Contemporary Mathematical Model of Human Behavior Under Some Environmental Constraints Research Paper

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Introduction

The research article of Kozma, Harter & Achunala (2007) presents the rather interesting notion that when subject to particular constraints via an external environment or increased task difficulty the human brain actually shows a rather amazing ability to adapt to a given task at hand despite the increasing arduousness of the endeavor (Kozma, Harter & Achunala, 2007).

From their perspective, cognition is a dynamic process and as such through complex trajectories in a high dimensional attractor landscape a person is capable of continuing to adapt to increasingly difficult situations or actions as a direct result of this inherent aspect of the human brain (Kozma, Harter & Achunala, 2007).

Such a perspective actually reflects the views of Dutt (2011) which examined the correlation between human performance during tests (i.e. quizzes, exams and cognitive decision making problems instead of playing Tetris as utilized by Kozma, Harter & Achunala (2007)) (Dutt, 2011).

Decision Making Process

For Dutt (2011) human decision making is inherently dynamic in that it can happen “on the fly” so to speak wherein in the face of a several possible outcomes in performing an immediate action the human brain is able to tune out all other possible variables and pick the solution it perceives as the most accurate, all of which is done within a split second.

While such a feature is also present in animals as evidenced by the study “Decision ecology: foraging and the ecology of animal decision making (2008)” as well as De Waal (2005) which both examined animal responses in light of sudden inexplicable changes to their environment the fact is that it is only in humans that a measurable degree of cognitive decision making can be made using complex tests that animals would otherwise be incapable of performing (Decision ecology: foraging and the ecology of animal decision making, 2008) (De Waa, 2005).

Human Performance

It must also be noted though that studies such as those by Lubinski (2004) and Roberts (1999) indicate that there are inherent limits to the degree of cognitive adaptability that humans are capable of performing (Lubinski, 2004) (Roberts, 1999).

Such a situation was seen in the Kozma, Harter & Achunala (2007) study wherein their model of human performance was able to show both the inherent adaptability of human performance in light of increasingly difficult tasks and its inherent limitations with performance ratios dropping significantly when the speed of the Tetris blocks was increased as well as the general availability of certain block types were reduced. As such it can be stated that their model fit the human performance data rather well.

Solution Algorithm

When it comes to determining what their solution algorithm consists of, you must first examine the data results from their experiment and the subsequent methods they utilized to obtain their desired result. What Kozma, Harter & Achunala (2007) were trying to accomplish was to measure the degree in which human performance changes as new external factors are introduced.

For the purpose of this experiment this came in the form of increased block speed, limited block resources and noise. When applying such factors as variables in the experiment the results showed how particular performances were affected when subject to certain variables.

They then attempted to utilize human results as the basis for subsequent computer algorithmic tests in order to mimic the response mechanisms of humans under a similar condition. Based on the process utilized to compare both results it can be stated that their solution algorithm utilized error reductions between input and output states as the means of producing a quantifiable data result.

Reference List

Decision ecology: foraging and the ecology of animal decision making. (2008). Cognitive, Affective & Behavioral Neuroscience, 8(4), 475-484.

De Waal, F. M. (2005). How Animals Do Business. Scientific American, 292(4), 72.

Dutt, V. (2011). Explaining Human Behavior in Dynamic Tasks through Reinforcement Learning. Journal Of Advances In Information Technology, 2(3), 177-188.

Kozma , R., Harter, D., & Achunala, S. (2007). Dynamical aspects of behavior generation under constraints. Cogn Neurodyn, 1(213), 213–223.

Lubinski, D. (2004). Introduction to the Special Section on Cognitive Abilities: 100 Years

After Spearman’s (1904) “General Intelligence,’ Objectively Determined and Measured”. Journal Of Personality & Social Psychology, 86(1), 96.

Roberts, R. (1999). Individual differences in speed of mental processing human and cognitive abilities: Toward a taxonomic model. Learning & Individual Differences, 11(1), 1.

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