Formal RTPA Models for a Set of Meta-Cognitive Processes of the Brain

2011 ◽  
pp. 308-324
Author(s):  
Yingxu Wang
Keyword(s):  
Mathematical Model ◽  
Knowledge Representation ◽  
Cognitive Processes ◽  
Process Model ◽  
Object Identification ◽  
Time Process ◽  
Metacognitive Processes ◽  
Internal Information ◽  
The Brain ◽  
Object Attribute

The cognitive processes modeled at the metacognitive level of the layered reference mode of the brain (LRMB) encompass those of object identification, abstraction, concept establishment, search, categorization, comparison, memorization, qualification, quantification, and selection. It is recognized that all higher layer cognitive processes of the brain rely on the metacognitive processes. Each of this set of fundamental cognitive processes is formally described by a mathematical model and a process model. Real-time process algebra (RTPA) is adopted as a denotational mathematical means for rigorous modeling and describing the metacognitive processes. All cognitive models and processes are explained on the basis of the object-attribute-relation (OAR) model for internal information and knowledge representation and manipulation.

On the Cognitive Processes of Human Perception with Emotions, Motivations, and Attitudes

2011 ◽  
pp. 65-77 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Software Engineering ◽  
Mathematical Models ◽  
Cognitive Processes ◽  
Reference Model ◽  
Human Perception ◽  
Comprehensive Model ◽  
Time Process ◽  
Rigorous Model ◽  
The Brain ◽  
Object Attribute

An interactive motivation-attitude theory is developed based on the Layered Reference Model of the Brain (LRMB) and the Object-Attribute-Relation (OAR) model. This chapter presents a rigorous model of human perceptual processes such as emotions, motivations, and attitudes. A set of mathematical models and formally described cognitive processes are developed. The interactions and relationships between motivation and attitude are formally described in real-time process algebra (RTPA). Applications of the mathematical models of motivations and attitudes in software engineering are demonstrated. This work is the detailed description of a part of the layered reference model of the brain (LRMB) that provides a comprehensive model for explaining the fundamental cognitive processes of the brain and their interactions. This work demonstrates that the complicated human emotional and perceptual phenomena can be rigorously modeled in mathematics and be formally treated and described.

Semantic and Formal Representation of Cognitive Models for the Metacognitive Architecture CARINA

2021 ◽  
pp. 54-72
Author(s):  
Alba J. Jerónimo ◽  
María P. Barrera ◽  
Manuel F. Caro ◽  
Adán A. Gómez
Keyword(s):  
Knowledge Representation ◽  
Semantic Memory ◽  
Cognitive Model ◽  
Semantic Knowledge ◽  
Cognitive Models ◽  
Formal Representation ◽  
Object Properties ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain

A cognitive model is a computational model of internal information processing mechanisms of the brain for the purposes of comprehension and prediction. CARINA metacognitive architecture runs cognitive models. However, CARINA does not currently have mechanisms to store and learn from cognitive models executed in the past. Semantic knowledge representation is a field of study which concentrates on using formal symbols to a collection of propositions, objects, object properties, and relations among objects. In CARINA Beliefs are a form of represent the semantic knowledge. The aim of this chapter is to formally describe a CARINA-based cognitive model through of denotational mathematics and to represent these models using a technique of semantic knowledge representation called beliefs. All the knowledge received by CARINA is stored in the semantic memory in the form of beliefs. Thus, a cognitive model represented through beliefs will be ready to be stored in semantic memory of the metacognitive architecture CARINA. Finally, an illustrative example is presented.

An Exemplar-Based Random-Walk Model of Categorization and Recognition

2015 ◽  
Author(s):  
Robert M. Nosofsky ◽  
Thomas J. Palmeri
Keyword(s):  
Mathematical Model ◽  
Decision Making ◽  
Random Walk ◽  
Cognitive Processes ◽  
Object Identification ◽  
Process Models ◽  
Response Time Models ◽  
Individual Object ◽  
Evidence Accumulation ◽  
Skilled Performance

In this chapter, we provide a review of a process-oriented mathematical model of categorization known as the exemplar-based random-walk (EBRW) model (Nosofsky & Palmeri, 1997a). The EBRW model is a member of the class of exemplar models. According to such models, people represent categories by storing individual exemplars of the categories in memory, and classify objects on the basis of their similarity to the stored exemplars. The EBRW model combines ideas ranging from the fields of choice and similarity, to the development of automaticity, to response-time models of evidence accumulation and decision-making. This integrated model explains relations between categorization and other fundamental cognitive processes, including individual-object identification, the development of expertise in tasks of skilled performance, and old-new recognition memory. Furthermore, it provides an account of how categorization and recognition decision-making unfold through time. We also provide comparisons with some other process models of categorization.

On the Cognitive Processes of Human Perception with Emotions, Motivations, and Attitudes

2009 ◽  
pp. 141-153
Author(s):  
Yingxu Wang
Keyword(s):  
Software Engineering ◽  
Mathematical Models ◽  
Real Time ◽  
Cognitive Processes ◽  
Reference Model ◽  
Human Perception ◽  
Comprehensive Model ◽  
Time Process ◽  
Rigorous Model ◽  
The Brain

An interactive motivation-attitude theory is developed based on the Layered Reference Model of the Brain (LRMB) and the object-attributerelation (OAR) model. This paper presents a rigorous model of human perceptual processes such as emotions, motivations, and attitudes. A set of mathematical models and formal cognitive processes of perception is developed. Interactions and relationships between motivation and attitude are formally described in real-time process algebra (RTPA). Applications of the mathematical models of motivations and attitudes in software engineering are demonstrated. This work is a part of the formalization of LRMB, which provides a comprehensive model for explaining the fundamental cognitive processes of the brain and their interactions. This work demonstrates that the complicated human emotional and perceptual phenomena can be rigorously modeled and formally treated based on cognitive informatics theories and denotational mathematics.

On the Cognitive Processes of Human Perception with Emotions, Motivations, and Attitudes

2009 ◽  
pp. 685-697
Author(s):  
Yingxu Wang
Keyword(s):  
Software Engineering ◽  
Mathematical Models ◽  
Real Time ◽  
Cognitive Processes ◽  
Reference Model ◽  
Human Perception ◽  
Comprehensive Model ◽  
Time Process ◽  
Rigorous Model ◽  
The Brain

An interactive motivation-attitude theory is developed based on the Layered Reference Model of the Brain (LRMB) and the object-attributerelation (OAR) model. This paper presents a rigorous model of human perceptual processes such as emotions, motivations, and attitudes. A set of mathematical models and formal cognitive processes of perception is developed. Interactions and relationships between motivation and attitude are formally described in real-time process algebra (RTPA). Applications of the mathematical models of motivations and attitudes in software engineering are demonstrated. This work is a part of the formalization of LRMB, which provides a comprehensive model for explaining the fundamental cognitive processes of the brain and their interactions. This work demonstrates that the complicated human emotional and perceptual phenomena can be rigorously modeled and formally treated based on cognitive informatics theories and denotational mathematics.

The Cognitive Processes of Formal Inferences

2011 ◽  
pp. 92-105
Author(s):  
Yingxu Wang
Keyword(s):  
Mathematical Models ◽  
Causal Inference ◽  
Real Time ◽  
Cognitive Processes ◽  
Cognitive Process ◽  
Process Algebra ◽  
Applied Research ◽  
Theoretical Research ◽  
Time Process ◽  
The Brain

Theoretical research is predominately an inductive process, while applied research is mainly a deductive process. Both inference processes are based on the cognitive process and means of abstraction. This chapter describes the cognitive processes of formal inferences such as deduction, induction, abduction, and analogy. Conventional propositional arguments adopt static causal inference. This chapter introduces more rigorous and dynamic inference methodologies, which are modeled and described as a set of cognitive processes encompassing a series of basic inference steps. A set of mathematical models of formal inference methodologies is developed. Formal descriptions of the 4 forms of cognitive processes of inferences are presented using Real-Time Process Algebra (RTPA). The cognitive processes and mental mechanisms of inferences are systematically explored and rigorously modeled. Applications of abstraction and formal inferences in both the revilement of the fundamental mechanisms of the brain and the investigation of next generation cognitive computers are explored.

Distinguishing The Cognitive Processes Of Mindfulness: Developing A Standardised Mindfulness Technique For Use In Longitudinal Randomised Control Trials

2019 ◽  
Author(s):  
Ben Isbel ◽  
Mathew J Summers
Keyword(s):  
Cognitive Processes ◽  
Mindfulness Training ◽  
Mindfulness Practice ◽  
Explanatory Framework ◽  
Cognitive Faculty ◽  
Metacognitive Processes ◽  
Capacity Model ◽  
Developmental Progression ◽  
Open Monitoring ◽  
Randomised Control Trials

A capacity model of mindfulness is adopted to differentiate the cognitive faculty of mindfulness from the metacognitive processes required to cultivate this faculty in mindfulness training. The model provides an explanatory framework incorporating both the developmental progression from focussed attention to open monitoring styles of mindfulness practice, along with the development of equanimity and insight. A standardised technique for activating these processes without the addition of secondary components is then introduced. Mindfulness-based interventions currently available for use in randomised control trials introduce components ancillary to the cognitive processes of mindfulness, limiting their ability to draw clear causative inferences. The standardised technique presented here does not introduce such ancillary factors, rendering it a valuable tool with which to investigate the processes activated in this practice.

Embodying algorithms, enactive artificial intelligence and the extended cognition: You can see as much as you know about algorithm

2021 ◽  
pp. 016555152098549
Author(s):  
Donghee Shin
Keyword(s):  
Artificial Intelligence ◽  
Embodied Cognition ◽  
Cognitive Processes ◽  
Process Model ◽  
Extended Cognition ◽  
Dual Process ◽  
Personalized Recommendation ◽  
Normative Values ◽  
And Performance ◽  
Concise Guide

The recent proliferation of artificial intelligence (AI) gives rise to questions on how users interact with AI services and how algorithms embody the values of users. Despite the surging popularity of AI, how users evaluate algorithms, how people perceive algorithmic decisions, and how they relate to algorithmic functions remain largely unexplored. Invoking the idea of embodied cognition, we characterize core constructs of algorithms that drive the value of embodiment and conceptualizes these factors in reference to trust by examining how they influence the user experience of personalized recommendation algorithms. The findings elucidate the embodied cognitive processes involved in reasoning algorithmic characteristics – fairness, accountability, transparency, and explainability – with regard to their fundamental linkages with trust and ensuing behaviors. Users use a dual-process model, whereby a sense of trust built on a combination of normative values and performance-related qualities of algorithms. Embodied algorithmic characteristics are significantly linked to trust and performance expectancy. Heuristic and systematic processes through embodied cognition provide a concise guide to its conceptualization of AI experiences and interaction. The identified user cognitive processes provide information on a user’s cognitive functioning and patterns of behavior as well as a basis for subsequent metacognitive processes.

scholarly journals Development of training method for vessel traffic service based on cognitive process

2021 ◽  
Author(s):  
Binbing Song ◽  
Hiroko Itoh ◽  
Yasumi Kawamura
Keyword(s):  
Cognitive Processes ◽  
Cognitive Process ◽  
Process Model ◽  
Situational Awareness ◽  
International Association ◽  
Tokyo Bay ◽  
Training Methods ◽  
Training Method ◽  
Oral Transmission ◽  
Maritime Traffic

AbstractVessel traffic service (VTS) is important to protect the safety of maritime traffic. Along with the expansion of monitoring area per VTS operator in Tokyo Bay, Japan, inexperienced operators must acquire the ability to quickly and accurately detect conditions that requires attention (CRAs) from a monitoring screen. In our previous study (Song B, Itoh H, Kawamura Y, Fukuto J (2018) Analysis of Cognitive Processes of Operators of Vessel Traffic Service. In: Proceedings of the 2018 International Association of Institutes of Navigation. IAIN 2018, pp 529–534, Song et al., J Jpn Inst Navig 140:48–54, 2019), we established a task analysis method based on the assumption that the cognitive process model consists of three stages: “situational awareness”, “situation judgment”, and “decision making”. A simulation experiment was conducted for VTS operators with different levels of ability and their cognitive processes were compared based on the observation of eye movements. The results showed that the inexperienced operators’ abilities to predict situation changes were lower. And it was considered that oral transmission of the knowledge is difficult, thus new training methods are needed to help the inexperienced operators to understand the prediction methods of experienced operators. In this study, based on the cognitive process of an experienced operator, we analyzed the prediction procedures of situation changes and developed an educational tool called vessel traffic routine (VTR). The training method learning VTR aims to quickly improve inexperienced VTS operators’ abilities to predict situation changes. A simulation verification experiment of the VTR effect was conducted for four inexperienced operators, who were divided into two groups with and without prior explanation of VTR. By evaluating the cognitive processes of inexperienced operators, it was confirmed that those given prior explanations of VTR were better at detecting CRAs.

Sign in / Sign up

Export Citation Format

Share Document