Editorial

A language and reasoning can be said as some of the characteristics of human abilities. On the other hand, the ability of human thinking can be modeled as computation. The development of cognitive science that combines scientific development with technology began to appear in the 1960s. In those years, human behavior did not adequately explain cognitive processes. Although, there has been much debate by behaviorist experts regarding the cognitive science approach. However, with a variety of approaches, there is something quite encouraging that computer models of cognition can be used as an alternative approach to these various models. Furthermore, computers can be used to test hypotheses where computation itself is the subject of the mind. So that there are various kinds of models developed in the field of cognitive science with different fields of science, including anthropology, artificial intelligence (AI), philosophy, linguistics, neuroscience, and psychology. Even though there are different scientific fields, it turns out that they can work together in explaining various kinds of cognitive science models. AI is a part of the field of computer science that can describe intelligent computer systems. This system can show characteristics related to intelligence in human behavior, such as reasoning, understanding language, learning, solving problems, and so on. This intelligent system has a long-term goal of equaling or surpassing human intelligence. The approach used in simulating this system uses mathematical approaches, discursive reasoning, language, and so on. New developments related to the paradigm in this field emerged in the mid-80s, bringing together developments in the fields of philosophy, AI, and cognitive science.

Human intelligence is illustrated as a result of a program running on the human brain. In connectionist's view, information processing on computer devices is a fundamental difference from the brain. In the context-sensitive cognition model, human intelligence depends on the physical properties of the neurons. So that artificial intelligence requires brain-like computer skills, better known as neurocomputers. The purpose of this terminology is to design hardware compatible with neuro-computing. In this case, the model that is later known massively is an artificial neural network in which this model is trained, not programmed. Much information is extracted deeper than a representation that is presented in various forms that can be understood by humans. In the past, artificial emotions were somewhat neglected in AI and cognitive science. However, currently, emotional intelligence is one of the things that is raised with relevant information indicators in solving a case or problem. Emotion has an important domain in motivating and directing behavior. So that discussions in cognitive science and AI become one of the raw materials in representing information, then use it in social interactions. This representation is a language capable of thinking about problem-solving and social processes. This explains the systematics or methods used are very important in understanding cognition and communication in the context of social interaction. This pattern has appeared in the childhood phase in the learning process until later understanding their identity and interacting with others in the form of communication. The basis for this transformation is then essential in solving many cases in the world of science and technology.

Editor of 2020 3rd ISRITI

Ferry Wahyu Wibowo
ORCID ID: 0000-0003-1913-436X