Meaningful Objects Research Articles

Boundary Devices for Reflexive Teachers

Reflexivity appears to be a key focus when designing teachers’ training; therefore, designers and trainers need to plan and put in place situated and proactive learning contexts in which reflexivity is supported by specific participatory devices. During a 1st level master’s degree for teachers and educators, based on the Trialogical Learning Approach, learners are involved in several collaborative activities to create meaningful objects. Divided into 14 groups of four members covering specific roles (the coordinator, the researcher, the storyboarder, and the diarist), the participants collaboratively built a multimedia teaching resource about a chosen methodological–didactic theme. Applying a qualitative approach, this study analyzes the online diaries compiled during the activity to understand its impact as a “meta” boundary object able to support reflexivity on one’s professionalism. The content analysis focused on analyzing how objects and practices enabled learning and participation and how students’ identity evolved during group work. The results show that a diary may act as a reflexive tool, allowing for the externalization of the processes that underly the construction of individual and collective knowledge and promoting reflection on practices and identity positioning within a community composed of professionals working in educational fields. In the end, practical implications and recommendations are provided to enhance the reflexive diaries both in teachers’ training and in the daily practice with students.

THE THINGS WE KEEP: AN EXAMINATION INTO THE USE OF MATERIAL POSSESSIONS IN QUALITATIVE GERONTOLOGICAL INTERVIEWS

Abstract Qualitative interviews are a useful research method because they allow researchers to gather in-depth data beyond the use of traditional survey methods. In this way, qualitative research is an effective method to understand complex and intricate aging-related concepts because there is room for developing new methodological ways for gathering data. A possible way to expand data collections is through the incorporation of material possessions as research aids. Older adults likely accumulate a series of material objects throughout their life course. These objects can take on a supportive role for older adults by conveying important aspects of meaning and are often attached to their identity. Some items have direct associations with later life changes (canes, glasses, etc) while other items (photographs and mementos) are linked with an individual’s identity and accrue a sense of meaning. While previous researchers have documented the importance of objects in later life through material convoys, little has been done to incorporate these meaningful objects into qualitative interview methodology. This paper assesses how incorporations of material possessions into qualitative interviews can assist researchers in gathering rich data. Through a structured literature review, this paper highlights the key findings, past incorporations, and benefits for qualitative interview methodology. Findings suggest that positive outcomes and enriched data result from incorporating material possessions as aides to qualitative interviews. Future researchers should consider how their studies can benefit from the incorporation of meaningful objects in later life.

Analysis of Current Advancement in 3D Point Cloud Semantic Segmentation

INTRODUCTION: The division of a 3D point cloud into various meaningful regions or objects is known as point cloud segmentation.
 OBJECTIVES: The paper discusses the challenges faced in 3D point cloud segmentation, such as the high dimensionality of point cloud data, noise, and varying point densities.
 METHODS: The paper compares several commonly used datasets in the field, including the ModelNet, ScanNet, S3DIS, and Semantic 3D datasets, ApploloCar3D, and provides an analysis of the strengths and weaknesses of each dataset. Also provides an overview of the papers that uses Traditional clustering techniques, deep learning-based methods, and hybrid approaches in point cloud semantic segmentation. The report also discusses the benefits and drawbacks of each approach.
 CONCLUSION: This study sheds light on the state of the art in semantic segmentation of 3D point clouds.

Comparing memory capacity across stimuli requires maximally dissimilar foils: Using deep convolutional neural networks to understand visual working memory capacity for real-world objects.

The capacity of visual working and visual long-term memory plays a critical role in theories of cognitive architecture and the relationship between memory and other cognitive systems. Here, we argue that before asking the question of how capacity varies across different stimuli or what the upper bound of capacity is for a given memory system, it is necessary to establish a methodology that allows a fair comparison between distinct stimulus sets and conditions. One of the most important factors determining performance in a memory task is target/foil dissimilarity. We argue that only by maximizing the dissimilarity of the target and foil in each stimulus set can we provide a fair basis for memory comparisons between stimuli. In the current work we focus on a way to pick such foils objectively for complex, meaningful real-world objects by using deep convolutional neural networks, and we validate this using both memory tests and similarity metrics. Using this method, we then provide evidence that there is a greater capacity for real-world objects relative to simple colors in visual working memory; critically, we also show that this difference can be reduced or eliminated when non-comparable foils are used, potentially explaining why previous work has not always found such a difference. Our study thus demonstrates that working memory capacity depends on the type of information that is remembered and that assessing capacity depends critically on foil dissimilarity, especially when comparing memory performance and other cognitive systems across different stimulus sets.

Sequential encoding aids working memory for meaningful objects’ identities but not for their colors

Sequential encoding aids working memory for meaningful objects’ identities but not for their colors

Pareidolias are a function of visuoperceptual impairment.

Pareidolias, or the misperception of ambiguous stimuli as meaningful objects, are complex visual illusions thought to be phenomenologically similar to Visual Hallucination (VH). VH are a major predictor of dementia in Parkinson's Disease (PD) and are included as a core clinical feature in Dementia with Lewy Bodies (DLB). A newly developed Noise Pareidolia Test (NPT) was proposed as a possible surrogate marker for VH in DLB patients as increased pareidolic responses correlated with informant-corroborated accounts of VH. This association could, however, be mediated by visuoperceptual impairment. To understand the drivers of performance on the NPT, we contrasted performances in patient groups that varied both in terms of visuoperceptual ability and rates of VH. N = 43 patients were studied of whom n = 13 had DLB or PD with Dementia (PDD); n = 13 had PD; n = 12 had typical, memory-onset Alzheimer's Disease (tAD); and n = 5 had Posterior Cortical Atrophy (PCA) due to Alzheimer's disease. All patient groups reported pareidolias. Within the Lewy body disorders (PD, DLB, PDD), there was no significant difference in pareidolic response rates between hallucinating and non-hallucinating patients. Visuoperceptual deficits and pareidolic responses were most frequent in the PCA group-none of whom reported VH. Regression analyses in the entire patient cohort indicated that pareidolias were strongly predicted by visuoperceptual impairment but not by the presence of VH. These findings suggest that pareidolias reflect the underlying visuoperceptual impairment of Lewy body disorders, rather than being a direct marker for VH.

A - 63 Neuropsychiatric and Cognitive Correlates of Pareidolias in Dementia with Lewy Bodies.

Pareidolias represent visual illusions of meaningful objects in ambiguous stimuli. Prior research has demonstrated phenomenological similarities between pareidolias and visual hallucinations (VH) and the potential clinical utility of pareidolias in discriminating dementia with Lewy bodies (DLB) from Alzheimer's dementia (ad). Though pareidolias have been linked with VH severity in DLB, the relationships between pareidolias and other neuropsychiatric symptoms have not been explored in large DLB samples. Individuals meeting 2017 McKeith criteria for probable DLB (n = 114) were enrolled in a multi-site systematic longitudinal study of the US DLB Consortium. Study assessments included an informant-rated psychiatric inventory (NPI), Montreal Cognitive Assessment (MoCA), and Noise Pareidolia Task (NPT). Ninety-five participants completed all clinical assessments. To examine baseline relationships between pareidolias responses (NPT) and the presence of 12 neuropsychiatric symptom dimensions (NPI), we used Poisson regression models with robust standard error estimates adjusted for age, education, disease duration, cognition (MoCA), and cholinesterase inhibitor use. Contrary to expectation, the presence of VH was not associated with pareidolias in DLB (Incidence Rate Ratio = 1.37, p = 0.275). The presence of both delusions (IRR = 2.11, p = 0.01) and depression (IRR = 1.79, p = 0.015) was associated with greater pareidolias. Additionally, overall cognitive dysfunction was associated with pareidolias (IRR = 0.90, p < 0.001). Our findings indicate that pareidolias are associated with poorer cognition and neuropsychiatric dimensions beyond VH, including depression and delusions. As pareidolias have been put forth as a surrogate marker of VH in DLB, future studies examining pareidolias in conjunction with comprehensive psychiatric/cognitive assessment and neuroimaging will further elucidate the clinical utility of pareidolia testing.

The testing effect for visual materials depends on preexisting knowledge.

Remembering facilitates future remembering. This benefit of practicing by active retrieval, as compared to more passive relearning, is known as the testing effect and is one of the most robust findings in the memory literature. It has typically been assessed using verbal materials such as word pairs, sentences, or educational texts. We here investigate if memory for visual materials equally benefits from retrieval-mediated learning. Based on cognitive and neuroscientific theories, we hypothesize that testing effects will be limited to meaningful visual images that can be related to preexisting knowledge. In a series of four experiments, we systematically varied the type of material (meaningless "squiggle" shapes vs. meaningful object images) and the format of the test used to probe memory (a visually driven alternative forced-choice test vs. a remember/know recognition test). Within each experiment, we assessed the effects of practice type (retrieval or restudy) and the delay of the final test (immediate vs. 1 week) on the resulting practice benefits. Abstract shapes never showed a significant testing benefit, irrespective of test format. Meaningful object images did benefit from testing, particularly at long delays, and with a test format probing the recollective component of recognition memory. Together, our results indicate that retrieval can facilitate the recollection of visual images when they represent meaningful semantic units. This pattern of results is predicted by cognitive and neurobiologically motivated theories proposing that retrieval's benefits emerge through spreading activation in semantic networks, producing more easily accessible and longer-lasting memory traces. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

52 ‘I remember when. . .’—environmental, event-based and personally relevant objects that support reminiscence in virtual reality and other digital settings

Abstract Background Limited but growing evidence is emerging to support technology-supported reminiscence. Although some studies involved older adults, few included people with dementia. Little guidance is available for facilitators regarding the themes and objects that best support reminiscence in a digital environment or those that are best used with a cognitively impaired population. This study aimed to address this research gap. Methods Thirty-seven older adults, with and without cognitive impairment, collaborated with our research team across two rounds of participatory workshops; held in each of two locations (urban and rural town). The first involved capturing and probing memories from young childhood to present day. Workshop discussion and outputs were thematically analysed, themes with higher levels of engagement were identified and content analysis of each theme identified associated objects. These results were reviewed in the second workshop and recommendations were made as to how they could best be used in digital environments. Results Engaged and positive reminiscence experiences were supported by: Environmental Objects—prompting memories closely linked with specific settings (e.g. school, childhood home, playing on the street, old shops); Event Objects—triggering memories relating to specific occasions of types of events (e.g. holidays, dances, sports, hobbies); and Meaningful Objects—items with personal relevance for individuals (e.g. first bike, record player, telegram). Object-rich digital environments were deemed to best support VR reminiscence, rather than single item interactions which can be effective for in-person sessions. Conclusion Objects recommended for use in digital VR reminiscence support the limited findings that are available from previous research. Fewer cross cultural differences were noted, and we recommend that this research is extended to learn more about reminiscence prompts in more ethnically diverse groups. Nevertheless, a catalogue of objects, environments and meaningful events are now available to support clinical and technical developers of digital reminiscence.

Meaningful objects avoid attribute amnesia due to incidental long-term memories

Attribute amnesia describes the failure to unexpectedly report the attribute of an attended stimulus, likely reflecting a lack of working memory consolidation. Previous studies have shown that unique meaningful objects are immune to attribute amnesia. However, these studies used highly dissimilar foils to test memory, raising the possibility that good performance at the surprise test was based on an imprecise (gist-like) form of long-term memory. In Experiment 1, we explored whether a more sensitive memory test would reveal attribute amnesia in meaningful objects. We used a four-alternative-forced-choice test with foils having mis-matched exemplar (e.g., apple pie/pumpkin pie) and/or state (e.g., cut/full) information. Errors indicated intact exemplar, but not state information. Thus, meaningful objects are vulnerable to attribute amnesia under the right conditions. In Experiments 2A-2D, we manipulated the familiarity signals of test items by introducing a critical object as a pre-surprise target. In the surprise trial, this critical item matched one of the foil choices. Participants selected the critical object more often than other items. By demonstrating that familiarity influences responses in this paradigm, we suggest that meaningful objects are not immune to attribute amnesia but instead side-step the effects of attribute amnesia.

Social construction of places as meaningful objects: a symbolic interactionist approach

ABSTRACT Places play a crucial role in the formation of both individual and collective identities. This article seeks to specify how a collective identity is constructed and sustained on the basis of the emotional bonds that the members of the group grow to the place. Drawing on the basic tenets of symbolic interactionism, the article argues that in order to function as the foundation of a group identity, a place needs to be perceived as a meaningful object of particular value. Furthermore, the article argues that the meaningfulness of the place occurs through the articulation of a narrative of uniqueness, that, drawing on the bulk of shared place-related life experiences, celebrates the special character of the place in question and shores up its unique meaning and value.

Learning-Induced Plasticity Enhances the Capacity of Visual Working Memory

Visual working memory (VWM) is limited in capacity, though memorizing meaningful objects may refine this limitation. However, meaningful and meaningless stimuli typically differ perceptually, and objects’ associations with meaning are usually already established outside the laboratory, potentially confounding experimental findings. Here, in two experiments with young adults (N = 45 and N = 20), we controlled for these influences by having observers actively learn associations of (for them) initially meaningless stimuli: Chinese characters, half of which were consistently paired with pictures of animals or everyday objects in a learning phase. This phase was preceded and followed by a (pre- and postlearning) change-detection task to assess VWM performance. The results revealed that short-term retention was enhanced after learning, particularly for meaning-associated characters, although participants did not quite reach the accuracy level attained by native Chinese observers (young adults, N = 20). These results thus provide direct experimental evidence that participants’ VWM of objects is boosted by them having acquired a long-term-memory association with meaning.

Autistic individuals show less grouping-induced bias in numerosity judgments

IntroductionWhen items are connected together, they tend to be perceived as an integrated whole rather than as individual dots, causing a strong underestimation of the numerosity of the ensemble. Previous evidence on grouping-induced biases of numerosity has shown a dependency on autistic-like personality traits in neurotypical adults, with a weaker tendency for grouping into meaningful segmented objects in individuals with strong autistic traits. Here we asked whether this result would generalize to the autistic population.MethodsTwenty-two adults with a diagnosis of Autism Spectrum Disorder (ASD) and 22 matched neurotypical controls judged the numerosity of clouds of dot-pairs connected by thin lines.ResultsResults showed no significant group difference in discrimination precision, suggesting that both groups were equally capable performing the task. However, while connecting pairs of dots at moderate numerosities caused large changes in apparent numerosity in the neurotypical controls, particularly those with low autistic-like traits, it had little effect in the group of autistic participants, suggesting significant differences in numerosity estimation between autistic and neurotypical perception. Consistent with earlier studies, the magnitude of the effect covaried strongly with AQ-defined autistic traits in the neurotypical range, reinforcing the idea that autistic traits predict the strength of grouping.DiscussionThese results provide strong support for the theories of autistic perception that highlight dissimilarities in global vs. local processing, and open the door to study grouping mechanisms indirectly, by asking participants to report on the apparent numerosity rather than on the grouping organization per se.

The Extraction of Foreground Regions of the Moving Objects Based on Spatio-Temporal Information under a Static Camera

The rapid development of computer vision technology provides a basic guarantee for public security reliance on video surveillance. In current video surveillance based on static cameras, accurate and quick extractions of foreground regions of moving objects enable quicker analysis of the behavior of meaningful objects and thus improve the intelligent analysis level of video surveillance. However, there would always occur false detection in the extraction of foreground regions, because of the shaking of tree branches and leaves in the scene and the “ghosting” area caused by the delayed updating of the background model. To solve this problem, this paper proposes a method for the extraction of foreground regions by using spatio-temporal information. This method can accurately extract foreground regions of moving objects by utilizing the difference and complementarity between spatial domain methods and temporal domain methods and further in combination with image processing technology. Specifically, the foreground regions of moving objects can be extracted by the morphological processing of the combination of the spatial information and the morphologically processed temporal information in the video. The experimental results show that the proposed method for the extraction of foreground regions of moving objects in view of the spatio-temporal information can reduce false detections caused by the shaking of tree branches and leaves, and thus effectively extract foreground regions of moving objects.

Meaningful objects are privileged in working memory: better incidental memory of recognizable relative to unrecognizable objects

Meaningful objects are privileged in working memory: better incidental memory of recognizable relative to unrecognizable objects

Raw Object Identification.

This paper attempts to deal with a specific kind of pathological identification-"raw object identification"-which tends to appear as concrete physiological phenomena, trying to escape meaning and integration. These somatic manifestations stem from early traumatic experiences with a meaningful object and entrap-as revealed through analysis-specific significant qualities of that object. A massive splitting ensues between body and mind, self and object, relation and identification. Certain properties of the object are then experienced as a foreign body in the subject and are defensively identified with. Thus, raw object identification is often manifested in stubborn bodily symptoms.

3D LIDAR SEGMENTATION BASED ON EUCLIDEAN CLUSTERING FOR EMBEDDED SYSTEM

This paper presents a method for 3D LiDAR segmentation based on Euclidean clustering specifically designed for embedded systems. LiDAR sensors are widely used for perception tasks in autonomous vehicles, robotics, and other applications, providing dense point cloud data of the environment. Segmentation of the point cloud into meaningful objects is essential for understanding the surroundings and making informed decisions. Euclidean clustering is an effective approach for grouping points based on spatial proximity, enabling object segmentation. However, implementing such algorithms on embedded systems poses challenges due to limited computational resources. In this work, we propose an optimized implementation of the Euclidean clustering algorithm tailored for embedded systems to achieve real-time performance on the embedded system. The proposed approach involves acquiring raw point cloud data from the LiDAR sensor and preprocessing it to reduce noise and size. Adaptive Euclidean clustering is then applied to group points into clusters based on their spatial proximity. Extracted features such as centroids and bounding boxes are utilized for object classification and segmentation. Post-processing steps refine the segmentation results, improving accuracy and removing spurious clusters

Connected pixels-based image smoothing filter

Digital image processing heavily relies on the connectivity of pixels, as it is a vital component for accurate object identification and analysis within an image. Grouping together pixels with similar features such as colour and intensity, allows for the formation of meaningful patterns or objects, which is essential for object recognition and segmentation. This approach is particularly valuable in photogrammetric imaging, video surveillance, deep learning as it facilitates the isolation of regions of interest and object tracking. Image smoothing is also a crucial aspect in enhancing visual quality by reducing noise and enhancing details, especially in applications such as aerial mapping, medical imaging, video compression, image resizing and computer vision. The absence of connected pixels and image smoothing would make image processing tasks more challenging and less reliable, making them fundamental to digital image processing and critical to various applications in diverse fields. This paper introduces a novel image smoothing filter called Connected Pixels Based Image Smoothing Filter (CPF), which is based on gray connected pixels. The success of the CPF was compared to that of the Non-Local Means Filter (NLMF) in terms of Structural Similarity Index (SSIM) for the same Mean Squared Error (MSE). The experimental results showed that CPF has a better ability to preserve image details compared to NLMF.

GPTR: Gestalt-Perception Transformer for Diagram Object Detection

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection.

LA TRADUCCIÓN DE LA NOVELA GRÁFICA: PERSPECTIVA SEMIOLÓGICA Y MULTIMODAL

LA TRADUCCIÓN DE LA NOVELA GRÁFICA: PERSPECTIVA SEMIOLÓGICA Y MULTIMODAL