Estimating the size of bodies is crucial for interactions with physical and social environments. Body size perception is malleable and can be altered using visual adaptation paradigms. However, it is unclear whether such visual adaptation effects also transfer to other modalities and influence, for example, the perception of tactile distances. In this study we employed a visual adaptation paradigm. Participants were exposed to images of expanded or contracted versions of self-or other-identity bodies. Before and after this adaptation they were asked to manipulate the width of body images to appear as "normal" as possible. We replicated an effect of visual adaptation, such that the body size selected as most "normal" was larger after exposure to expanded and thinner after exposure to contracted adaptation stimuli. In contrast, we did not find evidence that this adaptation effect transfers to distance estimates for paired tactile stimuli delivered to the abdomen. A Bayesian analysis showed that our data provide moderate evidence that there is no effect of visual body size adaptation on the estimation of spatial parameters in a tactile task. This suggests that visual body size adaptation effects do not transfer to somatosensory body size representations.
Deep learning-based approaches to markerless 3D pose estimation have taken neuroscience by storm. Yet many of these tools remain unvalidated. Here, we report on the validation of one increasingly popular tool (DeepLabCut) against simultaneous measurements obtained from a reference measurement system (Fastrak) with well-known performance characteristics. Our results confirm close (mm range) agreement between the two, indicating that deep learning-based approaches can be used by the research community with confidence.
Deep learning-based approaches to markerless 3D pose estimation are being adopted by researchers in psychology and neuroscience at an unprecedented rate. Yet many of these tools remain unvalidated. Here, we report on the validation of one increasingly popular tool (DeepLabCut) against simultaneous measurements obtained from a reference measurement system (Fastrak) with well-known performance characteristics. Our results confirm close (mm range) agreement between the two, indicating that under specific circumstances deep learning-based approaches can match more traditional motion tracking methods. Although more work needs to be done to determine their specific performance characteristics and limitations, this study should help build confidence within the research community using these new tools.
Estimating the size of bodies is crucial for interactions with physical and social environments. Body size perception is malleable and can be altered using visual adaptation paradigms. However, it is unclear whether such visual adaptation effects also transfer to other modalities and influence, for example, the perception of tactile distances. In this study we employed a visual adaptation paradigm. Participants were exposed to images of expanded or contracted versions of self- or other-identity bodies. Before and after this adaptation they were asked to manipulate the width of body images to appear as “normal” as possible. We replicated an effect of visual adaptation, such that the body size selected as most “normal” was larger after exposure to expanded and thinner after exposure to contracted adaptation stimuli. In contrast, we did not find evidence that this adaptation effect transfers to distance estimates for paired tactile stimuli delivered to the abdomen. A Bayesian analysis showed that our data provide moderate evidence that there is no effect of visual body size adaptation on the estimation of spatial parameters in a tactile task. This suggests that visual body size adaptation effects do not transfer to somatosensory body size representations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.