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.

Philosophy of Neuroscience

2016 ◽  
Author(s):  
Adina L. Roskies ◽  
Carl F. Craver
Keyword(s):  
Experimental Study ◽  
Experimental Techniques ◽  
Central Research ◽  
Philosophy Of Neuroscience ◽  
The Past ◽  
Research Areas ◽  
Potential Impact ◽  
Rich Material ◽  
The Mind ◽  
The Brain

The experimental study of the brain has exploded in the past several decades, providing rich material for both philosophers of science and philosophers of mind. In this chapter, the authors summarize some central research areas in philosophy of neuroscience. Some of these areas focus on the internal practice of neuroscience, that is, on the assumptions underlying experimental techniques, the accepted structures of explanations, the goals of integrating disciplines, and the possibility of a unified science of the mind-brain. Other areas focus outwards on the potential impact that neuroscience is having on our conception of the mind and its place in nature.

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.

Searching for Memory: The Brain, the Mind, and the Past

1999 ◽  
Vol 1 (1) ◽  
pp. 125-128
Author(s):  
C. Brooks Brenneis
Keyword(s):  
The Past ◽  
The Mind ◽  
The Brain

scholarly journals Emergent processes in cognitive-emotional interactions

2010 ◽  
Vol 12 (4) ◽  
pp. 433-448 ◽  
Keyword(s):  
Basal Forebrain ◽  
Significant Progress ◽  
Brain Organization ◽  
The Past ◽  
Mind And Brain ◽  
The Mind ◽  
The Brain ◽  
Anatomical Evidence ◽  
Made In

Emotion and cognition have been viewed as largely separate entities in the brain. Within this framework, significant progress has been made in understanding specific aspects of behavior. Research in the past two decades, however, has started to paint a different picture of brain organization, one in which network interactions are key to understanding complex behaviors. From both basic and clinical perspectives, the characterization of cognitive-emotional interactions constitutes a fundamental issue in the investigation of the mind and brain. This review will highlight the interactive and integrative potential that exists in the brain to bring together the cognitive and emotional domains. First, anatomical evidence will be provided, focusing on structures such as hypothalamus, basal forebrain, amygdala, cingulate cortex, orbitofrontal cortex, and insula. Data on functional interactions will then be discussed, followed by a discussion of a dual competition framework, which describes cognitive-emotional interactions in terms of perceptual and cognitive competition mechanisms.

scholarly journals OTAK DAN AKAL DALAM AYAT-AYAT NEUROSAINS

2018 ◽  
Vol 18 (1) ◽  
pp. 115-140
Author(s):  
Fu`ad Arif Noor
Keyword(s):  
Spinal Cord ◽  
Nervous System ◽  
Molecular Biology ◽  
Spinal Nerve ◽  
Nerve Cells ◽  
Genetic Studies ◽  
The Past ◽  
Reasonable Use ◽  
Brain And Spinal Cord ◽  
The Brain

Neuroscience, are simply the science devoted to learning Neoron(nerve cells). Nerve cells make up the nervous system, both thecentral nervous system (brain and spinal cord) and the peripheralnerves (31 pairs and 12 pairs of spinal nerve head). A nerve cellitself is not the smallest unit away from the nerve cell, the smallestunit of nerve cells (neurons) are the synapses ie the meeting point oftwo nerve cells move and pass the information (neurotransmitters).At the level of molecular biology, the smallest unit is like genes(genetic studies). Generally, the neuroscientist focused on nervecells in the brain. In the Qur'an sense has a glorious position. It wasevident the word "reasonable" in the Qur'an is mentioned insignificant amounts. The word "reasonable" in the Qur'an is called49 times. All in the form mudhari deed '(a verb that indicates thecurrent and future), but one that is shaped madhi verb (a verb thatindicates the past).Although the Qur'an does not mention the "sense" in its form as' acertain part of the human self '(سفنلا ىف لاقتسم ارهوج), which becamethe source of the birth of any acts rationally, but the Qur'anmentions "reasonable" in its meaning as' activities reasonable use'(لقعتلا ةيلمع), the appeal that invites use of reason as the path to truth(لقعتلا), thinking (ركفتلا), watching (رظنلا), to understand and learn(هقفتلا), take the wisdom and lessons from each incident (رابتعلاا) andetc.

Philosophy of Neuroscience

2016 ◽  
Author(s):  
Adina L. Roskies ◽  
Carl F. Craver
Keyword(s):  
Experimental Study ◽  
Experimental Techniques ◽  
Central Research ◽  
Philosophy Of Neuroscience ◽  
The Past ◽  
Research Areas ◽  
Potential Impact ◽  
Rich Material ◽  
The Mind ◽  
The Brain

The experimental study of the brain has exploded in the past several decades, providing rich material for both philosophers of science and philosophers of mind. In this chapter, the authors summarize some central research areas in philosophy of neuroscience. Some of these areas focus on the internal practice of neuroscience, that is, on the assumptions underlying experimental techniques, the accepted structures of explanations, the goals of integrating disciplines, and the possibility of a unified science of the mind-brain. Other areas focus outwards on the potential impact that neuroscience is having on our conception of the mind and its place in nature.

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 A Systematic Methodology on Class Imbalanced Problems involved in the Classification of Real-World Datasets

2019 ◽  
Vol 8 (3) ◽  
pp. 7071-7081
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Imbalanced Data ◽  
Past Research ◽  
Future Research ◽  
Data Sets ◽  
Time Data ◽  
Real World Data ◽  
The Past ◽  
Research Studies

Current generation real-world data sets processed through machine learning are imbalanced by nature. This imbalanced data enables the researchers with a challenging scenario in the context of perdition for both the machine learning and data mining algorithms. It is observed from the past research studies most of the imbalanced data sets consists of the major classes and minor classes and the major class leads the minor class. Several standards and hybrid prediction algorithms are proposed in various application domains but in most of the real-time data sets analyzed in the studies are imbalanced by nature thereby affecting the accuracy of the prediction. This paper presents a systematic survey of the past research studies to analyze intrinsic data characteristics and techniques utilized for handling class-imbalanced data. In addition, this study reveals the research gaps, trends and patterns in existing studies and discusses briefly on future research directions

scholarly journals Not One, but Many Critical States: A Dynamical Systems Perspective

2021 ◽  
Vol 15 ◽  
Author(s):  
Thilo Gross
Keyword(s):  
Dynamical Systems ◽  
Mathematical Theory ◽  
Bifurcation Theory ◽  
Neural Dynamics ◽  
Transfer Of Knowledge ◽  
Critical States ◽  
Systems Perspective ◽  
The Past ◽  
Dynamical Regimes ◽  
The Brain

The past decade has seen growing support for the critical brain hypothesis, i.e., the possibility that the brain could operate at or very near a critical state between two different dynamical regimes. Such critical states are well-studied in different disciplines, therefore there is potential for a continued transfer of knowledge. Here, I revisit foundations of bifurcation theory, the mathematical theory of transitions. While the mathematics is well-known it's transfer to neural dynamics leads to new insights and hypothesis.

scholarly journals Analysis of Neuromorphic Computing Systems and its Applications in Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 5309-5312
Author(s):  
Pranava Bhat
Keyword(s):  
Machine Learning ◽  
Human Brain ◽  
Neural Systems ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
The Past ◽  
Computational Capability ◽  
Biological World ◽  
Areas Of Interest ◽  
The Brain

The domain of engineering has always taken inspiration from the biological world. Understanding the functionalities of the human brain is one of the key areas of interest over time and has caused many advancements in the field of computing systems. The computational capability per unit power per unit volume of the human brain exceeds the current best supercomputers. Mimicking the physics of computations used by the nervous system and the brain can bring a paradigm shift to the computing systems. The concept of bridging computing and neural systems can be termed as neuromorphic computing and it is bringing revolutionary changes in the computing hardware. Neuromorphic computing systems have seen swift progress in the past decades. Many organizations have introduced a variety of designs, implementation methodologies and prototype chips. This paper discusses the parameters that are considered in the advanced neuromorphic computing systems and the tradeoffs between them. There have been attempts made to make computer models of neurons. Advancements in the hardware implementation are fuelling the applications in the field of machine learning. This paper presents the applications of these modern computing systems in Machine Learning.

Sign in / Sign up

Export Citation Format

Share Document