5월 넷째주 금요일인 25일에 Purdue University의 Robert W. Proctor 교수님을 초청하여 아래와 같이 강연을 개최하오니 많은 관심과 참여 부탁드립니다.
문의 사항이 있으실 경우 메일(nohji568@gmail.com보내주시길 바랍니다.


- 아 래 -

1. 날짜 및 시간: 5월 25일 금요일 오후 5시

2. 장소: 백주년 기념관 국제원격 회의실 (지하 1층)

3. 연사: Robert W. Proctor (Psychological Sciences, Purdue University)

4. 내용:

Title:  Development of the Language and Analytical Tools of Cognitive Psychology in the Information Age and Implications for the Era of Big Data

Abstract:

The information age can be dated to the work of Norbert Wiener and Claude Shannon in the 1940s. Their work on cybernetics and information theory, and advances in experimental design and inferential statistical testing stemming from Ronald Fisher, provided the conceptual and methodological tools for the information-processing revolution in psychology. Since the 1950s, much of the progress produced by the information-processing approach has been based on small-scale studies of behavior conducted in controlled laboratory settings. However, recent technological developments in the areas of computer science and artificial intelligence have allowed the collection of large amounts of various kinds of behavioral data outside of the laboratory. These Big Data sets provide unique opportunities to understand people in less controlled settings, potentially allowing verification of principles established in the lab and revealing relations among phenomena on a large scale that have not been evident in the small data sets collected in the laboratory. For Big Data to be informative about human behavior, it is vital for psychologists to work together with computer scientists, engineers, and data scientists to develop a common language and derive scientific methods to guide the research. Moreover, psychologists must determine how Big Data and small data laboratory research can be conducted to complement each other. I will describe historically significant advances that led to the widespread adoption of the information-processing approach, propose that human information processing and inferential statistical testing are necessary but not sufficient to understand human behavior in the Big Data era, and examine implications for psychological research accordingly.