Representational dynamics in the domain of iterated mental paper folding

Abstract Successful problem solving relies on the availability of suitable mental representations of the task domain. Especially for more complex problems, there might be a wide variety of possible problem representations, and it might even be beneficial to change them during problem solving. In a first part, we argue that investigating the dynamics of understanding in terms of dynamically changing problem representations is an underexplored aspect of problem solving research, and that most classic tasks even preclude the opportunity of such dynamics to occur. Continuing this theoretical discussion, as an illustrative example of a task designed for the exploration of such representational dynamics, the second part of the paper discusses a novel, complex spatial transformation and problem solving task. In this task, one is asked to repeatedly mentally cross-fold a sheet of paper, and to predict the resulting sheet geometry without the use of external aids. Through its deliberate openness and difficulty, this task requires finding new and more efficient representations – ranging from kinaesthetic and visuospatial imagery to symbolic notions. We present an overview of the task domain and discuss various ways of representing the domain as well as potential dynamics between them.

[1]  G. Lakoff,et al.  Metaphors We Live By , 1980 .

[2]  James A. Dixon,et al.  On the spontaneous discovery of a mathematical relation during problem solving , 2004, Cogn. Sci..

[3]  Herbert A. Simon,et al.  Understanding written problem instructions. , 1974 .

[4]  M. Jeannerod Mental imagery in the motor context , 1995, Neuropsychologia.

[5]  Christian D. Schunn,et al.  Analogy as a strategy for supporting complex problem solving under uncertainty , 2012, Memory & cognition.

[6]  Philip E. Agre,et al.  Interview with Allen Newell , 1993, Artif. Intell..

[7]  J. F. Voss Toulmin’s Model and the Solving of Ill-Structured Problems , 2005 .

[8]  Rafael Núñez,et al.  Mathematical Idea Analysis: What Embodied Cognitive Science Can Say about the Human Nature of Mathematics. , 2000 .

[9]  R. Sternberg,et al.  Recognizing, defining, and representing problems. , 2003 .

[10]  D. Calvillo,et al.  Are complex decisions better left to the unconscious? Further failed replications of the deliberation-without-attention effect , 2009, Judgment and Decision Making.

[11]  Anja Jamrozik,et al.  Metaphor: Bridging embodiment to abstraction , 2016, Psychonomic Bulletin & Review.

[12]  Stellan Ohlsson,et al.  The Problems with Problem Solving: Reflections on the Rise, Current Status, and Possible Future of a Cognitive Research Paradigm , 2012, J. Probl. Solving.

[13]  B. Burns,et al.  The Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology Goal Specificity Effects on Hypothesis Testing in Problem Solving , 2022 .

[14]  U. Neisser,et al.  Cognition and thought : an information-processing approach , 1966 .

[15]  Erik D. Demaine,et al.  Geometric folding algorithms - linkages, origami, polyhedra , 2007 .

[16]  D. Gentner,et al.  Metaphor and knowledge change , 2000 .

[17]  Samuel T. Moulton,et al.  Imagining predictions: mental imagery as mental emulation , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[18]  Andrea A. diSessa,et al.  Microgenetic Learning Analysis: A Methodology for Studying Knowledge in Transition , 2013, Human Development.

[19]  Ulrich Kortenkamp,et al.  An explorative study of search of model space in problem-solving , 2013, CogSci.

[20]  K. VanLehn Rule acquisition events in the discovery of problem-solving strategies , 1991 .

[21]  Paul S. Rosenbloom,et al.  Universal Subgoaling and Chunking: The Automatic Generation and Learning of Goal Hierarchies , 2012 .

[22]  Paul J. Feltovich,et al.  Categorization and Representation of Physics Problems by Experts and Novices , 1981, Cogn. Sci..

[23]  Bipin Indurkhya,et al.  Creativity and Cognitive Development: The Role of Perceptual Similarity and Analogy , 2013, AAAI Spring Symposium: Creativity and Cognitive Development.

[24]  Bipin Indurkhya,et al.  Emergent representations, interaction theory and the cognitive force of metaphor , 2006 .

[25]  Herbert A. Simon,et al.  The Structure of Ill Structured Problems , 1973, Artif. Intell..

[26]  Christian D. Schunn,et al.  A 4-Space Model of Scientific Discovery , 1995 .

[27]  Stephen K. Reed,et al.  The Structure of Ill-Structured (and Well-Structured) Problems Revisited , 2016 .

[28]  N. Kompa The Myth of Embodied Metaphor , 2017 .

[29]  Alan H. Schoenfeld,et al.  Learning: The Microgenetic Analysis of One Student’s Evolving Understanding of a Complex Subject Matter Domain , 2019, Advances in Instructional Psychology.

[30]  Tetsuo Ida,et al.  Formalizing polygonal knot origami , 2015, J. Symb. Comput..

[31]  A. Newell Unified Theories of Cognition , 1990 .

[32]  Linda B. Smith,et al.  A Dynamic Systems Approach to the Development of Cognition and Action , 2007, Journal of Cognitive Neuroscience.

[33]  R. Shepard,et al.  A chronometric study of mental paper folding , 1972 .

[34]  James L. McClelland,et al.  Letting structure emerge: connectionist and dynamical systems approaches to cognition , 2010, Trends in Cognitive Sciences.

[35]  Herbert A Simon,et al.  The understanding process: Problem isomorphs , 1976, Cognitive Psychology.

[36]  A. Luchins Mechanization in problem solving: The effect of Einstellung. , 1942 .

[37]  C. E. Bethell-Fox,et al.  Mental rotation: effects of stimulus complexity and familiarity , 1988 .

[38]  Brian F. Bowdle,et al.  Metaphor is like analogy , 2001 .

[39]  David Klahr,et al.  Dual Space Search During Scientific Reasoning , 1988, Cogn. Sci..

[40]  Andrea A. diSessa,et al.  The Construction of Causal Schemes: Learning Mechanisms at the Knowledge Level , 2014, Cogn. Sci..

[41]  G. Lakoff,et al.  Where mathematics comes from : how the embodied mind brings mathematics into being , 2002 .

[42]  K. A. Ericsson,et al.  Protocol Analysis: Verbal Reports as Data , 1984 .

[43]  Kurt VanLehn,et al.  Analogy Events: How Examples are Used During Problem Solving , 1998, Cogn. Sci..

[44]  K. Duncker,et al.  On problem-solving , 1945 .

[45]  Allen Newell,et al.  Human Problem Solving. , 1973 .

[46]  William H. Batchelder,et al.  Insight Problem Solving: A Critical Examination of the Possibility of Formal Theory , 2012, J. Probl. Solving.

[47]  Ut Na Sio,et al.  Does incubation enhance problem solving? A meta-analytic review. , 2009, Psychological bulletin.

[48]  Thora Tenbrink,et al.  Conceptual Transformation and Cognitive Processes in Origami Paper Folding , 2015, J. Probl. Solving.

[49]  Kathy Hirsh-Pasek,et al.  Understanding spatial transformations: similarities and differences between mental rotation and mental folding , 2013, Cognitive Processing.

[50]  James N. MacGregor,et al.  Human Performance on Insight Problem Solving: A Review , 2011, J. Probl. Solving.

[51]  Kurt VanLehn,et al.  Seeing Deep Structure From the Interactions of Surface Features , 2012 .

[52]  Antonio Lieto,et al.  The knowledge level in cognitive architectures: Current limitations and possible developments , 2018, Cognitive Systems Research.

[53]  Martha W. Alibali,et al.  Language, gesture, action! A test of the Gesture as Simulated Action framework , 2010 .

[54]  A. Dijksterhuis,et al.  A Theory of Unconscious Thought , 2006, Perspectives on psychological science : a journal of the Association for Psychological Science.

[55]  Vinod Goel,et al.  Neural basis of thinking: laboratory problems versus real-world problems. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[56]  Autumn B. Hostetter,et al.  Visible embodiment: Gestures as simulated action , 2008, Psychonomic bulletin & review.

[57]  Barbara Tversky,et al.  Cognitive Maps, Cognitive Collages, and Spatial Mental Models , 1993, COSIT.

[58]  Sylvia Fitting,et al.  Spatial Strategy Selection: Interesting Incremental Information , 2003 .

[59]  Evelyne Clément Knowledge of Domain Effects in Problem Representation: The Case of Tower of Hanoi Isomorphs , 1997 .

[60]  Holger Schultheis,et al.  Differences between Spatial and Visual Mental Representations , 2013, Front. Psychol..

[61]  Antonio Chella,et al.  Representational Issues in the Debate on the Standard Model of the Mind , 2018, AAAI Fall Symposia.

[62]  Bipin Indurkhya,et al.  Metaphor as change of representation : an arti ® cial intelligence perspective , 2003 .

[63]  B. Rittle-Johnson,et al.  Developing Conceptual Understanding and Procedural Skill in Mathematics: An Iterative Process. , 2001 .

[64]  Arthur B. Markman,et al.  Cognitive Dynamics : Conceptual and Representational Change in Humans and Machines , 2014 .

[65]  K. Dunbar The analogical paradox: Why analogy is so easy in naturalistic settings yet so difficult in the psychological laboratory. , 2001 .

[66]  Stellan Ohlsson,et al.  Deep Learning - How the Mind Overrides Experience , 2011 .

[67]  Frank J. Lee,et al.  Production Compilation: A Simple Mechanism to Model Complex Skill Acquisition , 2003, Hum. Factors.