Optic Flow Drives Human Visuo-Locomotor Adaptation

Bruggeman, Hugo; Zosh, Wendy D.; Warren, William H.

doi:10.1016/j.cub.2007.10.059

Cited by 95 publications

(87 citation statements)

References 20 publications

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“…Our results are consistent with these previous findings but add significant insight by identifying the underlying processes that produce these behaviors, namely, a fast gait selection mechanism that responds to the altered visual stimuli and a slower process that gradually returns subjects back to steady state over several minutes. Gait speed selection is then similar to the control of heading in that visual flow rate is used rapidly for online correction (Bruggeman et al 2007). The outcome of this study also provides functional knowledge for using virtual reality to enhance rehabilitative gait training (Lamontagne et al 2007) and suggests that reducing the visually perceived walking speed through a multiplicative gain will have a significant, albeit temporary, effect on increasing walking speed during a training session.…”

Section: Discussionmentioning

confidence: 76%

Fast visual prediction and slow optimization of preferred walking speed

O’Connor

Donelan

2012

Journal of Neurophysiology

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O'Connor SM, Donelan JM. Fast visual prediction and slow optimization of preferred walking speed. J Neurophysiol 107: 2549-2559, 2012. First published February 1, 2012 doi:10.1152/jn.00866.2011.-People prefer walking speeds that minimize energetic cost. This may be accomplished by directly sensing metabolic rate and adapting gait to minimize it, but only slowly due to the compounded effects of sensing delays and iterative convergence. Visual and other sensory information is available more rapidly and could help predict which gait changes reduce energetic cost, but only approximately because it relies on prior experience and an indirect means to achieve economy. We used virtual reality to manipulate visually presented speed while 10 healthy subjects freely walked on a self-paced treadmill to test whether the nervous system beneficially combines these two mechanisms. Rather than manipulating the speed of visual flow directly, we coupled it to the walking speed selected by the subject and then manipulated the ratio between these two speeds. We then quantified the dynamics of walking speed adjustments in response to perturbations of the visual speed. For step changes in visual speed, subjects responded with rapid speed adjustments (lasting Ͻ2 s) and in a direction opposite to the perturbation and consistent with returning the visually presented speed toward their preferred walking speed, when visual speed was suddenly twice (one-half) the walking speed, subjects decreased (increased) their speed. Subjects did not maintain the new speed but instead gradually returned toward the speed preferred before the perturbation (lasting Ͼ300 s). The timing and direction of these responses strongly indicate that a rapid predictive process informed by visual feedback helps select preferred speed, perhaps to complement a slower optimization process that seeks to minimize energetic cost. visual feedback; energetics; locomotion OF ALL THE POSSIBLE WAYS TO WALK, people prefer specific patterns. For example, rather than use our full range of possible walking speeds, we prefer a narrow speed range and will switch to a running gait if we have to move much faster

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Section: Discussionmentioning

confidence: 76%

Fast visual prediction and slow optimization of preferred walking speed

O’Connor

Donelan

2012

Journal of Neurophysiology

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“…For example, visual feedback is sufficient to recalibrate visually guided throwing (Martin et al, 1996), while optic flow can recalibrate self-motion toward a target (Bruggeman et al, 2007). Prisms that alter the relationship between convergence and distance cause perceptual aftereffects (e.g., , primarily via changes in oculomotor muscle tone (Ebenholtz and Wolfson, 1975).…”

Section: Discussionmentioning

confidence: 99%

Efficient Visual Recalibration from Either Visual or Haptic Feedback: The Importance of Being Wrong

Adams¹,

Kerrigan²,

Graf³

2010

J. Neurosci.

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The human visual system adapts to the changing statistics of its environment. For example, the light-from-above prior, an assumption that aids the interpretation of ambiguous shading information, can be modified by haptic (touch) feedback. Here we investigate the mechanisms that drive this adaptive learning. In particular, we ask whether visual information can be as effective as haptics in driving visual recalibration and whether increased information (feedback from multiple modalities) induces faster learning.During several hours' training, feedback encouraged observers to modify their existing light-from-above assumption. Feedback was one of the following: (1) haptic only, (2) haptic and stereoscopic (providing binocular shape information), or (3) stereoscopic only. Haptic-only feedback resulted in substantial learning; the perceived shape of shaded objects was modified in accordance with observers' new light priors. However, the addition of continuous visual feedback (condition 2) substantially reduced learning. When visual-only feedback was provided intermittently (condition 3), mimicking the time course of the haptic feedback of conditions 1 and 2, substantial learning returned.The intermittent nature of conflict information, or feedback, appears critical for learning. It causes an initial, erroneous percept to be corrected. Contrary to previous proposals, we found no particular advantage for cross-modal feedback. Instead, we suggest that an "oops" factor drives efficient learning; recalibration is prioritized when a mismatch exists between sequential representations of an object property. This "oops" factor appears important both across and within sensory modalities, suggesting a general principle for perceptual learning and recalibration.

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“…Visually guided locomotion has received considerable attention in the past [10,27,40,22,39,6], and two main strategies to move towards a goal have been identified. The first one, proposed by Gibson [10], leverages the optical flow created by the apparent motion of each point composing the sequence of images perceived by the walker during his motion [17].…”

Section: Goal-directed Locomotionmentioning

confidence: 99%

Kinematic Evaluation of Virtual Walking Trajectories

Cirio

Olivier

Marchal

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. The objective of this paper is to evaluate various virtual locomotion conditions by comparing reference with virtual trajectories formed during goal-directed locomotion tasks. Reference trajectories (left) can be recorded through motion capture or be generated through a numerical model of human locomotion. The paper demonstrates the framework (center) over a set of experimental trajectories (right). For the purpose of demonstration, this paper compares frequently used virtual locomotion conditions. Abstract-Virtual walking, a fundamental task in Virtual Reality (VR), is greatly influenced by the locomotion interface being used, by the specificities of input and output devices, and by the way the virtual environment is represented. No matter how virtual walking is controlled, the generation of realistic virtual trajectories is absolutely required for some applications, especially those dedicated to the study of walking behaviors in VR, navigation through virtual places for architecture, rehabilitation and training. Previous studies focused on evaluating the realism of locomotion trajectories have mostly considered the result of the locomotion task (efficiency, accuracy) and its subjective perception (presence, cybersickness). Few focused on the locomotion trajectory itself, but in situation of geometrically constrained task. In this paper, we study the realism of unconstrained trajectories produced during virtual walking by addressing the following question: did the user reach his destination by virtually walking along a trajectory he would have followed in similar real conditions? To this end, we propose a comprehensive evaluation framework consisting on a set of trajectographical criteria and a locomotion model to generate reference trajectories. We consider a simple locomotion task where users walk between two oriented points in space. The travel path is analyzed both geometrically and temporally in comparison to simulated reference trajectories. In addition, we demonstrate the framework over a user study which considered an initial set of common and frequent virtual walking conditions, namely different input devices, output display devices, control laws, and visualization modalities. The study provides insight into the relative contributions of each condition to the overall realism of the resulting virtual trajectories.

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Optic Flow Drives Human Visuo-Locomotor Adaptation

Cited by 95 publications

References 20 publications

Fast visual prediction and slow optimization of preferred walking speed

Fast visual prediction and slow optimization of preferred walking speed

Efficient Visual Recalibration from Either Visual or Haptic Feedback: The Importance of Being Wrong

Kinematic Evaluation of Virtual Walking Trajectories

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