Towards Characterizing the Canonical Computations Generating Phenomenal Experience

2021 ◽  
Author(s):  
Megan A. K. Peters
Keyword(s):  
Computational Models ◽  
Subjective Experience ◽  
Phenomenal Experience ◽  
Early Step ◽  
Generative Process ◽  
Metacognitive Processes ◽  
Starting Point ◽  
Computational Cognitive Science ◽  
The Brain ◽  
Computational Basis

Few people tackle the neural or computational basis of qualitative experience (Frith, 2019). Why? One major reason is that science and philosophy have both struggled to propose how we might even begin to start studying it. Here I propose that metacognitive computations, and the subjective feelings that go along with them, give us a solid starting point. Specifically, perceptual metacognition possesses unique properties that provide a powerful and unique opportunity for studying the neural and computational correlates of subjective experience, falling into three categories: (1) Metacognition is subjective: there is something it is like to feel ‘confident’; (2) Metacognitive processes are objectively characterizable: We can objectively observe metacognitive reports and define computational models to fit to empirical data; (3) Metacognition has multiple hierarchically-dependent “anchors”, presenting a unique computational opportunity for developing sensitive, specific models. I define this Metacognition as a Step Toward Explaining Phenomenology (M-STEP) approach to state that, given these properties, computational models of metacognition represent an empirically-tractable early step in identifying the generative process that constructs qualitative experience. By applying decades of developments in computational cognitive science and formal computational model comparisons to the specific properties of perceptual metacognition, we may reveal new and exciting insights about how the brain constructs subjective conscious experiences and the nature of those experiences themselves.

TEORI KECERDASAN, PENDIDIKAN ANAK, DAN KOMUNIKASI DALAM KELUARGA

1970 ◽  
Vol 6 (1) ◽  
Author(s):  
Muskinul Fuad
Keyword(s):  
Multiple Intelligences ◽  
Educational Activity ◽  
Howard Gardner ◽  
Human Beings ◽  
Brain Functions ◽  
Starting Point ◽  
The Family ◽  
Education Today ◽  
The Brain ◽  
Spatial Logics

The education system in Indonesia emphasize on academic intelligence, whichincludes only two or three aspects, more than on the other aspects of intelligence. For thatreason, many children who are not good at academic intelligence, but have good potentials inother aspects of intelligence, do not develop optimally. They are often considered and labeledas "stupid children" by the existing system. This phenomenon is on the contrary to the theoryof multiple intelligences proposed by Howard Gardner, who argues that intelligence is theability to solve various problems in life and produce products or services that are useful invarious aspects of life.Human intelligence is a combination of various general and specific abilities. Thistheory is different from the concept of IQ (intelligence quotient) that involves only languageskills, mathematical, and spatial logics. According to Gardner, there are nine aspects ofintelligence and its potential indicators to be developed by each child born without a braindefect. What Gardner suggested can be considered as a starting point to a perspective thatevery child has a unique individual intelligence. Parents have to treat and educate theirchildren proportionally and equitably. This treatment will lead to a pattern of education that isfriendly to the brain and to the plurality of children’s potential.More than the above points, the notion that multiple intelligences do not just comefrom the brain needs to be followed. Humans actually have different immaterial (spiritual)aspects that do not refer to brain functions. The belief in spiritual aspects and its potentialsmeans that human beings have various capacities and they differ from physical capacities.This is what needs to be addressed from the perspective of education today. The philosophyand perspective on education of the educators, education stakeholders, and especially parents,are the first major issue to be addressed. With this step, every educational activity andcommunication within the family is expected to develop every aspect of children'sintelligence, especially the spiritual intelligence.

scholarly journals Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 229
Author(s):  
JunHyuk Woo ◽  
Hyesun Cho ◽  
YunHee Seol ◽  
Soon Ho Kim ◽  
Chanhyeok Park ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Mitochondrial Dysfunction ◽  
Computational Models ◽  
Neuronal Damage ◽  
Living Organism ◽  
The Body ◽  
Mitochondrial Functions ◽  
A Cell ◽  
The Brain ◽  
And Oxidative Stress

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5′-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

scholarly journals Identification of Novel Cathepsin B Inhibitors with Implications in Alzheimer’s Disease: Computational Refining and Biochemical Evaluation

Cells ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1946
Author(s):  
Nitin Chitranshi ◽  
Ashutosh Kumar ◽  
Samran Sheriff ◽  
Veer Gupta ◽  
Angela Godinez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hydrogen Bond ◽  
Virtual Screening ◽  
Cathepsin B ◽  
Amyloid Β ◽  
Proteolytic Degradation ◽  
Hydrogen Bond Acceptor ◽  
Starting Point ◽  
The Brain

Amyloid precursor protein (APP), upon proteolytic degradation, forms aggregates of amyloid β (Aβ) and plaques in the brain, which are pathological hallmarks of Alzheimer’s disease (AD). Cathepsin B is a cysteine protease enzyme that catalyzes the proteolytic degradation of APP in the brain. Thus, cathepsin B inhibition is a crucial therapeutic aspect for the discovery of new anti-Alzheimer’s drugs. In this study, we have employed mixed-feature ligand-based virtual screening (LBVS) by integrating pharmacophore mapping, docking, and molecular dynamics to detect small, potent molecules that act as cathepsin B inhibitors. The LBVS model was generated by using hydrophobic (HY), hydrogen bond acceptor (HBA), and hydrogen bond donor (HBD) features, using a dataset of 24 known cathepsin B inhibitors of both natural and synthetic origins. A validated eight-feature pharmacophore hypothesis (Hypo III) was utilized to screen the Maybridge chemical database. The docking score, MM-PBSA, and MM-GBSA methodology was applied to prioritize the lead compounds as virtual screening hits. These compounds share a common amide scaffold, and showed important interactions with Gln23, Cys29, His110, His111, Glu122, His199, and Trp221. The identified inhibitors were further evaluated for cathepsin-B-inhibitory activity. Our study suggests that pyridine, acetamide, and benzohydrazide compounds could be used as a starting point for the development of novel therapeutics.

scholarly journals Pain Detection and the Privacy of Subjective Experience

2007 ◽  
Vol 33 (2-3) ◽  
pp. 433-456 ◽  
Author(s):  
Adam J. Kolber
Keyword(s):  
Subjective Experience ◽  
Brain Regions ◽  
Physical Pain ◽  
Medical Evidence ◽  
Functional Magnetic Resonance ◽  
Positron Emission ◽  
Pain Symptoms ◽  
The Brain ◽  
Insight Into ◽  
Neuroimaging Techniques

A neurologist with abdominal pain goes to see a gastroenterologist for treatment. The gastroenterologist asks the neurologist where it hurts. The neurologist replies, “In my head, of course.” Indeed, while we can feel pain throughout much of our bodies, pain signals undergo most of their processing in the brain. Using neuroimaging techniques like functional magnetic resonance imaging (“fMRI”) and positron emission tomography (“PET”), researchers have more precisely identified brain regions that enable us to experience physical pain. Certain regions of the brain's cortex, for example, increase in activation when subjects are exposed to painful stimuli. Furthermore, the amount of activation increases with the intensity of the painful stimulus. These findings suggest that we may be able to gain insight into the amount of pain a particular person is experiencing by non-invasively imaging his brain.Such insight could be particularly valuable in the courtroom where we often have no definitive medical evidence to prove or disprove claims about the existence and extent of pain symptoms.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

Computational basis of decision-making impairment in multiple sclerosis

2021 ◽  
pp. 135245852110593
Author(s):  
Rodrigo S Fernández ◽  
Lucia Crivelli ◽  
María E Pedreira ◽  
Ricardo F Allegri ◽  
Jorge Correale
Keyword(s):  
Multiple Sclerosis ◽  
Decision Making ◽  
Computational Models ◽  
Iowa Gambling Task ◽  
Memory Impairments ◽  
Hierarchical Bayesian Analysis ◽  
Control Subjects ◽  
Neurocognitive Impairments ◽  
Neurocognitive Evaluation ◽  
Computational Basis

Background: Multiple sclerosis (MS) is commonly associated with decision-making, neurocognitive impairments, and mood and motivational symptoms. However, their relationship may be obscured by traditional scoring methods. Objectives: To study the computational basis underlying decision-making impairments in MS and their interaction with neurocognitive and neuropsychiatric measures. Methods: Twenty-nine MS patients and 26 matched control subjects completed a computer version of the Iowa Gambling Task (IGT). Participants underwent neurocognitive evaluation using an expanded version of the Brief Repeatable Battery. Hierarchical Bayesian Analysis was used to estimate three established computational models to compare parameters between groups. Results: Patients showed increased learning rate and reduced loss-aversion during decision-making relative to control subjects. These alterations were associated with: (1) reduced net gains in the IGT; (2) processing speed, executive functioning and memory impairments; and (3) higher levels of depression and current apathy. Conclusion: Decision-making deficits in MS patients could be described by the interplay between latent computational processes, neurocognitive impairments, and mood/motivational symptoms.

scholarly journals Similarities Between the AdS/CFT Correspondence and the Binding Problem

2019 ◽  
Author(s):  
Michael Elliott
Keyword(s):  
Black Holes ◽  
Subjective Experience ◽  
Condensed Matter ◽  
Boundary Surface ◽  
Theoretical Physics ◽  
Binding Problem ◽  
Physical Systems ◽  
Combine Information ◽  
The Brain

The binding problem refers to the puzzle of how the brain combines objects’ properties such as motion, color, shape, location, sound, etc., from diverse regions of the brain and forms a unified subjective experience. Holographic physical systems, recently discovered darlings of theoretical physics, began with research into black holes but have since evolved into the study of condensed matter systems in the laboratory like superfluids and superconductors. A primary example is the AdS/CFT correspondence. A recent conjecture of this correspondence suggests that holographic systems combine information from across a boundary surface, sort out the simplest description of said information, and, in turn, use it to determine the geometry of spacetime itself in the interior - a kind of geometric hologram. Although we would never tend to think of these two processes as related, in this paper we point out ten similarities between the two and show that holographic systems are the only physical systems that match the subjective and computational characteristics of the binding problem.

scholarly journals The joy of sharing knowledge: But what if there is no knowledge to share? A critical reflection on human capacity building in Africa

2007 ◽  
Vol 7 ◽  
pp. 18-28
Author(s):  
Johannes J Britz
Keyword(s):  
Research And Development ◽  
Capacity Building ◽  
Capacity Development ◽  
Clear Understanding ◽  
Political Will ◽  
Current Trends ◽  
Long Run ◽  
Starting Point ◽  
Human Capacity ◽  
The Brain

This article focuses on the current trends and initiatives in human capacity building in Africa. It takes as it starting point that human capacity development is essential for Africa to become an information and know-ledge society and therefore an equal partner in the global sharing of knowledge. Four knowledge areas are identified and discussed. These are education, research and development, brain drain and information and documentation drain. The paper concludes that there is a clear understanding in Africa that its future lies with education and that most African leaders have a strong political will to invest in human capacity building on the continent. It is also clear that much has been done, particularly primary education. Africa will most defi-nitely benefit from this in the long run. Problem areas remain however. These are in the needed growth of research and development and how to address the brain and information drain phenomena.

scholarly journals Distributed Code for Semantic Relations Predicts Neural Similarity

2019 ◽  
Author(s):  
Jeffrey N. Chiang ◽  
Yujia Peng ◽  
Hongjing Lu ◽  
Keith J. Holyoak ◽  
Martin M. Monti
Keyword(s):  
Computational Models ◽  
Semantic Network ◽  
Building Blocks ◽  
Semantic Relations ◽  
Human Cognition ◽  
Sequential Presentation ◽  
Specific Content ◽  
Distributed Representations ◽  
Relational Similarity ◽  
The Brain

AbstractThe ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coded as atomistic links in a semantic network, or as distributed patterns over some core set of abstract relations. The form and content of the conceptual and neural representations of semantic relations remains to be empirically established. The present study combined computational modeling and neuroimaging to investigate the representation and comparison of abstract semantic relations in the brain. By using sequential presentation of verbal analogies, we decoupled the neural activity associated with encoding the representation of the first-order semantic relation between words in a pair from that associated with the second-order comparison of two relations. We tested alternative computational models of relational similarity in order to distinguish between rival accounts of how semantic relations are coded and compared in the brain. Analyses of neural similarity patterns supported the hypothesis that semantic relations are coded, in the parietal cortex, as distributed representations over a pool of abstract relations specified in a theory-based taxonomy. These representations, in turn, provide the immediate inputs to the process of analogical comparison, which draws on a broad frontoparietal network. This study sheds light not only on the form of relation representations but also on their specific content.SignificanceRelations provide basic building blocks for language and thought. For the past half century, cognitive scientists exploring human semantic memory have sought to identify the code for relations. In a neuroimaging paradigm, we tested alternative computational models of relation processing that predict patterns of neural similarity during distinct phases of analogical reasoning. The findings allowed us to draw inferences not only about the form of relation representations, but also about their specific content. The core of these distributed representations is based on a relatively small number of abstract relation types specified in a theory-based taxonomy. This study helps to resolve a longstanding debate concerning the nature of the conceptual and neural code for semantic relations in the mind and brain.

Sign in / Sign up

Export Citation Format

Share Document