Humans are daily presented with tasks that they complete with little effort or even consideration of the planning that goes into the movement. Movements such as manual manipulation tasks are completed with ease, even though the complexities and years of learned behavior are largely hidden from the person. Digital human modeling (DHM) and specifically optimization-based posture and motion prediction methodologies have employed numerical methods in order to simulate/predict/analyze human movements. However, these movements are heavily constrained such that the planning of the motion/posture is explicitly provided in the formulation of the problem. This paper presents the addition of cognitive principles into the optimization-based posture and motion prediction formulations. The simulation/prediction of manual manipulation tasks is considered such that a single formulation can accomplish multiple tasks. It adopts a theory from cognitive psychology referred to as the end-state comfort effect in order to derive general constraints for the prediction of the initial and final posture states that frame the movement related to the manual manipulation task. It considers multiple tasks from the literature that have been heavily studied through experimentation in order to evaluate the efficacy of the formulation. The results show strong correlation with observations reported in the literature.