The Processes of Scientific Discovery: The Strategy of Experimentation

Abstract

Hans Krebs' discovery, in 1932, of the urea cycle was a major event in biochemistry. This article describes a program, KEKADA, which models the heuristics Hans Krebs used in this discovery. KEKADA reacts to surprises, formulates explanations, and carries out experiments in the same manner as the evidence in the form of laboratory notebooks and interviews indicates Hans Krebs did. Furthermore, we answer a number of questions about the nature of the heuristics used by Krebs, in particular: How domain‐specific are the heuristics? To what extent are they idiosyncratic to Krebs? To what extent do they represent general strategies of problem‐solving search? The relative generality of KEKADA allows us to view the control structure of KEKADA and its domain‐independent heuristics as a model of scientific experimentation that should apply over a broad domain.

Inflexibility of experts—Reality or myth? Quantifying the Einstellung effect in chess masters

“…Given such characteristics, the creative product should go beyond previous knowledge and expertise, break links with the past and move away from stereotypical thinking. Implicit in this notion of creativity is that knowledge is necessary but not sufficient (for a different view see Hayes, 1989;Kulkarni & Simon, 1988;Weisberg, 2006).…”

“…More knowledge empowers creativity rather than hurting it (e.g. Kulkarni & Simon, 1988;Simonton, 1997;Weisberg, 1993Weisberg, , 1999.…”

Concept Discovery in a Scientific Domain

“…Recently, a number of models of scientific discovery have been proposed that have specified general heuristics that are used in reasoning scientifically (e.g., Holland, Holyoak, Nisbett & Thagard, 1986;Klahr & Dunbar, 1988;Kulkarni & Simon, 1988;Langley, Simon, Bradshaw, & Zytkow, 1987;Shrager & Langley, 1990). All of these models incorporate mechanisms for inducing hypotheses from data.…”

“…A major challenge for all these models has been to characterize the constraints that operate on the types of hypotheses proposed, experiments conducted, and data to make inductions over. One approach to the issue of what types of experiments to conduct and what types of data should be used to induce new hypotheses has been to build in heuristics that will take advantage of surprising results (e.g., Kulkarni & Simon, 1988). When an experimental result falls outside the bounds of those predicted by the current hypothesis, the model will focus on the surprising results and attempt to explain the cause of the puzzling event.…”

Untitled

“…The state of the art as of a dozen years ago is summarized in Tweney, Doherty, and Mynatt (1981). The more recent resurgence of interest in the "cognitive science of science," particulary in ML can be attributed to Simon and his colleagues (Langley, Simon, Bradshaw & Zytkow, 1987;Kulkarni & Simon, 1988;Qin & Simon, 1990, Cheng & Simon 1992Valdes-Perez, Simon & Murphy, 1992). However, beyond the projects that focus on the construction of computational models of the discovery process (c.f., Shrager & Langley, 1990), there is a substantial amount of psychological research on various aspects of scientific discovery (e.g., Chin & Brewer, 1992;Brewer & Samarapungavan, 1991;Dunbar, 1989;Dunbar & Schunn, 1990;Gholson, Standish, Neimeyer & Houts, 1989;Giere, 1988;Gorman, 1992;Holland, Holyoak, Nisbett & Thagard, 1986;Klayman & Ha, 1987;Kuhn, 1989;Schauble, 1990;Schauble, Klopfer & Raghavan, 1991;Sodian, Zaitchik & Carey, 1991;Vosniadou & Brewer, 1992).…”