A Hierarchical Characterization of Knowledge for Cognition

Hancock, Monte; Stiers, Jared; Higgins, Tyler; Swarr, Fiona; Shrider, Michael; Sood, Suraj

doi:10.1007/978-3-030-22419-6_5

Cited by 3 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…See[15] for a critique of the embodied view of knowledge, and[16] for a systematic illustration of the (computational) cognitivist approach to it.8 Formula (6) drew from Freud's topographical model of mind[18] on one hand-where mental content passes between the unconscious and conscious sub-minds via the intermediary subconscious-and Revelle's recent attempt to synthesize Plato's tripartite model of mind (consisting of precursors for affect, cognition, and motivation) into a formal personality framework[19].…”

mentioning

confidence: 99%

The Platonic-Freudian Model of Mind: Defining “Self” and “Other” as Psychoinformatic Primitives

Sood¹

2020

Augmented Cognition. Theoretical and Technological Approaches

Self Cite

View full text Add to dashboard Cite

As "nurtural" (rather than merely natural) kinds of human beings, people are complex and multifaceted. Any complete human science would require a complete theory of persons. Accomplishing the latter is the core objective of the present article. First, a feature list first laid out in [1] is summarized. This list is briefly critiqued. Next, the concept of person engaged with is expanded with the addition of nine novel features. These features follow from "holarchic psychoinformatics" [2], which was first propounded as a step forward from Sood's analytic treatment of third-force, existential-humanistic psychology. Person is formalized as a function of self and other; they are also granted to be romantic, existential, humanistic, chemical, environmental, hedonic and eudaimonic (happiness-seeking), conservative, and liberal. These are in addition to persons being physical, biological, psychological, social, cultural, and spiritual. Sood's holarchic view of persons is enlarged. Psychologically, augmented cognition as an established field of research and practice begets the formal studies of augmented affect, augmented behavior, and augmented motivation. All such interdisciplinary fields are required for the human-computer interactionist's study of augmented mind, more broadly. Additionally, this article builds on the person-situation interaction framework formalized in [1]. It does so by adding a formalization following from the discussion of psychological situations put forward by Rauthmann, Sherman, and Funder in [3]. The formalization of psychological situations sets them as a function of cues, characteristics, and classes. Further psychological equations that follow from this article's formalisms of person and situation, when considered along with Sood's formulae for mind and behavior, are then presented.

show abstract

mentioning

confidence: 99%

The Platonic-Freudian Model of Mind: Defining “Self” and “Other” as Psychoinformatic Primitives

Sood¹

2020

Augmented Cognition. Theoretical and Technological Approaches

Self Cite

View full text Add to dashboard Cite

show abstract

Relationship Recognition between Knowledge and Ability Based on the Modularity of Complex Networks

2023

View full text Add to dashboard Cite

The purpose of formal education is to increase students’ abilities, and its content is to impart knowledge through various courses. Thus, it is essential to accurately identify the relationship between knowledge and students’ ability increment to ensure the quality of education and the sustainable development of education. Currently, this relationship is mainly established based on previous educational data and teachers’ experience, which is often imprecise. This paper proposes a framework for knowledge and ability recognition based on the structural characteristics of complex network modules. The proposed framework utilizes a knowledge cognitive-interdependent network model (KCIN) as its object. First, the key knowledge nodes are identified via cognitive convergence flow of knowledge nodes in KCIN. Subsequently, the module structure of the knowledge network is identified by taking the key knowledge nodes as the core. Finally, the relationship between knowledge and ability is established by identifying the similar attributes of nodes in complex network modules. To validate the framework, we use teaching process data on the Data Structure course, which is a fundamental course for Information majors. The results show that the framework can effectively optimize the knowledge–ability relationship acquired from previous data and teacher experience.

show abstract

Predicting Dependent Edges in Nonequilibrium Complex Systems Based on Overlapping Module Characteristics

Zou,

Yan,

Gong

et al. 2024

Systems

View full text Add to dashboard Cite

Problem: Predicting dependency relationships in nonequilibrium systems is a critical challenge in complex systems research. Solution proposed: In this paper, we propose a novel method for predicting dependent edges in network models of nonequilibrium complex systems, based on overlapping module features. This approach addresses the many-to-many dependency prediction problem between nonequilibrium complex networks. By transforming node-based network models into edge-based models, we identify overlapping modular structures, enabling the prediction of many-to-many dependent edges. Experimental evaluation: This method is applied to dependency edge prediction in power and gas networks, curriculum and competency networks, and text and question networks. Results: The results indicate that the proposed dependency edge prediction method enhances the robustness of the network in power–gas networks, accurately identifies supporting relationships in curriculum–competency networks, and achieves better information gain in text–question networks. Conclusion: These findings confirm that the overlapping module-based approach effectively predicts dependencies across various nonequilibrium complex systems in diverse fields.

show abstract

A Hierarchical Characterization of Knowledge for Cognition

Cited by 3 publications

References 2 publications

The Platonic-Freudian Model of Mind: Defining “Self” and “Other” as Psychoinformatic Primitives

The Platonic-Freudian Model of Mind: Defining “Self” and “Other” as Psychoinformatic Primitives

Relationship Recognition between Knowledge and Ability Based on the Modularity of Complex Networks

Predicting Dependent Edges in Nonequilibrium Complex Systems Based on Overlapping Module Characteristics

Contact Info

Product

Resources

About