The influence of retention intervals and warning signals on prospective memory.

Prospective memory, memory for future events, is used for remembering duties and obligations that all people must complete. Past research has contributed to our understanding of the bases of prospective memory tasks (time versus event) and the kinds of situations requiring prospective memory (appointments, chores, deadlines, and medications). However, research has yet to examine how prospective remembering unfolds over time. For example, very little is known about how such remembering is affected by the time from when the task is encoded to the time that a task must be conducted (the retention interval), the length of the time in which a response can be counted as correct (the response window), and the time from a warning signal, if given, to the time that the prospective task must be completed (the anticipatory lag). This research explored the accuracy and temporal precision to remember to complete a prospective memory task. An accurate prospective remembering involves responding within a response window. The precision of a prospective response refers to how close in time a response is to the ideal time expected of a response. Participants completed prospective memory tasks with three retention intervals ( 45 second, 60 second, and 7 5 second) and attempted to respond within a response window often seconds. Warning signals were either not presented or presented at five and fifteen seconds prior to the expected reaction time. The results indicated that a warning signal affected both the accuracy and precision of prospective remebering such that shorter anticipatory lags created greater accuracy and lower failure rates.Michael Andrew SarapataHermann,DouglasNot ListedMaster of ScienceDepartment of PsychologyCunningham Memorial library, Terre Haute,Indiana State UniversityILL-ETD-037MastersTitle from document title page. Document formatted into pages: contains 56 p.: ill. Includes abstract and appendix

Inferring parameters for a lattice-free model of cell migration and proliferation using experimental data

Collective cell spreading takes place in spatially continuous environments, yet it is often modelled using discrete lattice-based approaches. Here, we use data from a series of cell proliferation assays, with a prostate cancer cell line, to calibrate a spatially continuous individual based model (IBM) of collective cell migration and proliferation. The IBM explicitly accounts for crowding effects by modifying the rate of movement, direction of movement, and the rate of proliferation by accounting for pair-wise interactions. Taking a Bayesian approach we estimate the free parameters in the IBM using rejection sampling on three separate, independent experimental data sets. Since the posterior distributions for each experiment are similar, we perform simulations with parameters sampled from a new posterior distribution generated by combining the three data sets. To explore the predictive power of the calibrated IBM, we forecast the evolution of a fourth experimental data set. Overall, we show how to calibrate a lattice-free IBM to experimental data, and our work highlights the importance of interactions between individuals. Despite great care taken to distribute cells as uniformly as possible experimentally, we find evidence of significant spatial clustering over short distances, suggesting that standard mean-field models could be inappropriate