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Developing Expertise with Objective Knowledge: Motive Generators and Productive Practice

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From Animals to Robots and Back: Reflections on Hard Problems in the Study of Cognition

Part of the book series: Cognitive Systems Monographs ((COSMOS,volume 22))

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Abstract

Experts seek to derive manifold benefits from objective knowledge. Viewed as progressive problem solvers (Bereiter and Scardamalia 1993), they are not immune to psychological and practical challenges to learning in depth, particularly given demands for breadth and a lack of cognitive productivity tools. What mental changes occur when one understands deeply and develops new skills, new attitudes and implicit knowledge? With a few scenarios, I propose that deep understanding of conceptual artifacts, in the sense of Bereiter (2002), establishes and configures diverse motive generators that enable the valenced detection of gaps of understanding, cognitive infelicities and opportunities (cognitive itches). This proposal, derived from a designer-based approach to motivation (Sloman 1987; Beaudoin and Sloman 1993), is significantly different from how motivation is typically treated in psychology. It raises many questions about how motivational mechanisms develop and operate in the propensities of expertise. I suggest that experts facing great cognitive productivity demands can benefit from productive practice.

This is a slightly updated version of the paper I presented at Aaron Sloman’s festschrift (Beaudoin 2011). Many of the ideas presented here are developed further in Beaudoin (2013a, b).

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Notes

  1. 1.

    I include psychological (e.g., affect) theory in cognitive science if it can be expressed as information processing.

  2. 2.

    The term knowledge resource means a conceptual artifact—such as a paper, book, podcast, audiobook, video, design, illustration, web page—that conveys knowledge. To process knowledge (resources) means to read, view, listen to, converse about, build knowledge with, discuss or otherwise “think” about a knowledge resource.

  3. 3.

    “Mechanism” here does not refer to a physical or biological layer, but virtual machine layers (Sloman and Chrisley 2010; Sloman 2010a). It does not preclude but underpins teleology (Boden 1972).

  4. 4.

    Space does not allow me to demonstrate that the circularity in this concept of understanding is virtuous.

  5. 5.

    Sloman admonishes readers and listeners to beware of the tendency to falsely assume that they are dealing with continuity as opposed to richly structured discontinuous spaces (e.g., Sloman 1984)

  6. 6.

    Thus, the designer-stance (Sloman 1993a, b) to the mind can be applied to other objects of understanding. This is consistent with the elaborate concept of understanding in Bereiter (2002). Problem-centred knowledge becomes requirements-driven knowledge.

  7. 7.

    Sloman (1996; 2011) undermines the distinction between declarative and procedural information.

  8. 8.

    Existing annotation, practice and “cognitive fitness” software applications address narrow requirements (e.g., types of document and types of learning outcomes) Beaudoin (2010a, b, 2013a).

  9. 9.

    By general annotation, I mean to link any information item accessible from the local host to any existing or self-authored one (e.g., a new note). An example of general annotation is to link a paragraph in an iBooks document to a snip in an email message. My colleagues and I have created several personal learning environments with extensive annotation capabilities, e.g. Beaudoin and Winne (2009).

  10. 10.

    A simple example of this would for readers to be able to choose for any scholarly document whether to view it in MLA, APA or some other format. This entails the separation of data from its presentation, as is commonly done with XML and cascading style sheets (CSS).

  11. 11.

    Reading is just a special case. This applies to processing knowledge resources in general. Beaudoin (2013a, b) explicitly deals with knowledge processing in general.

  12. 12.

    Why should the annotator need to switch to a limited editor in a special-purpose annotation tool? An annotation system could easily leverage the user’s preferred word processor, outliner and diagramming tools. This is in the spirit of Poplog Ved, emacs and OpenDoc.

  13. 13.

    This is related to promisingness in Bereiter and Scardamalia (1993) and potential in Sinclair (2006).

  14. 14.

    The concept of cognitive itch needs to be articulated in designer terms, to surpass the limitations of conceptual analysis. The term “cognitive itch” has been used independently by Beaman and Williams (2010). The “itch” I am describing is a state in which one detects a cognitive infelicity and wants to do something about it (whether or not the motives surface or one deals with it). Beaman’s “itch” is better renamed and classified as a cognitive perturbance (Beaudoin 1994), which is one of many possible states that a class of motive processing systems can generate, rather than merely as an arbitrary phenomenological state.

  15. 15.

    I have suggested that clinical psychologists in particular should be trained in conceptual analysis (Beaudoin 2013a, b) and the designer stance (Beaudoin 2013b).

  16. 16.

    Notice that some of the epithets that a designer uses in his quest for understanding are a modernization of Wertheimer’s “internal structure” talk. The designer is concerned with internal functional architecture.

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Acknowledgments

Thank you to Jeremy Wyatt for the initiative and all the effort in organizing this important event in the history of cognitive science. Thank you to Carl Bereiter, Alissa Ehrenkranz, Robert Hoffman, Claude Lamontagne, Carrie Spencer, Phil Winne and Carol Woodworth for their feedback.

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  1. Simon Fraser University and CogZest, Burnaby, BC, Canada

    Luc P. Beaudoin

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  1. School of Computer Science, University of Birmingham, Birmingham, United Kingdom

    Jeremy L. Wyatt

  2. School of Social Sciences, University of Northampton, Northampton, United Kingdom

    Dean D. Petters

  3. School of Computing, University of Leeds, Leeds, United Kingdom

    David C. Hogg

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Beaudoin, L.P. (2014). Developing Expertise with Objective Knowledge: Motive Generators and Productive Practice. In: Wyatt, J., Petters, D., Hogg, D. (eds) From Animals to Robots and Back: Reflections on Hard Problems in the Study of Cognition. Cognitive Systems Monographs, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-06614-1_12

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