Maximizing our Chances of Learning from Errors in Language Faculty Science: Suggestions and Illustration *
Hajime Hoji · 2011
This paper is concerned with how hypotheses about the language faculty can be made testable. Adopting the general model of the Computational System in Chomsky 1993, a model of judgment-making suggested in Ueyama 2010, and a research heuristic Maximize our chances of learning from errors, the paper suggests how we can try to identify what is likely a reflection of properties of the Computational System hypothesized at the center of the language faculty. Testability pursued here is in terms of point-value rather than predictions about a difference or a tendency. The paper argues, as a consequence of the three starting assumptions, that we can obtain categorical judgments, but only if we recognize a fundamental asymmetry between a *Schema-based prediction and an ok Schema-based prediction. The paper provides some illustration by applying the suggested method to lexical hypotheses in Japanese, by making reference to results of on-line experiments. The paper also provides an illustration that it is indeed possible to obtain categorical judgments from informants, contrary to the generally accepted view in the field that evidence for or against hypotheses about language (or the language faculty) can be assessed only in the terms of statistically significant contrasts, as in the general tradition of social, behavioral and life sciences.