View Of Representation Research Articles

The Episodic Prototypes Model (EPM): On the nature and genesis of facial representations.

Faces undergo massive changes over time and life events. We need a mental representation which is flexible enough to cope with the existing visual varieties, but which is also stable enough to be the basis for valid recognition. Two main theoretical frameworks exist to describe facial representations: prototype models assuming one central item comprising all visual experiences of a face, and exemplar models assuming single representations of each visual experience of a face. We introduce a much more ecological valid model dealing with episodic prototypes (the Episodic Prototypes Model—EPM), where faces are represented by a low number of prototypes that refer to specific Episodes of Life (EoL, e.g., early adulthood, mature age) during which the facial appearance shows only moderate variation. Such an episodic view of mental representation allows for efficient storage, as the number of needed prototypes is relatively low, and it allows for the needed variation within a prototype that keeps the everyday and steadily ongoing changes across a certain period of time. Studies 1–3 provide evidence that facial representations are highly dependent on temporal aspects which is in accord with EoL, and that individual learning history generates the structure and content of respective prototypes. In Study 4, we used implicit measures (RT) in a face verification task to investigate the postulated power of the EPM. We could demonstrate that episodic prototypes clearly outperformed visual depictions of exhaustive prototypes, supporting the general idea of our approach.

Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification

Dictionary learning has been an important role in the success of data representation. As a complete view of data representation, hybrid dictionary learning (HDL) is still in its infant stage. In previous HDL approaches, the scheme of how to learn an effective hybrid dictionary for image classification has not been well addressed. In this paper, we proposed a locality preserving and label-aware constraint-based hybrid dictionary learning (LPLC-HDL) method, and apply it in image classification effectively. More specifically, the locality information of the data is preserved by using a graph Laplacian matrix based on the shared dictionary for learning the commonality representation, and a label-aware constraint with group regularization is imposed on the coding coefficients corresponding to the class-specific dictionary for learning the particularity representation. Moreover, all the introduced constraints in the proposed LPLC-HDL method are based on the l2-norm regularization, which can be solved efficiently via employing an alternative optimization strategy. The extensive experiments on the benchmark image datasets demonstrate that our method is an improvement over previous competing methods on both the hand-crafted and deep features.

Predictive processing and anti-representationalism

Many philosophers claim that the neurocomputational framework of predictive processing entails a globally inferentialist and representationalist view of cognition. Here, I contend that this is not correct. I argue that, given the theoretical commitments these philosophers endorse, no structure within predictive processing systems can be rightfully identified as a representational vehicle. To do so, I first examine some of the theoretical commitments these philosophers share, and show that these commitments provide a set of necessary conditions the satisfaction of which allows us to identify representational vehicles. Having done so, I introduce a predictive processing system capable of active inference, in the form of a simple robotic “brain”. I examine it thoroughly, and show that, given the necessary conditions highlighted above, none of its components qualifies as a representational vehicle. I then consider and allay some worries my claim could raise. I consider whether the anti-representationalist verdict thus obtained could be generalized, and provide some reasons favoring a positive answer. I further consider whether my arguments here could be blocked by allowing the same representational vehicle to possess multiple contents, and whether my arguments entail some extreme form of revisionism, answering in the negative in both cases. A quick conclusion follows.

Long-term plasticity in the hippocampus: maintaining within and 'tagging' between synapses.

Synapses between neurons are malleable biochemical structures, strengthening and diminishing over time dependent on the type of information they receive. This phenomenon known as synaptic plasticity underlies learning and memory, and its different forms, long-term potentiation (LTP) and long-term depression (LTD), perform varied cognitive roles in reinforcement, relearning and associating memories. Moreover, both LTP and LTD can exist in an early transient form (early-LTP/LTD) or a late persistent form (late-LTP/LTD), which are triggered by different induction protocols, and also differ in their dependence on protein synthesis and the involvement of key molecular players. Beyond homosynaptic modifications, synapses can also interact with one another. This is encapsulated in the synaptic tagging and capture hypothesis (STC), where synapses expressing early-LTP/LTD present a 'tag' that can capture the protein synthesis products generated during a temporally proximal late-LTP/LTD induction. This 'tagging' phenomenon forms the framework of synaptic interactions in various conditions and accounts for the cellular basis of the time-dependent associativity of short-lasting and long-lasting memories. All these synaptic modifications take place under controlled neuronal conditions, regulated by subcellular elements such as epigenetic regulation, proteasomal degradation and neuromodulatory signals. Here, we review current understanding of the different forms of synaptic plasticity and its regulatory mechanisms in the hippocampus, a brain region critical for memory formation. We also discuss expression of plasticity in hippocampal CA2 area, a long-overlooked narrow hippocampal subfield and the behavioural correlate of STC. Lastly, we put forth perspectives for an integrated view of memory representation in synapses.

Interface Theory vs Gibson: An Ontological Defense of the Ecological Approach

ABSTRACT Interface theory is the hypothesis that inferential, representational theories of perception entail that fitness, not truth, dictates the evolution of perceptual systems. They show, with simulations, that “veridical” perceptual mappings (ones that preserve at least some of the structure of the world) are routinely out-competed by “non-veridical” interfaces (ones that make no attempt to preserve that structure). They therefore take particular aim at the direct perception, ecological approach to perception and work to show that such a system, even if technically an option, would never be selected for by evolution. This paper defends the ecological approach from this novel, existential attack by showing that the ecological hypothesis is so different in kind to the inferential, representational view of perception that it simply falls outside the scope of interface theory’s critiques; ecological psychology remains a viable scientific endeavor. This analysis will show that, far from being a radical new approach to perception, interface theory is simply a clear and elegant formalization of mainstream representational psychology, and any implications interface theory may have belong solely to that branch of science.

Stereotypical Representation of a Female Politician in the Language Consciousness of Twitter Users

The study focuses on issues related to gender stereotypes of the blog community based on bloggers’ individual reactions and their impact on the perception of female politicians. The sample comprises tweets of the Russian- and English-speaking segments of this microblog, selected by keywords (based on the researcher’s view on the component structure of the thematic group «women in politics» and the representativeness of the sample) and collected with the help of the Python programming language. The classification of female politicians’ characteristics significant for users is made on the basis of feminine-masculine and psychophysiological criteria (according to E. G. Bakhteeva’s classification). The distribution of characteristics attributed to a female politician allows the researcher to determine which characteristics help women to fulfil their potential in politics or, on the contrary, hinder a successful political career. The results are interpreted in two aspects. On the one hand, from the point of view of the quantitative representation (percentage of characteristic groups), the field structure of the gender stereotype is compiled. On the other hand, in terms of qualitative indicator (content of characteristic’s groups, connotative components), the correlation of characteristics is identified and their visualization is performed with the help of Gephi software. As a result, the study builds models of a female-politician’s stereotypical image in the Russian- and English-speaking segments of Twitter and carries out their their comparative analysis. According to the first type of model, personal characteristics that may be inherent in both men and women are important for Russian- and English-speaking Twitter users, while typical masculine characteristics are the least relevant. For Russian-speaking users, high moral qualities are particularly relevant, while English-speaking bloggers appreciate high professional qualities. The graphs demonstrate that the language consciousness of the blog community tends to masculinize the stereotypical image of a female politician. Thus, a contradiction arises: bloggers explicitly acclaim the equal right for both men and women to fulfill their potential in politics, they recognize and accept the fact that women can be successful in the highest political positions and establishments and approve of the activity of particular women politicians. At the same time, traditional gender stereotypes, claiming that politics is a male sphere, implicitly retain their influence.

Improving classifier training efficiency for automatic cyberbullying detection with Feature Density

We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods in order to estimate dataset complexity, which in turn is used to comparatively estimate the potential performance of machine learning (ML) classifiers prior to any training. We hypothesize that estimating dataset complexity allows for the reduction of the number of required experiments iterations. This way we can optimize the resource-intensive training of ML models which is becoming a serious issue due to the increases in available dataset sizes and the ever rising popularity of models based on Deep Neural Networks (DNN). The problem of constantly increasing needs for more powerful computational resources is also affecting the environment due to alarmingly-growing amount of CO2 emissions caused by training of large-scale ML models. The research was conducted on multiple datasets, including popular datasets, such as Yelp business review dataset used for training typical sentiment analysis models, as well as more recent datasets trying to tackle the problem of cyberbullying, which, being a serious social problem, is also a much more sophisticated problem form the point of view of linguistic representation. We use cyberbullying datasets collected for multiple languages, namely English, Japanese and Polish. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.

Засоби мовної репрезентації категорії процесу в англійській термінології сфери біотехнології

The article is devoted to the study of the category of process in English terminology of biotechnology in the cognitive aspect. The study of biotechnological terminology from the standpoint of cognitive linguistics is associated with the study of the role of epistemological categories and their linguistic expression in the studied terminology. It is established that certain categories play an important role both in the birth of a scientific concept and in the formation of the name that reflects it. The number of categories can be different and change over time, which in turn depends on the degree of coverage of terminology belonging to a particular field of scientific knowledge. Each science has its own conceptual apparatus, special, specific categories, which is a concentrated expression of the general, most essential features, qualities, patterns of the field that this science studies. Such a field is biotechnology and its terminology. The purpose of the article is to study the role of the category of processes and its linguistic expression in English biotechnological terminology from the standpoint of cognitive linguistics. Materials and methods of research . The source base of the work were texts from scientific literature in English (publications of scientific journals, materials of the Internet) and biotechnological dictionaries. During the analysis we relied on the methods of continuous sampling of language material, classification, definition and comparative analysis. R esults of the research. Organizing biotechnological terminology, we have identified the following categories: "biological object", "object (tool) of activity", "process", "substance", "property and characteristics", category "subject of activity", category "method of activity"," t echnology ","product of human activity". Within the terminology of biotechnology, the category "process" is the most frequently represented among these categories, as it has a pragmatic significance, which is explained by the need to accurately indicate the movement of biological objects and processes occurring in the use of biotechnology. Biotechnology as a subject area specifies this category at the conceptual level in relation to its conceptual system. In biotechnology terminology, this category has a binary structure, formed by two concepts that reflect the understanding of the processes used to form the final biotechnology product and the processes associated with monitoring the safety of biotechnology. The author aims to reveal the functional features of the category of process in the language of biotechnology and to identify a set of tools in English that serve to express this category. A significant number of terms representing the concept of "process" are also represented by metaphors. Metaphorical terms in the field of biotechnology, which verbalize the idea of objects and processes, reflect the desire to synthesize in the metaphor of the new and already known. The use of a ready-made linguistic name is often based on "usual" stereotypical associations. Сonclusions. The category of process is a universal terminological category. Biotechnology as a subject area specifies this category at the conceptual level in relation to its conceptual system. In the terminology of biotechnology, the process category has a binary structure, which is formed by two concepts. The first concept reflects an understanding of the processes used to form the final biotechnology product. The second - nominates the processes associated with monitoring the safety of biotechnology. The analysis of biotechnological terminology in English from the point of view of representation of a category of process gives the bases to consider as terms those units in which semantics the information on actions contains. At the language level, various means are involved in the verbalization of the concept of "process": special procedural vocabulary; specialized morphemes; terms created on the basis of metaphorical transference and syntactic constructions. The studied category is widely used in biotechnological terminology, as it is one of the basic categories used to classify the concepts of biotechnology.

Three Dimensional Terrain Modeling for Tectonic Geomorphology of Chinara Anticline, Northern Iraq

Analysis of the terrain using three-dimensional models offers a deep insight view of ground surface topography and terrain representation. The Chinara anticline is one of the main structures of NW-SE trends for the highly folded zone in northeastern Iraq. The objective of this study is to understand the interrelationship between topography and morphotectonic features using three-dimensional models. This research employed fourth generates principal raster derivative products from the DEM using ArcGIS. To understand the undulating of this anticline with the morphotectonic style, the adaptive equation has been suggested to determine the direction and amount of the main tectonic forces, which can be applied to other undulated anticlines. The values of northeastern and southwestern limbs undulating index UI are 11.7 and 7.8 respectively that indicates the strong tectonic force towards the northeast. Two listric faults have been conducted via the field survey that confirmed by remotely sensed interpretation and DEM products. These listric faults had an intensive impact in comparison with concluded strike-slip faults, and then the Chinara anticline would be less structural undulating in a region of vicinity syncline to Perat undulation. The morphotectonic landscapes reveal that the listric fault has branched into two parts, the first one extending to form the anticline and the other comprises the structural dilemma.

Tense in Khorchin Mongolian: an interpersonal perspective

This paper examines the tense system in Khorchin Mongolian from an interpersonal perspective based on the theoretical assumptions of Systemic Functional Linguistics. Previous studies on tense in Khorchin Mongolian have mainly taken a representational view, focusing primarily on the way different tense suffixes construe relationships between events. However, this perspective cannot sufficiently address the distinction between participle and non-participle tense suffixes (e.g. -x vs. -n for non-past, -sɘn vs. -tʃϵ for past). This paper, on the other hand, interprets the difference in meaning between the participle and non-participle realizations of tense from an interpersonal perspective in relation to the co-occurring modal particles and interrogative particles at the level of clause by drawing on the discourse patterns in exchange and the phenomenon of embedding. It is shown that while the non-participle tense suffixes in Khorchin Mongolian are involved in the process of settling propositions, the participle tense suffixes are not. The paper complements the representational perspective on tense in Khorchin Mongolian and models a methodology for interpreting morphological distinctions in the context of larger and more abstract units.

Representing the People: Latin American Councilors and Their Pathway to Power and Political Representation in Spain

Latin Americans are one of the most relevant migrant minorities in Spain. In this article, we analyze their political representation at the local level by describing how councilors of Latin American councilors perceive three stages on their “pathway to power”: the selection method most frequently used by them to become electoral candidates, their ranking as candidates in the Spanish closed and blocked lists system, and their view of political representation once in office. The article contributes to a better understanding of the political incorporation of sizable minority groups in politics in recent immigration countries by implementing a mixed method strategy with survey data and in-depth interviews. The results show how candidates of Latin American origin are included in electoral lists following more participative ways of internal selection than their native-born counterparts, who are more frequently appointed by a party leader. Despite this, those who eventually get elected perceive that they have been ranked in “unsafe positions” of the electoral lists, and therefore with no guarantee of being appointed. Interestingly, once in office, councilors of Latin Americans perceive that they represent immigrants to a lesser degree, compared with their native-born counterparts.

Deep multi-view document clustering with enhanced semantic embedding

Multi-view clustering, which aims to group data with multiple views, has recently attracted intense research attention. Text documents bring additional difficulties to multi-view clustering due to the sparseness, high dimensionality, and inconsistency of document views. In this paper, we introduced a novel model on multi-view document clustering with enhanced semantic embedding, namely, MDCE, to address all of the above difficulties of clustering text documents with more than one representation view. Enhanced semantic embedders are designed to learn and improve the semantic mapping from higher-dimensional document space to lower-dimensional feature space with complementary semantic information. Specifically, three types of complementary semantic information are involved in an unsupervised manner: neighbour-wise, view-wise, and cluster-wise complementary information. A deep network is designed to optimize the enhanced semantic mapping, integrate lower-dimensional features from multiple views, and discover document clustering assignments simultaneously. We conducted extensive experiments on our proposed MDCE model by using realistic datasets compared with a number of state-of-the-art multi-view clustering approaches. Experimental results demonstrate that the MDCE-related models perform substantially better than all other models.

Alcohol and sweet reward are encoded by distinct meta-ensembles

Cue-reward associations form distinct memories that can drive appetitive behaviors and cravings for both drugs and natural rewards. It is still unclear how such memories are encoded in the brain's reward system. We trained rats to concurrently self-administer either alcohol or a sweet saccharin solution as drug or natural rewards, respectively. Memory recall due to cue exposure reactivated reward-associated functional ensembles in reward-related brain regions, marked by a neural cFos response. While the local ensembles activated by cue presentation for either reward consisted of similar numbers of neurons, using advanced statistical network theory, we found robust reward-specific co-activation patterns across brain regions. Interestingly, the resulting meta-ensemble networks differed by the most influential regions, which in case of saccharin comprised the prefrontal cortex, while for alcohol seeking control shifted to insular cortex with strong involvement of the amygdala. Our results support the view of memory representation as a differential co-activation of local neuronal ensembles.This article is part of the special issue on ‘Neurocircuitry Modulating Drug and Alcohol Abuse’.

Quantitative and Qualitative Approaches to Generalization and Replication-A Representationalist View.

In this paper, we provide a re-interpretation of qualitative and quantitative modeling from a representationalist perspective. In this view, both approaches attempt to construct abstract representations of empirical relational structures. Whereas quantitative research uses variable-based models that abstract from individual cases, qualitative research favors case-based models that abstract from individual characteristics. Variable-based models are usually stated in the form of quantified sentences (scientific laws). This syntactic structure implies that sentences about individual cases are derived using deductive reasoning. In contrast, case-based models are usually stated using context-dependent existential sentences (qualitative statements). This syntactic structure implies that sentences about other cases are justifiable by inductive reasoning. We apply this representationalist perspective to the problems of generalization and replication. Using the analytical framework of modal logic, we argue that the modes of reasoning are often not only applied to the context that has been studied empirically, but also on a between-contexts level. Consequently, quantitative researchers mostly adhere to a top-down strategy of generalization, whereas qualitative researchers usually follow a bottom-up strategy of generalization. Depending on which strategy is employed, the role of replication attempts is very different. In deductive reasoning, replication attempts serve as empirical tests of the underlying theory. Therefore, failed replications imply a faulty theory. From an inductive perspective, however, replication attempts serve to explore the scope of the theory. Consequently, failed replications do not question the theory per se, but help to shape its boundary conditions. We conclude that quantitative research may benefit from a bottom-up generalization strategy as it is employed in most qualitative research programs. Inductive reasoning forces us to think about the boundary conditions of our theories and provides a framework for generalization beyond statistical testing. In this perspective, failed replications are just as informative as successful replications, because they help to explore the scope of our theories.

The visual and semantic features that predict object memory: Concept property norms for 1,000 object images

Humans have a remarkable fidelity for visual long-term memory, and yet the composition of these memories is a longstanding debate in cognitive psychology. While much of the work on long-term memory has focused on processes associated with successful encoding and retrieval, more recent work on visual object recognition has developed a focus on the memorability of specific visual stimuli. Such work is engendering a view of object representation as a hierarchical movement from low-level visual representations to higher level categorical organization of conceptual representations. However, studies on object recognition often fail to account for how these high- and low-level features interact to promote distinct forms of memory. Here, we use both visual and semantic factors to investigate their relative contributions to two different forms of memory of everyday objects. We first collected normative visual and semantic feature information on 1,000 object images. We then conducted a memory study where we presented these same images during encoding (picture target) on Day 1, and then either a Lexical (lexical cue) or Visual (picture cue) memory test on Day 2. Our findings indicate that: (1) higher level visual factors (via DNNs) and semantic factors (via feature-based statistics) make independent contributions to object memory, (2) semantic information contributes to both true and false memory performance, and (3) factors that predict object memory depend on the type of memory being tested. These findings help to provide a more complete picture of what factors influence object memorability. These data are available online upon publication as a public resource.

Reinventing 2D Convolutions for 3D Images.

There have been considerable debates over 2D and 3D representation learning on 3D medical images. 2D approaches could benefit from large-scale 2D pretraining, whereas they are generally weak in capturing large 3D contexts. 3D approaches are natively strong in 3D contexts, however few publicly available 3D medical dataset is large and diverse enough for universal 3D pretraining. Even for hybrid (2D + 3D) approaches, the intrinsic disadvantages within the 2D/3D parts still exist. In this study, we bridge the gap between 2D and 3D convolutions by reinventing the 2D convolutions. We propose ACS (axial-coronal-sagittal) convolutions to perform natively 3D representation learning, while utilizing the pretrained weights on 2D datasets. In ACS convolutions, 2D convolution kernels are split by channel into three parts, and convoluted separately on the three views (axial, coronal and sagittal) of 3D representations. Theoretically, ANY 2D CNN (ResNet, DenseNet, or DeepLab) is able to be converted into a 3D ACS CNN, with pretrained weight of a same parameter size. Extensive experiments validate the consistent superiority of the pretrained ACS CNNs, over the 2D/3D CNN counterparts with/without pretraining. Even without pretraining, the ACS convolution can be used as a plug-and-play replacement of standard 3D convolution, with smaller model size and less computation.

Remediation of a phonological representation deficit in Chinese children with dyslexia: A comparison between metalinguistic training and working memory training.

A form-preparation task in the language production field was adopted to examine output phonological representations in Chinese dyslexia and their susceptibility to training. Forty-one Chinese children with dyslexia (7-11years old) and 36 chronological age controls completed this task. The controls demonstrated a marginally significant syllable facilitation effect (d=-0.13), indicating their use of syllable-sized phonological representations during speech production, while the group with dyslexia showed a significantly different pattern (d=0.04), opposite to the direction of a facilitation effect. The children with dyslexia were then randomly assigned to either metalinguistic training (N=22) or working memory training (N=19). Only the metalinguistic training subgroup demonstrated a significant syllable facilitation effect afterward (metalinguistic: d=-0.13; working memory: d=-0.01). The results suggest the presence of a phonological representation deficit at the syllable level in Chinese dyslexia and its possible remediation by metalinguistic training. Such a phonological deficit in readers of a logographic script strongly supports the impaired phonological representation view of developmental dyslexia. A video abstract of this article can be viewed at https://youtu.be/zT2Be0xMkh0.

An Interview with Angela Tucker

This is an interview between Emily N. Bartz and Angela Tucker, an author and documentarian as well as a consultant and activist. The two address Tucker’s work, their views of adoptee representation and voice, and how far adoption studies has come in the past few years.

Visual and Affective Multimodal Models of Word Meaning in Language and Mind.

One of the main limitations of natural language‐based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different kinds of models account for people's representations of both concrete and abstract concepts. The models we compare include unimodal distributional linguistic models as well as multimodal models which combine linguistic with perceptual or affective information. There are two types of linguistic models: those based on text corpora and those derived from word association data. We present two new studies and a reanalysis of a series of previous studies. The studies demonstrate that both visual and affective multimodal models better capture behavior that reflects human representations than unimodal linguistic models. The size of the multimodal advantage depends on the nature of semantic representations involved, and it is especially pronounced for basic‐level concepts that belong to the same superordinate category. Additional visual and affective features improve the accuracy of linguistic models based on text corpora more than those based on word associations; this suggests systematic qualitative differences between what information is encoded in natural language versus what information is reflected in word associations. Altogether, our work presents new evidence that multimodal information is important for capturing both abstract and concrete words and that fully representing word meaning requires more than purely linguistic information. Implications for both embodied and distributional views of semantic representation are discussed.

Exchanging humpty dumpties is not a solution: Why a representational view of knowledge must be replaced with an action-based approach.

In arguing for knowledge representation before belief, Phillips et al. presuppose a representational theory of knowledge, a view that has been extensively criticized. As an alternative, we propose an action-based approach to knowledge, conceptualized in terms of skill. We outline the implications of this approach for children's developing social understanding, beginning with sensorimotor interaction and extending to the verbal level.