A Comparison of Head Movement Classification Methods.

To understand human behavior, it is essential to study it in the context of natural movement in immersive, three-dimensional environments. Virtual reality (VR), with head-mounted displays, offers an unprecedented compromise between ecological validity and experimental control. However, such technological advancements mean that new data streams will become more widely available, and therefore, a need arises to standardize methodologies by which these streams are analyzed. One such data stream is that of head position and rotation tracking, now made easily available from head-mounted systems. The current study presents five candidate algorithms of varying complexity for classifying head movements. Each algorithm is compared against human rater classifications and graded based on the overall agreement as well as biases in metrics such as movement onset/offset time and movement amplitude. Finally, we conclude this article by offering recommendations for the best practices and considerations for VR researchers looking to incorporate head movement analysis in their future studies.

Oh, the number of things you will process (in parallel)!

We highlight the importance of considering the variance produced during the parallel processing stage in vision and present a case for why it is useful to consider the "item" as a meaningful unit of study when investigating early visual processing in visual search tasks.

Towards a better understanding of parallel visual processing in human vision: Evidence for exhaustive analysis of visual information.

Most current models of visual processing propose that there are 2 main stages of visual processing, the first consisting of a parallel visual analysis of the scene and the second being a precise scrutiny of a few elements in the scene. Here, we present novel evidence that the first stage of processing adds systematic variance to visual processing times. When searching for a specific target, it has a behaviorally unique signature: RTs increase logarithmically with the number of items in the display and this increase is modulated by target-distractor similarity. This signature is characteristic of unlimited capacity parallel and exhaustive processing of all the elements in the scene. The function of this processing is to identify the locations in the scene containing items that are sufficiently similar to the target as to merit focused scrutiny, while discarding those that do not. We also demonstrate that stage-1 variability is sensitive to the observers' top-down goals: with identical displays, whereas RTs increase logarithmically with set size when observers are asked to find a specific target, they decrease exponentially when asked to find a unique item in the scene. (PsycINFO Database Record