This study tested the hypothesis that affordances for grasping with the corresponding hand are activated more strongly by three-dimensional (3D) real objects than by two-dimensional (2D) pictures of the objects. In Experiment 1, participants made left and right keypress responses to the handle or functional end (tip) of an eating utensil using compatible and incompatible mappings. In one session, stimuli were spoons mounted horizontally on a blackboard with the sides to which the handle and tip pointed varying randomly. In the other, stimuli were pictures of spoons displayed on a black computer screen. Three-dimensional and 2D sessions showed a similar benefit for compatible mapping when the tip was relevant and a small cost of compatible mapping when the handle was relevant. Experiment 2 used a flanker task in which participants responded compatibly to the location of the handle or the tip, and spoons located above and below the target spoon could have congruent or incongruent orientations. The difference between 3D and 2D displays was not obtained in the flanker effect for reaction time. There was little evidence that 3D objects activate grasping affordances that 2D images do not. Instead, we argue that visual salience of the tip is the critical factor determining these correspondence effects.
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http://dx.doi.org/10.1177/1747021820959599 | DOI Listing |
Exp Neurobiol
December 2024
Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea.
Research on brain aging using resting-state functional magnetic resonance imaging (rs-fMRI) has typically focused on comparing "older" adults to younger adults. Importantly, these studies have often neglected the middle age group, which is also significantly impacted by brain aging, including by early changes in motor, memory, and cognitive functions. This study aims to address this limitation by examining the resting state networks in middle-aged adults via an exploratory whole-brain ROI-to-ROI analysis.
View Article and Find Full Text PDFEur J Radiol
January 2025
School of Biomedical Engineering & Imaging Sciences, King's College London, London, the United Kingdom of Great Britain and Northern Ireland; Department of Neuroradiology, King's College Hospital National Health Service Foundation Trust, London, the United Kingdom of Great Britain and Northern Ireland. Electronic address:
Artificial intelligence (AI) tools can triage radiology scans to streamline the patient pathway and also relieve clinician workload. Validated AI tools can mitigate the delays in reporting scans by flagging time-sensitive and actionable findings. In this study, we aim to investigate current stakeholder perspectives and identify obstacles to integrating AI in clinical pathways.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, 90127, Italy. Electronic address:
Comput Methods Programs Biomed
December 2024
CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Zhejiang, Hangzhou, China.
Background: Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational demands hinder their practical use in clinical environments.
Objective: This study aims to address the computational inefficiencies of deep neural networks by proposing a lightweight model that integrates model compression techniques, ConvLSTM layers, and ConvNext Blocks, all optimized through Knowledge Distillation (KD).
Atten Percept Psychophys
January 2025
Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, Van Der Boechorststraat 7, 1081 BT, Amsterdam, The Netherlands.
In previous studies, it was established that individuals can implicitly learn spatiotemporal regularities related to how the distribution of target locations unfolds across the time course of a single trial. However, these regularities were tied to the appearance of salient targets that are known to capture attention in a bottom-up way. The current study investigated whether the saliency of target is necessary for this type of learning to occur.
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