Precrastination, as opposed to procrastination, is the tendency to embark on tasks as soon as possible,evenat the expense of extra physical effort. We examined the generality of this recently discovered phenomenon by extending the methods used to study it, mainly to test the hypothesis that precrastination is motivated by cognitive load reduction. Our participants picked up two objects and brought them back together. Participants in Experiment 1 demonstrated precrastination by picking up the near object first, carrying it back to the farther object, and then returning with both. Also, participants given an additional cognitive task (memory load) had a higher probability of precrastinating than those not given the added cognitive task. The objects in Experiment 1 were buckets with balls that had a very low chance of spillage; carrying them required low demands on attention. The near-object-first preference was eliminated in Experiment 2, where the near and far objects were cups with water that had a high chance of spillage; carrying them required higher demands on attention. Had precrastination occurred in this case, it would have greatly increased cognitive effort. The results establish the generality of precrastination and suggest that it is sensitive to cognitive load. Our results complement others showing that people tend to structure their behavior tominimize cognitive effort.The mainnew discovery is that people expend more physical effort to do so. We discuss the applied implications of our findings, as well as the possibility that precrastination may be a default, automatic behavior.
Smart environments offer valuable technologies for activity monitoring and health assessment. Here, we describe an integration of robots into smart environments to provide more interactive support of individuals with functional limitations. RAS, our Robot Activity Support system, partners smart environment sensing, object detection and mapping, and robot interaction to detect and assist with activity errors that may occur in everyday settings. We describe the components of the RAS system and demonstrate its use in a smart home testbed. To evaluate the usability of RAS, we also collected and analyzed feedback from participants who received assistance from RAS in a smart home setting as they performed routine activities.
This article introduces RAS, a cyber-physical system that supports individuals with memory limitations to perform daily activities in their own homes. RAS represents a partnership between a smart home, a robot, and software agents. When smart home residents perform activities, RAS senses their movement in the space and identifies the current activity. RAS tracks activity steps to detect omission errors. When an error is detected, the RAS robot finds and approaches the human with an offer of assistance. Assistance consists of playing a video recording of the entire activity, showing the omitted activity step, or guiding the resident to the object that is required for the current step. We evaluated RAS performance for 54 participants performing three scripted activities in a smart home testbed and for 2 participants using the system over multiple days in their own homes. In the testbed experiment, activity errors were detected with a sensitivity of 0.955 and specificity of 0.992. RAS assistance was performed successfully with a rate of 0.600. In the in-home experiments, activity errors were detected with a combined sensitivity of 0.905 and a combined specificity of 0.988. RAS assistance was performed successfully for the in-home experiments with a rate of 0.830.
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