Visual Feedback Modulates Aftereffects and Electrophysiological Markers of Prism Adaptation

Aziz, Jasmine R.; MacLean, Stephane; Krigolson, Olave E.; Eskes, Gail A.

doi:10.3389/fnhum.2020.00138

Cited by 14 publications

(11 citation statements)

References 57 publications

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“…Effects around the P300 peak were characterized by increased P300 amplitude in response to visuomotor rotations in the placebo condition but not in the levodopa condition. This result replicates previous findings that visuomotor rotations increase the amplitude of P300 responses to feedback, and additionally suggests that this effect is dependent on dopaminergic signaling (Palidis, Cashaback, and Gribble 2019; Aziz et al 2020; MacLean et al 2015). However, disruption of P300 amplitude modulation by dopaminergic perturbation did not correspond to any behavioral changes, indicating that the modulation of P300 amplitude by sensory error is not essential for adaptation.…”

Section: Discussionsupporting

confidence: 91%

“…This was expected, as trial-by-trial error correction induced by relatively small visuomotor rotations is thought to be driven primarily by sensory error-based learning mechanisms as opposed to dopaminergic reinforcement learning circuits (Diedrichsen et al, 2005;Ito, 2000;Krakauer et al, 2004;Tanaka et al, 2009;Jordan A. Taylor et al, 2010;Wong et al, 2019). It has previously been shown that visuomotor rotation increases the amplitude of the P300 ERP component, a centro-parietal ERP deflection peaking approximately 300-400ms following feedback presentation (Aziz et al, 2020;MacLean et al, 2015;Palidis et al, 2019). In the present study, we observed an interaction effect between feedback rotation and drug condition on the P300 amplitude.…”

Section: Discussionmentioning

confidence: 83%

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Null effects of levodopa on reward- and error-based motor adaptation, savings, and anterograde interference

et al. 2020

Preprint

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Dopamine signaling is thought to mediate reward-based learning. We tested for a role of dopamine in motor adaptation by administering the dopamine precursor levodopa to healthy participants in two experiments involving reaching movements. Levodopa has been shown to impair reward-based learning in cognitive tasks. Thus, we hypothesized that levodopa would selectively impair aspects of motor adaptation that depend on reinforcement of rewarding actions.In the first experiment, participants performed two separate tasks in which adaptation was driven either by visual feedback of the hand position or binary reward feedback. We used EEG to measure event-related potentials evoked by task feedback. We hypothesized that levodopa would specifically diminish adaptation and the neural responses to feedback in the reward learning task. However, levodopa did not affect motor adaptation in either task nor did it diminish event-related potentials elicited by reward outcomes.In the second experiment, participants learned to compensate for mechanical force field perturbations applied to the hand during reaching. Previous exposure to a particular force field can result in savings during subsequent adaptation to the same force field and interference during adaptation to an opposite force field. We hypothesized that levodopa would diminish savings and anterograde interference, as previous work suggests that these phenomena result from a reinforcement learning process. However, we found no reliable effects of levodopa.These results suggest that reward-based motor adaptation, savings, and interference may not depend on the same dopaminergic mechanisms which have been shown to be disrupted by levodopa during various cognitive tasks.New and NoteworthyMotor adaptation relies on multiple processes including reinforcement of successful actions. Cognitive reinforcement learning is impaired by levodopa-induced disruption of dopamine function. We administered levodopa to healthy adults who participated in multiple motor adaptation tasks. We found no effects of levodopa on any component of motor adaptation. This suggests that motor adaptation may not depend on the same dopaminergic mechanisms as cognitive forms or reinforcement learning which have been shown to be impaired by levodopa.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 83%

Null effects of levodopa on reward- and error-based motor adaptation, savings, and anterograde interference

et al. 2020

Preprint

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“…The effect of realistic body representation does not seem to be due to the fact that the error feedback is more direct in the realistic representation. Aziz et al (2020) compared three types of feedback in three prism experiments: At the end of the pointing movement, either both the pointing finger and the target line were shown (direct error feedback, Experiment 1), or only the pointing finger without the target line (indirect error feedback, Experiment 2), or a representation of the pointing finger as a line and the target line (low-fidelity direct error feedback, Experiment 3). Although participants in Experiment 3 received direct error feedback, the aftereffect was significantly smaller than in the other two experiments, which did not differ significantly from each other.…”

Section: Vr As a Research Toolmentioning

confidence: 99%

Sensorimotor adaptation in VR: magnitude and persistence of the aftereffect increase with the number of interactions

Wähnert

Gerhards

2022

Virtual Reality

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In both prism and virtual reality experiments, it has been observed that visual displacement leads to an adaptation of the sensorimotor system. A characteristic of adaptation is the occurrence of the aftereffect, which is the spatial deviation of the movements in the direction opposite to the visual displacement. Prism adaptation experiments have shown that a higher number of interactions lead to an increased magnitude and persistence of the aftereffect. The aim of the present study was to investigate this relationship in virtual reality. After baseline measurement, the virtual environment was displaced visually. During this adaptation phase, the participants performed either zero, five, or thirty-five pointing movements. Afterwards, all participants performed the pointing movements without the visual displacement in the virtual environment. Performing five pointing movements during the adaptation phase was already sufficient to produce an aftereffect. With thirty-five pointing movements, both magnitude and persistence of the aftereffect increased. These results replicate studies of prism adaptation. Considering this, we briefly discuss the suitability of virtual reality as a research tool to study prism adaptation.

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“…It was first introduced by Hermann von Helmholtz in late 19th-century Germany as supportive evidence for his perceptual learning theory [64]. Studies have shown that humans quickly adapt to these sensory-motor transformations, to the point of having to adapt back to normal after the displacement ends [6,24,50,83]. Prism adaptation has been researched in detail with VR headsets instead of prism lenses [1,11,20,31,32].…”

Section: Direct and Indirect Inputmentioning

confidence: 99%

“…Among the first projects in smartphone-based head-mounted display (HMD) design started in 2011 in USC Institute for Creative Technologies by Olson et al [62]. They devised the FOV2GO device 6 , which was the first low-cost smartphone-based "cardboard" HMD prototype. Further evolution of the idea led to the creation of the first versions of modern VR headsets such as the Oculus Rift DK1 [12] and Google Cardboard 7 .…”

Section: Chapter 1: Introductionmentioning

confidence: 99%

Selection Performance Using a Smartphone in VR with Redirected Input

Kyian¹

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We present a method to track a smartphone in VR using a fiducial marker displayed on the screen. Using WebRTC transmission protocol, we capture input on the smartphone touchscreen as well as the screen contents, copying them to a virtual representation in VR. We present two Fitts' law experiments assessing the performance of selecting targets displayed on the virtual smartphone screen using this method. The first compares direct vs. indirect input (i.e., virtual smartphone co-located with the physical smartphone, or not), and reveals there is no significant difference in performance due to input indirection. The second experiment assesses the influence of input scaling, i.e., decoupling the virtual cursor from the actual finger position on the smartphone screen so as to provide a larger virtual tactile surface. Results indicate a small effect for extreme scale factors. We discuss implications for the use of smartphones as input devices in VR.

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Visual Feedback Modulates Aftereffects and Electrophysiological Markers of Prism Adaptation

Cited by 14 publications

References 57 publications

Null effects of levodopa on reward- and error-based motor adaptation, savings, and anterograde interference

Null effects of levodopa on reward- and error-based motor adaptation, savings, and anterograde interference

Sensorimotor adaptation in VR: magnitude and persistence of the aftereffect increase with the number of interactions

Selection Performance Using a Smartphone in VR with Redirected Input

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