A fundamental question about the nature of learning is how practice and prior experiences change an organism. The field of perceptual learning has long explored such issues with a specific focus on perception, and has typically found learning that does not broadly generalize. Human psychophysics research (e.g., Fahle and Poggio, 2002
; Li et al., 2004
) and computational models (e.g., Dosher and Lu, 1999
) have revealed that prolonged practice with detecting or discriminating visual stimuli results in greater sensitivity to those stimuli, but that these benefits are highly specific. For example, training on discrimination of horizontal stimuli does not improve sensitivity to vertically oriented stimuli (Crist et al., 1997
). Learning, therefore, appears to occur for the trained stimuli, with only a limited amount of transfer to other untrained tasks or stimuli (Goldstone, 1998
; Ahissar and Hochstein, 2004
). In recent years however, there have been a number of reports of generalized learning in which practice on specific tasks can result in beneficial transfer of learned abilities to other untrained domains (reviewed in Green and Bavelier, 2008
). For example, the effects of musical training have been shown to improve spatiotemporal reasoning skills (Rauscher et al., 1997
), verbal memory (Ho et al., 2003
), and even general intelligence (Schellenberg, 2004
). Similarly, extensive athletic training has been shown to improve performance on a number of measures of perception and cognition (reviewed in Mann et al., 2007
), including covert attentional orienting (Lum et al., 2002
), visual search and anticipation (Savelsbergh et al., 2002
), motion speed and direction discrimination (Kioumourtzoglou et al., 1998
), and response inhibition (Kida et al., 2005
; but see Memmert et al., 2009
In addition to the above examples1
, a rapidly growing body of research has reported a strong link between experience with action video games and enhanced behavioral performance on a wide variety of perceptual and attention tasks (Green and Bavelier, 2003
; Ferguson, 2007
; Green et al., 2009
) While this is still a developing enterprise, with much left to be determined (Boot et al., 2011
), empirical demonstrations have shown that, among other cognitive benefits, avid action video game players possess increased visual sensitivity (Li et al., 2009
), improved allocation of attention resources (Green and Bavelier, 2006a
; Feng et al., 2007
; Donohue et al., 2010
), and more precise smooth pursuit eye movements (Tsoi et al., 2011
). Recent computational models (Green et al., 2010
) and theoretical reviews (Green et al., 2009
) have led to the proposal that video game players are better able to form perceptual templates and derive probabilistic inference, suggesting that such generalized learning occurs where information is integrated and actions are selected.
Based upon the emerging video game training literature, there is a suggestion that activities that hold qualitative features that engage a high degree of perceptual or cognitive load in the service of accurate performance may lead to generalized learning effects. In the present research we explore this capacity for generalized learning by measuring perceptual and cognitive abilities that may be enhanced as a result of visual–motor training under stroboscopic visual conditions.
Fast-paced activities, such as those involved in competitive sports, place great demands on human vision. Actions and reactions are dependent on a constant supply of accurate and reliable information from the environment and, therefore, a premium is placed on rapid, distributed, and precise visual perception and attention abilities. One key aspect is the role of visual feedback – it is often critical to be able to assess and update the relative movements, distances, and masses of objects in the visual environment in order to anticipate the appropriate forces required for a successful motor plan (Desmurget and Grafton, 2000
). Motor actions are guided by a combination of central planning and online control provided in the form of feedback from the visual system about the moment-to-moment state of the world. Extensive research investigating the role of feedback on visual–motor control has demonstrated that movements become progressively more dependent on visual feedback with greater amounts of practice under conditions in which visual feedback is available (Proteau and Cournoyer, 1990
; Proteau et al., 1992
). Given this, we ask what might the implications be if visual feedback was interrupted, and individuals did not have access to this important control signal.
A new sports training tool (see Materials and Methods
, below) has been designed based upon the premise that stroboscopic interruption of vision might enhance visual–motor control. The logic is that the interruption of visual information may force individuals to reduce their reliance on online visual feedback. It is thought that by practicing under situations of impoverished visual input, individuals will be forced to make better use of the limited visual information that is available. This, in turn, may train perceptual and attentional abilities that support basic visual–motor control. Previous research has indicated that stroboscopic training does, indeed, have benefits toward improved visual–motor actions such as driving performance (Tsimhoni et al., 1999
), as well as reductions in symptoms that result from visual–motor disagreement, such as motion sickness (Reschke et al., 2006
). Despite these premises and promising findings of specific visual–motor learning, it has not yet been explored whether stroboscopic training could result in generalized learning effects.
The goal of the present research was to begin to assess for improvements in visual cognitive abilities following stroboscopic training. For this purpose we used the recently developed Nike Vapor Strobe®
eyewear. This eyewear uses battery-powered liquid crystal filtered lenses that alternate between clear and opaque states and provides varying lengths of occlusion that are under the users’ control. The strobe effect is defined by opaque states that can vary through eight levels (67–900
ms), while the transparent state is fixed at a constant 100
In theory, stroboscopic exposure may influence any of a number of perceptual or cognitive abilities. As such, we took an exploratory approach in order to assess for generalized transfer cognitive abilities due to stroboscopic training. We devised a series of computer-based and physical assessments to measure abilities before and after training. Participants either trained while wearing the Strobe eyewear or while wearing Control eyewear, an identical product except that it contained transparent lenses. For each assessment, the critical question was whether individuals who trained under stroboscopic conditions would improve significantly more from the pre-training assessments to the post-training assessments than those trained with the transparent eyewear. We adopted a broad methodological approach by using a variety of assessments and extensively piloting these measures. Findings from several of these assessments are reported here, and we discuss additional measures and future directions in the General Discussion.
With the exploratory nature of this project, we took an approach that would allow the data to speak to the possible mechanisms that might be affected by stroboscopic visual training. We hypothesized a priori that stroboscopic training would influence aspects of visual cognition related to temporal processing of the visual environment, and/or the allocation of attention, to appropriate visual elements. However, it is entirely possible that other aspects would be equally or more influenced. To begin to address this issue, in the present paper we report the results from three computer-based assessments that measured perceptual and attentional faculties hypothesized to benefit from stroboscopic training. These tasks include measures of central and peripheral motion sensitivity (motion coherence tests), distributed transient attention [useful field of view (UFOV) – dual-target task], and distributed sustained attention (multiple-object tracking).
Before delving into the details of the current study, it is worth noting a few specific aspects of this research project. Primarily, by using battery-powered eyewear as our training tool we were able to conduct training outside of the laboratory and utilize athletic groups already engaged in highly interactive visual–motor activities. As well, because our research questions are the first steps of a much larger enterprise, we opted to take several experimental approaches that would afford us the best chance of measuring generalized training effects. First, we always administered the post-training assessments immediately after the last training session in order to avoid potential loss of the trained effects over time. Second, we always compared our experimental Strobe participants to matched Control participants that engaged in the identical training regimen, but wore eyewear containing transparent lenses. Finally, in line with previous research indicating that sleep facilitates the consolidation of learned skills (e.g., Marshall and Born, 2007
), we structured training so that it spanned multiple days and always included at least one night of sleep.
The primary goal of our training procedure was to have individuals perform tasks that actively engaged the visual–motor system (e.g., catching and throwing) under stroboscopic visual conditions. We were able to achieve this through two different types of training: in-lab
(see Materials and Methods
for full detail). In-lab training consisted of a ball catching game that was conducted in a controlled indoor environment. The in-lab training provided several benefits, as we were able to control the experimental timing and the training environment, and we were able to test Strobe and Control participants independently. However, in-lab training also had disadvantages in that it was logistically difficult to run multiple training sessions in the laboratory setting, and it presented limits on the nature of the physical activities that could be undertaken.
For the team-based training we partnered with campus athletic groups to administer our training regimen during their already established practice schedules. For the currently presented data we worked with the Duke University Varsity Football team and with the Duke Men’s and Women’s Club Ultimate Frisbee teams. This allowed us to provide training in highly engaging visual–motor activities as well as allowing up to 20 individuals to undergo training at the same time. The team-based training also had disadvantages, as the Strobe and Control participants were trained in the same practice sessions and therefore could be aware of the experimental condition assignments. By conducting both in-lab and team-based training regimens, we looked to overcome the shortcomings of each. Further, by using multiple training cohorts (in-lab, Club Ultimate Frisbee, and Football), we can compare performance to quantify any potential differences in training effects that are due to differences between these three different cohorts.