fMRI Brian Visuals as Fields for Interaction

A clear understanding of how fMRI scans work calls for the involvement of different faculties. First, it is important to note that the fMRI scans are a combination of two major elements in that they are not only visual or digital, but they are both visual and digital. Advertising We will write a custom essay sample on fMRI Brian Visuals as Fields for Interaction specifically for you for only $16.05 $11/page Learn More This means that the functioning of the fMRI scans is conceptualized on the clear understanding of the interplay of how the scans are a combination of the above-mentioned aspects. Understanding the fMRI scans as fields for interactions, also entails the understanding that digital scientific visuals cannot be understood in a representational manner, but must have an in depth participation of their readers and writers. Those tasked with the responsibility of deciphering the meaning of the fMRI scans must ensure that they invoke several facult ies so that they can accurately deduce the brain activities that are depicted by the fMRI scan technology. In the article, the author stresses that the entire concept of understanding the fMRI scan technology, should be explained in an indexical manner as opposed to iconic or as a sign manner. The argument here is that when the technological results are given the iconic meaning, they tend to have a direct relationship between the scan and the icon representing them. Signs, according to the author, just like icons give a vague representation between what is in question and what should be represented. To avoid this, fMRI scans should have an indexical representation (Alac 14). In presenting the views of this article, it is clear that the author has invested in a good structure. The paper starts with a defense of why the concept of the fMRI scan technology as part of the modern neuroscience should be viewed as fields for interactions. When the technology is viewed in this manner, inde xing as the main method of reference should be adopted. This makes it easy for all the concerned stakeholders to accurately decipher the indented meaning of the brain scans. In conclusion, the writer is of the view that the adoption of indexical interpretation of the fMRI scans is beneficial to the field of modern neurosurgery in that, this technology makes it easy for brain experts to conceptualize the functioning of the brain. This means that the concept of brain mapping is developed higher due to the technology that is adopted by the neurosurgery experts hence, producing greater insights into how the brain works. Scientific use of fMRI scans can be used to identify the specific areas of the human cortex that process certain types of information. Such an examination also helps the experts doing the analysis to decipher the relatedness or otherwise of information that is being processed. Advertising Looking for essay on computer science? Let's see if we can help you! Get your first paper with 15% OFF Learn More This is done by analyzing the areas of the brain that are in activity, a fact that is enabled by the analysis of the fMRI scans. Active areas of the brain are more pronounced especially due to the increased blood flow in such parts. This way, the technology has been hailed for its role in expounding on the functioning of the brain. Question In digital imaging, the main areas of activity can easily be denoted through the observation of the colors of the brain being imaged. Briefly explain how different colors such as bright red areas, blue areas or green areas of the brain mean in relation to brain activity. Work Cited Alac, Morana. fMRI brain visuals as fields for interaction, In Handling Digital Brains.Massachusetts: MIT Press. ND. Print. This essay on fMRI Brian Visuals as Fields for Interaction was written and submitted by user Aginar to help you with your own studies. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly. You can donate your paper here.

Bell Curve and Normal Distribution Definition

Bell Curve and Normal Distribution Definition The term bell curve is used to describe the mathematical concept called normal distribution, sometimes referred to as Gaussian distribution. Bell curve refers to the shape that is created when a line is plotted using the data points for an item that meets the criteria of normal distribution. The center contains the greatest number of a value and, therefore, would be the highest point on the arc of the line. This point is referred to the mean, but in simple terms, it is the highest number of occurrences of an element (in statistical terms, the mode). Normal Distribution The important thing to note about a normal distribution is the curve is concentrated in the center and decreases on either side. This is significant in that the data has less of a tendency to produce unusually extreme values, called outliers, as compared to other distributions. Also, the bell curve signifies that the data is symmetrical. This means that you can create reasonable expectations as to the possibility that an outcome will lie within a range to the left or right of the center, once you have measured the amount of deviation contained in the data.This is measured in terms of standard deviations. A bell curve graph depends on two factors: the mean and the standard deviation. The mean identifies the position of the center and the standard deviation determines the height and width of the bell. For example, a large standard deviation creates a bell that is short and wide while a small standard deviation creates a tall and narrow curve. Bell Curve Probability and Standard Deviation To understand the probability factors of a normal distribution, you need to understand the following rules: The total area under the curve is equal to 1 (100 percent)About 68 percent of the area under the curve falls within one standard deviation.About 95 percent of the area under the curve falls within two standard deviations.About 99.7 percent of the area under the curve falls within three standard deviations. Item Nos. 2,3 and 4 are sometimes referred to as the empirical rule or the 68-95-99.7 rule. Once you determine that the data is normally distributed (bell curved) and calculate the mean and standard deviation, you can determine the probability that a single data point will fall within a given range of possibilities. Bell Curve Example A good example of a bell curve or normal distribution is the roll of two dice. The distribution is centered around the number seven and the probability decreases as you move away from the center. Here is the percent chance of the various outcomes when you roll two dice. Two: 2.78 percentThree: percentFour: 8.33 percentFive: 11.11 percentSix: 13.89 percentSeven: 16.67 percentEight: 13.89 percentNine: 11.11 percentTen: 8.33 percentEleven: 5.56 percentTwelve: 2.78 percent Normal distributions have many convenient properties, so in many cases, especially in physics and astronomy, random variations with unknown distributions are often assumed to be normal to allow for probability calculations. Although this can be a dangerous assumption, it is often a good approximation due to a surprising result known as the central limit theorem. This theorem states that the mean of any set of variants with any distribution having a finite mean and variance tends to the normal distribution. Many common attributes such as test scores or height follow roughly normal distributions, with few members at the high and low ends and many in the middle. When You Shouldn't Use the Bell Curve There are some types of data that dont follow a normal distribution pattern. These data sets shouldnt be forced to try to fit a bell curve. A classic example would be student grades, which often have two modes. Other types of data that dont follow the curve include income, population growth, and mechanical failures.