Shape Of Object Research Articles

Multimodality Driven Impedance-Based Sim2Real Transfer Learning for Robotic Multiple Peg-in-Hole Assembly.

Robotic rigid contact-rich manipulation in an unstructured dynamic environment requires an effective resolution for smart manufacturing. As the most common use case for the intelligence industry, a lot of studies based on reinforcement learning (RL) algorithms have been conducted to improve the performances of single peg-in-hole assembly. However, existing RL methods are difficult to apply to multiple peg-in-hole issues due to more complicated geometric and physical constraints. In addition, previously limited solutions for multiple peg-in-hole assembly are hard to transfer into real industrial scenarios flexibly. To effectively address these issues, this work designs a novel and more challenging multiple peg-in-hole assembly setup by using the advantage of the Industrial Metaverse. We propose a detailed solution scheme to solve this task. Specifically, multiple modalities, including vision, proprioception, and force/torque, are learned as compact representations to account for the complexity and uncertainties and improve the sample efficiency. Furthermore, RL is used in the simulation to train the policy, and the learned policy is transferred to the real world without extra exploration. Domain randomization and impedance control are embedded into the policy to narrow the gap between simulation and reality. Evaluation results demonstrate the effectiveness of the proposed solution, showcasing successful multiple peg-in-hole assembly and generalization across different object shapes in real-world scenarios.

Point Cloud Attacks in Graph Spectral Domain: When 3D Geometry Meets Graph Signal Processing.

With the increasing attention in various 3D safety-critical applications, point cloud learning models have been shown to be vulnerable to adversarial attacks. Although existing 3D attack methods achieve high success rates, they delve into the data space with point-wise perturbation, which may neglect the geometric characteristics. Instead, we propose point cloud attacks from a new perspective-the graph spectral domain attack, aiming to perturb graph transform coefficients in the spectral domain that correspond to varying certain geometric structures. Specifically, leveraging on graph signal processing, we first adaptively transform the coordinates of points onto the spectral domain via graph Fourier transform (GFT) for compact representation. Then, we analyze the influence of different spectral bands on the geometric structure, based on which we propose to perturb the GFT coefficients via a learnable graph spectral filter. Considering the low-frequency components mainly contribute to the rough shape of the 3D object, we further introduce a low-frequency constraint to limit perturbations within imperceptible high-frequency components. Finally, the adversarial point cloud is generated by transforming the perturbed spectral representation back to the data domain via the inverse GFT. Experimental results demonstrate the effectiveness of the proposed attack in terms of both the imperceptibility and attack success rates.

Characterizing structure formation through instance segmentation

Dark matter haloes form from small perturbations to the almost homogeneous density field of the early universe. Although it is known how large these initial perturbations must be to form haloes, it is rather poorly understood how to predict which particles will end up belonging to which halo. However, it is this process that determines the Lagrangian shape of proto-haloes and it is therefore essential to understand their mass, spin, and formation history. We present a machine learning framework to learn how the proto-halo regions of different haloes emerge from the initial density field. We developed one neural network to distinguish semantically which particles become part of any halo and a second neural network that groups these particles by halo membership into different instances. This instance segmentation is done through the Weinberger method, in which the network maps particles into a pseudo-space representation where different instances can easily be distinguished through a simple clustering algorithm. Our model reliably predicts the masses and Lagrangian shapes of haloes object by object, as well as other properties such as the halo-mass function. We find that our model extracts information close to optimally by comparing it to the degree of agreement between two N-body simulations with slight differences in their initial conditions. We publish our model open source and suggest that it can be used to inform analytical methods of structure formation by studying the effect of systematic manipulations of the initial conditions.

A point cloud semantic segmentation method with augmented local geometry

With the development of 3D sensors, complex spatial structures can be established from point cloud data. Accurate semantic segmentation of 3D point clouds is beneficial for tasks such as robotic scene understanding. To extract the geometric features of the point cloud, most prior works use the xyz coordinates to potentially learn the shapes of 3D objects, which is not insufficient to handle complex scenes. In this paper, a Local Geometry Encoding (LGE) module is proposed to provide local normal information and express 3D shapes explicitly. Furthermore, considering that most prior methods use a single decoder to recover from abstract features, a Multi-Decoder (MD) ensemble module is proposed to restore multiple segmentation results from various scale feature maps in the encoder, and then a multi-attention fusion mechanism is utilized to produce single segmentation results. Benefiting from the local geometry encoding module and Multi-Decoder ensemble module, our network performs 6-fold cross-validation on the S3DIS dataset and achieves 75% mIoU with fewer parameters.

A method for grasp detection of flexible four-finger gripper

The flexible four-finger gripper, as a specialized robotic end-effector, is highly valued for its ability to passively adapt to the shape of objects and perform non-destructive grasping. However, the development of grasping detection algorithms for flexible four-finger grippers remains relatively unexplored. This paper addresses the unique characteristics of the flexible four-finger gripper by proposing a grasping detection method based on deep learning. Firstly, the Acute Angle Representation model (AAR-model), which is based on the structure of the flexible four-finger gripper and consists of grasp points and angles, is designed as the grasping representation model that reduces unnecessary rotations of the gripper and improves its versatility in grasping objects. Then, the Flexible Gripper Adaptive Attribute model (FGAA-model) is proposed to represent the grasping attributes of objects, calculate the grasp angles that meet the criteria of the AAR-model, and aggregate the AAR-models on the image data into a unified set, thereby circumventing the time-consuming process of pixel-level annotation. Finally, the Adaptive Grasping Neural Net (AGNN), which is based on Adaptive Feature Fusion and the Grasp Aware Network (AFFGA), is introduced by eliminating redundant network detection headers, fusing color and depth images as inputs, and incorporating a Series Atrous Spatial Pyramid (SASP) structure to produce more accurate grasp poses. Our method not only attains a remarkable accuracy of 97.62% on the Cornell dataset but also swiftly completes grasping detection within 25 ms. In practical robotic arm grasping tests, where a robot is outfitted with a flexible four-finger gripper, it successfully grasps unknown objects with a 96% success rate. These results underscore the reliability and real-time performance of our method, significantly enhancing the gripper's adaptability and precision when handling objects of varying sizes and shapes. This advancement provides a powerful technical solution for robots utilizing flexible four-finger grippers, enabling autonomous, real-time, and highly accurate grasping maneuvers. Moreover, it addresses the persistent challenge of the scarcity of efficient grasping detection techniques tailored for flexible four-finger grippers.

Pengaruh Model Project Science Based Learning Terhadap Keterampilan Berpikir Kritis dan Kreatif Siswa pada Topik Materi dan Perubahannya

level with the topic of forms of matter and their changes. Based on the results of pre-research conducted through observations and interviews with science and natural sciences class IV A SD Tamansiswa Turen teachers, it was said that after students received the material concept regarding the shape of objects and their materials. changes, students are not yet fully able to identify the process by which substances form and the causes that result in changes in form. that substance. To make it easier for students to understand material about the forms of substances and their changes, a learning model is needed that can increase students' understanding. One learning model that can be used is the project based learning (PjBL) learning model. This is because the project based learning (PjBL) model can improve student skills, make students more active and creative, and can improve critical thinking skills when completing projects that have been designed and prepared individually or in groups. This research aims to determine whether or not there is an influence of the project-based learning model on students' critical and creative thinking abilities in class IV of the material changes at SD Tamansiswa Turen.The type of research used was quantitative research with a pre-experimental method of type one group pre-test and post-test design. This research was given to all students in grade IV A at SD Tamansiswa Turen which amounted to 22 students. The instruments used in this study are: 1) Processing instruments: Teaching Module, LKPD, Interview Guidelines, 2) Measurement instruments: written tests (pre-test and post-test) in the form of 10 essay questions for pre-test and 1 essay question for post-test. Data analysis used the liliefors analysis test for normality test while for hypothesis test used paired sample t-test. The results of the study showed that there was an influence of the project-based learning (PjBL) model on students' critical and creative thinking skills with an average pre-test score of 67,840 and an average post-test score of 80. It is known that the t-count value for critical and creative thinking skills is 5,430 with a probability (Sig.) of 0.001. So that the probability value is 0.05, then H0 is rejected and Ha is accepted. In addition, the results of the study also show the influence of PjBL on critical and creative thinking skills with the emergence of indicators of critical and creative thinking skills in the learning process. Thus, the project-based learning (PjBL) model can be used by teachers in schools for learning options or alternatives in improving students' critical and creative thinking skills.

MUSEUM DAN SEKOLAH: SINERGI KEBIJAKAN DEMOKRATISASI KEBUDAYAAN MELALUI PROGRAM PEMBELAJARAN SENI DAN BUDAYA DI KOTA LA ROCHELLE - PRANCIS

Our article describes the relationship between museums and schools as a policy of democratisation of French culture, a policy that has encouraged the ministry of education and the ministry of culture to work together in designing arts and cultural learning programmes in museums and in schools. The case study of the implementation of the national policy of democratisation of culture by the municipality of La Rochelle is an important consideration that can help us to understand how the locality of public policy implementation in France can contribute specific characteristics to the cultural development and management of museums and schools. Arts and culture learning in museums aims to bring students into direct contact with museum collections. The aim is to give students real-life experiences, encouraging them to easily express or talk about them, both in the classroom and beyond. Learning in museums is designed to build sensitivity to emotions and impressions that arise when dealing directly with museum objects, when looking at the shape, colour or material of museum objects. Students will comment and express their experiences. This is the virtue of the learning process.

MEDIA SIBEDA BERBASIS AUGMENTED REALITY MENGGUNAKAN LC5E MATERI PERUBAHAN WUJUD BENDA UNTUK MENINGKATKAN SELF-AUTHENTICITY DAN ADVERSITY QUOTIENT SISWA KELAS IV SEKOLAH DASAR

The aim of this research is to develop augmented reality-based Sibeda Media (Object Shape Transformation) using LC5E on material object shape transformation to increase the self-authenticity and adversity quotient of Class IV students. The approach applied in this research is Research and Development (R&D) with steps in developing the ASSURE model. The field experimental design used was a one group pretest-posttest design. Based on the research results, it is known that the media validity score by media experts achieved 98%, while material experts gave a score of 96%, both of which were categorized as very valid. While, teacher responses to the feasibility aspect achieved 97.5%, with a very feasible category. Based on student responses, Sibeda media based on augmented reality using LC5E received positive results, namely being able to be effective with a significant influence in increasing self-authenticity in the high category based on the average N-gain score reaching 0.767, and the results of the paired t-test showed significance. with Sig value. (2-tailed) of 0.000 < 0.05. Likewise, this product is also able to be effective with a significant effect in increasing the adversity quotient in the high category of students which is based on an average N-gain score of 0.768 and the results of the paired t-test get significant criteria with Sig. (2-tailed) is 0.000 < 0.05.

“Two-in-one” high stretchable healable WPU based conductive hydrogel for intelligent wearable sensing

In recent years, conductive hydrogel based sensors have been widely used because of their flexibility and convenience, especially in strain sensing, handwriting recognition, object shape recognition and so on. In this research, the conductive hydrogel with “two-in-one” structure was well designed to avoid the agglomeration and leakage of liquid metal by using polyvinylpyrrolidone modified liquid gallium indium alloy/polydopamine composite hydrogel as the top layer and hydrogen bonding crosslinked waterborne polyurethane/poly(vinyl alcohol) composite hydrogel as the bottom layer, respectively. Finally, a hydrogel based sensor with excellent stretchability and sensitivity was successfully obtained. The tensile strength of the prepared composite hydrogel exceeded 2 MPa. The elongation at break could be greater than 500 %. The toughness was greatly improved due to the introduction of waterborne polyurethane, which was greater than 7.5 MJ/m3. The GF value of the sensors reached 4.54. More interestingly, the mechanical self-healing ability of composite hydrogel prepared by adding polydopamine, reached about 70 % of the original. The hydrogel based sensor was able to use for gesture recognition and controlling the manipulator in human-computer interaction. This research can provide a practical idea to achieve advanced intelligent wearable sensing.

Calculation and experiment of carpet illusionary cloaking modeling based on coding metasurface

Traditional metasurface cloaking mainly focuses on carpet-like reflective cloaking, while reflective illusionary cloaking remains a challenge. This paper proposes a method to achieve stealth or simulate other virtual objects by pre-designing the electromagnetic shape of virtual objects. We simulated three kinds of carpet-like illusionary virtual objects through metasurface phase regulation. At 8 GHz, simulations are carried out, and it is verified by experiments that the cloak has good stealth performance when the incident wave is between 0° and 15°. The technique can also be extended to the visible and terahertz domains.

Eating Disorders and Disordered Eating Behaviors Among Undergraduate Students in Pakistan as Measured by Eating Disorder Examination Questionnaire (EDE-Q).

Objectives Eating disorders (ED) are an emerging public health issue globally, especially in young adults studying at the undergraduate level. This study aims to assess the frequency of eating disorders, their types, and disordered eating behaviors among such students. Moreover, it aims to identify factors like weight concern, shape concern, eating concern, and restraint, along with assessing the shifting trend of BMI impact on eating disorders using a standardized Eating Disorder Examination Questionnaire (EDE-Q). Methods In this cross-sectional study, 400 undergraduate students (aged 18-25) from four public universities participated from July 2022 to November 2023. Data was collected using the Eating Disorder Examination Questionnaire (EDE-Q). The frequency of eating disorders was computed using SPSS version 27. Results Among the participants, 21.75% (n=84) were identified as having a score surpassing the clinical cut-off. This group comprised 5.5% males (n=22) and 16% females (n=64). The highest prevalence among the four subscales was observed in the Shape Concern subscale (10.5%). Objective binge episodes (19.3%) emerged as the most notable disordered eating attitude. Atypical anorexia nervosa accounted for 13.8% of different eating disorders, while disordered eating was noted in 19.5% (n=78) of individuals. Discussion This study offers critical insights into eating disorders among Pakistanundergraduate students, utilizing the EDE-Q 6.0. Disordered eating behaviors, particularly shape concern and objective binge eating, exhibit significant correlations with these disorders. Weight dissatisfaction emerges as a prominent predictor, suggesting societal influence. The study also reveals a moderate correlation between BMI and eating disorders, challenging conventional assumptions. Furthermore, a changing trend in the prevalence of eating disorders is observed among the male population.

Neural representations of perspectival shapes and attentional effects: Evidence from fMRI and MEG

Does the human brain represent perspectival shapes, i.e., viewpoint-dependent object shapes, especially in relatively higher-level visual areas such as the lateral occipital cortex? What is the temporal profile of the appearance and disappearance of neural representations of perspectival shapes? And how does attention influence these neural representations? To answer these questions, we employed functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and multivariate decoding techniques to investigate spatiotemporal neural representations of perspectival shapes. Participants viewed rotated objects along with the corresponding objective shapes and perspectival shapes (i.e., rotated round, round, and oval) while we measured their brain activities. Our results revealed that shape classifiers trained on the basic shapes (i.e., round and oval) consistently identified neural representations in the lateral occipital cortex corresponding to the perspectival shapes of the viewed objects regardless of attentional manipulations. Additionally, this classification tendency toward the perspectival shapes emerged approximately 200 ms after stimulus presentation. Moreover, attention influenced the spatial dimension as the regions showing the perspectival shape classification tendency propagated from the occipital lobe to the temporal lobe. As for the temporal dimension, attention led to a more robust and enduring classification tendency towards perspectival shapes. In summary, our study outlines a spatiotemporal neural profile for perspectival shapes that suggests a greater degree of perspectival representation than is often acknowledged.

Spatial and temporal motion characterization for x-ray CT.

Motion induced image artifacts have been the focus of many investigations for x-ray computed tomography (CT). Methodologies of combating patient motion include the use of gating devices to optimize the data acquisition, reduction in patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post-processing algorithms to minimize motion artifacts. Previously proposed approaches are generally "global" in nature in that motion is characterized for the entire image. It is well known, however, that the presence of motion artifact in a CT image is highly nonuniform. When there is a lack of automated and quantitative local measure indicating the presence and the severity of motion artifacts in a local region, the quality of the reconstructed images depends heavily on the CT operator's rigor and experience. Even when an operator is informed of the presence of motion, little information is provided about the nature of the motion artifact to understand its relevance to the clinical task at hand. In this paper, we propose an image-space spatial- and temporal-consistency metric (CM) to detect and characterize the local motion. In a non-rigid human organ, such as the lung, there are many small and rigid objects (target objects), such as blood vessels and nodules, distributed throughout the organ. If motion can be characterized for these target objects, we obtain a complete motion map for the organ. To accomplish this, a preliminary image reconstruction is carried out to identify the target objects and establish region-of-interests for consistency-metric calculation. The CM is then obtained based on the backprojected intensity difference between the object region and its circular background. For a stationary object, the accumulation of this quantity over views is linear. When a target object moves, nonlinear behavior exhibits and a quantitative measure of linearity indicates the severity of motion. Extensive computer simulation was utilized to confirm the validity of the theory. These tests stress the sensitivity of the proposed CM to the target object size, object shape, in-plane motion, cross-plane motion, cone-beam effect, and complex background. Results confirm that the proposed approach is robust under different testing conditions. The proposed CM is further validated using a cardiac scan of a swine, and the proposed CM correlates well with the visual inspection of the artifact in the reconstructed images. In this paper, we have demonstrated the efficacy of the proposed CM for motion detection. Unlike previously proposed approaches where the consistency condition is derived for the entire image or the entire imaging volume, the proposed metric is well localized so that different zones in a patient anatomy can be individually characterized. In addition, the proposed CM provides a quantitative measure on a view-by-view basis so that the severity of motion is consistently estimated over time. Such information can be used to optimize the image reconstruction process and minimize the motion artifact.

Design, analysis, and testing of a variable-stiffness soft grabbing robot coupling particle jamming and layer jamming

Variable-stiffness soft robots feature high flexibility in motion and high stiffness in task execution, so they are in wide demand. A coupled variable stiffening method and actuator structures based on layer jamming and particle jamming were developed. The influences of different coupled modes of particles and layers on variable stiffening were evaluated, and coupled variable-stiffness soft actuators were designed. Then, the finite element method was used to simulate the multi-airbag driving structure and variable-stiffness mechanical models were established for three coupled structures to optimize parameters influencing the stiffness. Furthermore, the prototypes of the coupled variable-stiffness soft actuators were prepared, and the test platform was built to estimate the bending performance and variable stiffening capacity. Finally, a soft grabbing robot was prepared using the coupled variable-stiffness soft actuator and application tests were performed. The theoretical analysis and test results show that the soft grabbing robot can grab objects in diverse shapes and the maximum mass of objects that can be grasped is 1.25 kg, which verifies the variable stiffening capacity of the coupled soft actuator. The research provides new theoretical and technological support for the design and application of variable-stiffness soft robots.

Strange property of physical quantities of some petal shape objects

We study various physical quantities of objects with petal shapes. N-petal shapes exhibit N-fold rotational symmetry. Furthermore, they might have an additional characteristic: the equation defining their boundaries could be represented by F(N θ). We will show that physical quantities of objects with these characteristics may show strange properties. By ‘physical quantities’, we refer to aspects such as electric potential and electric field due to a charged petal-shaped plate or cylinder on the rotation axis, their mass and moment of inertia. We are going to show that for such objects, these physical observables do not depend on the number of petals, N. This intriguing result has a simple reason.

From Food to “Detoxification”: A Framework for Understanding and Shaping Social Practices and Their Networking

Understanding how social practices, like cooking, evolve and network in our daily lives is crucial for addressing sustainability and well-being challenges. While existing research prioritizes swift and holistic transformation of the network of practices, a critical gap exists in comprehending how these networks form and how they can be analyzed in practice. This study addresses this gap by introducing a novel analytical framework. This framework, which moves beyond analyzing the ‘performative’ aspects of practices (e.g., food sharing), sheds light on how interactions with objects shape and contribute to the emergence and interconnectedness of practices. By applying this framework to the case study of note-by-note cooking, we showcase its utility in three key scenarios: Unveiling the “Why”: Analyzing how objects become “ideologically coded” within practice networks allows us to understand the underlying factors shaping them. This empowers researchers and practitioners to identify and potentially “recode” unsustainable or undesirable practices towards desired outcomes. Strategic Intervention: By strategically introducing new objects into the network, the framework provides insights into “neutralizing” the influence of undesirable practices. This targeted approach allows for more nuanced interventions within existing practice networks. Cultivating New Practices: The framework empowers researchers and practitioners to develop or “encode” alternative practices by leveraging object–practitioner interactions. This enables the creation of entirely new practices or the expansion of existing ones, fostering positive societal transformations.

Monocular reconstruction of shapes of natural objects from orthographic and perspective images.

Human subjects were tested in perception of shapes of 3D objects. The subjects reconstructed 3D shapes by viewing orthographic and perspective images. Perception of natural shapes was very close to veridical and was clearly better than perception of random symmetrical polyhedra. Viewing perspective images led to only slightly better performance than viewing orthographic images. In order to account for subjects' performance, we elaborated the previous computational models of 3D shape reconstruction. The previous models used as constraints mirror-symmetry and 3D compactness. The critical additional constraint was the use of a secondary mirror-symmetry that exists in most natural shapes. It is known that two planes of mirror symmetry are sufficient for a unique and veridical shape reconstruction. We also generalized the model so that it applies to both orthographic and perspective images. The results of our experiment suggest that the human visual system uses two planes of symmetry in addition to two forms of 3D compactness. Performance of the new model was highly correlated with subjects' performance with both orthographic and perspective images, which supports the claim that the most important 3D shape constraints that are used by the human visual system have been identified.

Evaluating tactile feedback in addition to kinesthetic feedback for haptic shape rendering: a pilot study.

In current virtual reality settings for motor skill training, only visual information is usually provided regarding the virtual objects the trainee interacts with. However, information gathered through cutaneous (tactile feedback) and muscle mechanoreceptors (kinesthetic feedback) regarding, e.g., object shape, is crucial to successfully interact with those objects. To provide this essential information, previous haptic interfaces have targeted to render either tactile or kinesthetic feedback while the effectiveness of multimodal tactile and kinesthetic feedback on the perception of the characteristics of virtual objects still remains largely unexplored. Here, we present the results from an experiment we conducted with sixteen participants to evaluate the effectiveness of multimodal tactile and kinesthetic feedback on shape perception. Using a within-subject design, participants were asked to reproduce virtual shapes after exploring them without visual feedback and with either congruent tactile and kinesthetic feedback or with only kinesthetic feedback. Tactile feedback was provided with a cable-driven platform mounted on the fingertip, while kinesthetic feedback was provided using a haptic glove. To measure the participants' ability to perceive and reproduce the rendered shapes, we measured the time participants spent exploring and reproducing the shapes and the error between the rendered and reproduced shapes after exploration. Furthermore, we assessed the participants' workload and motivation using well-established questionnaires. We found that concurrent tactile and kinesthetic feedback during shape exploration resulted in lower reproduction errors and longer reproduction times. The longer reproduction times for the combined condition may indicate that participants could learn the shapes better and, thus, were more careful when reproducing them. We did not find differences between conditions in the time spent exploring the shapes or the participants' workload and motivation. The lack of differences in workload between conditions could be attributed to the reported minimal-to-intermediate workload levels, suggesting that there was little room to further reduce the workload. Our work highlights the potential advantages of multimodal congruent tactile and kinesthetic feedback when interacting with tangible virtual objects with applications in virtual simulators for hands-on training applications.

Modeling the Temperature-Dependent Size Change of Polydisperse Nano-objects using a Deep Generative Model.

Modern microscopy techniques can be used to investigate soft nano-objects at the nanometer scale. However, time-consuming microscopy measurements combined with low numbers of observable polydisperse objects often limit the statistics. We propose a method for identifying the most representative objects from their respective point clouds. These point cloud data are obtained, for example, through the localization of single emitters in super-resolution fluorescence microscopy. External stimuli, such as temperature, can cause changes in the shape and properties of adaptive objects. Due to the demanding and time-consuming nature of super-resolution microscopy experiments, only a limited number of temperature steps can be performed. Therefore, we propose a deep generative model that learns the underlying point distribution of temperature-dependent microgels, enabling the reliable generation of unlimited samples with an arbitrary number of localizations. Our method greatly cuts down the data collection effort across diverse experimental conditions, proving invaluable for soft condensed matter studies.

Conceptual information of meaningful objects is stored incidentally.

Prior research has shown that visual working memory capacity is enhanced for meaningful stimuli (i.e., real-world objects) compared to abstract shapes (i.e., colored circles). Here, we hypothesized that the shape of meaningful objects would be better remembered incidentally than the shape of nonmeaningful objects in a color memory task where the shape of the objects is task-irrelevant. We used a surprise-trial paradigm in which participants performed a color memory task for several trials before being probed with a surprise trial that asked them about the shape of the last object they saw. Across three experiments, we found a memory advantage for recognizable shapes relative to scrambled versions of these shapes (Experiment 1) that was robust across different encoding times (Experiment 2), and the addition of a verbal suppression task (Experiment 3). Interestingly, this advantage disappeared when all objects were from the same category (Experiment 4), suggesting that people are incidentally encoding broad conceptual information about object identities, but not visual details. Finally, when we asked about the location of objects in a surprise trial, we did not observe any difference between the two stimulus types (Experiment 5). Overall, these results show that conceptual information about the categories of meaningful objects is incidentally encoded into working memory even when task-irrelevant. This privilege for meaningful information does not exhibit a trade-off with location memory, suggesting that meaningful features influence representations of visual working memory in higher-level visual regions without altering the use of spatial reference frames at the lower level. (PsycInfo Database Record (c) 2024 APA, all rights reserved).