Visual working memory (VWM) has been extensively studied in the context of memory capacity. However, less research has been devoted to the metacognitive processes involved in VWM. Most metacognitive studies of VWM studies tested simple, impoverished stimuli, whereas outside of the laboratory setting, we typically interact with meaningful, complex objects. Thus, the present study aimed to explore the extent to which people are able to monitor VWM of real-world objects that are more ecologically valid and further afford less inter-trial interference. Specifically, in three experiments, participants viewed a set of either four or six memory items, consisting of images of unique real-world objects that were not repeated throughout the experiment. Following the memory array, participants were asked to indicate where the probe item appeared (Experiment 1) whether it appeared at all (Experiment 2) or whether it appeared and what was its temporal order (Experiment 3). VWM monitoring was assessed by subjective confidence judgments regarding participants' objective performance. Similar to common metacognitive findings in other domains, we found that subjective judgments overestimated performance and underestimated errors, even for real-world, complex items held in VWM. These biases seem not to be task-specific as they were found in temporal, spatial, and identity VWM tasks. Yet, the results further showed that meaningful, real-world objects were better remembered than distorted items, and this memory advantage also translated to metacognitive measures.
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http://dx.doi.org/10.3389/fpsyg.2020.00179 | DOI Listing |
Sci Rep
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PSYCLE, Aix Marseille Univ, Aix en Provence, France.
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January 2025
Experimental Psychology, University College London, London, United Kingdom.
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February 2025
North Carolina Agricultural and Technical State University, 1601 E Market St, Greensboro, NC 27411, United States.
Contemporary research in 3D object detection for autonomous driving primarily focuses on identifying standard entities like vehicles and pedestrians. However, the need for large, precisely labelled datasets limits the detection of specialized and less common objects, such as Emergency Medical Service (EMS) and law enforcement vehicles. To address this, we leveraged the Car Learning to Act (CARLA) simulator to generate and fairly distribute rare EMS vehicles, automatically labelling these objects in 3D point cloud data.
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December 2024
Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, Nuevo Leon, Mexico.
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March 2025
Department of Computer Engineering, Hongik University, Seoul, 04066, Republic of Korea. Electronic address:
Automated Vehicles (AVs) are on the cusp of commercialization, prompting global governments to organize the forthcoming mobility phase. However, the advancement of technology alone cannot guarantee the successful commercialization of AVs without insights into the accidents on the read roads where Human-driven Vehicles (HV) coexist. To address such an issue, The New Car Assessment Program (NCAP) is currently in progress, and scenario-based approaches have been spotlighted.
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