A Dual Model of Relational Concept Representation

Relational concepts pervade daily life, as people are regularly required to comprehend, articulate, and reason about relational ideas and scenarios. Critically, these processes might be altered by how such concepts are represented. The dominant theories of relational learning have been built on the assumption that relational concepts are represented compositionally, based on the relationships among a concept’s components. However, these theories have typically neglected the possibility that a concept’s components can be consolidated or chunked into a unitized concept, producing a representation that is devoid of the concept’s component parts. The distinction between compositional and unitary representations of relational concepts is a natural consequence of structure-mapping theory, but its psychological implications have not been explored. This paper reports 7 studies that examine how people represent relational concepts and how such representations affect relational learning. The general take away from these studies is that people do indeed appear to be capable of representing relational concepts in two fundamentally different ways, unitarily and compositionally. Furthermore, unitary representations seem to lead to better relational learning than compositional representations, especially for inference-based tasks. However, the data suggest that there might be various factors that interact with how representation affects relational learning (e.g., individual differences in representation, type of task, type of comparison). The conclusion that follows from these studies is that unitary representations might incur less cognitive load than structural alignment of compositional representations, and thus may be the default for everyday relational reasoning

A Dual Model of Relational Concept Representation

Relational concepts pervade daily life, as people are regularly required to comprehend, articulate, and reason about relational ideas and scenarios. Critically, these processes might be altered by how such concepts are represented. The dominant theories of relational learning have been built on the assumption that relational concepts are represented compositionally, based on the relationships among a concept’s components. However, these theories have typically neglected the possibility that a concept’s components can be consolidated or chunked into a unitized concept, producing a representation that is devoid of the concept’s component parts. The distinction between compositional and unitary representations of relational concepts is a natural consequence of structure-mapping theory, but its psychological implications have not been explored. This paper reports 7 studies that examine how people represent relational concepts and how such representations affect relational learning. The general take away from these studies is that people do indeed appear to be capable of representing relational concepts in two fundamentally different ways, unitarily and compositionally. Furthermore, unitary representations seem to lead to better relational learning than compositional representations, especially for inference-based tasks. However, the data suggest that there might be various factors that interact with how representation affects relational learning (e.g., individual differences in representation, type of task, type of comparison). The conclusion that follows from these studies is that unitary representations might incur less cognitive load than structural alignment of compositional representations, and thus may be the default for everyday relational reasoning

Integrative priming occurs rapidly and uncontrollably during lexical processing

Lexical priming, whereby a prime word facilitates recognition of a related target word (e.g., nurse ? doctor), is typically attributed to association strength, semantic similarity, or compound familiarity. Here, the authors demonstrate a novel type of lexical priming that occurs among unassociated, dissimilar, and unfamiliar concepts (e.g., horse ? doctor). Specifically, integrative priming occurs when a prime word can be easily integrated with a target word to create a unitary representation. Across several manipulations of timing (stimulus onset asynchrony) and list context (relatedness proportion), lexical decisions for the target word were facilitated when it could be integrated with the prime word. Moreover, integrative priming was dissociated from both associative priming and semantic priming but was comparable in terms of both prevalence (across participants) and magnitude (within participants). This observation of integrative priming challenges present models of lexical priming, such as spreading activation, distributed representation, expectancy, episodic retrieval, and compound cue models. The authors suggest that integrative priming may be explained by a role activation model of relational integration

Linguistic Matrix Theory

32 pages, 3 figures32 pages, 3 figures32 pages, 3 figuresRecent research in computational linguistics has developed algorithms which associate matrices with adjectives and verbs, based on the distribution of words in a corpus of text. These matrices are linear operators on a vector space of context words. They are used to construct the meaning of composite expressions from that of the elementary constituents, forming part of a compositional distributional approach to semantics. We propose a Matrix Theory approach to this data, based on permutation symmetry along with Gaussian weights and their perturbations. A simple Gaussian model is tested against word matrices created from a large corpus of text. We characterize the cubic and quartic departures from the model, which we propose, alongside the Gaussian parameters, as signatures for comparison of linguistic corpora. We propose that perturbed Gaussian models with permutation symmetry provide a promising framework for characterizing the nature of universality in the statistical properties of word matrices. The matrix theory framework developed here exploits the view of statistics as zero dimensional perturbative quantum field theory. It perceives language as a physical system realizing a universality class of matrix statistics characterized by permutation symmetry

Categorical Ontology of Complex Systems, Meta-Systems and Theory of Levels: The Emergence of Life, Human Consciousness and Society

Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments.\ud \ud \ud KEYWORDS: Emergence of Life and Human Consciousness;\ud Proteomics; Artificial Intelligence; Complex Systems Dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell ‘cycling’; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patients’ possible improvements of the designs for future clinical trials and cancer treatments. \ud \u

Proceedings of the International Conference Sensory Motor Concepts in Language & Cognition

This volume contains selected papers of the 2008 annual conference of the German Association for Social Science Research on Japan (Vereinigung für sozialwissenschaftliche Japanforschung e.V. – VSJF). The academic meeting has addressed the issue of demographic change in Japan in comparison to the social developments of ageing in Germany and other member states of the European Union. The conference was organized by the Institute for Modern Japanese Studies at Heinrich-Heine-University of Duesseldorf and took place at the Mutter Haus in Kaiserswerth (an ancient part of Duesseldorf). Speakers from Germany, England, Japan and the Netherlands presented their papers in four sessions on the topics “Demographic Trends and Social Analysis”, “Family and Welfare Policies”, “Ageing Society and the Organization of Households” and “Demographic Change and the Economy”. Central to all transnational and national studies on demographic change is the question of how societies can be reconstructed and be made adaptive to these changes in order to survive as solidarity communities. The authors of this volume attend to this question by discussing on recent trends of social and economic restructuring and giving insight into new research developments such as in the area of households and housing, family care work, medical insurance, robot technology or the employment sector