New perspectives on the old data in psycholinguistics

Empirical data in a scientific inquiry hide many things that cannot be uncovered until the field reaches a certain level of maturity. NASA scientists have collected seismic signals from the lunar surface from the 1970s, but they had to wait 40 years before they finally came to learn, thanks to the state-of-the-art technology of the computer analysis, that the moon has a liquid core like the earth. A classic example of applying ‘new and arguably better’ technology to the old data to obtain improved understanding of human language was the Empty Category Principle in the time of Government and Binding theory (Chomsky 1981) that provided principled account for such old data as that-trace effects, island constraints, etc. The primary goal of this symposium is two-fold: (1) To see what we can uncover when we analyze the old data of psycholinguistics by using new or better methods of inferential statistics; and (2) To draw some consensus from the linguists' community that we must use best-possible statistical methods since advanced statistical analyses can reveal what we normally cannot see with basic statistics. Unlike theoretical linguists who always attempt to explain old and well-known phenomena with new and less-known frameworks, psycholinguists rarely apply new technology to the old data in the existing studies. What could we find out if we performed log-linear regression analyses of the multiple frequency tables in a journal article whose author carried out just as many Chi-square tests as the number of cross-tabulations? What could we find out if we transformed the raw data so that they could fulfill the crucial assumptions of certain statistical tests? What could we find out if we tested complicated causal models using Structural Equation Modeling for the data that used to be analyzed using basic regression methods? Papers presented in this symposium focus on applying new and less-known techniques to the old data in published works. The presenters analyze either their own data set or the raw data in earlier publications of other scholars. Notice