Formation Of Representations Research Articles

Pembentukan Representasi Adjoin pada Aljabar Lie

Algebra is a branch of mathematics that can provide understanding in solving problems. This research discusses one form of algebra, namely Lie algebra. Where Lie algebra 𝗀 is a vector space over the field 𝔽 equipped with a bilinear mapping equipped by a commutator operation commonly called the Lie bracket operation denoted [−, −] from 𝗀 × 𝗀 to 𝗀 if it satisfies the anti-symmetry axiom and satisfies Jacobi Identity. One model of Lie algebra is the set of all linear operators of a vector space 𝑉 denoted by 𝑔𝑙(𝑉). In addition, one of the discussions related to Lie algebra in this research is representation theory. Lie algebra uses representation theory with the aim of simplifying abstract algebraic problems into linear algebra by presenting each of its members in the form of linear mappings on vector spaces. There are various forms of representation theory in Lie algebra, one of which is adjoin representation. And this adjoin representation is formed from a derivation and Lie homomorphism. The purpose of this research is to find out how the formation of adjoin representation on Lie algebra. This research uses a qualitative method, where the method applies a way to collect data or library materials as a reference in the form of articles, journals, and even books related to Lie algebra.

Can human brain connectivity explain verbal working memory?

ABSTRACT The ability of humans to store spoken words in verbal working memory and build extensive vocabularies is believed to stem from evolutionary changes in cortical connectivity across primate species. However, the underlying neurobiological mechanisms remain unclear. Why can humans acquire vast vocabularies, while non-human primates cannot? This study addresses this question using brain-constrained neural networks that realize between-species differences in cortical connectivity. It investigates how these structural differences support the formation of neural representations for spoken words and the emergence of verbal working memory, crucial for human vocabulary building. We develop comparative models of frontotemporal and occipital cortices, reflecting human and non-human primate neuroanatomy. Using meanfield and spiking neural networks, we simulate auditory word recognition and examine verbal working memory function. The “human models”, characterized by denser inter-area connectivity in core language areas, produced larger cell assemblies than the “monkey models”, with specific topographies reflecting semantic properties of the represented words. Crucially, longer-lasting reverberant neural activity was observed in human versus monkey architectures, compatible with robust verbal working memory, a necessary condition for vocabulary building. Our findings offer insights into the structural basis of human-specific symbol learning and verbal working memory, shedding light on humans’ unique capacity for large vocabulary acquisition.

Working memory as a representational template for reinforcement learning

Working memory (WM) and reinforcement learning (RL) both influence decision-making, but how they interact to affect behaviour remains unclear. We assessed whether RL is influenced by the format of visual stimuli held in WM, either feature-based or unified, object-based representations. In a pre-registered paradigm, participants learned stimulus-action combinations that provided reward through 80% probabilistic feedback. In parallel, participants retained the RL stimulus in WM and were asked to recall this stimulus after each RL choice. Crucially, the format of representation probed in WM was manipulated, with blocks encouraging either separate features or bound objects to be remembered. Incentivising a feature-based WM representation facilitated feature-based learning, shown by an improved choice strategy. This reveals a role of WM in providing sustained internal representations that are harnessed by RL, providing a framework by which these two cognitive processes cooperate.

Visual Processing by Hierarchical and Dynamic Multiplexing.

The complexity of natural environments requires highly flexible mechanisms for adaptive processing of single and multiple stimuli. Neuronal oscillations could be an ideal candidate for implementing such flexibility in neural systems. Here, we present a framework for structuring attention-guided processing of complex visual scenes in humans, based on multiplexing and phase coding schemes. Importantly, we suggest that the dynamic fluctuations of excitability vary rapidly in terms of magnitude, frequency and wave-form over time, i.e., they are not necessarily sinusoidal or sustained oscillations. Different elements of single objects would be processed within a single cycle (burst) of alpha activity (7-14 Hz), allowing for the formation of coherent object representations while separating multiple objects across multiple cycles. Each element of an object would be processed separately in time-expressed as different gamma band bursts (>30 Hz)-along the alpha phase. Since the processing capacity per alpha cycle is limited, an inverse relationship between object resolution and size of attentional spotlight ensures independence of the proposed mechanism from absolute object complexity. Frequency and wave-shape of those fluctuations would depend on the nature of the object that is processed and on cognitive demands. Multiple objects would further be organized along the phase of slower fluctuations (e.g., theta), potentially driven by saccades. Complex scene processing, involving covert attention and eye movements, would therefore be associated with multiple frequency changes in the alpha and lower frequency range. This framework embraces the idea of a hierarchical organization of visual processing, independent of environmental temporal dynamics.

Touch to text: Spatiotemporal evolution of braille letter representations in blind readers.

Visual deprivation does not silence the visual cortex, which is responsive to auditory, tactile, and other nonvisual tasks in blind persons. However, the underlying functional dynamics of the neural networks mediating such crossmodal responses remain unclear. Here, using braille reading as a model framework to investigate these networks, we presented sighted (N=13) and blind (N=12) readers with individual visual print and tactile braille alphabetic letters, respectively, during MEG recording. Using time-resolved multivariate pattern analysis and representational similarity analysis, we traced the alphabetic letter processing cascade in both groups of participants. We found that letter representations unfolded more slowly in blind than in sighted brains, with decoding peak latencies ∼200 ms later in braille readers. Focusing on the blind group, we found that the format of neural letter representations transformed within the first 500 ms after stimulus onset from a low-level structure consistent with peripheral nerve afferent coding to high-level format reflecting pairwise letter embeddings in a text corpus. The spatiotemporal dynamics of the transformation suggest that the processing cascade proceeds from a starting point in somatosensory cortex to early visual cortex and then to inferotemporal cortex. Together our results give insight into the neural mechanisms underlying braille reading in blind persons and the dynamics of functional reorganization in sensory deprivation.

A lokális média kérdőjelei. A székelyföldi nyomtatott sajtóorgánumok néhány jellemzője

In this study, I analyze the connection between local media organizations and the rural areas these local media organizations are operating, assuming that these rural media organizations contribute to the formation of representations about the region in which the respective media organizations operate. Therefore, in the case of Szeklerland, the local Romanian and Hungarian-language media organizations contribute to the development and distribution of different images of Szeklerland. In this article, I present some features of the Romanian and Hungarian media ecosystem in Romania, an ecosystem a part of which are the local, rural media organizations, therefore the media outlets operating in Szeklerland as well. I present some of the characteristics of the media ecosystem – ownership system, demographic characteristics of journalists, professional self-image of journalists, the perception of journalism among journalists and readers, general characteristics of media discourse – based on the Romanian and Hungarian literature published in the last more than three decades in Romania.

Knowledge-guided Deep Temporal Clustering for Alzheimer's Disease Subtypes in Completed Clinical Trials.

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder with varied patient progression. We aim to test the hypothesis that AD patients can be categorized into subgroups based on differences in progression. We leveraged data from three randomized clinical trials (RCTs) to develop a knowledge-guided, deep temporal clustering (KG-DTC) framework for AD subtyping. This model combined autoencoders for contextual information capture, k-means clustering for representation formation, and clinical outcome classification for clinical knowledge integration. The derived representations, encompassing demographics, APOE genotype, cognitive assessments, brain volumes, and biomarkers, were clustered using the Gaussian Mixture Model to identify AD subtypes. Our novel KG-DTC framework was developed using placebo data from 2,087 AD patients across three solanezumab clinical trials (EXPEDITION, EXPEDITION2, and EXPEDITION3), achieving high performance in outcome prediction and clustering. The KG-DTC model demonstrated superior clustering structures, especially when combined with k-means clustering loss. External validation with independent clinical trial data showed consistent clustering results, with a 0.33 silhouette score for three clusters. The model's stability was confirmed through a leave-one-out approach, with an average adjusted Rand Index around 0.945. Three distinct AD subtypes were identified, each exhibiting unique patterns of cognitive function, neurodegeneration, and amyloid beta levels. Notably, Subtype 3 (S3) showed rapid cognitive decline across multiple clinical measures (e.g., 0.64 in S1 vs. -1.06 in S2 vs. 15.09 in S3 of average ADAS total change score, p<.001). This innovative approach offers promising insights for understanding variability in treatment outcomes and personalizing AD treatment strategies.

Maintenance and transformation of representational formats during working memory prioritization

Visual working memory depends on both material-specific brain areas in the ventral visual stream (VVS) that support the maintenance of stimulus representations and on regions in the prefrontal cortex (PFC) that control these representations. How executive control prioritizes working memory contents and whether this affects their representational formats remains an open question, however. Here, we analyzed intracranial EEG (iEEG) recordings in epilepsy patients with electrodes in VVS and PFC who performed a multi-item working memory task involving a retro-cue. We employed Representational Similarity Analysis (RSA) with various Deep Neural Network (DNN) architectures to investigate the representational format of prioritized VWM content. While recurrent DNN representations matched PFC representations in the beta band (15–29 Hz) following the retro-cue, they corresponded to VVS representations in a lower frequency range (3–14 Hz) towards the end of the maintenance period. Our findings highlight the distinct coding schemes and representational formats of prioritized content in VVS and PFC.

Aging delays the formation of object representations

Aging delays the formation of object representations

Common representational format in object-selective visual cortex for photographs and dynamic sketches

Common representational format in object-selective visual cortex for photographs and dynamic sketches

Abstract representations emerge in human hippocampal neurons during inference

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning and how they relate to behaviour2,3. Here we characterized the representational geometry of populations of neurons (single units) recorded in the hippocampus, amygdala, medial frontal cortex and ventral temporal cortex of neurosurgical patients performing an inferential reasoning task. We found that only the neural representations formed in the hippocampus simultaneously encode several task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consists of disentangled directly observable and discovered latent task variables. Learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behaviour suggests that abstract and disentangled representational geometries are important for complex cognition.

Oxytocin induces the formation of distinctive cortical representations and cognitions biased toward familiar mice

Social recognition is essential for the formation of social structures. Many times, recognition comes with lesser exploration of familiar animals. This lesser exploration has led to the assumption that recognition may be a habituation memory. The underlying memory mechanisms and the thereby acquired cortical representations of familiar mice have remained largely unknown, however. Here, we introduce an approach directly examining the recognition process from volatile body odors among male mice. We show that volatile body odors emitted by mice are sufficient to identify individuals and that more salience is assigned to familiar mice. Familiarity is encoded by reinforced population responses in two olfactory cortex hubs and communicated to other brain regions. The underlying oxytocin-induced plasticity promotes the separation of the cortical representations of familiar from other mice. In summary, neuronal encoding of familiar animals is distinct and utilizes the cortical representational space more broadly, promoting storage of complex social relationships.

Flexible encoding of multiple task dimensions in human cerebral cortex

IntroductionCognitive models have proposed that behavioral tasks can be categorized along at least three dimensions: the sensory-motor modality of the information, its representational format (e.g., location vs. identity), and the cognitive processes that transform it (e.g., response selection). Moreover, we can quickly and flexibly encode, represent, or manipulate information along any of these dimensions. How is this flexibility in encoding such information implemented in the cerebral cortex?MethodsTo address this question, we devised a series of functional magnetic resonance imaging (fMRI) experiments in each of which participants performed two distinct tasks that differed along one of the three dimensions.ResultsUsing multivariate pattern analysis of the fMRI data, we were able to decode between tasks along at least one task dimension within each of the cortical regions activated by these tasks. Moreover, the multiple demand network, a system of brain regions previously associated with flexible task encoding, was largely composed of closely juxtaposed sets of voxels that were specialized along each of the three tested task dimensions.DiscussionThese results suggest that flexible task encoding is primarily achieved by the juxtaposition of specialized representations processing each task dimension in the multiple demand network.

Learners' Spontaneous Gesture Before a Math Lesson Predicts the Efficacy of Seeing Versus Doing Gesture During the Lesson.

Gestures-hand movements that accompany speech and express ideas-can help children learn how to solve problems, flexibly generalize learning to novel problem-solving contexts, and retain what they have learned. But does it matter who is doing the gesturing? We know that producing gesture leads to better comprehension of a message than watching someone else produce gesture. But we do not know how producing versus observing gesture impacts deeper learning outcomes such as generalization and retention across time. Moreover, not all children benefit equally from gesture instruction, suggesting that there are individual differences that may play a role in who learns from gesture. Here, we consider two factors that might impact whether gesture leads to learning, generalization, and retention after mathematical instruction: (1) whether children see gesture or do gesture and (2) whether a child spontaneously gestures before instruction when explaining their problem-solving reasoning. For children who spontaneously gestured before instruction, both doing and seeing gesture led to better generalization and retention of the knowledge gained than a comparison manipulative action. For children who did not spontaneously gesture before instruction, doing gesture was less effective than the comparison action for learning, generalization, and retention. Importantly, this learning deficit was specific to gesture, as these children did benefit from doing the comparison manipulative action. Our findings are the first evidence that a child's use of a particular representational format for communication (gesture) directly predicts that child's propensity to learn from using the same representational format.

Formation of conceptual representations of reality on the basis of reflective practice (research experience)

Introduction. The transition from ordinary perceptions of reality to conceptualized ones is a significant problem of philosophical education. The aim of this article is to identify the ways of this transition in the course of doctoral students' work with modern philosophical literature, to substantiate ontological and epistemological principles of understanding reality, and to summarize modern approaches to solving this problem. Materials and Methods. On the basis of teaching practice, which goes back to Socrates' Maieutic method, the principle of “pro et contra”, and substantially determined by the discussions of realists and anti-realists about the nature of reality, the ways of strengthening the discipline of judgment, improving the reflexive culture, clarifying the bases of one's own ideas about the world and formalizing them in a thoughtful system of judgments are revealed. The method of research into the formation of conceptual representations of reality involves analyzing the ways of transition from unconsidered opinions through the recognition of their insufficiency and unsatisfactoriness to such knowledge, which Plato called ‘theory’ (θεωρία), i.e. rationally constructed principles of analytics. In this methodological approach (communicative-competence - in the modern formulation), the very sequence of cognitive acts is important: from ordinary ‘naïve’ perceptions - to a clear understanding of the complexity of the problem - then to the formation of questions in the audience that lead beyond a surface view of reality - and to the possible conceptual coherence and validity of perceptions of reality. Results. In the course of History and Philosophy of Science for doctoral students, the author proposed and justified the use of a three-level construct of moving from everyday representations of reality to conceptually meaningful ones. At the first level it was proposed to give an intuitive definition of the concept of ‘reality’; at the second level to analyze it, relying on sources from among those recommended by the lecturer or chosen independently, then briefly formulate the essence of realistic and anti-realistic approaches and identify the logic of one of them, and at the third level to move to the opposite position and present the relevant arguments. The author summarized the results of this procedure, which includes critical overcoming of ordinary statements about the nature of reality and dialogue between opposite conceptual solutions to the problem, and substantiated that such practice gives the effect of “awakened consciousness” and becomes the basis for readiness to conduct an internal dialogue on complex philosophical problems. Conclusions. The skills of reality analysis presented in the article develop and improve the reflexive practice of young researchers, clarify the basis of their own perceptions of the world, formalize them in a reasoned and critically elaborated system of judgments, and form their readiness to solve complex problems in science and philosophy.

Postcolonial Identity and Discourse: Foucauldian Analysis of Shamsie’s A God in Every Stone

This research article delves into the intricate interplay between postcolonial identity and discourse in Kamila Shamsie's novel, "A God in Every Stone," employing a Foucauldian analytical framework This paper presents a Foucauldian analysis of Kamila Shamsie's novel, "A God in Every Stone." Drawing upon the works of Michel Foucault (2016), the paper examines the novel through the lens of power, knowledge, and the construction of historical narratives. Through a close reading of key characters and events, this analysis elucidates how colonial power structures and systems of knowledge production intersect in the novel to shape individual and collective identities. The present study has been conducted on Shamsie’s A God in Every Stone to highlight the process of colonization. The interpreter has examined the ways by which the colonized resisted colonial rule. Foucault’s model of Power has been used as the conceptual framework of the study to analyze the postcolonial aspects of Shamsie’s (2014) novel. The present study is qualitative in nature. The data has been collected through the close reading technique of the A God in Every Stone. For data analysis, those textual lines have been selected in which the elements of power and resistance are present covertly. The interpreter has explored different dimensions of Foucault’s power, i.e., Sovereign power and disciplinary power. British Empire justified its colonial rule over the colonized through the colonial discourse which was prejudiced against non-white people. The colonized people resist colonial rule through their dialogues in this novel. In A God in Every Stone, the major protagonist is Vivian Rose Spencer. She is an English woman who carried out digging in Turkey to unearth the ancient silver circlet. The interpreter has inferred that the colonizers used many disciplinary strategies to subjugate the colonized people. The Indian soldiers were trained by the British imperialists to fight in the battlefield on the Western Front. Many innocent Indian soldiers lost their lives in fighting the enemies of the British Empire. The major character of the novel Qayyum Gul, fought bravely in the battlefield. Unfortunately, he lost an eye during the battle. When he returned to Peshawar, he joined a freedom movement, which aimed to liberate India from the British colonizers. Ultimately, this Foucauldian analysis of "A God in Every Stone" underscores the intricate dynamics of power and knowledge in the colonial context and sheds light on the complexities of historical representation and identity formation in a postcolonial world.

Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics

Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics.

Vertical is Beneficial but volume is Irrelevant: Optimization of urban guide signs based on spatial representation of road networks

Urban guide signs, a fundamental component of traffic sign systems, convey both directional and locational information. Previous studies mainly focused on the font or volume of information, while little attention was paid to the layout of text-based Chinese guide signs, which is an unregulated area but crucial in practical applications and related to people's travel safety. This study investigates the impact of text layout and information volume on the spatial representation of road networks through two experimental studies, examining the effects of different designs on path determination and global road network knowledge. The results indicate that the text layout of urban road guide signs significantly influences the formation of spatial representation of the road network. Specifically, vertical guide signs displaying road names on both sides proved more effective than horizontal ones. While the volume of road name information does not markedly affect the formation of spatial representation, the arrangement of road names does influence the determination of information volume, with vertical layouts facilitating the presentation of more information. It is anticipated that these design recommendations for road signs can effectively mitigate the incidence of road traffic accidents.

Adaptive modeling and inference of higher-order coordination in neuronal assemblies: A dynamic greedy estimation approach.

Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states.

Concurrent maintenance of both veridical and transformed working memory representations within unique coding schemes

Abstract In the dynamic environment we live in, the already limited information that human working memory can maintain needs to be constantly updated to optimally guide behaviour. Indeed, previous studies showed that leading up to a response, representations maintained in working memory representations are transformed continuously. This goes hand-in-hand with the removal of task-irrelevant items. However, does such removal also include the representations of stimuli as they were originally, prior to transformation? Here, we assessed the neural representation of task-relevant transformed representations, and the no-longer-relevant veridical representations they originated from. We applied multivariate pattern analysis to electroencephalographic data during maintenance of orientation gratings with and without mental rotation. During maintenance, we perturbed the representational network by means of a visual impulse stimulus, and were thus able to successfully decode veridical as well as imaginary, transformed orientation gratings from impulse-driven activity. The impulse response reflected only task-relevant (cued), but not task-irrelevant (uncued) items, suggesting that the latter were quickly discarded from working memory. By contrast, even though the original cued orientation gratings were also no longer task-relevant after mental rotation, these items continued to be represented next to the rotated ones, in different representational formats. This seemingly inefficient use of scarce working memory capacity was associated with reduced probe response times and may thus serve to increase precision and flexibility in guiding behaviour in dynamic environments.