scholarly journals The Future of Intelligence: The Central Meaning-Making Unit of Intelligence in the Mind, the Brain, and Artificial Intelligence

2021 ◽  
Author(s):  
Andreas Demetriou ◽  
hudson golino ◽  
Hudson Golino
Keyword(s):  
Artificial Intelligence ◽  
Meaning Making ◽  
Mental Content ◽  
Future Research ◽  
General Intelligence ◽  
Artificial Intelligence Research ◽  
Central Meaning ◽  
The Mind ◽  
Core Meaning ◽  
The Brain

This paper focuses on general intelligence, g. We first point to broadly accepted facts about g: it is robust, reliable, and sensitive to learning. We then summarize conflicting theories about its nature and development (Mutualism, Process Overlap Theory, and Dynamic Mental Field Theory) and suggest how future research may resolve their disputes. A model is proposed for g involving a core meaning-making mechanism, noetron, drawing on Alignment, Abstraction, and Cognizance, perpetually generating new mental content. Noetron develops through several levels of control: episodic attentional inferential truth epistemic control in infancy, preschool, childhood, adolescence, and adulthood, respectively. Finally, we propose an agenda for future brain, assuming a brain noetron, and artificial intelligence research, assuming an artificial noetron, that might uncover the underlying brain mechanisms of g and generate artificial general intelligence.

scholarly journals The Future of Intelligence: The Central Meaning-Making Unit of Intelligence in the Mind, the Brain, and Artificial Intelligence

2021 ◽  
Author(s):  
Andreas Demetriou ◽  
hudson golino ◽  
George Charilaos Spanoudis ◽  
Nikolaos Makris ◽  
Samuel Greiff
Keyword(s):  
Artificial Intelligence ◽  
Meaning Making ◽  
Mental Content ◽  
Future Research ◽  
General Intelligence ◽  
Artificial Intelligence Research ◽  
Central Meaning ◽  
The Mind ◽  
Core Meaning ◽  
The Brain

This paper focuses on general intelligence, g. We first point to broadly accepted facts about g: it is robust, reliable, and sensitive to learning. We then summarize conflicting theories about its nature and development (Mutualism, Process Overlap Theory, and Dynamic Mental Field Theory) and suggest how future research may resolve their disputes. A model is proposed for g involving a core meaning-making mechanism, noetron, drawing on Alignment, Abstraction, and Cognizance, perpetually generating new mental content. Noetron develops through several levels of control: episodic attentional inferential truth epistemic control in infancy, preschool, childhood, adolescence, and adulthood, respectively. Finally, we propose an agenda for future brain, assuming a brain noetron, and artificial intelligence research, assuming an artificial noetron, that might uncover the underlying brain mechanisms of g and generate artificial general intelligence.

scholarly journals The future of intelligence: The central meaning-making unit of intelligence in the mind, the brain, and artificial intelligence

Intelligence ◽  
2021 ◽  
Vol 87 ◽  
pp. 101562
Author(s):  
Andreas Demetriou ◽  
Hudson Golino ◽  
George Spanoudis ◽  
Nikolaos Makris ◽  
Samuel Greiff
Keyword(s):  
Artificial Intelligence ◽  
Meaning Making ◽  
The Future ◽  
Central Meaning ◽  
The Mind ◽  
The Brain

Teaching about Near-Death Experiences: The Effectiveness of Using the Day I Died

2011 ◽  
Vol 63 (4) ◽  
pp. 373-388 ◽  
Author(s):  
Janice Miner Holden ◽  
Kathy Oden ◽  
Kelly Kozlowski ◽  
Bert Hayslip
Keyword(s):  
Repeated Measures ◽  
Future Research ◽  
Control Group ◽  
Waitlist Control ◽  
Before And After ◽  
Waitlist Control Group ◽  
Quasi Experimental ◽  
The Mind ◽  
Near Death Experiences ◽  
The Brain

In this article, we reviewed results of research on near-death experiences (NDEs) over the past 3 decades and examined the effect of viewing the hour-long 2002 BBC documentary The Day I Died: The Mind, the Brain, and Near-Death Experiences on accurate knowledge about near-death experiences among advanced undergraduates at a southwestern university. In a quasi-experimental research design, the experimental group completed a 20-item questionnaire before and after viewing the documentary ( n = 66; 45 females, 21 males), and the waitlist control group completed the questionnaire as pre- and posttest before viewing the documentary ( n = 39; 36 female, 3 male). The two groups' scores at pretest were not significantly different ( p > .05). Group by occasion repeated measures ANOVA revealed the experimental group's posttest scores moved significantly in the direction of correctness with a large effect size ( p < .001; η2= .56), whereas waitlist control group posttest scores remained similar to pretest scores. We discuss two exceptions to the effectiveness of the documentary and recommendations for educators using it as well as for future research.

Ian Glynn, An anatomy of thought: The origin and machinery of the mind. New York: Oxford University Press, 1999. Pp. vi, 456. Hb $35.

2002 ◽  
Vol 31 (4) ◽  
pp. 613-616
Author(s):  
Ronald Gray
Keyword(s):  
Artificial Intelligence ◽  
New York ◽  
Interdisciplinary Approach ◽  
Oxford University ◽  
The Mind ◽  
The Brain ◽  
Oxford University Press

In this highly ambitious book, Glynn attempts to provide a description of both how the brain works and how it has developed. Taking an interdisciplinary approach (he is a physiologist by training), he relies on insights from a wide number of disciplines, including psychology, neurology, anthropology, linguistics, artificial intelligence, psychiatry, physiology, and even philosophy. He is interested in providing answers to some perennial and interconnected questions that relate to the mind: “What kind of thing is mind? What is the relation between our minds and our bodies and, more specifically, what is the relation between what goes on in our minds, and what goes on in our brains? How did brains and minds originate? Can our brains be regarded as nothing more than exceedingly complicated machines? Can minds exist without brains” (p. 4). Although his arguments are rather technical, the book is intended for a nonscientist audience.

scholarly journals It may be time to perfect the neuron of artificial neural network

2020 ◽  
Author(s):  
Gang Liu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Input Data ◽  
Basic Unit ◽  
Future Research ◽  
Brain Neuron ◽  
Artificial Neuron ◽  
The Neural Network ◽  
Artificial Neural ◽  
The Brain

In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence. The basic unit of an ANN is to mimic neurons in the brain. Neuron in ANNs is expressed as f(wx+b) or f(wx).This structure does not consider the information processing capabilities of dendrites. However, recently, studies shown that dendrites participate in pre-calculation in the brain. Concretely, biological dendrites play a role in the pre-processing to the interaction information of input data. Therefore, it's time to perfect the neuron of the neural network. This paper, add dendrite processing section, presents a novel artificial neuron, according to our previous studies (CR-PNN or Gang transform). The dendrite processing section can be expressed as WA.X. Because I perfected the basic unit of ANNs-neuron, there are so many networks to try, this article gives the basic architecture for reference in future research.

scholarly journals Contribution of Cognitive Science and Artificial Intelligence in the Simulation of the Complex Human Mind

2020 ◽  
Vol 9 (2) ◽  
pp. 928-932
Keyword(s):  
Artificial Intelligence ◽  
Cognitive Science ◽  
Real Time ◽  
Human Mind ◽  
Scientific Study ◽  
Computational Procedures ◽  
The Mind ◽  
And Behavior ◽  
Interdisciplinary Theory ◽  
The Brain

The research incorporated encircles the interdisciplinary theory of cognitive science in the branch of artificial intelligence. It has always been the end goal that better understanding of the idea can be guaranteed. Besides, a portion of the real-time uses of cognitive science artificial intelligence have been taken into consideration as the establishment for more enhancements. Before going into the scopes of future, there are many complexities that occur in real-time which have been uncovered. Cognitive science is the interdisciplinary, scientific study of the brain and its procedures. It inspects the nature, the activities, and the elements of cognition. Cognitive researchers study intelligence and behavior, with an emphasis on how sensory systems speak to, process, and change data. Intellectual capacities of concern to cognitive researchers incorporate recognition, language, memory, alertness, thinking, and feeling; to comprehend these resources, cognitive researchers acquire from fields, for example, psychology, artificial intelligence, philosophy, neuroscience, semantics, and anthropology. The analytic study of cognitive science ranges numerous degrees of association, from learning and choice to logic and planning; from neural hardware to modular mind organization. The crucial idea of cognitive science is that "thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures."

Supervenience

2018 ◽  
Author(s):  
Mark Jago
Keyword(s):  
Physical Properties ◽  
Mental Content ◽  
Technical Term ◽  
The Individual ◽  
The Mind ◽  
The Brain ◽  
The Way

Supervenience is a concept developed by philosophers to capture a way in which certain facts, events or properties rely or depend on others in a noncausal way. It is one way to capture the notion that certain phenomena seem to emerge from, or are determined by, others. Consider an example. The movement of one snooker ball depends on the way it is hit, either by the cue or by another ball. This is the familiar causal notion of dependence. But now suppose the balls make a perfect ‘W’ shape on the table. That ‘W’ depends on the arrangement of the individuals balls. It isn’t that the balls’ arrangement causes the ‘W’ to exist. Rather, the balls and their arrangement constitutes, or makes up the ‘W’. Their individual arrangements, taken together, brings it about that there is a ‘W’ shape on the table. These are all intuitive but imprecise ways of capturing the noncausal relationship between the individual balls and the ‘W’. The technical term philosophers use for this relationship is supervenience. It was used by Hare, and was put centre stage first by Davidson, and then by Kim and Lewis. Section 1 will explore different ways to define ‘supervenience’. Philosophers find the notion of supervenience useful because it can be used to describe and analyse a number of phenomena which seem to depend on other phenomena in an important, but noncausal, way. These might include: truth depending on reality; the mind depending on the brain; and moral and aesthetic truths depending on physical properties. Supervenience also provides a useful way to help clarify what is at stake in a number of debates, such as the internalist/externalist debate over mental content.

Expanding perspectives on cognition in humans, animals, and machines

2016 ◽  
Author(s):  
Alex Gomez-Marin ◽  
Zachary F Mainen
Keyword(s):  
Machine Learning ◽  
Dynamical Systems ◽  
Molecular Biology ◽  
Future Research ◽  
Learning Behavior ◽  
The Past ◽  
The Mind ◽  
The Brain

Over the past decade neuroscience has been attacking the problem of cognition with increasing vigor. Yet, what exactly is cognition, beyond a general signifier of anything seemingly complex the brain does? Here, we briefly review attempts to define, describe, explain, build, enhance and experience cognition. We highlight perspectives including psychology, molecular biology, computation, dynamical systems, machine learning, behavior and phenomenology. This survey of the landscape reveals not a clear target for explanation but a pluralistic and evolving scene with diverse opportunities for grounding future research. We argue that rather than getting to the bottom of it, over the next century, by deconstructing and redefining cognition, neuroscience will and should expand rather than merely reduce our concept of the mind.

scholarly journals It may be time to perfect the neuron of artificial neural network

2020 ◽  
Author(s):  
Gang Liu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Input Data ◽  
Basic Unit ◽  
Future Research ◽  
Brain Neuron ◽  
Artificial Neuron ◽  
The Neural Network ◽  
Artificial Neural ◽  
The Brain

In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence. The basic unit of an ANN is to mimic neurons in the brain. Neuron in ANNs is expressed as f(wx+b) or f(wx).This structure does not consider the information processing capabilities of dendrites. However, recently, studies shown that dendrites participate in pre-calculation in the brain. Concretely, biological dendrites play a role in the pre-processing to the interaction information of input data. Therefore, it's time to perfect the neuron of the neural network. This paper added dendrite processing section, and presented a novel artificial neuron, according to our previous studies (CR-PNN or Gang transform). The dendrite processing section can be expressed as WA.X. Because I perfected the basic unit of ANNs-neuron, there are so many networks to try, this article gives the basic architecture for reference in future research.

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