Experiment and the Making of Meaning

Gooding, David

doi:10.1007/978-94-009-0707-2

Cited by 411 publications

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“…In the actual practice of science (Gooding, 1990;Hanson, 1961;Simon, 1977;Thaggard, 1988), a major motivation for experimentation is to discover and characterize new phenomena.…”

Section: Phenomena and Theoriesmentioning

confidence: 99%

Expertise effects in memory recall: Comment on Vicente and Wang (1998).

Simon¹,

Gobet²

2000

Psychological Review

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proposed a "constraint attunement hypothesis" to explain the large effects of domain expertise on memory recall observed in a number of task domains. They claimed to have found serious defects in alternative explanations of these effects, which their theory overcomes. Reexamination of the evidence shows that their theory is not novel but has been anticipated by those they criticized and that other current published theories of the phenomena do not have the defects that Vicente and Wang attributed to them. Vicente and Wang's views reflect underlying differences about (a) emphasis on performance versus process in psychology and (b) how theories and empirical knowledge interact and progress with the development of a science.Vicente and Wang (1998) used an ecological approach to explain the "significant correlation between domain expertise and memory recall performance after a very brief exposure time" (p. 33). They claimed that "this constraint attunement hypothesis[CAH] . .. predicts ... a memory expertise advantage in cases in which experts are attuned to the goal-relevant constraints in the material to be recalled and that the more constraint available, the greater the expertise advantage can be" (p. 33) and explains "several findings in the literature [that] have no satisfactory theoretical explanation" (p. 33). We claim that, on the contrary, these findings have already been explained by process theories and the memory advantage has long been understood in terms of its goal relevance.After summarizing Vicente and Wang's (1998) theoretical proposal, we address several errors in their account of past and current research on expertise. We then show why the 10 experiments that Vicente and Wang reviewed do not support CAH unless one adds to it a number of auxiliary assumptions and why they provide better support for the contemporary process theories criticized by Vicente and Wang. Process theories need not wait and have not waited for product theories such as CAH to clear the way for progress.

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“…In the actual practice of science (Gooding, 1990;Hanson, 1961;Simon, 1977;Thaggard, 1988), a major motivation for experimentation is to discover and characterize new phenomena.…”

Section: Phenomena and Theoriesmentioning

confidence: 99%

Expertise effects in memory recall: Comment on Vicente and Wang (1998).

Simon¹,

Gobet²

2000

Psychological Review

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“…Exploring the potential of computer-based technology in constructing instruments in this way is another area of application for EM techniques (cf. [47]). …”

Section: Characteristics Of the Em Construal Processmentioning

confidence: 99%

“…Where AI is concerned, the sources include Rodney Brooks [31,32], Brian Smith [70,71], Mark Turner [73] and Peter Naur [63]. Other relevant philosophical ideas are drawn from William James's ideas on Radical Empiricism, first collected for publication shortly after his death in 1910 [53], and from more contemporary work of Gooding [47] and Hirschheim al. [52] on methodological issues in science and information systems development respectively.…”

Section: Critical Perspectives On Logicismmentioning

confidence: 99%

“…For instance: computer programming for AI is discussed in the context of Smith's Two Lessons in Logic in [7,20]; some of the issues for information systems design raised by Hirschheim al. in [52] have been considered in [17,18,64], and some relating to concurrent engineering in [1]; the status of constructed models in knowledge representation, as examined by Naur in [63], is discussed in [9,12]; the prospects for applying EM principles to meet Kent's challenges for data modelling [54] are considered in [11,45]; layers of intelligence serving a similar role to those in Brooks's subsumption architecture are introduced in [14,4]; the ideas of William James [53] and Gooding [47] on the relationship between theory and experiment are developed in [9,10,8].…”

Section: Critical Perspectives On Logicismmentioning

confidence: 99%

“…More generally, EM can be applied in a concurrent engineering context [1], where independent views may be subject to conflict, as in Gruber's shadow box experiment [47]. To account for the process by which such views might be reconciled through arbitration and management requires a hierarchical model for agent interaction in which an agent at one level acts in the role of the human modeller in relation to those at the level below [1].…”

Section: A Framework For Emmentioning

confidence: 99%

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Empirical Modelling and the Foundations of Artificial Intelligence

Beynon

1999

Computation for Metaphors, Analogy, and Agents

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Abstract. This paper proposes Empirical Modelling (EM) as a possible foundation for AI research outside the logicist framework. EM offers principles for constructing physical models, typically computer-based, by construing experience in terms of three fundamental concepts: observables, dependency and agency. EM is discussed in the context of critiques of logicism drawn from a variety of sources, with particular reference to the five foundational issues raised by Kirsh in his paper Foundations of AI: the Big Issues (AI, 47:3-30, 1991), William James's Essays on Radical Empiricism (Bison Books, 1996), and the controversy surrounding formal definitions for primitive concepts such as metaphor and agent that are recognised as fundamental for AI. EM principles are motivated and illustrated with reference to a historic railway accident that occurred at the Clayton Tunnel in 1861. The principal thesis of the paper is that logicist and non-logicist approaches to AI presume radically different ontologies. Specifically, EM points to a fundamental framework for AI in which experimentally guided construction of physical artefacts is the primary mode of knowledge representation. In this context, propositional knowledge is associated with phenomena that are perceived as circumscribed and reliable from an objective 'third-person' perspective. The essential need to incorporate subjective 'first-person' elements in an account of AI, and the role that commitment plays in attaching an objective meaning to phenomena, are seen to preclude a hybrid approach to AI in the conventional sense.

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Interpretations of graphs by university biology students and practicing scientists: Toward a social practice view of scientific representation practices

Bowen

Roth

McGinn

1999

J. Res. Sci. Teach.

100

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Using graphs is a key social practice of professional science. As part of a research program that investigates the development of graphing practices from elementary school to professional science activities, this study was designed to investigate similarities and differences in graph-related interpretations between scientists and college students engaged in collective graph interpretation. Forty-five students in a second-year university ecology course and four scientists participated in the study. Guided by domainspecific concerns, scientists' graph-related activities were characterized by a large number of experiencebased, domain-specific interpretive resources and practices. Students' group based activities were characterized by the lack of linguistic distinctions (between scientific terms) which led to ambiguities in group negotiations; there was also a lack of knowledge about specific organism populations which helped field ecologists construct meaning. Many students learned to provide correct answers to specific graphing questions but did not come to make linguistic distinctions or increase their knowledge of specific populations. In the absence of concerns other than to do well in the course, students did not appear to develop any general interpretive skills for graphs, but learned instead to apply the professor's interpretation. This is problematic because, as we have demonstrated, there are widely differing viable interpretations of the graph. Suggestions for changes in learning environments for graphing that should alleviate this problem are made. John Wiley & Sons, Inc. J Res Sci Teach 36: 1020-1043 Laboratory studies point to the generation and interpretation of inscriptions (e.g., graphs, tables, diagrams) as the central activity of scientists (Latour & Woolgar, 1986; Lynch, 1985). Graphs are probably the most important of scientists' ways of presenting their data because they pictorially illustrate the relationships between different measured variables in, for instance, scatter plots (Bastide, 1990;Lemke, 1998). Schank (1994) listed graphing-which by definition included producing, reading, and critiquing graphs-as one of the seven most important skills of a professional biologist. Learning to produce, read, and critique graphs should therefore be an important ingredient of any university biology program. However, there is evidence that even college graduates with B.Sc. and M.Sc. degrees by and large have not developed much competence in using graphs in contexts where scientists employ them by default (Roth, McGinn, & Bowen, 1998). JOURNAL OF RESEARCH IN SCIENCE TEACHING VOL. 36, NO. 9, PP. 1020 -1043 © 1999 John Wiley & Sons, Inc. CCC 0022-4308/99/091020-24Correspondence to: G. M. BowenStudents' deficiencies in graphing have been researched largely from an information-processing paradigm (Leinhardt, Zaslavsky, & Stein, 1990). Research from this perspective shows that few students arrive at the normative interpretations of graphs that researchers hold as referents for student-generated sol...

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Experiment and the Making of Meaning

Cited by 411 publications

References 0 publications

Expertise effects in memory recall: Comment on Vicente and Wang (1998).

Expertise effects in memory recall: Comment on Vicente and Wang (1998).

Empirical Modelling and the Foundations of Artificial Intelligence

Interpretations of graphs by university biology students and practicing scientists: Toward a social practice view of scientific representation practices

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