Previous studies investigating transfer of perceptual learning between luminance-defined (LD) motion and texture-contrast-defined (CD) motion tasks have found little or no transfer from LD to CD motion tasks but nearly perfect transfer from CD to LD motion tasks. Here, we introduce a paradigm that yields a clean double dissociation: LD training yields no transfer to the CD task, but more interestingly, CD training yields no transfer to the LD task. Participants were trained in two variants of a global motion task. In one (LD) variant, motion was defined by tokens that differed from the background in mean luminance. In the other (CD) variant, motion was defined by tokens that had mean luminance equal to the background but differed from the background in texture contrast. The task was to judge whether the signal tokens were moving to the right or to the left. Task difficulty was varied by manipulating the proportion of tokens that moved coherently across the four frames of the stimulus display. Performance in each of the LD and CD variants of the task was measured as training proceeded. In each task, training produced substantial improvement in performance in the trained task; however, in neither case did this improvement show any significant transfer to the nontrained task.
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http://dx.doi.org/10.3758/s13414-012-0290-3 | DOI Listing |
Nat Commun
January 2025
Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China.
Aerial manipulators can manipulate objects while flying, allowing them to perform tasks in dangerous or inaccessible areas. Advanced aerial manipulation systems are often based on rigid-link mechanisms, but the balance between dexterity and payload capacity limits their broader application. Combining unmanned aerial vehicles with continuum manipulators emerges as a solution to this trade-off, but these systems face challenges with large actuation systems and unstable control.
View Article and Find Full Text PDFFront Robot AI
January 2025
Interactive Robotics Laboratory, School of Computing and Augmented Intelligence (SCAI), Arizona State University (ASU), Tempe, AZ, United States.
We present WearMoCap, an open-source library to track the human pose from smartwatch sensor data and leveraging pose predictions for ubiquitous robot control. WearMoCap operates in three modes: 1) a Watch Only mode, which uses a smartwatch only, 2) a novel Upper Arm mode, which utilizes the smartphone strapped onto the upper arm and 3) a Pocket mode, which determines body orientation from a smartphone in any pocket. We evaluate all modes on large-scale datasets consisting of recordings from up to 8 human subjects using a range of consumer-grade devices.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
University of California, San Francisco Institute for Health & Aging, #123K, 490 Illinois Street, San Francisco, CA, 94158, USA.
Background: Mobile Health Clinics (MHCs) are an alternate form of healthcare delivery that may ameliorate current rural-urban health disparities in chronic diseases and have downstream impacts on the health system by reducing costs. Evaluations of providers' time allocation on MHCs are scarce, hindering knowledge transfer related to MHC implementation strategies.
Methods: Retrospective economic cost was assessed using business ledgers and expert assessments in 2023 US Dollar (USD) from 2022 to 2023.
Clin Biomech (Bristol)
January 2025
Health Sciences Department, Ribeirão Preto Medical School, University of São Paulo, Brazil.
Background: Upper limb fractures significantly alter movement, impacting function and recovery. Three-dimensional motion analysis allows precise assessment of these changes.
Methods: Sixty patients were divided into four groups: shoulder, elbow, wrist fractures, and controls.
J Neural Eng
January 2025
Department of Neuroscience, Northwestern University, 303 East Chicago Ave, Chicago, Illinois, 60611, UNITED STATES.
Objective: Creating an intracortical brain-computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI decoder. We aimed to develop a method that differs from a globally optimized decoder to address this issue.
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