Please respond to each of the 2 instructor questions “separately” using at least 250 words per response. Answers must be in APA format and include at least one citation for each question response.
***Respond to the instructor questions only…not the original question or my response.***
Original Question 1:
Kozma et al. present a contemporary mathematical model of human behavior under some environmental constraints. How well does their model fit the human performance data? Is their solution algorithm blind overall to error reduction between input and associated output states or is it based on propagation or another Hebbian learning model?
My Response:
The principle employed by Kazma is that a state of a personâ€s mind is viewed well as a set of difficult of values that are combined at all the times. The inputs in all tests of learning are supposed to have many qualities that will challenge someone. However, they should not be so many to make it hard for every type of work that is supervised in training. The environment should be controlled at all times. Everything that is outside the controlled environment is regarded as background noise.â€
Kozma’s approach insists in the existence of hard models that cannot be determined easily. The real human Performance however does not deal with the concept efficiently. However, this is a different case when it comes to the problems of the noise. The model as it is, also tries to find freedom from something already determined. That is the genetic structure as it is made. Any stochastic model usually assumes the case of a random element, but it is hard to know how to create them. Therefore, the performance of this model well resembles the human brain performance in almost every aspect as they can conceptualize and interpolate all that they come across.
Kozma refers to the algorithm as a mechanism for performance. The algorithms however are usually not common by their normal structure. However, it is possible to model every data that is missing and that they can make sense about. Usually, the object used for input has to be regulated and presented in a clear way that makes it easy to determine the important qualities and those that are not to the learner. Additionally, any algorithm present in a machine for learning deals with the outputs only and no inputs. It is possible to form a fully determined system if the system is closed. But this could not be the real case in life as there are things that we cannot always determine. Therefore, even the formulated model cannot determine n figure out everything at all times.
Reference:
Jensen, Eric, and Eric Jensen. 2008. Brain-based learning: the new paradigm of teaching. Thousand Oaks, CA.: Corwin Press.
Instructor Question:
What do you think of the hidden nodes that were in Kozma et al.’s model?
Original Question 2:
A person sees a barely visible human as he/she is approaching an isolated ranch house at twilight. How does the nature of declarative memory and the possibility of its use in parallel distributed processing (PDP) regarding formation affect the perception of this complex and potentially threatening scene. How can PDP augment memory in evaluating the potential risk in this and other potentially threatening situations?
My Response:
Parallel distributed processing is a mechanism that gives a way for thinking about the organization and nature of memory, perception, thought, and language. This discussion describes all the framework in brief and discusses its results for semantic, procedural, as well as the episodic memory. As per the parallel distributed processing approach, processing of any message happens through the interaction of many small processors that are organized as modules. The storage of the data occurs through modifying of the connected weights according to the response of the system to the input. The response also provides a way for incremental storage. Connection modification may as well give rise to learning in procedure and also to the formation of memories, structured by the use of semantic content through the course of experience. Discovery of semantic structure at times calls for gradual learning, with repeated exposure to samples of the structure to be understood. There are two neuropsychological effects of this approach. One is the possible modular of the organization in the brain of semantic. The hippocampus has a role in learning as well as in memory. At first, the PDP approach makes one know how the damage to the brain can produce apparent dissociations between the categories. However, the fact is that the organization of concern is by modality and not by category. In the other case, it will be understood that the approach enables us to understand a different way as to why it is important and unique. Arbitrary organizations cannot be stored at the same time in the same memory systems that are meant for semantic information.
Reference:
International Conference On Transportation Engineering, Qiyuan Peng, and Kelvin C. P. Wang. 2013. ICTE 2013: proceedings of the Fourth International Conference on Transportation
Instructor Question:
Great post, but how does this specifically tie into the danger aspect of this DQ? Do dangerous situations have a unique aspect here?
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