Mind - Wikipedia
dayline.info: Mental Biology: The New Science of How the Brain and Mind presents the latest research findings on this elusive brain-mind connection in a. Read chapter 5 Mind and Brain: First released in the Spring of , How We then look at language in learning as an example of the mind-brain connection. and molecular biology of the nervous system, with particular interest in how brain . As a tool for exploring the biology of the mind, neuroimaging has given brain science a strong cultural presence. As one scientist remarked.
When the mind is itself cognized properly, without misperceiving its mode of existence, it appears to exist like an illusion. There is a big difference however between being "space and illusion" and being "space-like" and "illusion-like". Mind is not composed of space, it just shares some descriptive similarities to space.
Mind is not an illusion, it just shares some descriptive qualities with illusions. Buddhism posits that there is no inherent, unchanging identity Inherent I, Inherent Me or phenomena Ultimate self, inherent self, Atman, Soul, Self-essence, Jiva, Ishvara, humanness essence, etc. In other words, human beings consist of merely a body and a mind, and nothing extra. Within the body there is no part or set of parts which is — by itself or themselves — the person.
Similarly, within the mind there is no part or set of parts which are themselves "the person". A human being merely consists of five aggregates, or skandhas and nothing else. In the same way, "mind" is what can be validly conceptually labelled onto our mere experience of clarity and knowing. There is something separate and apart from clarity and knowing which is "Awareness", in Buddhism. There is also not "objects out there, mind in here, and experience somewhere in-between".
There is a third thing called "awareness" which exists being aware of the contents of mind and what mind cognizes. There are five senses arising of mere experience: This awareness is deeply related to "no-self" because it does not judge the experience with craving or aversion.
Clearly, the experience arises and is known by mind, but there is a third thing calls Sati what is the "real experiencer of the experience" that sits apart from the experience and which can be aware of the experience in 4 levels.
Body Sensations Changes of the body mind. Mind, Contents of the mind. Changes of the body mind. To be aware of these four levels one needs to cultivate equanimity toward Craving and Aversion. This is Called Vipassana which is different from the way of reacting with Craving and Aversion. We first explain some basic concepts of neuroscience and new knowledge about brain development, including the effects of instruction and learning on the brain.
We then look at language in learning as an example of the mind-brain connection. Lastly, we examine research on how memory is represented in the brain and its implications for learning. Brain development and psychological development involve continuous interactions between a child and the external environment—or, more accurately, a hierarchy of environments, extending from the level of the individual body cells to the most obvious boundary of the skin. Greater understanding of the nature of this interactive process renders moot such questions as how much depends on genes and how much on environment.
As various developmental researchers have suggested, this question is much like asking which contributes most to the area of a rectangle, its height or its width Eisenberg, ? Several crucial questions about early learning particularly intrigue neuroscientists.
How does the brain develop? Are there stages of brain development? Are there critical periods when certain things must happen for the brain to develop normally? How is information encoded in the developing and the adult nervous systems? And perhaps most important: How does experience affect the brain? Page Share Cite Suggested Citation: Nerve cells are equipped with a cell body—a sort of metabolic heart—and an enormous treelike structure called the dendritic field, which is the input side of the neuron.
Information comes into the cell from projections called axons. Most of the excitatory information comes into the cell from the dendritic field, often through tiny dendritic projections called spines. The junctions through which information passes from one neuron to another are called synapses, which can be excitatory or inhibitory in nature.
The neuron integrates the information it receives from all of its synapses and this determines its output. At birth, the human brain has in place only a relatively small proportion of the trillions of synapses it will eventually have; it gains about two-thirds of its adult size after birth. The rest of the synapses are formed after birth, and a portion of this process is guided by experience.
Synaptic connections are added to the brain in two basic ways. The first way is that synapses are overproduced, then selectively lost. Synapse overproduction and loss is a fundamental mechanism that the brain uses to incorporate information from experience. It tends to occur during the early periods of development. In the visual cortex—the area of the cerebral cortex of the brain that controls sight—a person has many more synapses at 6 months of age than at adulthood.
This is because more and more synapses are formed in the early months of life, then they disappear, sometimes in prodigious numbers. The time required for this phenomenon to run its course varies in different parts of the brain, from 2 to 3 years in the human visual cortex to 8 to 10 years in some parts of the frontal cortex. Some neuroscientists explain synapse formation by analogy to the art of sculpture.
Classical artists working in marble created a sculpture by chiseling away unnecessary bits of stone until they achieved their final form. The nervous system sets up a large number of connections; experience then plays on this network, selecting the appropriate connections and removing the inappropriate ones.
What remains is a refined final form that constitutes the sensory and perhaps the cognitive bases for the later phases of development. The second method of synapse formation is through the addition of new synapses—like the artist who creates a sculpture by adding things together until the form is complete. This process is not only sensitive to experience, it is actually driven by experience. Synapse addition probably lies at the base of some, or even most, forms of memory. As discussed later in this chapter, the work of cognitive scientists and education researchers is contributing to our understanding of synapse addition.
Wiring the Brain The role of experience in wiring the brain has been illuminated by research on the visual cortex in animals and humans. In adults, the inputs entering the brain from the two eyes terminate separately in adjacent regions of the visual cortex. Subsequently, the two inputs converge on the next set of neurons. People are not born with this neural pattern. But through the normal processes of seeing, the brain sorts things out. Neuroscientists discovered this phenomenon by studying humans with visual abnormalities, such as a cataract or a muscle irregularity that deviates the eye.
If the eye is deprived of the appropriate visual experience at an early stage of development because of such abnormalitiesit loses its ability to transmit visual information into the central nervous system. When the eye that was incapable of seeing at a very early age was corrected later, the correction alone did not help—the afflicted eye still could not see.
When researchers looked at the brains of monkeys in which similar kinds of experimental manipulations had been made, they found that the normal eye had captured a larger than average amount of neurons, and the impeded eye had correspondingly lost those connections. This phenomenon only occurs if an eye is prevented from experiencing normal vision very early in development. The period at which the eye is sensitive corresponds to the time of synapse overproduction and loss in the visual cortex.
Out of the initial mix of overlapping inputs, the neural connections that belong to the eye that sees normally tend to survive, while the connections that belong to the abnormal eye wither away. When both eyes see normally, each eye loses some of the overlapping connections, but both keep a normal number. In the case of deprivation from birth, one eye completely takes over. The later the deprivation occurs after birth, the less effect it has. By about 6 months of age, closing one eye for weeks on end will produce no effect whatsoever.
The critical period has passed; the connections have already sorted themselves out, and the overlapping connections have been eliminated. This anomaly has helped scientists gain insights into normal visual development. By overproducing synapses then selecting the right connections, the brain develops an organized wring diagram that functions optimally.
The brain development process actually uses visual information entering from outside to become more precisely organized than it could with intrinsic molecular mechanisms alone.
This external information is even more important for later cognitive development. The more a person interacts with the world, the more a person needs information from the world incorporated into the brain structures.
Synapse overproduction and selection may progress at different rates in different parts of the brain Huttenlocher and Dabholkar, In the primary visual cortex, a peak in synapse density occurs relatively quickly. In the medial frontal cortex, a region clearly associated with higher cognitive functions, the process is more protracted: The selection process, which corresponds conceptually to the main organization of patterns, continues during the next 4—5 years and ends around early adolescence.
This lack of synchrony among cortical regions may also occur upon individual cortical neurons where different inputs may mature at different rates see Juraska,on animal studies. After the cycle of synapse overproduction and selection has run its course, additional changes occur in the brain.
They appear to include both the modification of existing synapses and the addition of entirely new synapses to the brain. Research evidence described in the next section suggests that activity in the nervous system associated with learning experiences somehow causes nerve cells to create new synapses. Unlike the process of synapse overproduction and loss, synapse addition and modification are lifelong processes, driven by experience.
Word meaning has of course also been characterized for thousands of years in many languages and cultures through the creation of dictionaries. Researchers in computational linguistics are vigorously pursuing the topic of conceptual ontologies [ ]. Yet, it remains to be established if and how formal ontological theories could map semantic spaces such as those generated by latent semantic analyses.
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Specifically, using a novel self-organization process, we constructed a semantic map of natural language that simultaneously represents synonymy and antonymy. Synonyms and antonyms are commonly listed in dictionaries for most terms. For each dictionary and language, we initially allocated words at random locations in a finite, multidimensional spherical space. Then we started moving the position of every word following a simple rule: Thus, pairs of synonyms would tend to move closer to each other, and pairs of antonyms would move farther apart within the bounds of the multidimensional sphere.
The semantic map is sufficiently robust to allow the automated extraction of synonyms and antonyms not originally present in the dictionaries used to construct the map, as well as to predict connotation from their coordinates. Both the semantic content and the main geometric features of the map are consistent between dictionaries, among tested Western languages, and with previously established psychometric measures. Some of the mathematical formalism and speculative interpretations are elaborated in a second follow-up paper [ ].
Interestingly, the main emerging dimensions of this semantic map loosely correspond to the primary modulatory neurotransmitter systems in the mammalian brain [ ].
Understanding Brain, Mind and Soul: Contributions from Neurology and Neurosurgery
The previous paradigm can be expanded with appropriate adaptations to extract additional, independent dimensions of word meaning by considering other linguistic relations besides synonyms and antonyms. However, hypernyms and hyponyms are seldom listed in immediately machine-readable form in digital collections, the way synonyms and antonyms are. One exception is provided once again by WordNet, which explicitly provides is-a relationships among many of its terms.
Unlike synonyms and antonyms, which are symmetric relations if A is synonym of B, B is synonym of Ahypernyms and hyponyms are directional and mutually antisymmetric if A is hypernym of B, B is hyponym of A.
Human behaviour: is it all in the brain – or the mind? | Science | The Guardian
We thus changed the form of the energy functional in the previously described optimization procedure [ ].
The resulting allocation of words in space yielded a ranking of all terms along a single dimension, that is, a simple scalar measure of their abstractness ontological generality.
The bottom 11 ex aequo of the list reads Edmontonia, Coelophysis, Deinocheirus, Struthiomimus, Deinonychus, dromaeosaur, Mononykus olecranus, oviraptorid, superslasher, Utahraptor, and Velociraptor.
Moreover, because the measure is quantitative, it allows evaluation of relative comparisons. This opens the possibility to establish a probabilistic estimate of whether a word is more abstract than another.
The metrics of context-independent word meaning along the principal dimensions described previously can be applied to characterize declarative mental states. The most straightforward application is to quantify the content of verbal examples along the main dimensions of the map. This can help in relating semantic content to neural signals.
It should be noted that the semantic map described here represents a complementary, rather than alternative, tool to more established latent semantic analyses. While maps produced by the latter are corpus and context dependent, this space adds general dimensions that are applicable to all corpora and context.
We indeed found that the first dimension good-bad was an excellent quantitative predictor not only of the movie critique score but also together with the second dimension of its genre high valence and arousal: A Radical View of Reality, Information, Consciousness, and Remaining Challenges Semantic mapping provides a possible approach to quantifying mental states that can be expressed declaratively.
In this framework, mental states and their relationships can be themselves represented as graphs of nodes and edges, respectively. If one believes that at least some mental states reflect properties of outer reality, it is possible to conceive reality itself as occurring in a giant graph in which any possible observable is a node, and edges correspond to probabilities that two observables would cooccur. We call this conceptual construct the Universal Reality Graph.
In this view, reality would unfold in time as a sequence of events constituting patterns of activation of subsets of nodes and all edges among them within the University Reality Graph. Any agent capable of observation will witness a subset of these activation patterns, that is, a sequence of partial events, each consisting of a collection of active nodes and edges.
Most importantly, the possibility to conceive reality as a graph offers interesting vistas on the solution of the mind-brain problem. If agents form graph-like minds to represent and therefore predict their experience of graph-like reality, it stands to reason that the fittest physical substrates selected by evolution to encode these representations be themselves graph like, namely, brain networks. The relationship between minds and brains could then be resolved as a mapping between their respective graphs and their embeddings.
In this framework, the fundamental operation to grow a mind is pairwise association between observables [ ], that is, establishment of edges between nodes in the mental graph based on corresponding experiences in the reality graph. An interesting aspect of mental representation is that we only learn a small fraction of associations from those observed in reality. In particular, our ability to learn is gated by previously acquired background information.
We have recently proposed that this constraint may be a consequence of the spatial relationship among the tree-like shaped neuronal axons and dendrites that underlie brain connectivity . Specifically, in order for new synapses to be formed, the axon of the presynaptic neuron must be sufficiently close to a dendrite of the postsynaptic neuron, arguably because of preexisting connectivity with other neurons encoding for related knowledge.
These ideas are also consonant with the Information Integration Theory IIT of consciousness [ ], which is emerging as a leading candidate among the fundamental theories of mental content.
The underlying assumption of IIT is that consciousness is fundamentally a property of information processing. Specifically, according to the IIT, when a brain or in principle any other computing device is in a particular state, its amount of consciousness, called Phi, depends not only on the actual content represented in that state but also on the absence of all contents represented in the states that are not being but could be instantiated.
Thus silent neurons contribute to the conscious state as much as the active neurons, because consciousness depends as much on the content that could be represented by the network as on the content that is actually being represented. Therefore, consciousness is a product of the integrated activity in the network and is measured by information integration, a property that has been defined in graph structures [ ].
While the IIT profoundly links consciousness to information [ ], its cognitive underpinning is shared by other theories e. A crucial and unique outcome of IIT, however, is that the definition of integrated information enables a geometric characterization of mental states or qualia [ ].
This can in principle provide a neurally based bottom-up correlate to the spaces that emerge from top-down semantic mapping of natural language. If the information processing product of neural network activity can be shown to correspond mathematically to a quantitative description of subjective mental content, the brain-mind problem would be effectively resolved.
Information is not only an essential element of consciousness, reality, and brain activity, but also of communication among conscious agents. Consider a dialogue between two individuals, in which one tells the other: I had such a day. More precisely, what does it mean to the speaking individual, and what does it mean to the listener? Assuming that the second individual had no idea of what time it was, whether the first person was rested or fatigued, and so forth, communication is indeed informative.
Mapping brain and mental states, however, opens another perspective on the meaning of communication. This simple example can be generalized to all of human communications. How much of the intended meaning is effectively transmitted between communicating conscious individuals on average? Even reminiscence and planning retrieval of autobiographic and prospective memories, resp.
Such a type of communication between two instances of the same individual at different points in time can be expected to be much more effective than between different human beings, but even in those situations it will not be perfectly effective, as the mind is in constant flux.
In all cases, mental state quantification by semantic mapping and its corresponding neural correlates in brain activity spaces could dramatically enhance communication effectiveness, deeply altering human relationship. We have proposed that a satisfactory answer can ultimately come from mathematics, if the abstract spaces of brain activity and mental content can be quantitatively characterized and geometrically mapped onto each other.
We argued that semantic maps constitute a useful initial framework to establish a rigorous description of the mind and that network connectivity provides the most informative constraint on brain dynamics. However, defining the proper mathematical states to effectively bridge brain and mind still constitutes a formidable challenge. State-of-the-art semantic maps only scratch the surface of the necessary quantification of the human mind.
Next-generation voice recognition and optical character recognition software programs might soon enable real-time acquisition and analysis of the complete life-long natural language corpus experienced by an individual. Exhaustive compendia of semantic relationships could be extracted from such a resource, enabling the creation of a comprehensive semantic map for that individual. Such a resource could then be used to systematically report subjective mental states.
While the main obstacle in quantifying mental content appears to be the required paradigm shift towards a science of first-person perspective, neuroscience faces mostly a technological hurdle in creating brain-wide neuron-level maps of network connectivity and activity. Specifically, existing techniques can indeed map all of synaptic circuitries, but only in a very small volume a fraction of cubic millimeter of nervous tissue [ ], only in animal models, and not in vivo.Meet Your Master: Getting to Know Your Brain - Crash Course Psychology #4
Other techniques to analyze neuronal anatomy, only hinting at the potential connectivity [ 75], are possibly scalable to entire brains of live animals , but again not human beings, let alone in normal behavioral conditions.
The only noninvasive imaging techniques available to investigate the human brain e. One present-day partial solution is to use molecular homology to identify existing correspondences between neuron types in rodents and humans by comprehensive genetic mapping [ ] and single-neuron sequencing [ ].
The subsequent extension of rodent brain connectivity to human cognitive architecture would only be tentative, requiring extensive computational testing and refinement by multiscale simulation [ ]. An initial pilot project in this regard might tackle a suitable brain region and related computational functionssuch as the mammalian hippocampus [ ].
Assuming that, at least in principle, technological advancements enabled accumulation of sufficient datasets to adequately map the neuronal activity of the human brain, such a feat would likely involve massive automation. High-throughput, machine-acquired, and large-scale data poses the outstanding matter of human interpretation [ — ]. This issue has recently promoted considerable growth in the field of neuroinformatics [ ], that is, the establishment of an information framework for neuroscience e.
Recent initiatives have proposed a formalism to represent connectivity structure in neuronal network models [ ] and seeded web-based multimodal connectivity databases [ ].