Communication and cognitive science research in Linköping

PurposeSecchi and Cowley (2016, 2018) propose a Radical approach to Organizational Cognition (ROC) as a way of studying cognitive processes in organizations. What distinguishes ROC from the established research on Organizational Cognition is that it remains faithful to radical, anti-representationalist principles of contemporary cognitive science. However, it is imperative for proponents of ROC to legitimize their approach by considering how it differs from the established research approach of Distributed Cognition (DCog). DCog is a potential contender to ROC in that it not only counters classical approaches to cognition but also provides valuable insights into cognition in organizational settings.Design/methodology/approachThe paper adopts a conceptual/theoretical approach that expands Secchi and Cowley's introduction of ROC.FindingsThe paper shows that DCog research presupposes a task-specification requirement, which entails that cognitive tasks are well-defined. Consequently, DCog research neglects cases of organizational becoming where tasks cannot be clearly demarcated for the or are well-known to the organization. This is the case with the introduction of novel tasks or technical devices. Moreover, the paper elaborates on ROC's 3M model by linking it with insights from the literature on organizational change. Thus, it explores how organizing can be explored as an emergent phenomenon that involves micro, meso and macro domain dynamics, which are shaped by synoptic and performative changes.Originality/valueThe present paper explores new grounds for ROC by not only expanding on its core model but also showing its potential for informing organizational theory and radical cognitive science research.

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A call to arms: time to do cognitive science in Latin America

International journal of psychological research ◽

10.21500/20112084.919 ◽

2008 ◽

Vol 1 (2) ◽

pp. 41-52 ◽

Cited By ~ 1

Author(s):

Fernando Marmolejo-Ramos

Keyword(s):

Latin America ◽

Cognitive Psychology ◽

Cognitive Science ◽

Latin American ◽

Science Research ◽

Chi Square ◽

World Region ◽

The World ◽

Social Organizational ◽

Research Domains

Previous theoretical reviews about the development of Psychology in Latin America suggest that Latin American psychology has a promising future. This paper empirically checks whether that status remains justified. In so doing, the frequency of programs/research domains in three salient psychological areas is assessed in Latin America and in two other regions of the world. A chi-square statistic is used to analyse the collected data. Programs/research domains and regions of the world are the independent variables and frequency of programs/research domains per world region is the dependent variable. Results suggest that whereas in Latin America the work on Social/Organizational Psychology is moving within expected parameters, there is a rather strong focus on Clinical/Psychoanalytical Psychology. Results also show that Experimental/Cognitive Psychology is much underestimated. In Asia, however, the focus on all areas of psychology seems to be distributed within expected parameters, whereas Europe outperforms regarding Experimental/Cognitive Psychology research. Potential reasons that contribute to Latin Americas situation are discussed and specific solutions are proposed. It is concluded that the scope of Experimental/Cognitive Psychology in Latin America should be broadened into a Cognitive Science research program.

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Resituating cognitive mechanisms within heterarchical networks controlling physiology and behavior

Theory & Psychology ◽

10.1177/0959354319873725 ◽

2019 ◽

Vol 29 (5) ◽

pp. 620-639 ◽

Cited By ~ 3

Author(s):

William Bechtel

Keyword(s):

Cognitive Control ◽

Cognitive Science ◽

Neural Control ◽

Science Research ◽

Control Mechanisms ◽

Cognitive Mechanisms ◽

Physiological Processes ◽

High Level ◽

And Behavior ◽

Production Mechanisms

Cognitive science has traditionally focused on mechanisms involved in high-level reasoning and problem-solving processes. Such mechanisms are often treated as autonomous from but controlling underlying physiological processes. I offer a different perspective on cognition which starts with the basic production mechanisms through which organisms construct and repair themselves and navigate their environments and then I develop a framework for conceptualizing how cognitive control mechanisms form a heterarchical network that regulates production mechanisms. Many of these control mechanisms perform cognitive tasks such as evaluating circumstances and making decisions. Cognitive control mechanisms are present in individual cells, but in metazoans, intracellular control is supplemented by a nervous system in which a multitude of neural control mechanisms are organized heterarchically. On this perspective, high-level cognitive mechanisms are not autonomous, but are elements in larger heterarchical networks. This has implications for future directions in cognitive science research.

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Nature's subtlety undermines the empirical relevance of both dynamical and computational hypotheses

Behavioral and Brain Sciences ◽

10.1017/s0140525x9843173x ◽

1998 ◽

Vol 21 (5) ◽

pp. 646-647

Author(s):

Gregory R. Mulhauser

Keyword(s):

Cognitive Science ◽

Science Research ◽

The Way

Technical hitches mar van Gelder's proposed map of the conceptual landscape, particularly with respect to descriptive levels and the trio of instantiation, realisation, and implementation. However, for all the formal quibbles, van Gelder is onto something important; the tension he notes between computationalism and a dynamical alternative threatens to transform the way we conduct cognitive science research.

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Overview of Global Supercomputing

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Research and Applications in Global Supercomputing ◽

10.4018/978-1-4666-7461-5.ch001 ◽

2015 ◽

pp. 1-32 ◽

Cited By ~ 5

Author(s):

Richard S. Segall ◽

Neha Gupta

Keyword(s):

Artificial Intelligence ◽

Cognitive Science ◽

Science Research ◽

The World

In this chapter, a discussion is presented of what a supercomputer really is, as well as of both the top few of the world's fastest supercomputers and the overall top 500 in the world. Discussions are also of cognitive science research using supercomputers for artificial intelligence, architectural classes of supercomputers, and discussion and visualization using tables and graphs of global supercomputing comparisons across different countries. Discussion of supercomputing applications and overview of other book chapters of the entire book are all presented. This chapter serves as an introduction to the entire book and concludes with a summary of the topics of the remaining chapters of this book.

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Chapter 1. Neural-Symbolic Learning and Reasoning: A Survey and Interpretation1

10.3233/faia210348 ◽

2021 ◽

Author(s):

Tarek R. Besold ◽

Artur d’Avila Garcez ◽

Sebastian Bader ◽

Howard Bowman ◽

Pedro Domingos ◽

...

Keyword(s):

Artificial Intelligence ◽

Computer Science ◽

Cognitive Science ◽

Symbolic Computation ◽

Software Verification ◽

Science Research ◽

Symbolic Learning ◽

Computer Science Research ◽

Science Philosophy ◽

Computational Systems

The study and understanding of human behaviour is relevant to computer science, artificial intelligence, neural computation, cognitive science, philosophy, psychology, and several other areas. Presupposing cognition as basis of behaviour, among the most prominent tools in the modelling of behaviour are computational-logic systems, connectionist models of cognition, and models of uncertainty. Recent studies in cognitive science, artificial intelligence, and psychology have produced a number of cognitive models of reasoning, learning, and language that are underpinned by computation. In addition, efforts in computer science research have led to the development of cognitive computational systems integrating machine learning and automated reasoning. Such systems have shown promise in a range of applications, including computational biology, fault diagnosis, training and assessment in simulators, and software verification. This joint survey reviews the personal ideas and views of several researchers on neural-symbolic learning and reasoning. The article is organised in three parts: Firstly, we frame the scope and goals of neural-symbolic computation and have a look at the theoretical foundations. We then proceed to describe the realisations of neural-symbolic computation, systems, and applications. Finally we present the challenges facing the area and avenues for further research.

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