Executive summary

            Computer systems of the present era are a reflection of our internal cognitive mechanisms and behavior. In addition, new and emerging computer technology systems have played a significant role in introducing new practices and the roles of human being. This paper analyzed the co-adaptation between individuals and the design of computers with a consideration of the various theories relating to human cognition. Basing on this the paper identified the various ways that knowledge of cognition can help in improving the design of computers. It was found that development of automated computer systems bases on the prediction of human behavior, computer systems embed human cognition, symbol recognition and cognitive science and human behavior are related. In addition, the paper discussed the relationship between artificial intelligence and expert in the light of situational and analytical knowledge. The overall conclusion was that knowledge of human cognition is imperative in improving the design of computers.


This report is aimed to illustrate the reader how our knowledge of human cognition helps in the design of computers. To have an understanding of the topic, the report shall discuss the several cognitive perspectives ranging from short to long timescales.

This report will first outline the nature of computers in relation to the aspects of human cognition basing on the fact computers are just like other invented artefacts. Basing on this, the paper will then discuss the various ways on how our knowledge of human cognition improves the design of computers.

The nature of computers in relation to aspects of human cognition

The functioning of socio-technical such as computers are basically is a reflection of the relationship between internal cognitive mechanism and human behavior. Innovative technological designs have imposed new practices and individual roles in the larger society. This implies that there is a correlation between the human cognition and the design of computers, which is constantly being changed time after time (Amit & Jain 2005). Descartes established an approach that transformed the concept of knowledge management through a reduction of most of the problems to mathematical expressions that could be solved by definition. Descartes method was technology oriented, and perhaps it could be the underlying reason behind the technological success of the 20th century (Eysenck & Keane 2005). It important to note that the computer was one of the ultimate productions of the Descartes method, which rehabilitated the Art of Memory because the  materialistic approach could no longer be adequate to the world (Harris 2011). Our knowledge of human cognition is ultimately important during the design of computers. We shall then discuss the various ways through which human cognition is related to effective design of computers.

Symbol recognition

Symbols are necessary during the design of computers. This is because communicating with the computer significantly relies on visual communication. This means that an effective design of computer systems should adopt symbols that can be easily recognizes. The underlying argument is that symbol recognition is an integral element of human cognition and solely depends on the manner through which we process information that has been recognized in the external environment. For instance, the use of graphical presentation in computer system such as desktop icons can be used to evaluate whether computer systems were designed effectively in such a way that the people understand the role of the icons without the text. The positioning of the icons must be easy for the user to identify and use the icon for the intended reason. Basing on this, it is arguably evident that having knowledge of human cognition plays an important role in improving the design of computers. The following discussion outlines how computers embed core aspects of human cognition.

Computer systems embed human cognition

The primary concern during the design of computers is not if people are currently using and they will use newer computer designs, rather, how they are using and they will use them wither to enhance their well being and increase their awareness of other people’s needs. The revolution from energy-based use of physical artifacts towards the use of cognitive artifacts that are information-based requires a further investigation (Smith & Gavriel 2001). The nature of present human practices are characterized by thinking more than directly acting, which is contrary to the traditional approaches to human-machine interaction whereby individuals were required to think and act simultaneously (Simon 1981). This implies that the emergence of new practices has compelled cognitive scientists to deploy a dissimilar human-machine interaction models into consideration, and the design of computers is an exception with respect to this. For instance, an effective computer design virtually does all the processing and just displays the output for the user. Taking this into consideration is helpful in the design of computers in the sense that designers have to put into consideration that the current practices of the computer users are more cognitive-oriented and less concrete-task oriented, which is somewhat related to the aspect of automation (Stillings et al 1995). The following diagram helps in understanding how the design of more cognitive computer systems can be considered effective.













Hard to use, therefore ill designed.






Figure 1: computers systems and human cognition

Cognitive science and human behavior

Cognitive science should devise appropriate theories that can be used to address the evolution of the aspect of automation. The Rasmussen model attempts to explain the nature of the behavior of a human operator that is charged with the responsibility of controlling a complex system that is dynamic (Smith & Gavriel 2001). According to Rasmussen, the human behavior, which mainly comprise of errors and faults, are divided into three groups, comprising of Skill based Slips, Rule based mistakes and Knowledge based mistakes, which are dependent on the individual worker and their respective performance concerning to the task requirements. The reliance on rules for controlling various workplace situations and the levels of the operator competency require to be critically analyzed in accordance to the situational variety, implying that a less competent operator needs rules that are less abstract. This means that a well designed computer system requires less expertise to use compared to an ill-designed computer system that requires more expertise. An example of this is the differences in difficulties between the command based systems and systems that use the Graphical user interface. Understanding the evolution of automated systems is essential during the design of computers because most of the potential users of computers are at the knowledge based level of the Rasmussen model because individuals are increasingly becoming managers of the cognitive systems such as computers (Simon 1981). Basing on this approach, it is arguably evident that having knowledge of human cognition is essential during the design of computer systems. It is also vital to note that functioning at the knowledge-based level of the Rasmussen model requires significant training. The following Figure 2 shows the relation that exists between the cognitive abilities of a individual and computer usage.











Expertise and artificial intelligence

Most of the models of human cognition are similar to the mechanical tools that currently exist. The computer is one of perfect tools that can be compared to human cognition. Alan Turing was the initial theoretician to equate the computer system to the human brain. The present conventional cognitive science strongly asserts that cognition is significantly related to the aspect of information processing (Posner 1993). This methodology of cognitive science is usually referred to as cognitivism, and played an important role during the launch of artificial intelligence. Equating artificial intelligence with human expertise is an important approach that can be deployed during the design of computers. Hubert Dreyfus criticized the aspect of comparing artificial intelligence with human expertise, noting that artificial intelligence cannot be implemented to make a replica human expertise. Hubert claimed that Human expertise is a developmental process that takes time and cannot just be implemented through IT-THEN rules (Harris 2011). The argument is that it is difficult to capture situational knowledge, implying that human expertise cannot be modeled using AI. In addition, situational knowledge is not analogous to analytical knowledge, which is mostly implemented in AI computer systems such as Decision Support Systems. Similarly, there is a clear difference between human information processing that is controlled and automatic. AI systems rely on representations of analytical knowledge, while situational knowledge is acquired through incremental learning and experience (Harris 2011). Knowing the distinction between situational knowledge and analytical knowledge helps in the effective design of computer systems. For instance, artificial intelligence can be used to design a computer system that can perform a task that humans cannot undertake, the differences between analytical and situational knowledge are vital. In addition, this facilitates the development of computer systems that are better off compared to human beings. A perfect example of such instance was the development of computer systems that are better off at playing chess compared to people, for instance the Deep Blue that was able to beat the chess world champion (Harris 2011).


The paper has proved that our knowledge of human cognition improves the process of designing computers. This is mainly because of the transformation from the energy based systems to information based systems, implying the tasks are becoming more cognitive that require intense thinking compared to traditional approaches that relied on concrete task undertaking that involved a lesser application of the human cognition. This plays an important role in the design of computer systems that are effective in undertaking short term tasks that are beyond the cognitive abilities of human beings.














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Harris, D 2011, Engineering Psychology and Cognitive Ergonomics: 9th International       Conference, EPCE 2011, Held as Part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011, Proceedings, Springer, New York.

Posner, M 1993, The Foundations of Cognitive Science, MIT Press, Cambridge.

Simon, H 1981, Sciences of the Artificial, MIT Press, Cambridge.

Smith, MJ & Gavriel, S 2001, Usability evaluation and interface design: cognitive engineering,      intelligent agents and virtual reality, Routlege, New York.

Stillings et al et al 1995, Cognitive Science: An introduction, MIT Press, Cambridge.

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