Situational-Context: A Unified View of Everything Involved at a Particular Situation

Author(s):  
Javier Berrocal ◽  
Jose Garcia-Alonso ◽  
Carlos Canal ◽  
Juan M. Murillo

2019 ◽  
Vol 42 ◽  
Author(s):  
Giulia Frezza ◽  
Pierluigi Zoccolotti

Abstract The convincing argument that Brette makes for the neural coding metaphor as imposing one view of brain behavior can be further explained through discourse analysis. Instead of a unified view, we argue, the coding metaphor's plasticity, versatility, and robustness throughout time explain its success and conventionalization to the point that its rhetoric became overlooked.



Author(s):  
Youssef A. Haddad

This chapter defines attitude datives as evaluative and relational pragmatic markers that allow the speaker to present material from a specific perspective and to invite the hearer to view the material from the same perspective. It identifies three types of context that are pertinent to the analysis of these datives. These are the sociocultural context (e.g., values, beliefs), the situational context (i.e., identities, activity types), and the co-textual context (e.g., contextualization cues). The chapter draws on Cognitive Grammar and Theory of Stance and puts forth a sociocognitive model called the stancetaking stage model. In this model, when a speaker uses an attitude dative construction, she directs her hearer’s attention to the main content of her message and instructs him to view this content through the attitude dative as a filter. In this sense, the attitude dative functions as a perspectivizer and the main content becomes a perspectivized thought.



2002 ◽  
Vol 7 (5-6) ◽  
pp. 45-53
Author(s):  
Claude Christment ◽  
Florence Sèdes


2004 ◽  
Author(s):  
Medhat A. Abuhantash ◽  
Matthew V. Shoultz
Keyword(s):  


1989 ◽  
Vol 6 (3) ◽  
pp. 161-178 ◽  
Author(s):  
D. J. DALEY ◽  
J. H. MAINDONALD
Keyword(s):  


2021 ◽  
Vol 15 (4) ◽  
pp. 1-46
Author(s):  
Kui Yu ◽  
Lin Liu ◽  
Jiuyong Li

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.



Synthese ◽  
2021 ◽  
Author(s):  
Matt Sims ◽  
Giovanni Pezzulo

AbstractPredictive processing theories are increasingly popular in philosophy of mind; such process theories often gain support from the Free Energy Principle (FEP)—a normative principle for adaptive self-organized systems. Yet there is a current and much discussed debate about conflicting philosophical interpretations of FEP, e.g., representational versus non-representational. Here we argue that these different interpretations depend on implicit assumptions about what qualifies (or fails to qualify) as representational. We deploy the Free Energy Principle (FEP) instrumentally to distinguish four main notions of representation, which focus on organizational, structural, content-related and functional aspects, respectively. The various ways that these different aspects matter in arriving at representational or non-representational interpretations of the Free Energy Principle are discussed. We also discuss how the Free Energy Principle may be seen as a unified view where terms that traditionally belong to different ontologies—e.g., notions of model and expectation versus notions of autopoiesis and synchronization—can be harmonized. However, rather than attempting to settle the representationalist versus non-representationalist debate and reveal something about what representations are simpliciter, this paper demonstrates how the Free Energy Principle may be used to reveal something about those partaking in the debate; namely, what our hidden assumptions about what representations are—assumptions that act as sometimes antithetical starting points in this persistent philosophical debate.



ACS Catalysis ◽  
2021 ◽  
pp. 6596-6601
Author(s):  
Alexander Bagger ◽  
Hao Wan ◽  
Ifan E. L. Stephens ◽  
Jan Rossmeisl


Author(s):  
Anna Zajenkowska ◽  
Radosław Rogoza ◽  
Noah J. Sasson ◽  
Philip D. Harvey ◽  
David L. Penn ◽  
...  


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