Model Of Concept Formation Research Articles

Concept formation through multimodal integration using multimodal BERT and VQ-VAE

Humans form concepts from multimodal information obtained from multiple sensory organs and understand the environment and language by predicting unobserved information through these concepts. Robots can also make better predictions by forming concepts from multimodal information in a bottom-up manner. In contrast, BERT, a language model, acquires semantic representations of sentences applicable to various language comprehension tasks by learning relationships between words through mask prediction learning. We believe that BERT can be extended to multimodality and learn multimodal representations for world understanding by learning relationships among data across modalities. Therefore, we propose a multimodal learning model using BERT and VQ-VAEs with multimodal information as input and evaluate the model in terms of model predictability and concept formation. To verify the usefulness of the proposed model, experiments were conducted using a multimodal dataset of objects acquired by a robot. The experimental results showed that the proposed model has higher category classification accuracy and word prediction performance than multimodal latent Dirichlet allocation (MLDA), which is used as a concept formation model. An analysis of the representation of the model showed that clusters of object categories were formed within the model.

The Nature and Status of Concepts in Phenomenology

Abstract This essay examines the debate that arose immediately following the publication of the first volume of Edmund Husserl's Ideas regarding the model of concept formation that Husserl sketches in that work. After a brief overview of the relevant passages from the Ideas, I take up essay-length responses to Husserl by August Messer, Theodor Elsenhans, and Heinrich Gustav Steinmann. Reflecting a variety of empiricist commitments, all three authors are skeptical that concepts can be expected to embody the essence of a corresponding phenomenon. Subsequently, I review the responses to these critiques offered by Husserl, his then-assistant Edith Stein, and Paul Linke. For Linke, it is at least highly probable that certain concepts derive their content from an apprehension of essence. The empiricist alternative, he argues, is fatally unstable. Husserl and Stein, meanwhile, offer a more forceful defense of this position. Unless we allow that certain kinds of concepts can originate from a grasp of essence, they argue, we will be unable to explain how certain manifest cognitive accomplishments are possible.

Use of the method for the formation of the concepts of size and shape in preschool children for clinical research

The goal of the present study was to test the method of forming or actualizing preverbal concepts in the conditions of an independent search for a task solution in order to use the method in clinical practice. The conditions of the absence of researchers instructions allow: 1) to compare in ontogenetic and clinical aspects; 2) to develop the skills of an independent search for solutions in problem situations; 3) to improve the creative potential of a child; 4) to develop the ability to achieve a goal. The advantage of using the preverbal concepts of size and shape is that this method can be applied to children of primary preschool age and children with a delay in speech development. To test the method, 7 healthy children of the middle preschool age (4-5-year-old children) were selected. The first task was to form or actualize the concept of a bigger or smaller size in children during the simultaneous presentation of 4 flat or volume geometrical figures. The second task was to form or actualize the concept of a flat or volumetric object among simultaneously presented 4 figures of the same size (small, medium or big). To get reward children should have chosen the figure, which was different from the other 3 ones in a trial during the formation of both the concepts. In the both tasks, we calculated the quantity of trials, which were needed for the single attainment or excess of the 70% level of the correct task performance per each type of learning. The behavioral results showed that the learning speeds during the formation of the concepts of the bigger/smaller size and flat/volumetric shape did not significantly differ from each other. It could be suggested that those concepts were equally important for the 4-5-year-old children. The study showed that the concept formation model is a convenient psychological method for testing the level of the development of the higher cognitive functions in participants and its use is possible in clinical practice with the parallel recording of the brain activity (EEG, evoked potentials etc.) in children in the process of their performing cognitive tasks. The described model is promising for identifying intellectual developmental delays in preschool children and can be used to test children with various cognitive abnormalities.

Information-Processing Model of Concept Formation – Is First Language Acquisition Universal?

Abstract The analysis of child’s speech corpora shows that the process of acquisition of English and French displays identical development of children’s expressions when the speech-utterances are presented as Fibonacci-weighted classes of concepts. A model of concept complexity and information processing based on principles of optimality is proposed to explain this statistical result.

Wittgenstein on epistemic intermediaries and categorisation

Wittgenstein has anticipated important recent developments in the philosophy of psychology in psychology and in cognitive science itself. Mis rejection of epistemic intermediaries makes him a precursor of Gibsonian theory. His model of concept formation has important links with contemporary work on categorisation and stereotypes.

Las prácticas de citación como interpretantes semióticos de acreditación de saberes locales en astronomía: México 1952-1972

Se estudia la literatura científica en el área de astronomía publicada en México. Para ello se utilizan los textos publicados en el Boletín de los Observatorios de Tonantzintla y Tacubaya, durante un periodo de cambios importantes en la producción y publicación de conocimientos científicos en México, 1952-1972. Se busca identificar, en los textos científicos del Boletín, marcas de afiliación del texto local a los patrones de la comunicación científica moderna. Para ello revisamos las prácticas de citación seguidas en las publicaciones, particularmente las funciones de comunicación de las referencias como elementos retóricos de persuasión, en el proceso de formación del texto científico. Se utiliza una metodología de análisis semiótico basada en el modelo tríadico de formación de conceptos. A través de esta metodología fue posible mostrar nuevos roles de los textos del Boletín en el sistema de la comunicación científica internacional. Interpretantes semióticos que dan cuenta de dos funciones de comunicación adquiridas por los textos: una como fuentes acreditadas y otra como fuentes acreditantes, en el proceso mutuo de certificación de conocimientos seguido en las revistas científicas.

Rethinking the role of context and definition in second language vocabulary acquisition (SLVA): An assimilation via a cognitive model of concept formation

The motivational base of this study lies in the real-life problem faced by many L2 learners: How can learners achieve target-like lexical competence? It does not take much to demonstrate that knowing a dictionary definition alone is not enough, but how learning from context actually leads to accuracy of production has remained unclear. The present article addresses this issue by proposing a cognitive model that illustrates the role of context and definition as well as L1 translation in the acquisition of conceptual knowledge. Drawing on studies of L1 acquisition, particularly the usage-based theory of language acquisition, this paper discusses in depth how L2 acquisition differs from L1 acquisition in various aspects. Meanwhile, it accounts for the development of accuracy by adopting a gap-filling-oriented acquisition view. In summary, by providing a systematic account for how various components contribute to the acquisition of a word, the proposed model offers a solution to several important debates in SLVA besides giving new insights into a range of pedagogical and learning issues.

Being seaward-handed: a computational model of the acquisition of language-specific spatial references

Empirical findings of cross-linguistic studies reveal three different frames of spatial reference: intrinsic, relative, and absolute. Of special interest are relative and absolute systems because they have antagonistic logical implications concerning the dependence on standpoint and orientation of the speaker/hearer. On the background of these findings it becomes crucial to show how an agent can form such language-specific spatial representations. In this paper, the system Locator is introduced as a model of concept formation in the spatial domain. It is assumed that an agent creates necessary discriminative features in processes of self-organization and selection and cannot just discover or “find” them in its environment. A number of simulations show that agents successfully create concepts of either a relative system (German) or an absolute system (Marquesan), relying solely on multimodal input (visual and linguistic).

Les espaces conceptuels

Conceptual spaces. Within cognitive science, there are currently two dominating approaches to the problem of modeling representations. The symbolic approach starts from the assumption that cognition is essentially involving symbol manipulation. The second approach is connectionism which models associations by artificial neuron networks. In this article, I advocate a third form of representing information that is based on using geometrical structures rather than symbols or connections between neurons. Using these structures similarity relations can be modelled in a natural way. A theory of conceptual spaces is outlined. Such a space is built up from geometrical representations based on a number of quality dimensions which are often derived from perceptual mechanisms. It will be outlined how conceptual spaces can represent various kind of information and how they can be used to describe cognitive processes like concept formation and induction. A "geometric" model of concept formation will be proposed and, among other things, its relation to prototype theory will be discussed.

A model of concept formation

At first we model the way an intelligence “I” constructs statements from phrases, and the how “I” interlocks these statements to form a string of statements to attain a concept. These strings of statements are called progressions. That is, starting with an initial stimulating relation between two phrases, we study how “I” forms the first statements of the progression and continues from this first statement to form the remaining statements in these progressions to construct a concept. We assume that “I” retains the progressions that it has constructed. Then we show how these retained progression provide “I” with a platform to incrementally constructs more and more sophisticated conceptual structures. The reason for the construction of these conceptual structures is to achieve additional concepts. Choice plays a very important role in the progression and concept formation. We shows that as “I” forms new concepts, it enriches its conceptual structure and makes further concepts attainable. This incremental attainment of concepts is a way in which we humans learn, and this paper studies the attainability of concepts from previously attained concepts. We also study the ability of “I” to apply its progressions and also the ability of “I” to selectivity manipulate its conceptual structure to achieve new concepts. Application and elective manipulation requires of “I” ingenuity and insight. We also show that as “I” attains new concepts, the conceptual structures change and circumstances arise where unanticipated conceptual discoveries are attainable. As the conceptual structure of “I” is developed, the logical and structural relationships between concepts embedded in this structure also develop. These relationships help “I” understand concepts in the context of other concepts and help “I 1” communicate to another “I 2” information and concept structures. The conceptual structures formed by “I” give rise to a directed web of statement paths which is called a convolution web. The convolution web provides “I” with the paths along which it can reason and obtain new concepts and alternative ways to attain a given concept. This paper is an extension of the ideas introduced in [1]. It is written to be self-contained and the required background is supplied as needed.

A model of concept formation

At first we model the way an intelligence “I” constructs statements from phrases, and then how “I” interlocks these statements to form a string of statements to attain a concept. These strings of statements are called progressions. That is, starting with an initial stimulating relation between two phrases, we study how “I” forms the first statement of the progression and continues from this first statement to form the remaining statements in these progressions to construct a concept. We assume that “I” retains the progressions that it has constructed. Then we show how these retained progressions provide “I” with a platform to incrementally constructs more and more sophisticated conceptual structures. The reason for the construction of these conceptual structures is to achieve additional concepts. Choice plays a very important role in the progression and concept formation. We show that as “I” forms new concepts, it enriches its conceptual structure and makes further concepts attainable. This incremental attainment of concepts is a way in which we humans learn, and this paper studies the attainability of concepts from previously attained concepts. We also study the ability of “I” to apply its progressions and also the ability of “I” to electively manipulate its conceptual structure to achieve new concepts. Application and elective manipulation requires of “I” ingenuity and insight. We also show that as “I” attains new concepts, the conceptual structures change and circumstances arise where unanticipated conceptual discoveries are attainable. As the conceptual structure of “I” is developed, the logical and structural relationships between concepts embedded in this structure also develop. These relationships help “I” understand concepts in the context of other concepts and help “I 1” communicate to another “I 2” information and concept structures. The conceptual structures formed by “I” give rise to a directed web of statement paths which is called a convolution web. The convolution web provides “I” with the paths along which it can reason and obtain new concepts and alternative ways to attain a given concept. This paper is an extension of the ideas introduced in [1]. It is written to be self-contained and the required background is supplied as needed.

FUZZ: a fuzzy-based concept formation system that integrates human categorization and numerical clustering

Recently, psychologists proposed the prototype theory of concept representation, in which a concept is organized around a best example or so-called prototype. Most proponents of the prototype theory conceive that objects may fall in a concept to some degree rather than the all-or-none membership in the classical theory. Fuzzy-set theory is compatible with the basic premises of the prototype theory of concept representation. Concept formation is defined as a machine learning task that captures concepts through categorizing the observation of objects and also uses them in classifying future experiences. A reasonable computational model of concept formation must reflect the characteristics of human concept learning and categorization. In this paper, the design and implementation of a fuzzy-set based concept formation system (FUZZ) is presented. The main feature of the FUZZ is that the concept hierarchy is nondisjoint, in which an instance may belong to two categories in different memberships. An information-theoretic evaluation measure called category-binding to direct-searches in the FUZZ is proposed. The learning and classification algorithms of the FUZZ are also given. In order to examine FUZZ's behavior, the results of some experiments are examined.

The relationship between connectionist models and a dynamic data-oriented theory of concept formation

In this paper I shall compare two models of concept formation, both inspired by basic convictions of philosophical empiricism. The first, the connectionist model, will be exemplified by Kohonen maps, and the second will be my own dynamic theory of concept formation. Both can be understood in probabilistic terms, both use a notion of convergence or stabilization in modelling how concepts are built up. Both admit destabilization of concepts and conceptual change. Both do not use a notion of representation in some pregiven language, such as a language of thought or some logical language. Representation in a formal language only plays a role on the meta-level, namely within the theory about concept formation.

Learning and discovery from a clinical database: An incremental concept formation approach

The main interest of this research is to discover clinical implications from a large PTCA (Percutaneous Transluminal Coronary Angioplasty) database. A case-based concept formation model D-UNIMEM, a modified version of Lebowitz's UNIMEM, is proposed for this purpose. In this model, we integrated two kinds of class memberships: the feature-disjunction class membership and the index-conjunction class membership. The former is a polythetic clustering approach and serves at the early stage of concept formation. The latter allows only relevant instances to be placed in the same cluster and serves as the later stage of concept formation. D-UNIMEM could extract interesting correlations among features from the learned concept hierarchy.

Analog computational models of concept formation

Analog computational models of concept formation

RCE CLASSIFIERS: THEORY AND PRACTICE

Restricted Coulomb Energy (RCE) classifiers, as described by Scofield et al. (1988), are shown to have a conceptual relationship with hyperspherical classifiers developed in the 1960s by Batchelor (1974). These classifiers are also shown to share similarities with networks of localized receptive fields and with psychological models of concept formation. Next, the performance of some RCE classifiers is examined. The ability of a trained RCE classifier to generalize to new instances is compared with that of several well-known classifiers. Then, four previously unexamined aspects of RCE classifiers are investigated empirically: (1) the influence of potential wells on training rate, (2) the influence of potential wells on storage requirement, (3) the influence of potential wells on generalization to new instances, and (4) rejection of an instance from an unknown class. Modifications of a traditional RCE classifier improve average correct classification at generalization from 83.2 to 90.7% without significant change in computational cost. By comparison, a nearest-neighbor performs at 93% and a feed-forward multilayer neural network at 88.4% on the same data. Surprisingly, when the improved RCE network is compared with its underlying adaptive nearest-neighbor component, one finds that the incorporation of potential wells into the RCE classifier does not reduce training time or instance storage requirement, nor does it improve generalization to new instances.

Connectionism and developmental theory

The main goal of this paper is to argue for an ‘epigenetic developmental interpretation’ of connectionist modelling of human cognitive processes, and to propose that Parallel Distributed Processing (PDP) models provide a superior account of developmental phenomena than that offered by cognitivist (symbolic) computational theories. After comparing some of the general characteristics of epigeneticist and cognitivist theories, we provide a brief overview of the operating principles underlying Artificial Neural Networks (ANNs) and their associated learning procedures. Four applications of different PDP architectures to developmental phenomena are described. First, we assess the current status of the debate between symbolic and connectionist accounts of the process of English past tense formation. Second, we introduce a connectionist model of concept formation and vocabulary growth and show how it provides an account of aspects of semantic development in early childhood. Next, we take up the problem of compositionality and structure dependency in connectionist nets, and demonstrate that PDP models can be architecturally designed to capture the structural principals characteristic of human cognition. Finally, we review a connectionist model of cognitive develop ment which yields stage‐like behavioural properties even though architectural and input assumptions remain constant throughout training. It is shown how the organizational characteristics of the model provide a simple but precise account of the equilibration of the processes of accommodation and assimilation. The article concludes that a coherent epigcnctic‐developmental interpretation of PDP modelling rquires the rejection of so‐called hybrid‐architecture theories of human cognition.

Symbol Grounding or the Emergence of Symbols? Vocabulary Growth in Children and a Connectionist Net

Abstract The Symbolic Grounding Problem is viewed as a by-product of the classical cognitivist approach to studying the mind. In contrast, an epigenetic interpretation of connectionist approaches to studying the mind is shown to offer an account of symbolic skills as an emergent, developmental phenomenon. We describe a connectionist model of concept formation and vocabulary growth that auto-associates image representations and their associated labels. The image representations consist of clusters of random dot figures, generated by distorting prototypes. Any given label is associated with a cluster of random dot figures. The network model is tested on its ability to reproduce image representations given input labels alone (comprehension) and to identify labels given input images alone (production). The model implements several well-documented findings in the literature on early semantic development; the occurrence of over- and under-extension errors; a vocabulary spurt; a comprehension/production asymmetry; and a prototype effect. It is shown how these apparently disparate findings can be attributed to the operation of a single underlying mechanism rather than by invoking separate explanations for each phenomenon. The model represents a first step in the direction of providing a formal explanation of the emergence of symbolic behaviour in young children.

Do we really need a ‘contingency model’ for concept formation? A reply to Richardson & Bhavnani (1984)

In a recent paper, Richardson & Bhavnani (1984) proposed a new model of concept formation christened ‘the contingency model’ which would supersede the so‐called ‘prototype model’. To support their model, they ran an experiment in which the contingency model apparently surpassed a prototype model. It is argued here that their conclusions are doubtful for several reasons. Firstly, some miscomputations have slipped into the data analysis. Secondly, the prototype model used is not appropriate. Precisely, Richardson & Bhavnani have computed their ‘Contingency Values' (CV) assuming that the features composing the exemplars are nominal variables, but have computed one ‘distance to prototype’ assuming the features are ordinal variables. The alleged superiority of the contingency model vanishes when a correct analysis is performed, or when it is evaluated against what we call a ‘mimimal prototype model’ in which the features are nominal variables. The ‘minimal prototype model’ can be interpreted as a particular and simple case of most of the current ‘prototype models’ (recalling that there is not one but several models). Consequently, it provides an overall test between the CV model and most of the current prototype models. The minimal model is shown to be a particular case of the ‘distance to prototype’ models, and the ‘family resemblance to exemplars models’; (including Tversky's contrast model), and the ‘distributed memory models’. Finally, we note that the contingency model (as tested by Richardson & Bhavnani) is a variant of a ‘distance to prototype model’, in which the distance from the prototype to an exemplar is given by an entropic measure. Thus the opposition proposed by Richardson & Bhavnani between the CV model and the prototype models is meaningless.

How a concept is formed: Prototype or contingency abstraction?

‘Prototype’ models of concept formation have been popular in recent years, in spite of several misgivings. In this paper a new model, called the contingency model, is introduced which meets some of these misgivings, and suggests that the empirical success of prototype models in the past may be explained by the fact of sharing certain computational processes with the new model. The ecological and evolutionary foundations of the new model are explained, and an experiment is reported which compared the predictions of the two models. The contingency model fared better than a representative prototype model.