by j.w.gibson, m.s.
Everyone wants to be happy. Unfortunately, Happiness is a habit just like anything else, the more you practice at it the better you get and the easier it is. This is probably the most true thing I know.
Neurologically, humans are wired to encode information about our environment, culture, and experience into complicated webs of inter and intra-relationships. We create nueronets that constantly link experiences together into a semblance of "reality". The emotional system we use determines the quality of our neuronets and directs HOW we encode future information. So, if you constantly bombard your neurons with negative emotions, those chemicals will fundamentally alter the chemistry of your neuro pathways and thus create a crappy outlook to every situation.
If you want to change your life its not hard to understand what to do, the trick is to constantly FOCUS your attention to do it.
Here's How to Create Happiness
1. Change your PHYSIOLOGY--Standing up straight or looking up causes the body to shift its level or awareness from the interior to the exterior. In primitive times, when humans focused on a detail task or the way they feel inside, it causes the posture of the body to literally shut out the external world. This makes the internal world, temporarily, seem more important and more real than it actually is.
Try this: Hunch you body over and look at the ground, let your head drop and see how you feel after 5 minutes. What do you think about? Then try to stand straight, tilt your head back and look up slightly, stand proud. Your body will release different chemicals into the blood stream and this will cause you to FEEL different.
2. REFRAME your thinking. Listen to how you talk, to what words you use often, and what picture you constantly paint in your head to explain your reality. Often, people do not realize the way they constantly create their own states of reality. Humans are adapted to create patterns of meaning to speed up the ability for them to make decisions about survival in the world. Whether the patterns produce positive or negative emotions is immaterial to the body, because the body simply establishes whatever pattern is most efficient for the particular person. Consciously Reframing how you tell your story will break unwanted behavior patterns.
3. LAUGH at everything. Evolutionary psychologists, microbiologists, medical doctors, neurologists, and just about everyone else with a formal training in humans agrees that laughter produces positive changes in body chemistry that cannot be duplicated in any other way. Laughter produces vibrations in the organs of the body that stimulate cells to process more efficiently, thus detoxifying the body. Laughter, released endorphins into the blood stream that have been shown to improve everything from the immune system to memory, sexual performance to strokes.
Choosing to laugh is the key.
--jwg
Thursday, February 18, 2010
Wednesday, February 17, 2010
The Evolution of Culture: Adaptive Socialization and Units of Inheritance
by j.w.gibson, copyright 2006
Introduction
The development of a highly complex set of social strategies has enabled humans to exert considerable control over their environments. Humans are inextricably social organisms, and as such must successfully develop species-normal functions across a variety of environmental and social domains (Scarr, 1993). Humans who do not develop species-normal behavior will be less successful at accessing social organizations and extra-genetic information, attracting potential mates, and/or gaining preferential access to resources. In order to become species-normal, human evolution has produced a singular, highly dynamic, mutable mechanism to provide the appropriate opportunities for species-normal development: culture. Culture is maintained by individuals’ ever changing personal ideas, beliefs, and behaviors. It does not exist on its own, but rather in the minds of each individual. Culture is a social adaptation that provides a generic framework for the processing of developmental information and experience necessary for species-normal development. The impact of culture on individual development cannot be underestimated. Cultural constructs influence every cognitive structure, the interpretation and appropriateness of behavioral actions, and the development of motivations. Because cultural ownership resides with individual members, a “culture” is in a perpetual state of flux, adapting through the actions of individuals.
In evolution, increasingly complex structures are always the result of natural selection. All life is a result of natural selection on the genetic design, or biology, of an organism. All extant behaviors of organisms must therefore be rooted in biology. Considering a mental module that contained the propensity for cultural participation would need to be exceedingly complex, the only likely explanation is that culture is a product of selective design. Imagine the different cognitive machines necessary to sort, categorize, input, recall, evaluate, and differentiate information! Klein, Cosmides, Tooby, and Chance (2002) identified factors in the evolution of memory, a necessary yet minute fraction, of culture. Current theories on memory suggest that there may be up to five different, isolable memory systems: procedural, perceptual-representational, primary, semantic, and episodic (Klein, Cosmides, Tooby, & Chance, 2002). Each of these memory systems entails, at least, dozens of sub-procedural mechanisms for acquiring, processing, evaluating, and making meaning from input. To describe the total complexity of the mechanisms behind human culture may prove nearly impossible. However, it is possible to develop theories of, what I term, super-organization. That is, what managerial or organizational structures are necessary in a mental module that has developed to support cultural knowledge transmission as an adaptive function?
The difficulty in convincing the “hard” evolutionary sciences that such a structure exists or could be materially identified rests on the fact that psychological structures, and especially adaptive cognitive architecture, are troublesome to describe and support empirically. While theory supports that individuals should demonstrate highly complex mechanisms, or specialized modules for dealing with a variety of domain specific problems (Tooby & Cosmides, 1990), proving those structures exist is another matter. However, several evolutionary studies have found evidence to support the existence of adaptive modules. Utilization of biological adaptive theory will be necessary if psychology fully wishes to explain the evolution of culture. This paper discusses the evolution of culture as an adaptive strategy which transmutes survival information and species-normal behaviors. It is suggested that if culture begins in the minds of individual members, then a genetic component is more than probable, and that certain cultural traits may be transmitted as “units of inheritance” (Griesemer, 2000).
Human Evolution: Cognitive Programs
The study of human evolution has finally come to understand that multidisciplinary theories are needed to explain the extent and magnitude of human adaptations. Evolutionary psychology attempts to identify and explain the development of cognitive structures. These programs adapted by natural selection in a particular environmental situation. Darwinian theorists unanimously agree “that [natural] selection is the explanatory law” (Fracchia & Lewontin, 1999).
Evolutionary biologists, geneticists, and physical anthropologists have dramatically strengthened the range of theoretical models of human evolution. Psychology and other social sciences, have suffered from data envy, leading to skepticism about the accuracy of psychological inferences. Given the recency of evolutionary theory across the social sciences, it will take some time before confidence in experimentation is acceptable to other divisions of evolutionary study. Theories in evolutionary psychology are treated cautiously due to “underexposed opportunities for falsification” (Conway & Schaller, 2002). Others have offered criticisms about the nature of cognitive programs. Citing methodological inadequacies, Fodor rejects the idea that the mind evolved due to massive modularity; instead arguing that a small genetic alteration could easily have produced the difference in cognitive capacity of hominids (Okasha, 2003). Considering the complexity, specificity, and interconnectedness of cognitive programs, the hypothesis that these programs are types of neurological software ready to respond to environmental and cultural input, offer an organism an array of appropriate behaviors, calculate their potential effectiveness and consequences and build memory experiences for future reference, in addition to abstract organization and evaluation, seem to be attributed to small genetic alteration.
While evolutionary biologists and physical anthropologists have collected immense physical data in support of the evolution of anatomical structures, psychologists have had a difficult time deciding how to collect their “invisible” data. The psychological structures of behavior are so mixed up in the dynamics of environmental, social, and cultural interactions that to define a concrete mechanism is nearly hopeless. What psychologists must do is search for measurable behavior components which can be subjected to positivist science.
It is hard to separate the human mind from other physical structures. The brain is clearly a result of natural selective processes. There can be no logical argument against the evolution of culture as a human adaptation. Scientists universally agree that human biological hardware is definitely a process of evolution Indeed, when evolutionary psychologists suggest that the myriad of cognitive structures that developed to solve adaptive problems in ancient environments are evident in modern populations, these claims are generally accepted; if still hard to fully support with experimentation.
Culture is a biological adaptation and must be explained in the biological sciences (Fracchia & Lewontin, 1999). One stated goal of evolutionary psychology is to answer the grand question: what is human nature? Tooby and Cosmides (1990) have described human nature as “a set of innate psychological mechanisms and developmental programs” (p.23). Investigation of how cultural knowledge is organized, created, and used is fundamental to the interpretation of all other human experiences.
At a fundamental level, all organisms are chunks of biology that interact in different capacities in their environments. They continuously process information from around them and devise behaviors that satisfy some end. It is completely reasonable to assume that if all organisms are chunks of biology and all biology is subject to evolutionary forces, then any behavior, which is rooted in biological actions, must be somehow represented genetically. The brain itself is an adaptation that directs the entire organism in a coordinated manner. Any evolved structure, cognitive or behavioral, is the result of genetic design, manufacture, and environmental influence.
Evolutionary psychology is advantageously placed to illustrate and explain the cognitive structures of culture as a pan-human phenomenon. Evolutionary theory can help to explain how culture functions as the super organizer of social experience by illustrating how such organization satisfies a number of adaptive problems. The application of evolutionary perspectives in psychology is especially powerful at the explanatory level (Tooby & Cosmides, 1990). Because genes are filtered through natural selection, they are constantly engaged by environmental influences (Tooby & Cosmides, 1990). The debate about the extent of the interaction between genes and environment is now over. Finally it seems psychologists are discovering ways to describe the nature of the relationship: culture.
Cultural Conceptions
The concept of culture has been tragically misdefined, as a static, causal agent, affecting humans, but not affected by them (Buss, 2001). Part of the problem stems from an earlier tendency of anthropology to favor descriptive methods over explanatory methods. Theoretical models utilizing descriptive rather than explanatory data naturally produce descriptive accounts, which are limited to cross-cultural comparisons. Comparisons of different cultural apparatus are only marginally useful because they draw dark lines between cultures while failing to identify universals. In addition, the sheer volumes of information cultures define make a clear and useful definition slippery. The existence of every human experience resides somewhere in culture! Every fleeting thought, darkest dream, most mundane actions are made meaningful by our utilization of cultural information.
How does one describe, explain, and differentiate all the cumulative behavior represented in the total collection of human culture? It consists of ever changing “content of what is to be valued and acquired; biology provides the motivation and intelligence for learning it. Cultures define what is desirable to be learned, what is to be believed, and how to behave.” (Scarr, 1993). Cultures also establish an available parameter of opportunities that provide both normal and abnormal species development (Scarr, 1993). Cultures form boundaries and expectations of human social behavior. Cultures are “go-betweens,” buffering humans from a geography where they are dependent on energy resources. Culture acts as the unifying human house from which all developmental information is passed, evolutionary objectives are translated, and through which humans build understanding about life.
Humans have developed and been developed by culture. Input from the socioenvironment interacts within a highly complex set of social, group, and interpersonal factors that are used to construct knowledge about species-appropriate behavior. In turn, the success or failure of behaviors to satisfy an adaptation issue are encoded as memories, useful for providing background information in future decision making situations (Klein et al., 2002). To the extent that memories are stored in the neurological tissues of the brain and governed by the activity of neural cells, it is very probable that some aspects of acquired environmental information can become encoded in DNA. If it were not possible for “learned” behavior to become encoded, then each individual would have to re-learn the entirety of species specific information. “Whether the evolution is variational or transformational there must be some mechanism by which a new generation of successors retains some vestige of the changes that occurred in a previous time” (Fracchia & Lewontin, 1999).
The utilization of evolutionary theory in pursuit of cultural mechanisms is mildly controversial, to say the least. The basis for an evolutionary perspective in psychology is justified by the fact that at least some aspects of human cognition are the result of Darwinian natural selection (Okasha, 2003). Culture may well prove to be the super-organizer of the cognitive and developmental programs, a highly evolved and specialized organizational adaptive response to manage constantly increasing amounts of social-species specific information. The challenge for researchers is to design experiments that effectively collect solid data on the nature of cognitive organization machines and identify the process by which environmental experience can change the genetic signature of heritable programs.
Niche Construction: How Culture Shapes the Environment
Human social groups develop specific strategies relevant to the social and environmental factors in a local area. The job of culture is to provide a general framework from which a range of strategies are available. For example, Cohen (1998) discussed the adaptive function of a culture of honor in the South and West of America. Cohen explains the high acceptance of violence and strong purveyance of individual honor as reactions to the “frontier” history and minimalism of law enforcement. In an environment where men were forced to protect their interests and solve conflict themselves, honor and the legitimization of violence increased one’s fitness (Cohen, 1998). Numerous localized (meaning indigent to a specific geography) cultural adaptations have been noted in anthropological studies. In summarizing the historical development of cultural understanding, Buss (2001) discusses the differential development of social aggression in neighboring villages of Yanomamo Indians of Venezuela. At least two groups show profoundly different personality traits. Lowland Yanomamo men are highly aggressive; they often hit their wives with sticks for minor infractions, often challenge other men to club or ax fights, and sometimes declare war on neighboring villages, killing the men and capturing their wives (Buss, 2001).
Highland Yanomamo villages are markedly different. Disliking fighting, they are more peaceful. These Yanomamo do not raid neighboring villages or engage in ax fights, rarely in club fights. Instead the virtues of cooperation are stressed (Buss, 2001). Food sources are more plentiful in the lowlands; aggression is advantageous for controlling as much food and reproduction resources as possible, and indeed, the most aggressive Yanomamo have the most wives (Buss, 2001). Highland food resources are much more limited. These Yanomamo have learned to pool their collective resources increasing the survival of the group over the individual. Cultural influences favored cooperative behavior where thin resources can be stretched more equally (Buss, 2001). The particular manifestation of culture depends, in part, upon resident ecological variables.
By altering human behavior, culture can impact the local ecology of an area. Laland, Odling-Smee, and Feldman (2001) suggest that there is ample evidence to argue that “cultural activities have influenced human genetic evolution by modifying sources of natural selection and altering genotype frequencies in some human populations” (Laland, Odling-Smee, & Feldman, 2001). Niche construction is the study of how organisms change their environments to better solve survival problems. Humans build homes, cities, shopping centers, hospitals, parks, schools; they change nature and live in an artificial environment advantageous to the species survival. The impact of organisms on the environment is not small. For instance, cyanobacteria are responsible for the creation of oxygen in Earth’s atmosphere; over millions of years of photosynthesis (Laland et al., 2001). In addition, the invention of nuclear weapons, digital information processing, and industrialization have begun environmental changes that are yet unquantifiable. Few species have changed the selective environment as drastically as human beings (Laland et al., 2001).
Cultural behaviors do affect the environments that humans live in. Evolutionary biologists stress that culture frequently affects evolution by modifying the selection pressures and biasing available resource situations (Laland et al., 2001). Agriculture and industry affect natural selection pressures on population size and structure (Laland et al., 2001). Gene-culture coevolution theories have been developed using mathematical and conceptual models to describe how genetic evolution influences culture and how culture drives genetic changes (Laland et al., 2001). Laland et al. (2001) make several important conclusions about heritable units. First, there is no simple function that relates frequency of cultural niche-constructing activity to the frequency of the population’s genes. Cultural activity is too broad to be explained by simple relationships. For example, the Kwa-speaking yam farmers of West Africa have a significantly high frequency of the sickle-cell allele (Laland et al., 2001). Traditionally, these people clear cut the rainforest to make room for fields, simultaneously creating new standing water resources which amplify the reproduction of malaria-carrying mosquitoes. Mosquito population is also dependent on environmental factors, such as rainfall, and rainfall is dependent on meteorological processes and so on. The selective pressure to increase the sickle-cell allele cannot be simply explained by a dual relationship between cultural activity and environment, there are too many variables in the system (Laland et al., 2001). Evidence of cultural processes can be seen in genetic variability when cultural activities work to change selective pressures.
“Cultural evolution does not exist as a distinct category from biological evolution” (Griesemer, 2000). Identification of the “units of heritance” of cultural transmission rely on a specific definition of a reproducer, which is a combination of genetic instructions and developmental opportunities (Griesemer, 2000). Genes do not, by themselves, develop into useful structures. Culture transmits developmental opportunities to individuals who in turn provide relevant genetic material with the interaction necessary to successfully develop into a phenotype. Units of heritable structures concerning culture are likely to be those which require environmental and social data to mature into their intended structure or purpose.
Conclusion
Finding conclusive cognitive structures, for which the biological sciences support, is a worthy yet problematic area of study for evolutionary psychologists. Until neurological functioning can be “grouped” into specific domains, physically and empirically delineated, and linked to cultural organization strategies, evolutionary psychologists will struggle with an acceptable explanation for the relationship between culture, genes, and evolution. The study of memory systems, information processing, personality, and social interactions are all benefited by the successful development of a theory that explains how cultural influences contribute to genetic variability. When looking for cultural units which may be coded in genetics, psychologists should focus on those structures whose genes require developmental experiences or other species-conveyed information. Future developments in biology will be crucial in providing psychologists with empirical connections between environmental pressures and genetic encoding processes. If the ultimate goal of the human sciences is to explain “human nature” then a synthesis of the life and social sciences is absolutely necessary.
The return of Darwinistic theory across the sciences has clearly established the cooperative ability and utility of cross-discipline perspectives in explaining complex evolutionary processes. If culture functions as a super-organizer of environmental experience and extra-genetic species knowledge, then its explanation is of considerable importance and should receive due attention by researchers.
References
Buss, D. M. (2001). Human nature and culture: an evolutionary psychological perspective. Journal of Personality, 69(6), 955-978.
Cohen, D. (1998). Culture, social organization, and patterns of violence. Journal of Personality and Social Psychology, 75(2), 408-419.
Conway, L. G. I., & Schaller, M. (2002). On the verifiability of evolutionary psychological theories: An analysis of the psychology of scientific persuasion. Personality and Social Psychology Review, 6(2), 152-166.
Fracchia, J., & Lewontin, R. C. (1999). Does culture evolve? History & Theory, 38(4).
Griesemer, J. (2000). Development, culture and the units of inheritance. Philosophy of Science, 67(Proceedings), S348-S368.
Klein, S. B., Cosmides, L., Tooby, J., & Chance, S. (2002). Decisions and the evolution of memory: Multiple systems, multiple functions. Psychological Review, 109(2), 306-329.
Laland, K. N., Odling-Smee, J., & Feldman, M. W. (2001). Cultural niche construction and human evolution. Journal of Evolutionary Biology, 14, 22-33.
Okasha, S. (2003). Fodor on cognition, modularity and adaptionism. Philosophy of Science, 70, 68-88.
Scarr, S. (1993). Biological and cultural diversity: The legacy of Darwin for development. Child Development, 64, 1333-1353.
Tooby, J., & Cosmides, L. (1990). On the universality of human nature and the uniqueness of the individual: The role of genetics and adaptation. Journal of Personality, 58(1), 17-67.
Introduction
The development of a highly complex set of social strategies has enabled humans to exert considerable control over their environments. Humans are inextricably social organisms, and as such must successfully develop species-normal functions across a variety of environmental and social domains (Scarr, 1993). Humans who do not develop species-normal behavior will be less successful at accessing social organizations and extra-genetic information, attracting potential mates, and/or gaining preferential access to resources. In order to become species-normal, human evolution has produced a singular, highly dynamic, mutable mechanism to provide the appropriate opportunities for species-normal development: culture. Culture is maintained by individuals’ ever changing personal ideas, beliefs, and behaviors. It does not exist on its own, but rather in the minds of each individual. Culture is a social adaptation that provides a generic framework for the processing of developmental information and experience necessary for species-normal development. The impact of culture on individual development cannot be underestimated. Cultural constructs influence every cognitive structure, the interpretation and appropriateness of behavioral actions, and the development of motivations. Because cultural ownership resides with individual members, a “culture” is in a perpetual state of flux, adapting through the actions of individuals.
In evolution, increasingly complex structures are always the result of natural selection. All life is a result of natural selection on the genetic design, or biology, of an organism. All extant behaviors of organisms must therefore be rooted in biology. Considering a mental module that contained the propensity for cultural participation would need to be exceedingly complex, the only likely explanation is that culture is a product of selective design. Imagine the different cognitive machines necessary to sort, categorize, input, recall, evaluate, and differentiate information! Klein, Cosmides, Tooby, and Chance (2002) identified factors in the evolution of memory, a necessary yet minute fraction, of culture. Current theories on memory suggest that there may be up to five different, isolable memory systems: procedural, perceptual-representational, primary, semantic, and episodic (Klein, Cosmides, Tooby, & Chance, 2002). Each of these memory systems entails, at least, dozens of sub-procedural mechanisms for acquiring, processing, evaluating, and making meaning from input. To describe the total complexity of the mechanisms behind human culture may prove nearly impossible. However, it is possible to develop theories of, what I term, super-organization. That is, what managerial or organizational structures are necessary in a mental module that has developed to support cultural knowledge transmission as an adaptive function?
The difficulty in convincing the “hard” evolutionary sciences that such a structure exists or could be materially identified rests on the fact that psychological structures, and especially adaptive cognitive architecture, are troublesome to describe and support empirically. While theory supports that individuals should demonstrate highly complex mechanisms, or specialized modules for dealing with a variety of domain specific problems (Tooby & Cosmides, 1990), proving those structures exist is another matter. However, several evolutionary studies have found evidence to support the existence of adaptive modules. Utilization of biological adaptive theory will be necessary if psychology fully wishes to explain the evolution of culture. This paper discusses the evolution of culture as an adaptive strategy which transmutes survival information and species-normal behaviors. It is suggested that if culture begins in the minds of individual members, then a genetic component is more than probable, and that certain cultural traits may be transmitted as “units of inheritance” (Griesemer, 2000).
Human Evolution: Cognitive Programs
The study of human evolution has finally come to understand that multidisciplinary theories are needed to explain the extent and magnitude of human adaptations. Evolutionary psychology attempts to identify and explain the development of cognitive structures. These programs adapted by natural selection in a particular environmental situation. Darwinian theorists unanimously agree “that [natural] selection is the explanatory law” (Fracchia & Lewontin, 1999).
Evolutionary biologists, geneticists, and physical anthropologists have dramatically strengthened the range of theoretical models of human evolution. Psychology and other social sciences, have suffered from data envy, leading to skepticism about the accuracy of psychological inferences. Given the recency of evolutionary theory across the social sciences, it will take some time before confidence in experimentation is acceptable to other divisions of evolutionary study. Theories in evolutionary psychology are treated cautiously due to “underexposed opportunities for falsification” (Conway & Schaller, 2002). Others have offered criticisms about the nature of cognitive programs. Citing methodological inadequacies, Fodor rejects the idea that the mind evolved due to massive modularity; instead arguing that a small genetic alteration could easily have produced the difference in cognitive capacity of hominids (Okasha, 2003). Considering the complexity, specificity, and interconnectedness of cognitive programs, the hypothesis that these programs are types of neurological software ready to respond to environmental and cultural input, offer an organism an array of appropriate behaviors, calculate their potential effectiveness and consequences and build memory experiences for future reference, in addition to abstract organization and evaluation, seem to be attributed to small genetic alteration.
While evolutionary biologists and physical anthropologists have collected immense physical data in support of the evolution of anatomical structures, psychologists have had a difficult time deciding how to collect their “invisible” data. The psychological structures of behavior are so mixed up in the dynamics of environmental, social, and cultural interactions that to define a concrete mechanism is nearly hopeless. What psychologists must do is search for measurable behavior components which can be subjected to positivist science.
It is hard to separate the human mind from other physical structures. The brain is clearly a result of natural selective processes. There can be no logical argument against the evolution of culture as a human adaptation. Scientists universally agree that human biological hardware is definitely a process of evolution Indeed, when evolutionary psychologists suggest that the myriad of cognitive structures that developed to solve adaptive problems in ancient environments are evident in modern populations, these claims are generally accepted; if still hard to fully support with experimentation.
Culture is a biological adaptation and must be explained in the biological sciences (Fracchia & Lewontin, 1999). One stated goal of evolutionary psychology is to answer the grand question: what is human nature? Tooby and Cosmides (1990) have described human nature as “a set of innate psychological mechanisms and developmental programs” (p.23). Investigation of how cultural knowledge is organized, created, and used is fundamental to the interpretation of all other human experiences.
At a fundamental level, all organisms are chunks of biology that interact in different capacities in their environments. They continuously process information from around them and devise behaviors that satisfy some end. It is completely reasonable to assume that if all organisms are chunks of biology and all biology is subject to evolutionary forces, then any behavior, which is rooted in biological actions, must be somehow represented genetically. The brain itself is an adaptation that directs the entire organism in a coordinated manner. Any evolved structure, cognitive or behavioral, is the result of genetic design, manufacture, and environmental influence.
Evolutionary psychology is advantageously placed to illustrate and explain the cognitive structures of culture as a pan-human phenomenon. Evolutionary theory can help to explain how culture functions as the super organizer of social experience by illustrating how such organization satisfies a number of adaptive problems. The application of evolutionary perspectives in psychology is especially powerful at the explanatory level (Tooby & Cosmides, 1990). Because genes are filtered through natural selection, they are constantly engaged by environmental influences (Tooby & Cosmides, 1990). The debate about the extent of the interaction between genes and environment is now over. Finally it seems psychologists are discovering ways to describe the nature of the relationship: culture.
Cultural Conceptions
The concept of culture has been tragically misdefined, as a static, causal agent, affecting humans, but not affected by them (Buss, 2001). Part of the problem stems from an earlier tendency of anthropology to favor descriptive methods over explanatory methods. Theoretical models utilizing descriptive rather than explanatory data naturally produce descriptive accounts, which are limited to cross-cultural comparisons. Comparisons of different cultural apparatus are only marginally useful because they draw dark lines between cultures while failing to identify universals. In addition, the sheer volumes of information cultures define make a clear and useful definition slippery. The existence of every human experience resides somewhere in culture! Every fleeting thought, darkest dream, most mundane actions are made meaningful by our utilization of cultural information.
How does one describe, explain, and differentiate all the cumulative behavior represented in the total collection of human culture? It consists of ever changing “content of what is to be valued and acquired; biology provides the motivation and intelligence for learning it. Cultures define what is desirable to be learned, what is to be believed, and how to behave.” (Scarr, 1993). Cultures also establish an available parameter of opportunities that provide both normal and abnormal species development (Scarr, 1993). Cultures form boundaries and expectations of human social behavior. Cultures are “go-betweens,” buffering humans from a geography where they are dependent on energy resources. Culture acts as the unifying human house from which all developmental information is passed, evolutionary objectives are translated, and through which humans build understanding about life.
Humans have developed and been developed by culture. Input from the socioenvironment interacts within a highly complex set of social, group, and interpersonal factors that are used to construct knowledge about species-appropriate behavior. In turn, the success or failure of behaviors to satisfy an adaptation issue are encoded as memories, useful for providing background information in future decision making situations (Klein et al., 2002). To the extent that memories are stored in the neurological tissues of the brain and governed by the activity of neural cells, it is very probable that some aspects of acquired environmental information can become encoded in DNA. If it were not possible for “learned” behavior to become encoded, then each individual would have to re-learn the entirety of species specific information. “Whether the evolution is variational or transformational there must be some mechanism by which a new generation of successors retains some vestige of the changes that occurred in a previous time” (Fracchia & Lewontin, 1999).
The utilization of evolutionary theory in pursuit of cultural mechanisms is mildly controversial, to say the least. The basis for an evolutionary perspective in psychology is justified by the fact that at least some aspects of human cognition are the result of Darwinian natural selection (Okasha, 2003). Culture may well prove to be the super-organizer of the cognitive and developmental programs, a highly evolved and specialized organizational adaptive response to manage constantly increasing amounts of social-species specific information. The challenge for researchers is to design experiments that effectively collect solid data on the nature of cognitive organization machines and identify the process by which environmental experience can change the genetic signature of heritable programs.
Niche Construction: How Culture Shapes the Environment
Human social groups develop specific strategies relevant to the social and environmental factors in a local area. The job of culture is to provide a general framework from which a range of strategies are available. For example, Cohen (1998) discussed the adaptive function of a culture of honor in the South and West of America. Cohen explains the high acceptance of violence and strong purveyance of individual honor as reactions to the “frontier” history and minimalism of law enforcement. In an environment where men were forced to protect their interests and solve conflict themselves, honor and the legitimization of violence increased one’s fitness (Cohen, 1998). Numerous localized (meaning indigent to a specific geography) cultural adaptations have been noted in anthropological studies. In summarizing the historical development of cultural understanding, Buss (2001) discusses the differential development of social aggression in neighboring villages of Yanomamo Indians of Venezuela. At least two groups show profoundly different personality traits. Lowland Yanomamo men are highly aggressive; they often hit their wives with sticks for minor infractions, often challenge other men to club or ax fights, and sometimes declare war on neighboring villages, killing the men and capturing their wives (Buss, 2001).
Highland Yanomamo villages are markedly different. Disliking fighting, they are more peaceful. These Yanomamo do not raid neighboring villages or engage in ax fights, rarely in club fights. Instead the virtues of cooperation are stressed (Buss, 2001). Food sources are more plentiful in the lowlands; aggression is advantageous for controlling as much food and reproduction resources as possible, and indeed, the most aggressive Yanomamo have the most wives (Buss, 2001). Highland food resources are much more limited. These Yanomamo have learned to pool their collective resources increasing the survival of the group over the individual. Cultural influences favored cooperative behavior where thin resources can be stretched more equally (Buss, 2001). The particular manifestation of culture depends, in part, upon resident ecological variables.
By altering human behavior, culture can impact the local ecology of an area. Laland, Odling-Smee, and Feldman (2001) suggest that there is ample evidence to argue that “cultural activities have influenced human genetic evolution by modifying sources of natural selection and altering genotype frequencies in some human populations” (Laland, Odling-Smee, & Feldman, 2001). Niche construction is the study of how organisms change their environments to better solve survival problems. Humans build homes, cities, shopping centers, hospitals, parks, schools; they change nature and live in an artificial environment advantageous to the species survival. The impact of organisms on the environment is not small. For instance, cyanobacteria are responsible for the creation of oxygen in Earth’s atmosphere; over millions of years of photosynthesis (Laland et al., 2001). In addition, the invention of nuclear weapons, digital information processing, and industrialization have begun environmental changes that are yet unquantifiable. Few species have changed the selective environment as drastically as human beings (Laland et al., 2001).
Cultural behaviors do affect the environments that humans live in. Evolutionary biologists stress that culture frequently affects evolution by modifying the selection pressures and biasing available resource situations (Laland et al., 2001). Agriculture and industry affect natural selection pressures on population size and structure (Laland et al., 2001). Gene-culture coevolution theories have been developed using mathematical and conceptual models to describe how genetic evolution influences culture and how culture drives genetic changes (Laland et al., 2001). Laland et al. (2001) make several important conclusions about heritable units. First, there is no simple function that relates frequency of cultural niche-constructing activity to the frequency of the population’s genes. Cultural activity is too broad to be explained by simple relationships. For example, the Kwa-speaking yam farmers of West Africa have a significantly high frequency of the sickle-cell allele (Laland et al., 2001). Traditionally, these people clear cut the rainforest to make room for fields, simultaneously creating new standing water resources which amplify the reproduction of malaria-carrying mosquitoes. Mosquito population is also dependent on environmental factors, such as rainfall, and rainfall is dependent on meteorological processes and so on. The selective pressure to increase the sickle-cell allele cannot be simply explained by a dual relationship between cultural activity and environment, there are too many variables in the system (Laland et al., 2001). Evidence of cultural processes can be seen in genetic variability when cultural activities work to change selective pressures.
“Cultural evolution does not exist as a distinct category from biological evolution” (Griesemer, 2000). Identification of the “units of heritance” of cultural transmission rely on a specific definition of a reproducer, which is a combination of genetic instructions and developmental opportunities (Griesemer, 2000). Genes do not, by themselves, develop into useful structures. Culture transmits developmental opportunities to individuals who in turn provide relevant genetic material with the interaction necessary to successfully develop into a phenotype. Units of heritable structures concerning culture are likely to be those which require environmental and social data to mature into their intended structure or purpose.
Conclusion
Finding conclusive cognitive structures, for which the biological sciences support, is a worthy yet problematic area of study for evolutionary psychologists. Until neurological functioning can be “grouped” into specific domains, physically and empirically delineated, and linked to cultural organization strategies, evolutionary psychologists will struggle with an acceptable explanation for the relationship between culture, genes, and evolution. The study of memory systems, information processing, personality, and social interactions are all benefited by the successful development of a theory that explains how cultural influences contribute to genetic variability. When looking for cultural units which may be coded in genetics, psychologists should focus on those structures whose genes require developmental experiences or other species-conveyed information. Future developments in biology will be crucial in providing psychologists with empirical connections between environmental pressures and genetic encoding processes. If the ultimate goal of the human sciences is to explain “human nature” then a synthesis of the life and social sciences is absolutely necessary.
The return of Darwinistic theory across the sciences has clearly established the cooperative ability and utility of cross-discipline perspectives in explaining complex evolutionary processes. If culture functions as a super-organizer of environmental experience and extra-genetic species knowledge, then its explanation is of considerable importance and should receive due attention by researchers.
References
Buss, D. M. (2001). Human nature and culture: an evolutionary psychological perspective. Journal of Personality, 69(6), 955-978.
Cohen, D. (1998). Culture, social organization, and patterns of violence. Journal of Personality and Social Psychology, 75(2), 408-419.
Conway, L. G. I., & Schaller, M. (2002). On the verifiability of evolutionary psychological theories: An analysis of the psychology of scientific persuasion. Personality and Social Psychology Review, 6(2), 152-166.
Fracchia, J., & Lewontin, R. C. (1999). Does culture evolve? History & Theory, 38(4).
Griesemer, J. (2000). Development, culture and the units of inheritance. Philosophy of Science, 67(Proceedings), S348-S368.
Klein, S. B., Cosmides, L., Tooby, J., & Chance, S. (2002). Decisions and the evolution of memory: Multiple systems, multiple functions. Psychological Review, 109(2), 306-329.
Laland, K. N., Odling-Smee, J., & Feldman, M. W. (2001). Cultural niche construction and human evolution. Journal of Evolutionary Biology, 14, 22-33.
Okasha, S. (2003). Fodor on cognition, modularity and adaptionism. Philosophy of Science, 70, 68-88.
Scarr, S. (1993). Biological and cultural diversity: The legacy of Darwin for development. Child Development, 64, 1333-1353.
Tooby, J., & Cosmides, L. (1990). On the universality of human nature and the uniqueness of the individual: The role of genetics and adaptation. Journal of Personality, 58(1), 17-67.
Tuesday, February 16, 2010
Attachment Level and the Experience of Death Anxiety In Elderly Individuals: Clinical Implications
Introduction
The largest growing section of the American population is the elderly. As a group, those aged 65 and older have increased by a factor of 11, from 3 million in the beginning of the 1900’s to 33 million in 1994 (Hobbs, 2001). In addition the oldest old (85+) are another small but rapidly expanding group making up just over 1% of the American population in 1994 (Hobbs, 2001). Between 1960 and 1994 the oldest old increased a staggering 274 percent making it the largest growing age group in the Nation. Indeed the future population projections see the oldest old age group as continuing to grow. This population group will have a tremendous impact on a number of national issues, including the economy, healthcare, geriatric related services, and will affect the way the American government decides its policies and programs. One simulation model has concluded that the interaction of current demographic, health, and income trends will mean a tripling of the number of elderly individuals needing of nursing home or other care between 1990 and 2030 (Hobbs & Damon, 1996).
The graying of America
One facet that needs further research is the area geriatric psychology. Developmentally, a number of changes take place in old age that directly affects the quality of life experienced by individuals in the later years of life. Recently, much more attention has been paid to the subdiscipline of geriatric psychology. A recent search on google.com displayed 103,000 hits for “geriatric psychology.”
A main feature associated with age and especially old age is that of impending biological decline and death. While a number of researchers disagree on the details of biological decline it is generally accepted that later life brings decreases in height and weight, mobility and muscle mass, bone mass, sensory acuity, cardiovascular and respiratory abilities, central nervous system (CNS) and autonomic nervous system (ANS) structures (Cavanaugh & Blanchard-Fields, 2002). Some changes are minor and unnoticeable, while other may be life changing.
Of particular interest to geriatric psychologists is the loss of neural material in the brain, especially the prefrontal cortex (PFC), and how this affects an individual’s psychological resiliency to other physical and environmental changes specific to aging populations. Changes to the PFC may or may not affect the quality of life of elderly individuals, but researchers have not specifically addressed this issue. One problem is how to determine the construct quality of life. A great deal of recent literature has focused on examining death attitudes/anxieties (Copp, 1998; Depaola, Griffin, Young, & Neimeyer, 2003; Fortner & Neimeyer, 1999; Fry, 1990; Hines, Babrow, Badzek, & Moss, 2001; Ingebretsen & Solem, 1998; Langs, 2003; Martinez de Pison Liebanas, 2002; Neimeyer & Fortner, 1995; Wass & Myers, 1982; Weiler, 2001) in order to determine variables and factors related to the experience of the inevitable final moment. Only one recent paper (Weiler, 2001) analyzes the relationship between perceived quality of life and death attitudes, accounting for personal meaning making as a significant factor. Because death is unavoidably a pan human experience, it may well serve as a point of research interest to determine what factors affect the resiliency of aging adults in experiencing death anxiety and hence be reflective of quality of life.
The future reality the geriatric population will be exposed to will more than likely impact, at least to some degree, the quality of life experienced in the last years of life. An examination of death attitudes and anxiety has already produced preliminary information on some factors that affect the development of anxiety vs. positive attitudes. These studies have invariably looked at age, gender, ethnicity, economics, psychological and physical health, religiosity, and other extravariables. Several studies (Bearon, 1996; Depner & Ingersoll-Dayton, 1988; Ryff & Singer, 1998; Smider, Essex, & Ryff, 1996) have noted the importance of the development and maintenance of close personal social relationships as a noteworthy factor in individuals with positive life evaluations. In addition, the prevalence of adult attachment literature in the psychological realm is truly overwhelming. Most psychologists would not disagree that attachment experiences (Ainsworth, 1989; Ainsworth & Bowlby, 1991; Bowlby, 1988) significantly affect the ability of an individual to make and maintain social relationships. Yet, the present author could find no studies directly linking attachment experiences to death anxieties. If positive attachment experiences tend to foster more stable and long term close supportive social relationships, and if social support structures in old age are negatively correlated with death anxiety, then it would seem that research focusing on attachment of adults and their experience of their own death would be beneficial in determining such a relationship.
This paper evaluates both death attitudes/anxiety studies concerned with the older populations and pertinent adult attachment literature in order to establish a worthwhile stream of inquiry for clinical geropsychologists. Implications for clinical changes and adaptations in other institutions will be offered as a way to improve the quality of life of the aging individuals who will make up a growing percentage of the American population, and face very unique obstacles to their successful aging and dying.
Components of Attachment
Bowlby first explored the relationship of parent-child dyad interactions based on ethological studies. He determined that the close social interactions that occur between children and caregivers have significant impact on the development of social expectations, attitudes and behaviors. This transactional relationship between offspring and caregiver functions on several levels: (1) there is an emotionally significant bond that has basic survival functions, (2) cybernetic systems that reside within the CNS of each partner act to maintain proximity or ready accessibility of the partner to the other, and (3) the systems can only operate if each individual has a “working model” of the others probable behaviors or responses based on past experiences (Bowlby, 1988). Attachments have four essential features, each of which is reflected in the behavioral patterns directed toward the primary attachment figure; proximity maintenance—the continual seeking of physical closeness; safe haven—seeking comfort or safety when needed;, separation distress—displaying overt grief or panic on unexpected or prolonged separations; and secure base— depending on the attachment figure as a base of security from which to explore the environment and other activities not related to attachment (Hazan & Diamond, 2000).
Attachment Development
The formation of attachment generally occurs between the 6th and 8th months of life and typically centers on one individual (Hazan & Diamond, 2000). During infancy and early childhood, children rely on their parents for protection, comfort, and assistance (Bowlby, 1988). They strive to remain in close physical contact, and the accumulation of their interactions helps both the mother and child to better approximate the other’s mental state. How parents respond to their children’s needs affects the specific nature of their attachment and consequently, their social learning about how future interactions will progress.
Infants are equipped with a number of species-characteristic signaling behaviors (i.e. crying) that serve to keep caregivers in very close proximity (Ainsworth, 1989). This proximity-keeping behavior has direct adaptive value since it ensures that parents will tend to the needs of the child. A frightening or stressful situation that produces crying, for instance, will signal the parent’s, and especially the mother’s, protective response mechanisms. Throughout the child’s experiences, gradually they build up “expectations of regularities in what happens to him or her” (Ainsworth, 1989). The consistency of the parent’s responsiveness acts to form the child’s comprehension of how they will be treated in a social environment.
Attachment behavior develops, in one of four ways, based on the consistency and responsiveness of the parents to a child’s needs. Secure attachment develops when parents are consistently available to their children, and are sensitive to their needs. A securely attached child develops a healthy trust that a parent will be around during a frightening or stressful situation (Bowlby, 1988). Because they are confident that their caretaker is protecting and available, they are more likely to explore the surrounding environment, aware that if they get into trouble, they have a safe haven to return to. Being more likely to explore, they will tend to interact with more developmentally appropriate stimulus.
Securely attached children are likely to be described by teachers as “cheerful and cooperative, popular with other children, resilient, and resourceful” (Bowlby, 1988). In addition, they are likely to be relatively indifferent to minimal separations from their mothers and happily greet them upon return (Hetherington & Parke, 1999). Their experience with the social nature of humans is positive and they develop confidence in their ability to navigate the social landscape.
Insecure attachment happens when primary caretakers are inconsistent in their responses to a child’s signals, who are indifferent or distant, who may respond to a child’s request in negative or frustrated ways, or who fails to soothe a child. Insecurely attached children suffer negative social experience and begin to form models that human society functions as they have experienced. Consequently, they are more likely to be described as “emotionally insulated, hostile, or antisocial and as unduly seeking of attention” (Bowlby, 1988). In addition, insecurely attached children may develop inadequate defense mechanisms that confound the relationship between themselves and parents because of their disadvantaged social experiences.
According to a now classic study by Ainsworth, The Strange Situation, children were exposed to a situation where their mothers left and returned twice. The degree of attachment relationship had considerable impacts on the child’s reaction to the situation of the “leavings” and upon the return of their mother. Securely attached children suffered the least amount of stress at being separated from their mothers, showing excitement and warmth upon their subsequent returns (Hetherington & Parke, 1999). In contrast, all the insecurely attached children maintained some degree of dissassociative behaviors directed at the mother for leaving them in strange environments.
Insecurely attached children can be further broken down into three distinct categories: Insecure-avoidant, insecure-resistant, and insecure-disorganized (Hetherington & Parke, 1999). Insecure-avoidant attachment children show little distress when their mothers are gone, and avoid them upon return, sometimes becoming visibly upset. Their past history has taught them that the mother is not concerned with their needs and so they are to be avoided. Insecure-resistant attached children show the opposite behavior, becoming extremely upset when mothers leave, but indifferent upon their return, as if to punish their mother’s for leaving them in the first place. The third type, insecure-disorganized attachment babies display random confusion and disorientation upon their mother’s return, often freezing or repeating their movements, such as rocking or tapping of an appendage (Hetherington & Parke, 1999).
The importance of bond formation between parent and child should not be underestimated because it sets the stage for neurological development in a number of social and cognitive domains. Personality aspects of children are heavily affected by their primary (parental) relationships. If children learn early that the social world is not stable, does not provide them safety, and is often cruel, they will tend to develop personality patterns that both represent this belief and adapt to survive in such a world. Bowlby (1988) believed that there is a strong emphasis of stability and continuity of how an individual’s existing internal experiences will affect the way in which they construe and respond to every new situation. While the extent of this truth has been both challenged and changed over the years, the impact of early social experiences has not been dismissed as a serious factor in the ability of children to develop species-normal behaviors. A child’s first experience of bond formation is not likely to be forgotten, in fact, how well the child forms bonds is a direct measure of how successful they will be as adults in a complex social landscape (Iwaniec & Sneddon, 2001).
Attachment in Adulthood
The ability to maintain and foster close healthy relationships is an integral part of adulthood. Attachment experiences in childhood have been shown to be predictive of the quality and nature of adult relationships in later life (Shaver & Mikulincer, 2002; Sperling & Berman, 1994). Sperling and Berman (1994) succinctly defined adult attachment as
“the stable tendency of an individual to make substantial efforts to seek and maintain proximity to and contact with one or a few specific individuals who provide the subjective potential for physical and/or psychological safety and security . . .[which is] regulated by internal working models of attachment, which are cognitive-affective-motivational schemata built from the individuals experience in his or her interpersonal world” (p.8).
Extant in this definition is the representational models that each individual uses with others in order to predict how they will act towards the individual. Past social relationship interactions build up a working model of how others tend to treat the individual. In addition, it is highly probable that these working models may be (somewhat) self-fulfilling as people tend to select environmental situations that are in line with their beliefs about self and process this information within their existing belief-system (Iwaniec & Sneddon, 2001). While adults may participate in a number of different types of relationships, attachment relationships are typically characterized between an adult and their parents, their children, and close love relationships which include security features (Sperling & Berman, 1994).
A wealth of research supports the existence of both secure and insecure attachment styles in adult romantic relationships (Ainsworth, 1989; Feeney, Noller, & Hanrahan, 1994; Hazan & Diamond, 2000; Hindy & Schwarz, 1994; Levitt, Coffman, Guacci-Franco, & Loveless, 1998; Mikulincer, 1998; Shaver & Mikulincer, 2002; Sperling & Berman, 1994). Other studies have directly linked the quality of attachment with psychological and physical health state (Ainsworth, 1989; Hazan & Diamond, 2000; Ryff & Singer, 1998; Shaver & Mikulincer, 2002). Insecure attachment in adults has been described as “undue jealousy, lack of self-disclosure, feelings of loneliness even during relationships, reluctance to commit in relationships, difficulty in making relationships in new settings, and tendencies to see partners as insufficiently attentive” (Iwaniec & Sneddon, 2001, p. 184). During stressful situations insecurely attached adults may suffer more depression, see their circumstances as deserved or insurmountable, and recoil from the support offered by social relationships.
Because social relationships make up such a significant part of adult life experience, and because individuals have participated in social relationships over the course of their life, the quality of their attachment directly affects the individual’s ability to maintain close, supportive, social relationships in adulthood and old age. In addition, it is hypothesized that adults who are securely attached and thus are able to maintain relationships and utilize their support systems in times of stress will be more likely to overcome obstacles in life and thus have more positive overall perspectives on their life course which will result in less anxiety about death in old age.
Death Anxiety
While death is statistically related to old age, thanatology literature has overwhelmingly centered on death at younger ages, primarily those related to cancer, aids, or traumatic occurrences (Ingebretsen & Solem, 1998). Older people are the most preoccupied with thoughts about death, though they tend to do so with little or no anxiety compared to adolescents or adults (Ingebretsen & Solem, 1998). Yet there is still considerable variance in the level of anxiety experienced by the heterogeneous group labeled elderly. Several researchers have attempted to describe the components of death anxiety in order to develop a basis for theory and ultimately to improve clinical practices with elderly patients.
Death anxiety has only recently been accented in psychological circles and especially so in geriatric focused research. Because of this deficit, Fortner and Neimeyer (1999) constructed a comprehensive quantitative review of death anxiety studies which allowed them to identify seven distinct psychological constructs specifically related to geriatric populations. Death anxiety was found to be related to age, ego integrity, gender, institutionalization, physical health, and religiosity. Higher levels of death anxiety in the elderly were found to be negatively related to ego integrity (r = -.30), and positively related to more physical problems (r = .28) and more psychological problems (r = .19) and may, to some extent, be related to institutionalization (Fortner & Neimeyer, 1999) though this may also be due to loss of self-efficacy and social relationships.
Death anxiety is higher in middle-aged people than in the elderly, but this does not suggest that all elderly have low levels of death anxiety (Depaola et al., 2003). Age comparisons of death anxiety do not seriously help to elucidate what causes death anxiety in older people.
Ego integrity refers to the psychosocial development of integrity versus despair in old age (Hetherington & Parke, 1999). Those individuals who have successfully developed levels of integrity are less prone to death anxiety according to Fortner and Neimeyer (1999). Developmentally, those who have shown greater satisfaction in overcoming life obstacles are naturally more able to deal with difficult situations. In addition, individuals who are satisfied with their life achievement may also be more resistant to death anxiety because they are buffered by positive feelings of meaningful accomplishment.
Differences in death anxiety between male and females have not been consistently shown. For example, some researchers have reasoned that death anxiety is higher in females than males (Neimeyer & Fortner, 1995), but others did not support this view (Fortner & Neimeyer, 1999). The mixed results may be attributed to differences in research design, or operationalization of anxiety, i.e. social experience—associated more with coping strategies of females, or overcoming obstacles—associated more with the coping strategies of males. In addition, different cultural experiences by women and men may significantly affect their access to resources and information, further complicating their experience of death. It seems likely that, globally, women might be more at risk of death anxiety because of the depressed state of freedoms and access to resources in many countries, yet no research conclusively rests this issue.
Being forcibly put into restrictive environments seriously undermines ones self-efficacy and autonomy. Therefore it comes as no surprise that some research studies
(Fortner & Neimeyer, 1999) have found a positive relationship between institutionalization and higher levels of death anxiety. However, the literature is still too vague to make any definitive argument on this issue. Past research has failed on many cases to discuss the difference between voluntary and involuntary commitment to an institution (such as a nursing home). Individuals with higher levels of physical and psychological problems are more often institutionalized which may compound the effects of death anxiety. Loss of bodily control, ability to control one’s environment, or care for one’s self adequately seriously affects autonomy and self-esteem or ego integrity negatively. Clearly, more research is needed before conclusions on these variables can be reached.
Religiosity has produced mixed results as a variable in death anxiety studies. Some researchers found those who are more religious to have lower levels of death anxiety (Neimeyer & Fortner, 1995), while other have not (Fortner & Neimeyer, 1999). The difference in results is most likely due to definitions of religiosity of belief or behaviors, and not to individual faiths. It may also be that the current geriatric cohort is relatively homogeneous in their faith as compared to younger cohorts which have also been included in the studies.
In general, elderly who are independent, have positive levels of ego integrity, and are sufficiently satisfied with their life’s path show lower levels death anxiety than elderly in different situations. On the whole, death anxiety is less in the elderly than other age groups. These results may reflect a closer death experience in the elderly, i.e.—lost loved ones. Socialization between elderly about their shared experiences probably naturally includes more death themed conversations than other age groups. Or, the decrease in overall death anxiety may be related to other personal factors such as difference in coping strategies, personality, or life-span experience. What seems clear though is that protective factors against death anxiety may be related to coping strategies.
Positive Health, Social Relationships, and Quality of Life
In the past, human health has been defined as a lack of physical or mental dysfunctions. This definition is clearly inadequate to account for the complexity of what makes up total human health. According to Ryff and Singer (1998), positive human health (PHH) is a complex philosophical, emotional, mental, and biological interaction of factors that are evaluated in social contexts and support “happiness”. Numerous studies on well-being (Bearon, 1996; Depner & Ingersoll-Dayton, 1988; Diener, Suh, Lucas, & Smith, 1999; Long & Martin, 2000; Shaver & Mikulincer, 2002; Shu, Huang, & Chen, 2003) have reached the same conclusions, namely, that one of the most important influences in the development of PHH, well-being, and life satisfaction is that of maintaining close personal relationships throughout the lifespan. The protective factor of social relationships is so fundamentally ingrained that it extends beyond the human experience, indeed, back into evolutionary history.
The history of life on the planet can only be written in terms of social interaction. Bacteria, the earliest forms of life, demonstrate incredibly complex social behaviors, including foraging for food, protecting resources, and mass communication (Bloom, 2000). When some bacteria colonies exhaust their immediate energy resources, they send out scouts in concentric circles in search of new environments to exploit. Those scouts that find food return to the center of the colony leaving behind a chemical trail that other bacteria will be able to follow. The bacteria who do not find anything of energy value to the colony do not come back, but instead undergo programmed cell death, excusing themselves, and their failure, from the rest of the colony (Bloom, 2000). The evolution of life is wrapped up in this social networking so much so that humans who do not form quality social relationships, those of love and support will not only tend to suffer health problems more often (Ryff & Singer, 1998), but also decrease their overall satisfaction with their own lives (Ainsworth, 1989; Depner & Ingersoll-Dayton, 1988).
Social relationships offer more than just support in times of stress. They have been shown to be affect the development of one’s self concept (Shu et al., 2003). Shu et al. (2003) demonstrated that elderly living in a retirement facility had more positive self concepts if they were more interactive with others. Social relationships also affect the biological functioning of individuals. Ryff and Singer (1998) discussed how a wide variety of stressors in animals (i.e. electric shocks, social defeat, maternal separation, etc.) can alter many different aspects of the immune system. However, the impact of stress on the immune system could not be fully explained in terms of biology, instead, there seems to be a strong correlation between positive social interaction and stressors as a protective function. Social interactions have been shown to affect emotional and personality stability as well (Diener et al., 1999).
The correlation between perceived quality of life and Intimacy (the ability to form close personal relationships) has been explored by Antonucci, Lansford, and Akiyama (2001, as cited in Weiler, 2001). Antonucci et al. found that women who did not have a friend in which to confide were less satisfied with life than those who did and more likely to report depressive symptomology. Weiler (2001) suggests that friendship may also constitute a facilitator of meaning, enabling people to formulate their belief systems and to find meaning in life’s challenges. In the absence of friends, finding meaning in life becomes complicated because the sharing process is non-existent. “Human flourishing, whether in the form of deeply engaged life purposes or richly experienced love relationships, likely affects multiple biological systems” (Ryff & Singer, 1998, p. 9).
Conclusions
Worry about death is perhaps the most universal and fundamental source of threat and anxiety humans may encounter. While younger individuals generally show more anxiety concerning death than older people, older people are closer to the final moment and therefore variables affecting their death anxiety levels may also affect their perceived quality of life. Social relationship competency is a necessary component in the development and maintenance of social support systems throughout life, and especially into old age. Attachment is a requisite for preventing loneliness because early social experiences form the individual’s perception and ability to use social relationships for protective functions (Long & Martin, 2000). Shaver and Mikulincer (2002) argue that attachment level in adulthood is highly correlated to an individual’s ability to use social relationships in times of stress. Considering that aging individuals are exposed to a number of age related stressors, i.e. biological decline, death of loved ones, it is no surprise that considerable attention is given to the role of social relationships in mediating these stressful events.
Death anxiety has been shown to be negatively correlated with close support systems as formed by past attachment experiences (Becker, 1992). Individuals classified as securely attached consistently rely on proximity seeking and social interaction to protect themselves from extreme distress (Shaver & Mikulincer, 2002). Elderly who have had inconsistent attachment experiences in childhood and therefore are insecurely attached, adopt and entirely different strategy for resolving of stressful situations. For example, Shaver and Mikulincer (2002) found that people who scored high on the avoidance-anxiety dimension tend to focus on their own distress, mull over on negative thoughts, and adopt emotion-avoiding coping strategies instead of diminishing the distress. This supports the idea that death anxiety in old age may well be negatively correlated with positive attachment experiences, and that insecurely attached individuals may show greater death anxiety because their coping mechanisms are not centered on the protective nature of social relationships.
A growing percentage of aging individuals will require some type of assisted living. Government and private agencies which develop retirement centers, assisted care facilities, or other elderly communities should consider the importance of social interactions in the design of such facilities so as to increase the opportunities to foster social interactions between their community members if they want to help increase the quality of life experienced by elderly in the last years of life. Currently, many elderly facilities may neglect to emphasize the socialness of their programs because of an insufficient understanding of the role that close personal relationships plays in protecting the elderly from the stresses of biological and social aging. Considering that elderly individuals are more likely to lose those close to them, it is suggested that further research be conducted on the best way to implement social programs in order to provide the elderly with access to a wide social matrix in the hopes of increasing their quality of life in the last years. In addition, increasing education programs for new parents in the formation of attachment may, in the future, increase the overall well being of the general population by increasing the quality and use of social relationships in order to overcome life stressors.
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In Vivo Research Techniques
by j.w.gibson, m.s.
Advanced technology in body imaging has greatly increased the ability of researchers and clinicians to investigate cognitive functioning and diagnose brain dysfunctions. Two techniques that are currently enjoying widespread use are Functional Magnetic Resonance Imaging (fMRI) and Positron Emission Tomography (PET). These techniques are in vivo, that is, they do not require invasive intrusion in to the body to study the underlying mechanisms. In vivo techniques are advantageous because they can illuminate cognitive processing while it is happening, giving cognitive scientists a tangible picture of which parts of the brain function to produce certain cognitive activities.
Magnetic Resonance Imaging (MRI) has been used for more than fifteen years to analyze the soft tissue of the human body (Noll, 2001). MRI is an imaging technique that uses the property of Nuclear Magnetic Resonance (NMR) to differentiate between atomic nuclei with different magnetic properties (Ugurbil, 2001). Some types of atoms have nuclei that have an odd number of neutrons, odd number of protons, or perhaps both, which leads to a "net magnetic moment and will therefore be NMR active" (Noll, 2001). Hydrogen1 or simply a proton, is the most common type of nuclei used for NMR because of its high concentration in the human body (Noll, 2001). When these nuclei are subjected to a magnetic field they will align themselves parallel or anti-parallel to the static magnetic field (Ugurbil, 2001). Electromagnetic radiation is used to create discrete resonances in different chemical nuclei (Ugurbil, 2001). Imaging is then performed by assigning specific spatial encoding and contrast mechanisms to the resonance information (Noll, 2001). Colors or gray-scale coding are used to create a visual image of different tissue based on resonation differences.
Functional MRI (fMRI) is the application of MRI technology to investigate the physiologic changes that occur in brain tissue. For instance, fMRI is able to measure changes in "phosphorus metabolism and metabolic byproducts, blood flow, blood volume, and blood oxygenation" (Noll, 2001). Blood oxygen level dependent (BOLD) contrast is the most frequently used technique. Hemoglobin that is oxygenated has different magnetic properties than hemoglobin that has been deoxygenated by the metabolic processes of the cells. Since neural processing requires the use of neural cells, which in turn use oxygen, it is possible to identify which parts of the brain are more active during a specific cognitive task. However, the differences between magnetic resonances are typically so small that it is necessary to perform numerous trials and then subtract out the difference from the control and experimental data (Noll, 2001). Only then is there enough statistical difference to be superimposed onto brain images thus highlighting activated brain structures (Noll, 2001; Ugurbil, 2001).
Researchers have used fMRI to study a wide range of cognitive processing including the fundamental layout and relationship of brain structures during normal processing (Salvador et al., 2005) to dysfunctional processing (Pantano et al., 2005). However, most studies using fMRI do not utilize multivariate methods for analysis and so may not be realizing the full potential of fMRI data (Friman, Cedefamn, Lundberg, Borga, & Knutsson, 2001). New methods for detecting neural activity are constantly being developed, such as the Canonical Correlation Analysis (CCA) suggested by Friman et al. (2001). It is clear that MRI and especially fMRI have enormous potential as an investigative and diagnostic tool.
Positron Emission Tomography (PET) is an imaging technique that utilizes the properties of annihilation radiation that occurs when positrons are absorbed into matter (Riachle, 2001). Subjects are injected with a radioactive substance, called a radiotracer, which then flows through the heart and eventually to the brain. The decay of positrons produces gamma rays that can then be detected by machines. The patterns of gamma rays provides data, which is then processed by algorithm, to create an image (Montandon & Zaidi, 2002). Because PET relies on regional cerebral blood flow (rCBF), it is understood that more active areas of the brain will require more rCBF and thus have greater concentrations of the radiotracer. In the early days of PET it was realized that while both blood flow and metabolism could be accurately measured, blood flow provided a greater practical advantage because of it could be measured in less than a minute, and with a widely available radiopharmaceutical (H215O) (Riachle, 2001).
Researchers typically use a subtractive methodology for the measurement of thought processes. That is, they take a base measurement, for instance, how long it takes to respond to a light, and then subtract the difference in response times for responding to a particular color of light (Riachle, 2001). Using this same technique, researchers have been aided in the studying the relationship between certain types of brain disorders. For example, low serotonergic activity seems to be related to both Borderline Personality Disorder (BPD) and Major Depressive Disorder (MDD) (Oquendo et al., 2005). PET technology has helped researchers to demonstrate that both BPD and MDD are associated with unusual activity in the parietotemporal and anterior cingulate cortical regions (Oquendo et al., 2005).
References
Barlow, H. (2001). Cerebral cortex. In R. A. Wilson & F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences (pp. 111-113). Cambridge, MA: The MIT Press.
Bloom, H. (2000). Global brain: The evolution of mass mind from the big band to the 21 century. New York: John Wiley & Sons, Inc.
Carlson, N. R. (2004). Physiology of behavior (8th ed.). Boston; MA: Pearson Education, Inc.
Eliassen, J. C., Baynes, K., & Gazzaniga, M. S. (2000). Anterior and posterior callosal contributions to simultaneous bimanual movements of the hands and fingers. Brain, 123(12), 2501-2511.
Friman, O., Cedefamn, J., Lundberg, P., Borga, M., & Knutsson, H. (2001). Detection of neural activity in functional MRI using canonical correlation analysis. Magnetic Resonance in Medicine, 45, 323-330.
Gandhi, S. P., & Stevens, C. F. (2003). Three modes of synaptic vesicular recycling revealed by single-vesicle imaging. Nature, 423, 607-613.
Gazzaniga, M. S. (1995). Principals of human brain organization derived from split-brain studies. Neuron, 14, 217-228.
Gazzaniga, M. S. (2002). The split-brain revisited. Retrieved September 1, 2006, from http://people.brandeis.edu/~teuber/splitbrain.pdf
Green, R., Clark, A., Hickey, W., Hutsler, J., & Gazzaniga, M. S. (1999). Braincutting for psychiatrists: The time is ripe. The Journal of Neuropsychiatry and Clinical Neuroscience, 11(3), 301-306.
Humphrys, M. (1997). AI is possible . .but AI won't happen: The future of artificial intelligence. Retrieved September 11, 2006, from http://www.computing.dcu.ie/~humphrys/newsci.html
Kalat, J. W. (2001). Biological psychology (7th ed.). Belmont, CA: Wadsworth/Thomson Learning.
Koch, C. (2004). The quest for consciousness: A neurobiological approach. Englewood: CO: Roberts and Company Publishers.
Montandon, M.-L., & Zaidi, H. (2002). Perspectives in quantitative brain positron emission tomography imaging. Business Briefing: Global Healthcare(3), 2-4.
Mycek, M. J., Harvey, R. A., & Champe, P. C. (Eds.). (2000). Pharmacology (2nd ed.). Philadelphia: Lippincott Williams & Wilkins.
Noll, D. C. (2001). A primer on MRI and functional MRI. Retrieved September 13, 2006, from http://www.eecs.umich.edu/~dnoll/primer2.pdf#search=%22a%20primer%20on%20MRI%20and%20functional%20MRI%22
Oquendo, M. A., Krunic, A., Parsey, R., Milak, M., Malone, K. M., Anderson, A., et al. (2005). Positron emission tomography of regional brain metabolic responses to a serotonergic challenge in major depressive disorder with and without borderline personality disorder. Neuropsychopharmacology, 30, 1163-1172.
Pantano, P., Mainero, C., Lenzi, D., Caramia, F., Iannetti, G. D., Piattella, M. C., et al. (2005). A longitudinal fMRI study on motor activity in patients with multiple sclerosis. Brain, 128(2146-2153).
Pinker, S. (1997). How the mind works. New York: W.W. Norton & Company.
Riachle, M. (2001). Positron emission tomography. In R. A. Wilson & F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences (pp. 656-659). Cambridge, MA: The MIT Press.
Rosenzweig, M. R., Leiman, A. L., & Breedlove, S. M. (1999). Biological psychology: An introduction to behavioral, cognitive, and clinical neuroscience (2nd ed.). Sunderland, MA: Sinauer Associates.
Salvador, R., Suckling, J., Coleman, M. R., Pickard, J. D., Menon, D., & Bullmore, E. (2005). Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex, 15, 1332-1342.
Shepard, G. (2001). Neuron. In R. A. Wilson & F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences (pp. 603-604). Cambridge: MA: The MIT Press.
Sperry, R. W. (1964). The great cerebral commissure. Scientific American, 210(1), 42-52.
Sternberg, R. J. (2003). Cognitive psychology (3rd ed.). Belmont, CA: Wadsworth/Thompson Learning.
Ugurbil, K. (2001). Magnetic resonance imaging. In R. A. Wilson & F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences (pp. 505-507). Cambridge, MA: The MIT Press.
Wills, T. A., DuHamel, K., & Vaccaro, D. (1995). Activity and mood temperament as predictors of adolescent substance use: Test of a self-regulation mediational model. Journal of Personality and Social Psychology, 68(5), 901-916.
Advanced technology in body imaging has greatly increased the ability of researchers and clinicians to investigate cognitive functioning and diagnose brain dysfunctions. Two techniques that are currently enjoying widespread use are Functional Magnetic Resonance Imaging (fMRI) and Positron Emission Tomography (PET). These techniques are in vivo, that is, they do not require invasive intrusion in to the body to study the underlying mechanisms. In vivo techniques are advantageous because they can illuminate cognitive processing while it is happening, giving cognitive scientists a tangible picture of which parts of the brain function to produce certain cognitive activities.
Magnetic Resonance Imaging (MRI) has been used for more than fifteen years to analyze the soft tissue of the human body (Noll, 2001). MRI is an imaging technique that uses the property of Nuclear Magnetic Resonance (NMR) to differentiate between atomic nuclei with different magnetic properties (Ugurbil, 2001). Some types of atoms have nuclei that have an odd number of neutrons, odd number of protons, or perhaps both, which leads to a "net magnetic moment and will therefore be NMR active" (Noll, 2001). Hydrogen1 or simply a proton, is the most common type of nuclei used for NMR because of its high concentration in the human body (Noll, 2001). When these nuclei are subjected to a magnetic field they will align themselves parallel or anti-parallel to the static magnetic field (Ugurbil, 2001). Electromagnetic radiation is used to create discrete resonances in different chemical nuclei (Ugurbil, 2001). Imaging is then performed by assigning specific spatial encoding and contrast mechanisms to the resonance information (Noll, 2001). Colors or gray-scale coding are used to create a visual image of different tissue based on resonation differences.
Functional MRI (fMRI) is the application of MRI technology to investigate the physiologic changes that occur in brain tissue. For instance, fMRI is able to measure changes in "phosphorus metabolism and metabolic byproducts, blood flow, blood volume, and blood oxygenation" (Noll, 2001). Blood oxygen level dependent (BOLD) contrast is the most frequently used technique. Hemoglobin that is oxygenated has different magnetic properties than hemoglobin that has been deoxygenated by the metabolic processes of the cells. Since neural processing requires the use of neural cells, which in turn use oxygen, it is possible to identify which parts of the brain are more active during a specific cognitive task. However, the differences between magnetic resonances are typically so small that it is necessary to perform numerous trials and then subtract out the difference from the control and experimental data (Noll, 2001). Only then is there enough statistical difference to be superimposed onto brain images thus highlighting activated brain structures (Noll, 2001; Ugurbil, 2001).
Researchers have used fMRI to study a wide range of cognitive processing including the fundamental layout and relationship of brain structures during normal processing (Salvador et al., 2005) to dysfunctional processing (Pantano et al., 2005). However, most studies using fMRI do not utilize multivariate methods for analysis and so may not be realizing the full potential of fMRI data (Friman, Cedefamn, Lundberg, Borga, & Knutsson, 2001). New methods for detecting neural activity are constantly being developed, such as the Canonical Correlation Analysis (CCA) suggested by Friman et al. (2001). It is clear that MRI and especially fMRI have enormous potential as an investigative and diagnostic tool.
Positron Emission Tomography (PET) is an imaging technique that utilizes the properties of annihilation radiation that occurs when positrons are absorbed into matter (Riachle, 2001). Subjects are injected with a radioactive substance, called a radiotracer, which then flows through the heart and eventually to the brain. The decay of positrons produces gamma rays that can then be detected by machines. The patterns of gamma rays provides data, which is then processed by algorithm, to create an image (Montandon & Zaidi, 2002). Because PET relies on regional cerebral blood flow (rCBF), it is understood that more active areas of the brain will require more rCBF and thus have greater concentrations of the radiotracer. In the early days of PET it was realized that while both blood flow and metabolism could be accurately measured, blood flow provided a greater practical advantage because of it could be measured in less than a minute, and with a widely available radiopharmaceutical (H215O) (Riachle, 2001).
Researchers typically use a subtractive methodology for the measurement of thought processes. That is, they take a base measurement, for instance, how long it takes to respond to a light, and then subtract the difference in response times for responding to a particular color of light (Riachle, 2001). Using this same technique, researchers have been aided in the studying the relationship between certain types of brain disorders. For example, low serotonergic activity seems to be related to both Borderline Personality Disorder (BPD) and Major Depressive Disorder (MDD) (Oquendo et al., 2005). PET technology has helped researchers to demonstrate that both BPD and MDD are associated with unusual activity in the parietotemporal and anterior cingulate cortical regions (Oquendo et al., 2005).
References
Barlow, H. (2001). Cerebral cortex. In R. A. Wilson & F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences (pp. 111-113). Cambridge, MA: The MIT Press.
Bloom, H. (2000). Global brain: The evolution of mass mind from the big band to the 21 century. New York: John Wiley & Sons, Inc.
Carlson, N. R. (2004). Physiology of behavior (8th ed.). Boston; MA: Pearson Education, Inc.
Eliassen, J. C., Baynes, K., & Gazzaniga, M. S. (2000). Anterior and posterior callosal contributions to simultaneous bimanual movements of the hands and fingers. Brain, 123(12), 2501-2511.
Friman, O., Cedefamn, J., Lundberg, P., Borga, M., & Knutsson, H. (2001). Detection of neural activity in functional MRI using canonical correlation analysis. Magnetic Resonance in Medicine, 45, 323-330.
Gandhi, S. P., & Stevens, C. F. (2003). Three modes of synaptic vesicular recycling revealed by single-vesicle imaging. Nature, 423, 607-613.
Gazzaniga, M. S. (1995). Principals of human brain organization derived from split-brain studies. Neuron, 14, 217-228.
Gazzaniga, M. S. (2002). The split-brain revisited. Retrieved September 1, 2006, from http://people.brandeis.edu/~teuber/splitbrain.pdf
Green, R., Clark, A., Hickey, W., Hutsler, J., & Gazzaniga, M. S. (1999). Braincutting for psychiatrists: The time is ripe. The Journal of Neuropsychiatry and Clinical Neuroscience, 11(3), 301-306.
Humphrys, M. (1997). AI is possible . .but AI won't happen: The future of artificial intelligence. Retrieved September 11, 2006, from http://www.computing.dcu.ie/~humphrys/newsci.html
Kalat, J. W. (2001). Biological psychology (7th ed.). Belmont, CA: Wadsworth/Thomson Learning.
Koch, C. (2004). The quest for consciousness: A neurobiological approach. Englewood: CO: Roberts and Company Publishers.
Montandon, M.-L., & Zaidi, H. (2002). Perspectives in quantitative brain positron emission tomography imaging. Business Briefing: Global Healthcare(3), 2-4.
Mycek, M. J., Harvey, R. A., & Champe, P. C. (Eds.). (2000). Pharmacology (2nd ed.). Philadelphia: Lippincott Williams & Wilkins.
Noll, D. C. (2001). A primer on MRI and functional MRI. Retrieved September 13, 2006, from http://www.eecs.umich.edu/~dnoll/primer2.pdf#search=%22a%20primer%20on%20MRI%20and%20functional%20MRI%22
Oquendo, M. A., Krunic, A., Parsey, R., Milak, M., Malone, K. M., Anderson, A., et al. (2005). Positron emission tomography of regional brain metabolic responses to a serotonergic challenge in major depressive disorder with and without borderline personality disorder. Neuropsychopharmacology, 30, 1163-1172.
Pantano, P., Mainero, C., Lenzi, D., Caramia, F., Iannetti, G. D., Piattella, M. C., et al. (2005). A longitudinal fMRI study on motor activity in patients with multiple sclerosis. Brain, 128(2146-2153).
Pinker, S. (1997). How the mind works. New York: W.W. Norton & Company.
Riachle, M. (2001). Positron emission tomography. In R. A. Wilson & F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences (pp. 656-659). Cambridge, MA: The MIT Press.
Rosenzweig, M. R., Leiman, A. L., & Breedlove, S. M. (1999). Biological psychology: An introduction to behavioral, cognitive, and clinical neuroscience (2nd ed.). Sunderland, MA: Sinauer Associates.
Salvador, R., Suckling, J., Coleman, M. R., Pickard, J. D., Menon, D., & Bullmore, E. (2005). Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex, 15, 1332-1342.
Shepard, G. (2001). Neuron. In R. A. Wilson & F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences (pp. 603-604). Cambridge: MA: The MIT Press.
Sperry, R. W. (1964). The great cerebral commissure. Scientific American, 210(1), 42-52.
Sternberg, R. J. (2003). Cognitive psychology (3rd ed.). Belmont, CA: Wadsworth/Thompson Learning.
Ugurbil, K. (2001). Magnetic resonance imaging. In R. A. Wilson & F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences (pp. 505-507). Cambridge, MA: The MIT Press.
Wills, T. A., DuHamel, K., & Vaccaro, D. (1995). Activity and mood temperament as predictors of adolescent substance use: Test of a self-regulation mediational model. Journal of Personality and Social Psychology, 68(5), 901-916.
Analysis of a Split-Brain Drawing Task with Both Right and Left Hands
by j.w.gibson, ms
A procedure to help alleviate seizures associated with severe epilepsy is to surgically sever the corpus callosum. The corpus callosum is a large bundle of nerves that connects corresponding parts of the brain to each other (Carlson, 2004). It allows for the brain to send messages back and forth between the right and left hemispheres. Patients suffering from severe epilepsy may suffer from seizures that occur numerous times in a day. Researchers have found that severing the corpus callosum drastically decreases the frequency and intensity of epileptic seizures since the different hemispheres of the brain are no longer able to transfer excessive electrical energy.
Lateralization of brain functioning has been known for quite some time. In 1826, a French doctor named Marc Dax noted that more than 40 of his patients suffering from loss of speech consistently had damage to the left side of the brain (Sternberg, 2003). It is now well supported that the left and right hemispheres are indeed specialized for different types of neural processing, and indeed in some respects can be considered as separate brains (Sternberg, 2003). Both hemispheres of the brain receive sensory information from the opposite side of the body. They share this information via the corpus callosum so that each knows what the other is "perceiving and doing" (Carlson, 2004). Extensive research of Sperry (Sperry, 1964) and Gazzaniga (Gazzaniga, 1995), among others has lead to a strong argument for hemispheric specialization.
The left hemisphere contains language processing while the right hemisphere seems to be dominant for spatial visualization (Green, Clark, Hickey, Hutsler, & Gazzaniga, 1999; Kalat, 2001; Rosenzweig, Leiman, & Breedlove, 1999; Sternberg, 2003; Wills, DuHamel, & Vaccaro, 1995). In addition to language processing, the left hemisphere is also important for smooth skilled movement (Gazzaniga, 1995). Researchers have found that while the right is far superior in terms of spatial processing, it does have some limited ability to comprehend verbal instructions; however, it is wholly incapable of producing speech (Carlson, 2004; Eliassen, Baynes, & Gazzaniga, 2000).
Cutting the corpus callosum leads to some interesting behaviors. Because the two hemispheres are incapable of communicating with each other, split-brain patients have noted that their left-hand seems to act on its own. For instance, "patients may find themselves putting down a book held in the left hand, even if they have been reading it with great interest. This conflict occurs because the right hemisphere, which controls the left hand, cannot read and therefore fids the book boring" (Carlson, 2004). The left-hemisphere receives sensory information from the right side, and vice versa. Because sensory information is processed on the opposite site, split-brain patients are not able to access certain types of information when asked to recall. An exception to this rule of crossed representation is olfaction.
Olfaction occurs on the same side of the brain that the nostril resides. Therefore, a scent detected in the right nostril is processed on the right side of the brain. When split-brain patients are asked to identify the odor of something presented to their left-hemisphere, there are able to name it. However, when the odor is presented to the right side they are unable to find the word, but they are able to physically find an object that represents the odor (Kalat, 2001; Rosenzweig, Leiman, & Breedlove, 1999).
When split-brain patients try to replicate drawings from pairs of words presented to different hemispheres there is no integration of the concepts. For instance, Kingstone and Gazzaniga conducted an experiment where they flashed two words "Bow" and "Arrow" to different hemispheres. They then asked the participant to draw what they had seen and surprisingly the participant drew a bow and arrow, leading the researchers to believe that the concepts had been integrated (Gazzaniga, 2002). However, upon further tasks with different word pairs (sky, scraper) the participants obviously did not integrate the concept into skyscraper, but instead drew a "comb-like scraper" with a sky above (Gazzaniga, 2002). Thus split-brain patients are not capable of integrating both hemispheres knowledge about visual information to make a unified concept or representation. Instead, it is as if the two hemispheres are unaware of each other's processing.
Split-brain patients cannot access the lexiconal information of words that reside in the left-hemisphere when information requiring this type of information is presented to the right-hemisphere. While the right side is capable of some language processing such as matching words to pictures, performing spelling and rhyming tasks, and categorizing objects, it is incapable of syntactical meaning, and indeed most people's right hemispheres "cannot handle even the most rudimentary language" (Gazzaniga, 2002).
If a patient was presented with a three-dimensional object to draw they would be successful with the left hand yet not the right. This is because of the contralateral wiring of the brain. Visual-spatial processing resides in the right-hemisphere, which controls the left hand. Without the corpus callosum intact, there exists virtually no communication or transfer of information from one hemisphere to the other. The right hand, which is controlled by the left-hemisphere, has no knowledge of the 3-dimensional object therefore it cannot replicate it. If instead the object was translated into a word such as "cube" the left side could process this language information and instruct the right hand to represent the three-dimensional object.
References
References
Barlow, H. (2001). Cerebral cortex. In R. A. Wilson & F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences (pp. 111-113). Cambridge, MA: The MIT Press.
Bloom, H. (2000). Global brain: The evolution of mass mind from the big band to the 21 century. New York: John Wiley & Sons, Inc.
Carlson, N. R. (2004). Physiology of behavior (8th ed.). Boston; MA: Pearson Education, Inc.
Eliassen, J. C., Baynes, K., & Gazzaniga, M. S. (2000). Anterior and posterior callosal contributions to simultaneous bimanual movements of the hands and fingers. Brain, 123(12), 2501-2511.
Friman, O., Cedefamn, J., Lundberg, P., Borga, M., & Knutsson, H. (2001). Detection of neural activity in functional MRI using canonical correlation analysis. Magnetic Resonance in Medicine, 45, 323-330.
Gandhi, S. P., & Stevens, C. F. (2003). Three modes of synaptic vesicular recycling revealed by single-vesicle imaging. Nature, 423, 607-613.
Gazzaniga, M. S. (1995). Principals of human brain organization derived from split-brain studies. Neuron, 14, 217-228.
Gazzaniga, M. S. (2002). The split-brain revisited. Retrieved September 1, 2006, from http://people.brandeis.edu/~teuber/splitbrain.pdf
Green, R., Clark, A., Hickey, W., Hutsler, J., & Gazzaniga, M. S. (1999). Braincutting for psychiatrists: The time is ripe. The Journal of Neuropsychiatry and Clinical Neuroscience, 11(3), 301-306.
Humphrys, M. (1997). AI is possible . .but AI won't happen: The future of artificial intelligence. Retrieved September 11, 2006, from http://www.computing.dcu.ie/~humphrys/newsci.html
Kalat, J. W. (2001). Biological psychology (7th ed.). Belmont, CA: Wadsworth/Thomson Learning.
Koch, C. (2004). The quest for consciousness: A neurobiological approach. Englewood: CO: Roberts and Company Publishers.
Montandon, M.-L., & Zaidi, H. (2002). Perspectives in quantitative brain positron emission tomography imaging. Business Briefing: Global Healthcare(3), 2-4.
Mycek, M. J., Harvey, R. A., & Champe, P. C. (Eds.). (2000). Pharmacology (2nd ed.). Philadelphia: Lippincott Williams & Wilkins.
Noll, D. C. (2001). A primer on MRI and functional MRI. Retrieved September 13, 2006, from http://www.eecs.umich.edu/~dnoll/primer2.pdf#search=%22a%20primer%20on%20MRI%20and%20functional%20MRI%22
Oquendo, M. A., Krunic, A., Parsey, R., Milak, M., Malone, K. M., Anderson, A., et al. (2005). Positron emission tomography of regional brain metabolic responses to a serotonergic challenge in major depressive disorder with and without borderline personality disorder. Neuropsychopharmacology, 30, 1163-1172.
Pantano, P., Mainero, C., Lenzi, D., Caramia, F., Iannetti, G. D., Piattella, M. C., et al. (2005). A longitudinal fMRI study on motor activity in patients with multiple sclerosis. Brain, 128(2146-2153).
Pinker, S. (1997). How the mind works. New York: W.W. Norton & Company.
Riachle, M. (2001). Positron emission tomography. In R. A. Wilson & F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences (pp. 656-659). Cambridge, MA: The MIT Press.
Rosenzweig, M. R., Leiman, A. L., & Breedlove, S. M. (1999). Biological psychology: An introduction to behavioral, cognitive, and clinical neuroscience (2nd ed.). Sunderland, MA: Sinauer Associates.
Salvador, R., Suckling, J., Coleman, M. R., Pickard, J. D., Menon, D., & Bullmore, E. (2005). Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex, 15, 1332-1342.
Shepard, G. (2001). Neuron. In R. A. Wilson & F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences (pp. 603-604). Cambridge: MA: The MIT Press.
Sperry, R. W. (1964). The great cerebral commissure. Scientific American, 210(1), 42-52.
Sternberg, R. J. (2003). Cognitive psychology (3rd ed.). Belmont, CA: Wadsworth/Thompson Learning.
Ugurbil, K. (2001). Magnetic resonance imaging. In R. A. Wilson & F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences (pp. 505-507). Cambridge, MA: The MIT Press.
Wills, T. A., DuHamel, K., & Vaccaro, D. (1995). Activity and mood temperament as predictors of adolescent substance use: Test of a self-regulation mediational model. Journal of Personality and Social Psychology, 68(5), 901-916.
A procedure to help alleviate seizures associated with severe epilepsy is to surgically sever the corpus callosum. The corpus callosum is a large bundle of nerves that connects corresponding parts of the brain to each other (Carlson, 2004). It allows for the brain to send messages back and forth between the right and left hemispheres. Patients suffering from severe epilepsy may suffer from seizures that occur numerous times in a day. Researchers have found that severing the corpus callosum drastically decreases the frequency and intensity of epileptic seizures since the different hemispheres of the brain are no longer able to transfer excessive electrical energy.
Lateralization of brain functioning has been known for quite some time. In 1826, a French doctor named Marc Dax noted that more than 40 of his patients suffering from loss of speech consistently had damage to the left side of the brain (Sternberg, 2003). It is now well supported that the left and right hemispheres are indeed specialized for different types of neural processing, and indeed in some respects can be considered as separate brains (Sternberg, 2003). Both hemispheres of the brain receive sensory information from the opposite side of the body. They share this information via the corpus callosum so that each knows what the other is "perceiving and doing" (Carlson, 2004). Extensive research of Sperry (Sperry, 1964) and Gazzaniga (Gazzaniga, 1995), among others has lead to a strong argument for hemispheric specialization.
The left hemisphere contains language processing while the right hemisphere seems to be dominant for spatial visualization (Green, Clark, Hickey, Hutsler, & Gazzaniga, 1999; Kalat, 2001; Rosenzweig, Leiman, & Breedlove, 1999; Sternberg, 2003; Wills, DuHamel, & Vaccaro, 1995). In addition to language processing, the left hemisphere is also important for smooth skilled movement (Gazzaniga, 1995). Researchers have found that while the right is far superior in terms of spatial processing, it does have some limited ability to comprehend verbal instructions; however, it is wholly incapable of producing speech (Carlson, 2004; Eliassen, Baynes, & Gazzaniga, 2000).
Cutting the corpus callosum leads to some interesting behaviors. Because the two hemispheres are incapable of communicating with each other, split-brain patients have noted that their left-hand seems to act on its own. For instance, "patients may find themselves putting down a book held in the left hand, even if they have been reading it with great interest. This conflict occurs because the right hemisphere, which controls the left hand, cannot read and therefore fids the book boring" (Carlson, 2004). The left-hemisphere receives sensory information from the right side, and vice versa. Because sensory information is processed on the opposite site, split-brain patients are not able to access certain types of information when asked to recall. An exception to this rule of crossed representation is olfaction.
Olfaction occurs on the same side of the brain that the nostril resides. Therefore, a scent detected in the right nostril is processed on the right side of the brain. When split-brain patients are asked to identify the odor of something presented to their left-hemisphere, there are able to name it. However, when the odor is presented to the right side they are unable to find the word, but they are able to physically find an object that represents the odor (Kalat, 2001; Rosenzweig, Leiman, & Breedlove, 1999).
When split-brain patients try to replicate drawings from pairs of words presented to different hemispheres there is no integration of the concepts. For instance, Kingstone and Gazzaniga conducted an experiment where they flashed two words "Bow" and "Arrow" to different hemispheres. They then asked the participant to draw what they had seen and surprisingly the participant drew a bow and arrow, leading the researchers to believe that the concepts had been integrated (Gazzaniga, 2002). However, upon further tasks with different word pairs (sky, scraper) the participants obviously did not integrate the concept into skyscraper, but instead drew a "comb-like scraper" with a sky above (Gazzaniga, 2002). Thus split-brain patients are not capable of integrating both hemispheres knowledge about visual information to make a unified concept or representation. Instead, it is as if the two hemispheres are unaware of each other's processing.
Split-brain patients cannot access the lexiconal information of words that reside in the left-hemisphere when information requiring this type of information is presented to the right-hemisphere. While the right side is capable of some language processing such as matching words to pictures, performing spelling and rhyming tasks, and categorizing objects, it is incapable of syntactical meaning, and indeed most people's right hemispheres "cannot handle even the most rudimentary language" (Gazzaniga, 2002).
If a patient was presented with a three-dimensional object to draw they would be successful with the left hand yet not the right. This is because of the contralateral wiring of the brain. Visual-spatial processing resides in the right-hemisphere, which controls the left hand. Without the corpus callosum intact, there exists virtually no communication or transfer of information from one hemisphere to the other. The right hand, which is controlled by the left-hemisphere, has no knowledge of the 3-dimensional object therefore it cannot replicate it. If instead the object was translated into a word such as "cube" the left side could process this language information and instruct the right hand to represent the three-dimensional object.
References
References
Barlow, H. (2001). Cerebral cortex. In R. A. Wilson & F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences (pp. 111-113). Cambridge, MA: The MIT Press.
Bloom, H. (2000). Global brain: The evolution of mass mind from the big band to the 21 century. New York: John Wiley & Sons, Inc.
Carlson, N. R. (2004). Physiology of behavior (8th ed.). Boston; MA: Pearson Education, Inc.
Eliassen, J. C., Baynes, K., & Gazzaniga, M. S. (2000). Anterior and posterior callosal contributions to simultaneous bimanual movements of the hands and fingers. Brain, 123(12), 2501-2511.
Friman, O., Cedefamn, J., Lundberg, P., Borga, M., & Knutsson, H. (2001). Detection of neural activity in functional MRI using canonical correlation analysis. Magnetic Resonance in Medicine, 45, 323-330.
Gandhi, S. P., & Stevens, C. F. (2003). Three modes of synaptic vesicular recycling revealed by single-vesicle imaging. Nature, 423, 607-613.
Gazzaniga, M. S. (1995). Principals of human brain organization derived from split-brain studies. Neuron, 14, 217-228.
Gazzaniga, M. S. (2002). The split-brain revisited. Retrieved September 1, 2006, from http://people.brandeis.edu/~teuber/splitbrain.pdf
Green, R., Clark, A., Hickey, W., Hutsler, J., & Gazzaniga, M. S. (1999). Braincutting for psychiatrists: The time is ripe. The Journal of Neuropsychiatry and Clinical Neuroscience, 11(3), 301-306.
Humphrys, M. (1997). AI is possible . .but AI won't happen: The future of artificial intelligence. Retrieved September 11, 2006, from http://www.computing.dcu.ie/~humphrys/newsci.html
Kalat, J. W. (2001). Biological psychology (7th ed.). Belmont, CA: Wadsworth/Thomson Learning.
Koch, C. (2004). The quest for consciousness: A neurobiological approach. Englewood: CO: Roberts and Company Publishers.
Montandon, M.-L., & Zaidi, H. (2002). Perspectives in quantitative brain positron emission tomography imaging. Business Briefing: Global Healthcare(3), 2-4.
Mycek, M. J., Harvey, R. A., & Champe, P. C. (Eds.). (2000). Pharmacology (2nd ed.). Philadelphia: Lippincott Williams & Wilkins.
Noll, D. C. (2001). A primer on MRI and functional MRI. Retrieved September 13, 2006, from http://www.eecs.umich.edu/~dnoll/primer2.pdf#search=%22a%20primer%20on%20MRI%20and%20functional%20MRI%22
Oquendo, M. A., Krunic, A., Parsey, R., Milak, M., Malone, K. M., Anderson, A., et al. (2005). Positron emission tomography of regional brain metabolic responses to a serotonergic challenge in major depressive disorder with and without borderline personality disorder. Neuropsychopharmacology, 30, 1163-1172.
Pantano, P., Mainero, C., Lenzi, D., Caramia, F., Iannetti, G. D., Piattella, M. C., et al. (2005). A longitudinal fMRI study on motor activity in patients with multiple sclerosis. Brain, 128(2146-2153).
Pinker, S. (1997). How the mind works. New York: W.W. Norton & Company.
Riachle, M. (2001). Positron emission tomography. In R. A. Wilson & F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences (pp. 656-659). Cambridge, MA: The MIT Press.
Rosenzweig, M. R., Leiman, A. L., & Breedlove, S. M. (1999). Biological psychology: An introduction to behavioral, cognitive, and clinical neuroscience (2nd ed.). Sunderland, MA: Sinauer Associates.
Salvador, R., Suckling, J., Coleman, M. R., Pickard, J. D., Menon, D., & Bullmore, E. (2005). Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex, 15, 1332-1342.
Shepard, G. (2001). Neuron. In R. A. Wilson & F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences (pp. 603-604). Cambridge: MA: The MIT Press.
Sperry, R. W. (1964). The great cerebral commissure. Scientific American, 210(1), 42-52.
Sternberg, R. J. (2003). Cognitive psychology (3rd ed.). Belmont, CA: Wadsworth/Thompson Learning.
Ugurbil, K. (2001). Magnetic resonance imaging. In R. A. Wilson & F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences (pp. 505-507). Cambridge, MA: The MIT Press.
Wills, T. A., DuHamel, K., & Vaccaro, D. (1995). Activity and mood temperament as predictors of adolescent substance use: Test of a self-regulation mediational model. Journal of Personality and Social Psychology, 68(5), 901-916.
How Neurons Communicate
How Neurons Communicate
Anatomy of the Neuron
The human body can be described on many levels. Macroscopic descriptions would focus on how our organ systems, such as our skin, heart, intestines, lungs, and others, work together to accomplish a specific task. As we descend to the microscopic level, we see how different tissues make up individual organs, and how those tissues themselves are made up of dense assemblages of cells. In the body, the cell is the central unit for biological study. Schwann first proposed the cell theory in 1839, which, stated that, "all body organs and tissues are composed of individual cells" (Shepard, 2001). Psychology owes a huge debt of gratitude to neurology and developmental biology for the amount of information that has been gained concerning the exact processes and structures of the cell.
The neuron is the cell of the nervous system and brain. Neurons are different then the other cells of the body in numerous ways, but it is their information-processing and transmitting ability (Carlson, 2004) that enables consciousness, communication, and indeed life to progress at all. Like other specialized cells of the body, neurons perform numerous tasks. Therefore they come in variegated shapes and sizes depending upon their function (Kalat, 2001). Neurons share the same basic structure; this includes the (1) cell body, otherwise known as the soma; (2) dendrites, (3) axons, and (4) terminal buttons (Carlson, 2004). Neurons might be described to look like tiny spindly sea stars, arms dangling delicately out in any direction that a connection with another neuron might be found.
The soma is the command center of the cell; it contains the nucleus, which houses the life operations of the cell including the cytoplasm, the mitochondria, the nucleus, the endoplasmic reticulum among other structures. The nucleus directs cell functioning via chemical messengers which pass through the nuclear membrane and deliver different chemical triggers to other parts of the cell (Mycek, Harvey, & Champe, 2000).
The word dendron comes from the Greek word for tree, and the dendrites of a neuron very much resemble branch like forms (Carlson, 2004). Dendrites are long, thin spider web-like structures that branch off from one end of the soma. They are the receivers of messages being sent from other neurons, through the synapse, the small gap between neurons. Axons, look like long slender tubes, that more often than not, are covered by a protective coating called myelin (Koch, 2004). Myelin, which is roughly 80 percent lipid and 20 percent protein, is made by oligodendrocytes (Rosenzweig, Leiman, & Breedlove, 1999), specialized cells that provide support to the axon. The myelin sheath surrounding axons serves to speed up transmission down the axon. The sheath of myelin is not continuous but rather segmented into small sections approximately 1-2 micrometers long with a gap in between the next segment of myelin. This gap is known as the node of Ranvier (Kalat, 2001). The axon is responsible for carrying information from the soma to the terminal buttons.
At the end of the axon are a collection of small branches which end in button-like structures called terminal buttons (Sternberg, 2003). Terminal buttons receive the information from the soma via the axon in the form of an action potential. This causes an electro-chemical change in the terminal button, which then releases certain chemicals into the synaptic gap, signaling other nearby dendrites of the message.
Neurons are typically classified according to the way in which the axons and dendrites are branched out from the soma. Multipolar neurons have numerous dendritic trees yet only one axon; bipolar neurons have only one dendritic tree and axon, while unipolar neurons consist of one axon that splits in two directions, both receiving and sending information to the central nervous system (CNS) (Carlson, 2004).
The Action Potential
On the most basic level in neuroscience is the acknowledgement that the action potential is "the primary means of conveying information rapidly form one neuron to the next" (Koch, 2004). Diffusion is the mechanism by which different molecules of a substance tend to spread out evenly, bouncing off of each other, until they are evenly spaced within a mixture (Carlson, 2004). When electrolytes are dissolved in water they break into component parts, separating into ions. For instance, NaCl (sodium chloride) will break into Na+ and Cl-, where sodium is now a positively charged ion, chlorine a negative charged ion. Because like charges repel each other, sodium ions push away from other sodium ions and chlorine ions push away from other chlorine ions. The net effect of diffusion and ionization of electrolytes in a cell is to create an even distribution of charges. Because the intracellular and extracellular fluid contain different ions, this contributes to the membrane potential, or the difference in electrical charge in and out of a cell (Carlson, 2004; Pinker, 1997). The difference in charge (about 70 millivolts) is caused by a higher concentration of Potassium ions (K+) outside the cell and a higher concentration of both Sodium (Na+) and Chlorine (Cl-) inside the neuron. Because like charged ions have a natural inclination to push away from each other, and diffusion serves to push ions to areas of higher concentration to areas of lower concentration, electrostatic pressure builds up on the membrane of the neuron.
The neural membrane is made up of linked molecules of lipids. This membrane has openings which are controlled by "gates" that allow the transport of different ions into and out of the neuron. Ion channels are controlled by a lock and key mechanism, that is, only certain chemical shapes can fit onto the outer structure of the membrane and thus activate the channel to open. When ion channels are opened, the electrostatic pressure forces ions through the channel causing a depolarization in the neuron. This depolarization of the membrane potential triggers an electric pulse down the neuron, this is known as an action potential (Kalat, 2001). Specific gates in the membrane actively pump out Sodium ions, which results in a low concentration of intracellular sodium. Because the membrane is not permeable to sodium ions (unless the gates are open) there exists a much higher level of Na+ outside the cell than inside. When the neuron is stimulated by an outside event, such as by pain receptors, the gates are opened up and the rush of sodium ions into the cell changes the polarization and thus begins the action potential.
Action potentials generally start in the dendrites spines, although they can begin in the axon itself, and travel through the soma, down the length of the axon. Passage of an action potential through a myelinated axon is achieved by a process called salutatory conduction (Kalat, 2001). Myelinated axons have two distinct advantages over non-myelinated axons. First, the myelin decreases the ability of Na ions to enter the cell since they may only enter a myelinated axon at the node of Ranvier. This means that the cell spends less energy pumping ions into and out of the axon (Carlson, 2004). Secondly, myelin speeds up the rate of transmission, reaching speeds of up to 100 meters per second (Sternberg, 2003).
When the signal finally reaches the terminal button, synaptic vesicles bind themselves to calcium channels on the synaptic membrane (Rosenzweig, Leiman, & Breedlove, 1999) causing their contents, neurotransmitters, to be released through the membrane into the synaptic cleft. These neurotransmitters travel small distances to the postsynaptic neuron where they dock with specific receptor sites and then trigger other ion channels to open, leading to yet another action potential. After some molecules of the neurotransmitters have docked to the postsynaptic neuron, a process of reuptake pulls back leftover chemicals into the cytoplasm of the terminal button for reuse (Carlson, 2004).
Released neurotransmitters can produce either excitory or inhibitory responses which lead to depolarizations (EPSPs) or hyperpolarizations (IPSPs) (Carlson, 2004). The specific combinations of EPSPs and IPSPs that occur thus determine the firing rate of neurons. Neurons communicate through a process of chemical and electrical changes. When the action potential (electrical) reaches the terminal button it activates channels that are voltage dependent. These channels open to release Calcium ions, which are able to bind to the synaptic vesicles and thus allow them to break open, spilling their contents into the synaptic cleft. Neurotransmitters then bind to sites on the postsynaptic membrane causing the opening of ion channels, which then cause another depolarization or hyperpolarization depending upon which ion channels are opened. The presynaptic neuron then releases molecules that retrieve left over neurotransmitters and return them to the cytoplasm for recycling (Gandhi & Stevens, 2003), this process normally prevents the re-stimulation of an action potential when the postsynaptic neuron resets. Other chemicals such as peptides, neuromodulators, and hormones can also trigger action potentials by proxy of second messengers (Mycek, Harvey, & Champe, 2000).
By j.w.gibson copyright 2006
References
Anatomy of the Neuron
The human body can be described on many levels. Macroscopic descriptions would focus on how our organ systems, such as our skin, heart, intestines, lungs, and others, work together to accomplish a specific task. As we descend to the microscopic level, we see how different tissues make up individual organs, and how those tissues themselves are made up of dense assemblages of cells. In the body, the cell is the central unit for biological study. Schwann first proposed the cell theory in 1839, which, stated that, "all body organs and tissues are composed of individual cells" (Shepard, 2001). Psychology owes a huge debt of gratitude to neurology and developmental biology for the amount of information that has been gained concerning the exact processes and structures of the cell.
The neuron is the cell of the nervous system and brain. Neurons are different then the other cells of the body in numerous ways, but it is their information-processing and transmitting ability (Carlson, 2004) that enables consciousness, communication, and indeed life to progress at all. Like other specialized cells of the body, neurons perform numerous tasks. Therefore they come in variegated shapes and sizes depending upon their function (Kalat, 2001). Neurons share the same basic structure; this includes the (1) cell body, otherwise known as the soma; (2) dendrites, (3) axons, and (4) terminal buttons (Carlson, 2004). Neurons might be described to look like tiny spindly sea stars, arms dangling delicately out in any direction that a connection with another neuron might be found.
The soma is the command center of the cell; it contains the nucleus, which houses the life operations of the cell including the cytoplasm, the mitochondria, the nucleus, the endoplasmic reticulum among other structures. The nucleus directs cell functioning via chemical messengers which pass through the nuclear membrane and deliver different chemical triggers to other parts of the cell (Mycek, Harvey, & Champe, 2000).
The word dendron comes from the Greek word for tree, and the dendrites of a neuron very much resemble branch like forms (Carlson, 2004). Dendrites are long, thin spider web-like structures that branch off from one end of the soma. They are the receivers of messages being sent from other neurons, through the synapse, the small gap between neurons. Axons, look like long slender tubes, that more often than not, are covered by a protective coating called myelin (Koch, 2004). Myelin, which is roughly 80 percent lipid and 20 percent protein, is made by oligodendrocytes (Rosenzweig, Leiman, & Breedlove, 1999), specialized cells that provide support to the axon. The myelin sheath surrounding axons serves to speed up transmission down the axon. The sheath of myelin is not continuous but rather segmented into small sections approximately 1-2 micrometers long with a gap in between the next segment of myelin. This gap is known as the node of Ranvier (Kalat, 2001). The axon is responsible for carrying information from the soma to the terminal buttons.
At the end of the axon are a collection of small branches which end in button-like structures called terminal buttons (Sternberg, 2003). Terminal buttons receive the information from the soma via the axon in the form of an action potential. This causes an electro-chemical change in the terminal button, which then releases certain chemicals into the synaptic gap, signaling other nearby dendrites of the message.
Neurons are typically classified according to the way in which the axons and dendrites are branched out from the soma. Multipolar neurons have numerous dendritic trees yet only one axon; bipolar neurons have only one dendritic tree and axon, while unipolar neurons consist of one axon that splits in two directions, both receiving and sending information to the central nervous system (CNS) (Carlson, 2004).
The Action Potential
On the most basic level in neuroscience is the acknowledgement that the action potential is "the primary means of conveying information rapidly form one neuron to the next" (Koch, 2004). Diffusion is the mechanism by which different molecules of a substance tend to spread out evenly, bouncing off of each other, until they are evenly spaced within a mixture (Carlson, 2004). When electrolytes are dissolved in water they break into component parts, separating into ions. For instance, NaCl (sodium chloride) will break into Na+ and Cl-, where sodium is now a positively charged ion, chlorine a negative charged ion. Because like charges repel each other, sodium ions push away from other sodium ions and chlorine ions push away from other chlorine ions. The net effect of diffusion and ionization of electrolytes in a cell is to create an even distribution of charges. Because the intracellular and extracellular fluid contain different ions, this contributes to the membrane potential, or the difference in electrical charge in and out of a cell (Carlson, 2004; Pinker, 1997). The difference in charge (about 70 millivolts) is caused by a higher concentration of Potassium ions (K+) outside the cell and a higher concentration of both Sodium (Na+) and Chlorine (Cl-) inside the neuron. Because like charged ions have a natural inclination to push away from each other, and diffusion serves to push ions to areas of higher concentration to areas of lower concentration, electrostatic pressure builds up on the membrane of the neuron.
The neural membrane is made up of linked molecules of lipids. This membrane has openings which are controlled by "gates" that allow the transport of different ions into and out of the neuron. Ion channels are controlled by a lock and key mechanism, that is, only certain chemical shapes can fit onto the outer structure of the membrane and thus activate the channel to open. When ion channels are opened, the electrostatic pressure forces ions through the channel causing a depolarization in the neuron. This depolarization of the membrane potential triggers an electric pulse down the neuron, this is known as an action potential (Kalat, 2001). Specific gates in the membrane actively pump out Sodium ions, which results in a low concentration of intracellular sodium. Because the membrane is not permeable to sodium ions (unless the gates are open) there exists a much higher level of Na+ outside the cell than inside. When the neuron is stimulated by an outside event, such as by pain receptors, the gates are opened up and the rush of sodium ions into the cell changes the polarization and thus begins the action potential.
Action potentials generally start in the dendrites spines, although they can begin in the axon itself, and travel through the soma, down the length of the axon. Passage of an action potential through a myelinated axon is achieved by a process called salutatory conduction (Kalat, 2001). Myelinated axons have two distinct advantages over non-myelinated axons. First, the myelin decreases the ability of Na ions to enter the cell since they may only enter a myelinated axon at the node of Ranvier. This means that the cell spends less energy pumping ions into and out of the axon (Carlson, 2004). Secondly, myelin speeds up the rate of transmission, reaching speeds of up to 100 meters per second (Sternberg, 2003).
When the signal finally reaches the terminal button, synaptic vesicles bind themselves to calcium channels on the synaptic membrane (Rosenzweig, Leiman, & Breedlove, 1999) causing their contents, neurotransmitters, to be released through the membrane into the synaptic cleft. These neurotransmitters travel small distances to the postsynaptic neuron where they dock with specific receptor sites and then trigger other ion channels to open, leading to yet another action potential. After some molecules of the neurotransmitters have docked to the postsynaptic neuron, a process of reuptake pulls back leftover chemicals into the cytoplasm of the terminal button for reuse (Carlson, 2004).
Released neurotransmitters can produce either excitory or inhibitory responses which lead to depolarizations (EPSPs) or hyperpolarizations (IPSPs) (Carlson, 2004). The specific combinations of EPSPs and IPSPs that occur thus determine the firing rate of neurons. Neurons communicate through a process of chemical and electrical changes. When the action potential (electrical) reaches the terminal button it activates channels that are voltage dependent. These channels open to release Calcium ions, which are able to bind to the synaptic vesicles and thus allow them to break open, spilling their contents into the synaptic cleft. Neurotransmitters then bind to sites on the postsynaptic membrane causing the opening of ion channels, which then cause another depolarization or hyperpolarization depending upon which ion channels are opened. The presynaptic neuron then releases molecules that retrieve left over neurotransmitters and return them to the cytoplasm for recycling (Gandhi & Stevens, 2003), this process normally prevents the re-stimulation of an action potential when the postsynaptic neuron resets. Other chemicals such as peptides, neuromodulators, and hormones can also trigger action potentials by proxy of second messengers (Mycek, Harvey, & Champe, 2000).
By j.w.gibson copyright 2006
References
References
Barlow, H. (2001). Cerebral cortex. In R. A. Wilson & F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences (pp. 111-113). Cambridge, MA: The MIT Press.
Bloom, H. (2000). Global brain: The evolution of mass mind from the big band to the 21 century. New York: John Wiley & Sons, Inc.
Carlson, N. R. (2004). Physiology of behavior (8th ed.). Boston; MA: Pearson Education, Inc.
Eliassen, J. C., Baynes, K., & Gazzaniga, M. S. (2000). Anterior and posterior callosal contributions to simultaneous bimanual movements of the hands and fingers. Brain, 123(12), 2501-2511.
Friman, O., Cedefamn, J., Lundberg, P., Borga, M., & Knutsson, H. (2001). Detection of neural activity in functional MRI using canonical correlation analysis. Magnetic Resonance in Medicine, 45, 323-330.
Gandhi, S. P., & Stevens, C. F. (2003). Three modes of synaptic vesicular recycling revealed by single-vesicle imaging. Nature, 423, 607-613.
Gazzaniga, M. S. (1995). Principals of human brain organization derived from split-brain studies. Neuron, 14, 217-228.
Gazzaniga, M. S. (2002). The split-brain revisited. Retrieved September 1, 2006, from http://people.brandeis.edu/~teuber/splitbrain.pdf
Green, R., Clark, A., Hickey, W., Hutsler, J., & Gazzaniga, M. S. (1999). Braincutting for psychiatrists: The time is ripe. The Journal of Neuropsychiatry and Clinical Neuroscience, 11(3), 301-306.
Humphrys, M. (1997). AI is possible . .but AI won't happen: The future of artificial intelligence. Retrieved September 11, 2006, from http://www.computing.dcu.ie/~humphrys/newsci.html
Kalat, J. W. (2001). Biological psychology (7th ed.). Belmont, CA: Wadsworth/Thomson Learning.
Koch, C. (2004). The quest for consciousness: A neurobiological approach. Englewood: CO: Roberts and Company Publishers.
Montandon, M.-L., & Zaidi, H. (2002). Perspectives in quantitative brain positron emission tomography imaging. Business Briefing: Global Healthcare(3), 2-4.
Mycek, M. J., Harvey, R. A., & Champe, P. C. (Eds.). (2000). Pharmacology (2nd ed.). Philadelphia: Lippincott Williams & Wilkins.
Noll, D. C. (2001). A primer on MRI and functional MRI. Retrieved September 13, 2006, from http://www.eecs.umich.edu/~dnoll/primer2.pdf#search=%22a%20primer%20on%20MRI%20and%20functional%20MRI%22
Oquendo, M. A., Krunic, A., Parsey, R., Milak, M., Malone, K. M., Anderson, A., et al. (2005). Positron emission tomography of regional brain metabolic responses to a serotonergic challenge in major depressive disorder with and without borderline personality disorder. Neuropsychopharmacology, 30, 1163-1172.
Pantano, P., Mainero, C., Lenzi, D., Caramia, F., Iannetti, G. D., Piattella, M. C., et al. (2005). A longitudinal fMRI study on motor activity in patients with multiple sclerosis. Brain, 128(2146-2153).
Pinker, S. (1997). How the mind works. New York: W.W. Norton & Company.
Riachle, M. (2001). Positron emission tomography. In R. A. Wilson & F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences (pp. 656-659). Cambridge, MA: The MIT Press.
Rosenzweig, M. R., Leiman, A. L., & Breedlove, S. M. (1999). Biological psychology: An introduction to behavioral, cognitive, and clinical neuroscience (2nd ed.). Sunderland, MA: Sinauer Associates.
Salvador, R., Suckling, J., Coleman, M. R., Pickard, J. D., Menon, D., & Bullmore, E. (2005). Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex, 15, 1332-1342.
Shepard, G. (2001). Neuron. In R. A. Wilson & F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences (pp. 603-604). Cambridge: MA: The MIT Press.
Sperry, R. W. (1964). The great cerebral commissure. Scientific American, 210(1), 42-52.
Sternberg, R. J. (2003). Cognitive psychology (3rd ed.). Belmont, CA: Wadsworth/Thompson Learning.
Ugurbil, K. (2001). Magnetic resonance imaging. In R. A. Wilson & F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences (pp. 505-507). Cambridge, MA: The MIT Press.
Wills, T. A., DuHamel, K., & Vaccaro, D. (1995). Activity and mood temperament as predictors of adolescent substance use: Test of a self-regulation mediational model. Journal of Personality and Social Psychology, 68(5), 901-916.
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