Dependant Pesonality Disorders Essay Example | Topics and Well Written Essays - 1000 words

Dependant Pesonality Disorders - Essay Example (DPD2006) DPD involves a pervasive and excessive need to be taken care of that leads to submissiveness, clinging, and fears of separation. Its primary diagnostic criteria include extreme difficulty making decisions without others' input, need for others to assume responsibility for most aspects of daily life, extreme difficulty disagreeing with others, inability to initiate projects due to lack of self-confidence, and going to excessive lengths to obtain the approval of others. An individual with DPD always seeks a relationship, in which he or she clings to this relationship in very dependent manner and goes to the utmost extent to keep both the ends meet. This disorder includes abnormally high levels of the neuroticism facets of anxiousness, self-consciousness, and vulnerability following dependent individuals desperately seeks another relationship as soon as one ends (i.e. dependent persons are high in extraversion). Excessively dependent behavior may be seen in persons who have developed debilitating mental and physical conditions, such as agoraphobia, schizophrenia, severe injuries, or dementia. However, a diagnosis of DPD requires the presence of the dependent traits since late childhood or adolescence. One can diagnose the presence of a personality disorder at any age during a person's lifetime, but if (for example) a DPD diagnosis is given to a person at the age of 75, this presumes that the dependent behavior was evident since the age of approximately 18 (i.e., predates the onset of a co morbid mental or physical disorder). Differences in personality due to differing cultural norms should not be confused with the presence of a personality disorder. Cultural groups differ greatly in the degree of importance attached to deferent behavior, politeness, and passivity. The diagnosis of DPD requires that the dependent behavior result in clinically significant functional impairment or distress. Etiology and Pathology Insecure interpersonal attachment is central to the etiology and pathology of DPD. Insecure attachment and helplessness may be generated through a parent-child relationship, perhaps by a clinging parent or a continued infantilization during a time in which individuation and separation normally occurs. However, the combination of an anxious and/or inhibited temperament with inconsistent or overprotective parenting may also generate or exacerbate dependent personality traits (Maddux, 2005, p. 219). Unable to generate feelings of security and confidence for themselves, dependent persons may rely on a parental figure or significant other for constant reassurance of their worth. Eventually, persons with DPD may come to believe that their self-worth is defined by their importance to another person. Five-Factor Model Reformulation DPD is characterized by maladaptively high levels of agreeableness and the neuroticism facets of anxiousness, self-consciousness, and vulnerability. Persons with DPD

Neural System Development Essay Example | Topics and Well Written Essays - 750 words

Neural System Development - Essay Example (Gill, 2008). To understand neural development different experimental approaches are done in different model system. The distinction between inherited genetic code (genotype) and the corresponding organism (phenotype) is the cornerstone of biology. Genotype is the one that is inherited from the parents. The complete individual that is formed according to the instructions specified in the genotype is the phenotype. One of the model is the simple genetic encoding, direct genotype-phenotype mapping. Deciding on how to encode the network in the genotype that suitable for the application of the genetic operators is important to evolve neural networks. To be able for an individual at the level of the genotype assumes the form of a string of identical elements; phenotypical characteristics are coded in a uniform manner. The transformation of the genotype into the phenotypical network is called genotype-to-phenotype mapping. (Canglenosi, Nolfi, Parisi). One to one mapping has several drawbacks one of the m is scalability. The space to be searched evolutionary process increases exponentially with the size of the network (Kitano, 1990), because of the length of the genotype is proportional to the complexity of the corresponding phenotype. Another method using the genotype and the phenotype is the growing method. The genotype-to-phenotype process in nature is not only an abstract mapping of information from genotype to phenotype but it is also a process of physical growth (growth in size and in physical structure).( Canglenosi, Nolfi, Parisi). By executing the inherited growing instruction the phenotype is progressively built. Below is the illustration of the development of an evolved neural network. The upper part is the growing and branching process of the axons. The lower part of the illustration is the resulting network after removal of nonconnecting branches and the elimination of isolated neurons and groups of interconnected neurons. Source: http://www.tech.plym.ac.uk/soc/staff/angelo/papers/cangelosi-in-bentley.pdf Cellular Encodings - In natural organisms folding in of the ectodermic tissue which forms the neural crest is the beginning of the development of the nervous system. This gives rise to the mature nervous system in succession of three phases: the genesis and proliferation of different classes of neurons by cellular duplication and differentiation, the migration of neurons toward their final destination, and the growth of neuritis (axons,dendrites). Several experimental approaches in modelling neural development are increasing. The above models are study focused by the paper written by Cangolosi, Nolfi, Parisi. Above models are only some of the studies done by several researchers. Reference: Cangelosi, Nolfi, Parisi, Artificial Life Models of Neural Development http://www.tech.plym.ac.uk/soc/staff/angelo/papers/cangelosi-in-bentley.pdf Kitano H. (1990). Designing neural networks using genetic