobility-related and mobility-unrelated scenarios. Similar to the classical Multi-Motive Grid, we selected 14 picture scenarios representing seven mobility and seven non-mobility situations. The scenarios were combined with 12 statements from the MMG. Both the MMG-M and MMG were assessed to (...) allow comparability between psychometric criteria. The results of confirmatory factor analyses yielded a good model fit for a six-factor solution with an additional mobility factor for the MMG-M. Internal consistency of the items was similar to the MMG. Lastly, we investigated associations between the motive components and mobility-related variables. We found that risk awareness was positively related to all fear components in both mobility and non-mobility scenarios. Most importantly, physical constraint was positively associated with fear of rejection and fear of power in mobility situations underlining the importance to create support systems to reduce these concerns in people’s everyday lives. (shrink)

An investigation is presented in which a computer simulation model (DIAGNOSER) is used to develop and test predictions for behavior of subjects in a task of medical diagnosis. The first experiment employed a process‐tracing methodology in order to compare hypothesis generation and evaluation behavior of DIAGNOSER with individuals at different levels of expertise (students, trainees, experts). A second experiment performed with only DIAGNOSER identified conditions under which errors in reasoning in the first experiment could be related to interpretation of specific (...) data items. Predictions derived from DIAGNOSER's performance were tested in a third experiment with a new sample of subjects. Data from the three experiments indicated that (1) form of diagnostic reasoning was similar for all subjects trained in medicine and for the simulation model, (2) substance of diagnostic reasoning employed by the simulation model was parable with that of the more expert subjects, and (3) errors in subjects' reasoning were attributable to deficiencies in disease knowledge and the interpretation of specific patient data cues predicted by the simulation model. (shrink)