Representing space in cognitive science: from empirical insights via computational models to human-centred assistance

2013 ◽  
pp. 1-7
Author(s):  
Thora Tenbrink ◽  
Jan Wiener ◽  
Christophe Claramunt
Keyword(s):  
Cognitive Science ◽  
Computational Models

Research work in the Applied Cognitive Modelling Lab

Impact ◽  
2020 ◽  
Vol 2020 (7) ◽  
pp. 9-11
Author(s):  
Junya Morita
Keyword(s):  
Mental Illness ◽  
Language Development ◽  
Cognitive Science ◽  
Graphic Design ◽  
Computational Models ◽  
Research Work ◽  
Theoretical Studies ◽  
Cognitive Modelling ◽  
Web Navigation ◽  
Fundamental Values

Dr Junya Morita is based at the Applied Cognitive Modelling Laboratory (ACML) within the Department of Behavior Informatics at Shizuoka University in Japan. His team is conducting investigations that use computational models in an effort to improve our understanding of human minds and their inner workings. There are currently two directions of study underway at ACML. The first is concerned with theoretical studies of cognitive modelling, where the team try to construct models that explain human minds as computational and algorithmic levels. The second direction of study is the application of computational cognitive models. Morita and his team believe that there are fundamental values within the basic endeavours of cognitive science and are working to prove these values exist and are valid. Current topics of application include education, driving, entertainment, graphic design, language development, web navigation and mental illness.

scholarly journals What is the role of computational models in Cognitive Science? A quantitative and qualitative analysis of the history of the TRACE model of speech segmentation

2019 ◽  
Author(s):  
Manisha Chawla ◽  
Richard Shillcock
Keyword(s):  
Qualitative Analysis ◽  
Cognitive Science ◽  
Computational Models ◽  
Computational Modelling ◽  
Speech Segmentation ◽  
Cognitive Modelling ◽  
Trace Model ◽  
History Of ◽  
Central Paradigm

Implemented computational models are a central paradigm of Cognitive Science. How do cognitive scientists really use such models? We take the example of one of the most successful and influential cognitive models, TRACE (McClelland & Elman, 1986), and we map its impact on the field in terms of published, electronically available documents that cite the original TRACE paper over a period of 25 years since its publication. We draw conclusions about the general status of computational cognitive modelling and make critical suggestions regarding the nature of abstraction in computational modelling.

scholarly journals Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines

Philosophies ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 17 ◽  
Author(s):  
Gordana Dodig-Crnkovic
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cognitive Science ◽  
Philosophy Of Mind ◽  
Computational Models ◽  
Common Ground ◽  
Natural Philosophy ◽  
Learning To Learn ◽  
Natural Systems ◽  
Integrative View

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.

scholarly journals Morphological, Natural, Analog, and Other Unconventional Forms of Computing for Cognition and Intelligence

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 30
Author(s):  
Gordana Dodig-Crnkovic
Keyword(s):  
Cognitive Science ◽  
Computational Models ◽  
The Body ◽  
Knowledge Generation ◽  
Symbol Manipulation ◽  
Physical Computation ◽  
Natural Analog ◽  
Morphological Computation ◽  
Living Organisms ◽  
High Level

According to the currently dominant view, cognitive science is a study of mind and intelligence focused on computational models of knowledge in humans. It is described in terms of symbol manipulation over formal language. This approach is connected with a variety of unsolvable problems, as pointed out by Thagard. In this paper, I argue that the main reason for the inadequacy of the traditional view of cognition is that it detaches the body of a human as the cognizing agent from the higher-level abstract knowledge generation. It neglects the dynamical aspects of cognitive processes, emotions, consciousness, and social aspects of cognition. It is also uninterested in other cognizing agents such as other living beings or intelligent machines. Contrary to the traditional computationalism in cognitive science, the morphological computation approach offers a framework that connects low-level with high-level approaches to cognition, capable of meeting challenges listed by Thagard. To establish this connection, morphological computation generalizes the idea of computation from symbol manipulation to natural/physical computation and the idea of cognition from the exclusively human capacity to the capacity of all goal-directed adaptive self-reflective systems, living organisms as well as robots. Cognition is modeled as a layered process, where at the lowest level, systems acquire data from the environment, which in combination with the already stored data in the morphology of an agent, presents the basis for further structuring and self-organization of data into information and knowledge.

scholarly journals Morphological, Natural, Analog, and Other Unconventional Forms of Computing for Cognition and Intelligence

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 30
Author(s):  
Gordana Dodig-Crnkovic
Keyword(s):  
Cognitive Science ◽  
Computational Models ◽  
The Body ◽  
Knowledge Generation ◽  
Symbol Manipulation ◽  
Physical Computation ◽  
Natural Analog ◽  
Morphological Computation ◽  
Living Organisms ◽  
High Level

According to the currently dominant view, cognitive science is a study of mind and intelligence focused on computational models of knowledge in humans. It is described in terms of symbol manipulation over formal language. This approach is connected with a variety of unsolvable problems, as pointed out by Thagard. In this paper, I argue that the main reason for the inadequacy of the traditional view of cognition is that it detaches the body of a human as the cognizing agent from the higher-level abstract knowledge generation. It neglects the dynamical aspects of cognitive processes, emotions, consciousness, and social aspects of cognition. It is also uninterested in other cognizing agents such as other living beings or intelligent machines. Contrary to the traditional computationalism in cognitive science, the morphological computation approach offers a framework that connects low-level with high-level approaches to cognition, capable of meeting challenges listed by Thagard. To establish this connection, morphological computation generalizes the idea of computation from symbol manipulation to natural/physical computation and the idea of cognition from the exclusively human capacity to the capacity of all goal-directed adaptive self-reflective systems, living organisms as well as robots. Cognition is modeled as a layered process, where at the lowest level, systems acquire data from the environment, which in combination with the already stored data in the morphology of an agent, presents the basis for further structuring and self-organization of data into information and knowledge.

What are Mental Representations?

2020 ◽  
Keyword(s):  
Cognitive Science ◽  
Cognitive Processes ◽  
Mental Representation ◽  
Computational Models ◽  
State Of The Art ◽  
Mental Representations ◽  
The Core ◽  
The Mind ◽  
The Brain

Mental representation is one of the core theoretical constructs within cognitive science and, together with the introduction of the computer as a model for the mind, is responsible for enabling the “cognitive turn” in psychology and associated fields. Conceiving of cognitive processes, such as perception, motor control, and reasoning, as processes that consist in the manipulation of contentful vehicles representing the world has allowed us to refine our explanations of behavior and has led to tremendous empirical advancements. Despite the central role that the concept plays in cognitive science, there is no unanimously accepted characterization of mental representation. Technological and methodological progress in the cognitive sciences has produced numerous computational models of the brain and mind, many of which have introduced mutually incompatible notions of mental representation. This proliferation has led some philosophers to question the metaphysical status and explanatory usefulness of the notion. This book contains state-of-the-art chapters on the topic of mental representation, assembling some of the leading experts in the field and allowing them to engage in meaningful exchanges over some of the most contentious questions. The collection gathers both proponents and critics of the concept of mental representation, allowing them to engage with topics such as the ontological status of representations, the possibility of formulating a general account of mental representation which would fit our best explanatory practices, and the possibility of delivering such an account in fully naturalistic terms.

Spazi di parole: metafore e rappresentazioni semantiche

PARADIGMI ◽  
2009 ◽  
pp. 83-100
Author(s):  
Alessandro Lenci
Keyword(s):  
Cognitive Science ◽  
Semantic Similarity ◽  
Computational Linguistics ◽  
Computational Models ◽  
Dimensional Space ◽  
High Dimensional ◽  
Semantic Representations ◽  
Topological Relations ◽  
Cognitive Sciences ◽  
Context Sensitive

- The aim of this paper is to analyse the analogy of the lexicon with a space defined by words, which is common to a number of computational models of meaning in cognitive science. This can be regarded as a case of constitutive scientific metaphor in the sense of Boyd (1979) and is grounded in the so-called Distributional Hypothesis, stating that the semantic similarity between two words is a function of the similarity of the linguistic contexts in which they typically co-occur. The meaning of words is represented in terms of their topological relations in a high-dimensional space, defined by their combinatorial behaviour in texts. A key consequence of adopting the metaphor of word spaces is that semantic representations are modelled as highly context-sensitive entities. Moreover, word space models promise to open interesting perspectives for the study of metaphorical uses in language, as well as of lexical dynamics in general. Keywords: Cognitive sciences, Computational linguistics, Distributional models of the lexicon, Metaphor, Semantics, Word spaces.

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