View Of Representation Research Articles

Space-time interference: The asymmetry we get out is the asymmetry we put in.

Temporal judgments are more affected by space than vice versa. This asymmetry has often been interpreted as primacy of spatial representations over temporal ones. This interpretation is in line with conceptual metaphor theory that humans conceptualize time by spatial metaphors, but is inconsistent with the assumption of a common neuronal magnitude system. Here we review the accumulating evidence for a genuinely symmetric interference between time and space and discuss potential explanations as to why asymmetric interference can arise, both with respect to the interaction between spatial size and temporal duration, and the interaction between traveled distance and travel time. Contrary to the view of hierarchical representations of time and space, our review suggests that asymmetric interference can be explained on the basis of working memory processes and the aspect of speed inherent in dynamic stimuli. We conclude that the asymmetry we often get out (space affects time more than vice versa) is a consequence of the asymmetry we put in (by using biased paradigms and stimuli facilitating spatial processing).

Self-adaptive label discovery and multi-view fusion for complementary label learning

Unlike traditional supervised classification, complementary label learning (CLL) operates under a weak supervision framework, where each sample is annotated by excluding several incorrect labels, known as complementary labels (CLs). Despite reducing the labeling burden, CLL always suffers a decline in performance due to the weakened supervised information. To overcome such limitations, in this study, a multi-view fusion and self-adaptive label discovery based CLL method (MVSLDCLL) is proposed. The self-adaptive label discovery strategy leverages graph-based semi-supervised learning to capture the label distribution of each training sample as a convex combination of all its potential labels. The multi-view fusion module is designed to adapt to various views of feature representations. In specific, it minimizes the discrepancies of label projections between pairwise views, aligning with the consensus principle. Additionally, a straightforward mechanism inspired by a teamwork analogy is proposed to incorporate view-discrepancy for each sample. Experimental results demonstrate that MVSLDCLL learns more discriminative label distribution and achieves significantly higher accuracies compared to state-of-the-art CLL methods. Ablation study has also been performed to validate the effectiveness of both the self-adaptive label discovery strategy and the multi-view fusion module.

Factors influencing the development, recruitment, integration, retention and career development of advanced practice providers in hospital health care teams: a scoping review

BackgroundAdvanced practice providers (APPs), including physician assistants/associates (PAs), nurse practitioners (NPs) and other non-physician roles, have been developed largely to meet changing healthcare demand and increasing workforce shortages. First introduced in primary care in the US, APPs are prevalent in secondary care across different specialty areas in different countries around the world. In this scoping review, we aimed to summarise the factors influencing the development, recruitment, integration, retention and career development of APP roles in hospital health care teams.MethodsWe conducted a scoping review and searched Ovid MEDLINE, Ovid Embase, Ovid Global Health, Ovid PsycINFO and EBSCOhost CINAHL to obtain relevant articles published between Jan 2000 and Apr 2023 that focused on workforce management of APP roles in secondary care. Articles were screened by two reviewers independently. Data from included articles were charted and coded iteratively to summarise factors influencing APP development, recruitment, integration, retention and career development across different health system structural levels (macro-, meso- and micro-level).ResultsWe identified and analysed 273 articles that originated mostly from high-income countries, e.g. the US (n = 115) and the UK (n = 52), and primarily focused on NP (n = 183) and PA (n = 41). At the macro-level, broader workforce supply, national/regional workforce policies such as work-hour restrictions on physicians, APP scope of practice regulations, and views of external collaborators, stakeholders and public representation of APPs influenced organisations’ decisions on developing and managing APP roles. At the meso-level, organisational and departmental characteristics, organisational planning, strategy and policy, availability of resources, local experiences and evidence as well as views and perceptions of local organisational leaders, champions and other departments influenced all stages of APP role management. Lastly at the micro-level, individual APPs’ backgrounds and characteristics, clinical team members’ perceptions, understanding and relationship with APP roles, and patient perceptions and preferences also influenced how APPs are developed, integrated and retained.ConclusionsWe summarised a wide range of factors influencing APP role development and management in secondary care teams. We highlighted the importance for organisations to develop context-specific workforce solutions and strategies with long-term investment, significant resource input and transparent processes to tackle evolving healthcare challenges.

Using drawings and deep neural networks to characterize the building blocks of human visual similarity.

Early in life and without special training, human beings discern resemblance between abstract visual stimuli, such as drawings, and the real-world objects they represent. We used this capacity for visual abstraction as a tool for evaluating deep neural networks (DNNs) as models of human visual perception. Contrasting five contemporary DNNs, we evaluated how well each explains human similarity judgments among line drawings of recognizable and novel objects. For object sketches, human judgments were dominated by semantic category information; DNN representations contributed little additional information. In contrast, such features explained significant unique variance perceived similarity of abstract drawings. In both cases, a vision transformer trained to blend representations of images and their natural language descriptions showed the greatest ability to explain human perceptual similarity-an observation consistent with contemporary views of semantic representation and processing in the human mind and brain. Together, the results suggest that the building blocks of visual similarity may arise within systems that learn to use visual information, not for specific classification, but in service of generating semantic representations of objects.

Perception and illusion: replies to Sethi, Speaks and Cutter

ABSTRACT I reply to comments on my book Perception (Routledge 2021) by Umrao Sethi, Jeff Speaks and Brian Cutter. Sethi objects to my representational view of perception on the ground that that having an experience of a color or shape can enable you to know what that color or shape is like only if it is actually present in the experience. Speaks has a very interesting discussion of my puzzle of the laws of appearance for the representational view. And Cutter asks what I have against 'neural sense datum theory'. In my responses, I try to take the discussion forward. At the end, I develop a new argument against reductive physicalist theories of sensible qualities.

'At first they felt barely relevant': teacher reflective sensemaking of justice-oriented science teaching

ABSTRACT This study explores three science teachers’ sensemaking of justice-oriented science teaching, as they reflect on their science teaching practices in the framework of justice as redistribution, recognition, and representation. Drawing on a constant comparative analysis of data generated from group and individual conversations with the teachers, findings report that teachers’ initial understanding of justice and its practices were more aligned with the view of justice as redistribution. This understanding gradually evolved to encompass the view of recognition and representation as the teachers explored the meanings of the culture, politics, and power within their own context of science teaching. Implications are discussed about how to support teachers’ practices toward ‘justice-oriented’ – particularly in the context where the expectation of teacher accountability for curricular completion and student test results prevails.

Language in Bioethics: Beyond the Representational View

Though assumptions about language underlie all bioethical work, the field has rarely partaken of theories of language. This article encourages a more linguistically engaged bioethics. We describe the tacit conception of language that is frequently upheld in bioethics—what we call the representational view, which sees language essentially as a means of description. We examine how this view has routed the field’s theories and interventions down certain paths. We present an alternative model of language—the pragmatic view—and explore how it expands and clarifies traditional bioethical concerns. To lend concreteness, we apply the pragmatic view to a pervasive concept in bioethics and adjacent fields: decision making. We suggest that problems of the decision-making approach to bioethical issues are grounded in adherence to the representational view. Drawing on empirical work in surgery and critical care, we show how the pragmatic view productively reframes bioethical questions about how medical treatments are pursued.

The Role of the Motor System in L1 and L2 Action Verb Processing for Chinese Learners of English: Evidence from Mu Rhythm Desynchronization.

The nature of semantic representation has long been a key question in linguistic research. The Embodied Cognition theory challenges the traditional view of language representation, stating that semantic information stems from the sensory-motor cortex, which is activated automatically during semantic processing. However, most of the evidence comes from monolingual studies; it remains unclear whether second-language (L2) comprehension involves different semantic representations or mirrors the pattern seen in first-language (L1) processing. Therefore, the present study investigated the role of the sensory-motor system in language processing via making Electroencephalography (EEG) recordings during the processing of L1 and L2 action verbs. The results showed that L1 (Chinese) action verbs generated higher mu-event-related desynchronization (ERD) than L1 abstract verbs in the early processing stage (250 ms after verb presentation), and the same phenomenon was also observed for L2 (English). The results also indicated that language modulated the processing of action verbs, with L1 action verbs eliciting stronger ERD than L2 action verbs. These results demonstrate that the sensory-motor cortex plays a crucial role in comprehending both L1 and L2 action verbs.

Cost analysis of water charge rates in the Czech Republic – Case study

The article was conceived as an initial insight into the issues related to the representation of individual type costs (e.g. material, labor costs, property depreciation, etc.) in the price of water in the Czech Republic. The aim of the article was to point out the possibility of dependence between the size of the company operating the infrastructural property of water supply and sewerage and the representation of individual costs in the water price within the framework of the case study. As a sample that formed the outputs of the case study, 14 companies were taken, which were selected according to the unified regions of the Czech Republic. Both basic mathematical methods and elementary methods used in financial analysis were used in the analysis. Within the scope of the case study, it can be stated that there is no dependence between the size of the companies and the representation of costs. Among the largest costs from the point of view of financial representation are other direct costs, where the costs of depreciation, property repairs, rental property, as well as wage costs and material costs are mainly represented. Insignificant costs include, for example, energy costs, which are only represented in the range of 1.39–5.70% of the total costs. Therefore, in order for the results included in the case study to be considered statistically relevant, it is necessary to expand the sample and confirm or refute the initial findings published in this article.

Hill on perceptual contents, Thouless properties, and representational pluralism

Part of a symposium on Christopher Hill's book, Perceptual experience. Hill argues that perceptual experiences typically represent objects as having exotic properties that he calls Thouless properties. This and his representational pluralism allow him to attribute less perceptual error than the view that experiences represent simple relational properties (only). However, I think it is plausible that perceptual systems do make these sorts of errors, which although pervasive and systematic, are relatively subtle and perfectly explicable. I also express some concerns about representational pluralism, especially in the context of a representationalist view of the phenomenal character of experience.

Missing Poetry: A historiography of Albanian Literature during the Communist Regime

The paper aims at a panoramic view of the partial representation of Albanian poetry in the texts of the history of Albanian literature published during the dictatorship period (1945–1990). The fragmentary presentation of Albanian poetry and poets in informative scholarly and university texts on literature highlights the complications and the problems derived from this one-sided representation. The information gained only after the fall of the dictatorship, changed the situation regarding the literary values specifically related to poetry. What crucial representative texts on Albanian literary history were published during the dictatorship? Who were the most noticeable authors and poets, and why were they set aside, censored, imprisoned, executed, and not published? The answers suggest that only thanks to political change and awareness of literature can the reader today reassess this missing part of Albanian literature in historical texts published during the communist regime.
 The methodology relies on a historical, analytical, and critical approach to the most representative authors and their texts that is intertwined with factual data. The conclusions reinforce the idea that any evaluation of the literary process dictated by extra-literary principles damages the natural literary process. The revitalisation of literature after the 1990s, although with much delay, restored the natural process of aesthetic assessment. Now we can reassess the best part of that missing poetry.

Transformer-Based Recognition Model for Ground-Glass Nodules from the View of Global 3D Asymmetry Feature Representation

Ground-glass nodules (GGN) are the main manifestation of early lung cancer, and accurate and efficient identification of ground-glass pulmonary nodules is of great significance for the treatment of lung diseases. In response to the problem of traditional machine learning requiring manual feature extraction, and most deep learning models applied to 2D image classification, this paper proposes a Transformer-based recognition model for ground-glass nodules from the view of global 3D asymmetry feature representation. Firstly, a 3D convolutional neural network is used as the backbone to extract the features of the three-dimensional CT-image block of pulmonary nodules automatically; secondly, positional encoding information is added to the extracted feature map and input into the Transformer encoder layer for further extraction of global 3D asymmetry features, which can preserve more spatial information and obtain higher-order asymmetry feature representation; finally, the extracted asymmetry features are entered into a support vector machine or ELM-KNN model to further improve the recognition ability of the model. The experimental results show that the recognition accuracy of the proposed method reaches 95.89%, which is 4.79, 2.05, 4.11, and 2.74 percentage points higher than the common deep learning models of AlexNet, DenseNet121, GoogLeNet, and VGG19, respectively; compared with the latest models proposed in the field of pulmonary nodule classification, the accuracy has been improved by 2.05, 2.05, and 0.68 percentage points, respectively, which can effectively improve the recognition accuracy of ground-glass nodules.

A Gestalt account of human behavior is supported by evidence from switching between single and dual actions

The question of how behavior is represented in the mind lies at the core of psychology as the science of mind and behavior. While a long-standing research tradition has established two opposing fundamental views of perceptual representation, Structuralism and Gestalt psychology, we test both accounts with respect to action representation: Are multiple actions (characterizing human behavior in general) represented as the sum of their component actions (Structuralist view) or holistically (Gestalt view)? Using a single-/dual-response switch paradigm, we analyzed switches between dual ([A + B]) and single ([A], [B]) responses across different effector systems and revealed comparable performance in partial repetitions and full switches of behavioral requirements (e.g., in [A + B] → [A] vs. [B] → [A], or [A] → [A + B] vs. [B] → [A + B]), but only when the presence of dimensional overlap between responses allows for Gestalt formation. This evidence for a Gestalt view of behavior in our paradigm challenges some fundamental assumptions in current (tacitly Structuralist) action control theories (in particular the idea that all actions are represented compositionally with reference to their components), provides a novel explanatory angle for understanding complex, highly synchronized human behavior (e.g., dance), and delimitates the degree to which complex behavior can be analyzed in terms of its basic components.

Minimalism's continued creep: Subject matter

AbstractThe problem of creeping minimalism is the problem of drawing a principled distinction between expressivists and non‐expressivists. Explanationism is a popular strategy for solving the problem, but two of its forms—ontological explanationism and representational explanationism—have fatal problems. Christine Tiefensee and Matthew Simpson have recently, and independently, endorsed a third form: subject matter explanationism. But this form also fails. At bottom, the problem is that it does not note the existence of non‐reductive expressivist views, just as earlier forms of explanationism did not note the existence of error theories, or non‐naturalist realists, or realists who wanted to endorse deflationary views of truth and representation. The failure of this latest version of explanationism—one that does indeed avoid problems with earlier versions—strengthens the case that we may not actually want a solution to the problem of creeping minimalism after all. Rather, a form of global expressivism—neopragmatism—might be regarded as yielding a version of non‐naturalist normative realism.

Attentional Strategies and the Transition From Subitizing to Estimation in Numerosity Perception.

The common view of the transition between subitizing and numerosity estimation regimes is that there is a hard bound on the subitizing range, and beyond this range, people estimate. However, this view does not adequately address the behavioral signatures of enumeration under conditions of attentional load or in the immediate post-subitizing range. The possibility that there might exist a numerosity range where both processes of subitizing and estimation operate in conjunction has so far been ignored. Here, we investigate this new proposal, that people strategically combine the processes of subitizing and estimation to maximize accuracy and precision, given time or attentional constraints. We present a process-level account of how subitizing and estimation can be combined through strategic deployment of attention to maximize the precision of perceived numerosity given time constraints. We then describe a computational model of this account and apply it in two experimental simulations to demonstrate how it can explain key findings in prior enumeration research. While recent modeling work has argued that the behavioral signatures of enumeration can best be explained through a single numerosity system with a single form of representation, we argue that our model demonstrates how the traditional two-systems view of numerical representation accounts for behavioral data through coordination with a unified attentional mechanism, rather than a unifiedrepresentation.

Integrative views of representations and processes in morphology: an introduction

AbstractOne of the most enduring conceptualisations of the language architecture rests on a modular subdivision of work between lexical representations of stored items on the one hand, and dynamic processes, modelled as procedural rules working on such items, on the other hand. In morphology, network-based approaches have suggested an alternative “integrative” view of word representations and processes, where lexical representations consist of partially overlapping activation patterns spreading over several processing units. From this integrative perspective, the resulting network is both a lexicon and a word processor. We argue that the network-based view provides a stimulating research framework for several complementary levels of language inquiry (including theoretical, computational and neuro-psychological approaches) to be fruitfully integrated into a novel, comprehensive understanding of morphology. We discuss some implications of this view and delineate prospects of progress in this area.

The Kantian view of dark representations and their function in practical life, according to the anthropological notes of the Critical Period

The Kantian view of dark representations and their function in practical life, according to the anthropological notes of the Critical Period

Outer and Inner Space in Craig Thompson’s Blankets: A Visual Representation Perspective

Written in 2003, Craig Thompson’s graphic novel Blankets manages to arouse readers’ attention through its complex emotional background that entwines the protagonist’s constant attempts to live a normal, happy life with his psychological instability, as well as his personal coming-of-age and first love with strict religious education and a detachment from his childhood memories. This paper, however, attempts to analyse the concepts of space and time from the point of view of visual representation, the author succeeding in creating the idea of temporal development through constant flashbacks, inner space representations, the novel being retrospectively narrated; at the same time, a series of artfully coined images that symbolise winter’s purity and cleanliness come to oppose the ugliness of the social context (unspoiled versus ravaged outer space) that traps the hero in an impossible universe that annihilates any outbursts of creativity, ingenuity and love.

The neural correlates of individual differences in numerosity perception: A voxel-based morphometry study

Numerosity perception is a fundamental cognitive function in humans and animals. Using an individual difference approach with a comprehensive dataset (N= 249), we performed a voxel-based morphometry analysis to unravel the neuroanatomical substrates associated with individual differences in numerosity perception sensitivity, measured by a classical non-symbolic numerical judgment task. Results showed that greater gray matter volume (GMV) in the left cerebellum, right temporal pole, and right parahippocampal was positively correlated to higher perceptual sensitivity to numerosity. In contrast, the GMV in the left intraparietal sulcus, and bilateral precentral/postcentral gyrus was negatively correlated to the sensitivity of numerosity perception. These findings indicate that a wide range of brain structures, rather than a specific anatomical structure or circuit, forms the neuroanatomical basis of numerosity perception, lending support to the emerging network view of the neural representation of numerosity. This work contributes to a more comprehensive understanding of how the brain processes numerical information.

Hierarchical graph transformer with contrastive learning for protein function prediction.

In recent years, high-throughput sequencing technologies have made large-scale protein sequences accessible. However, their functional annotations usually rely on low-throughput and pricey experimental studies. Computational prediction models offer a promising alternative to accelerate this process. Graph neural networks have shown significant progress in protein research, but capturing long-distance structural correlations and identifying key residues in protein graphs remains challenging. In the present study, we propose a novel deep learning model named Hierarchical graph transformEr with contrAstive Learning (HEAL) for protein function prediction. The core feature of HEAL is its ability to capture structural semantics using a hierarchical graph Transformer, which introduces a range of super-nodes mimicking functional motifs to interact with nodes in the protein graph. These semantic-aware super-node embeddings are then aggregated with varying emphasis to produce a graph representation. To optimize the network, we utilized graph contrastive learning as a regularization technique to maximize the similarity between different views of the graph representation. Evaluation of the PDBch test set shows that HEAL-PDB, trained on fewer data, achieves comparable performance to the recent state-of-the-art methods, such as DeepFRI. Moreover, HEAL, with the added benefit of unresolved protein structures predicted by AlphaFold2, outperforms DeepFRI by a significant margin on Fmax, AUPR, and Smin metrics on PDBch test set. Additionally, when there are no experimentally resolved structures available for the proteins of interest, HEAL can still achieve better performance on AFch test set than DeepFRI and DeepGOPlus by taking advantage of AlphaFold2 predicted structures. Finally, HEAL is capable of finding functional sites through class activation mapping. Implementations of our HEAL can be found at https://github.com/ZhonghuiGu/HEAL.