Immersive technologies, such as virtual and augmented reality, initially failed to live up to expectations, but have improved greatly, with many new head-worn displays and associated applications being released over the past few years. Unfortunately, ‘cybersickness’ remains as a common user problem that must be overcome if mass adoption is to be realized. This article evaluates the state of research on this problem, identifies challenges that must be addressed, and formulates an updated cybersickness research and development (R&D) agenda. The new agenda recommends prioritizing creation of powerful, lightweight, and untethered head-worn displays, reduction of visual latencies, standardization of symptom and aftereffect measurement, development of improved countermeasures, and improved understanding of the magnitude of the problem and its implications for job performance. Some of these priorities are unresolved problems from the original agenda which should get increased attention now that immersive technologies are proliferating widely. If the resulting R&D agenda is carefully executed, it should render cybersickness a challenge of the past and accelerate mass adoption of immersive technologies to enhance training, performance, and recreation.

Partial information can trigger a complete memory. At the same time, human memory is not perfect. A cue can contain enough information to specify an item in memory, but fail to trigger that item. In the context of word memory, we present experiments that demonstrate some basic patterns in human memory errors. We use cues that consist of word frag- ments. We show that short and long cues are completed more accurately than medium length ones and study some of the factors that lead to this behavior. We then present a novel computational model that shows some of the flexibility and patterns of errors that occur in human memory. This model iterates between bottom-up and top-down computations. These are tied together using a Markov model of words that allows memory to be accessed with a simple feature set, and enables a bottom-up process to compute a probability distribution of possible completions of word frag- ments, in a manner similar to models of visual perceptual completion.