Abstract
Fischbein (1977) in the opening epigraph is certainly correct in pointing out our natural predisposition toward constructing images in order to make sense of some knowledge that appears to us perhaps initially in either linguistic or alphanumeric form. In the case of Gemiliano, he liked mathematics despite his many struggles with its symbolic aspect because in most cases he understood what was happening, at least visually. I should note that visualizing facts and images does not necessarily imply the use of pictures alone. They could also be routed propositionally, that is, in either linguistic or algebraic form. But whether those images take the shape of pictures or language, I underscore a basic problem some learners have in the case of school mathematical objects, concepts, and processes, that make sense despite the absence of any natural mapping with the real world.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Two points are worth noting. First, Miller (1990) spoke about visualizing that arises from “non-veridical perception … simply the experience of visualizing something … in the absence of an actual object, scene, or person” (p. 4). For example, in (pure) mathematics, it is common among mathematicians to visualize objects that have no corresponding physical existence. However, their reification rests on an axiomatic, deductive system, which then allows us to classify them under structured visual representations. Second, the manner in which we characterize Level 0 visualizing is different from Nemirovsky and Ferrara’s (2009) notion of mathematical imagination. I share their view that imagination causes individual learners to “entertain possibilities for action” (p. 159), including the distinction they developed between pure and empirical possibilities with the latter referring to actions drawn from empirical evidence and the former to actions based on an unconditional acceptance of some assumptions. But then they went a step further in situating “mathematical imagination” under pure possibilities that have all the qualities of “logical necessity in all its deductive and inductive modalities” (Nemirovsky & Ferrara, 2009, p. 160). In my classification, what they consider to be mathematical imagination is a negotiated form of visualization, which could be either formational or transformational in context.
- 2.
Gibson (1986/1979), of course, was referring to visual perception involving everyday objects in a person’s (or an animal’s) environment. Land, for example, naturally affords particular kinds of animals to physically move or pose in ways that adapt to their intentions. In the case of mathematical objects, visual affordances of objects require an interpretive act of discerning and constructing some intention relevant to the objects.
- 3.
Begle’s (1979) notion of mathematical objects had him classifying various kinds of mathematical ideas, as follows: (1) facts (notation-based and deduced ones); (2) concepts; (3) operations (assignment of meaning from object to object); and (4) principles (relationships). Leushina (1991) also wrote about mathematical objects but in the context of visual aids. Leushina’s classification of aids depends on “how the surrounding reality is reflected,” as follows: (1) natural; (2) representational; and (3) graphic. My notion of mathematical objects does not conflate object and the meanings, associations, and ontological sources we derive from them. I leave aside conceptual meanings and intentions in favor of what I consider to be the raw dimension in which inscriptions and physical or material manifestations generally are reified forms of their ideal referents.
References
Alcock, L., & Simpson, A. (2004). Convergence of sequences and series: Interactions between visual reasoning and the learners’ beliefs about their own role. Educational Studies in Mathematics, 57, 1–32.
Arcavi, A. (2003). The role of visual representations in the learning of mathematics. Educational Studies in Mathematics, 52, 215–241.
Arnheim, R. (1971). Visual thinking. Berkeley, CA: University of California Press.
Arp, R. (2008). Scenario visualization: An evolutionary account of creative problem solving. Cambridge, MA: MIT Press.
Bakker, A. (2007). Diagrammatic reasoning and hypostasized abstraction in statistics education. Semiotica, 164(1/4), 9–29.
Begle, E. (1979). Critical variables in mathematics education: Findings from a survey of the empirical literature. Washington, DC: Mathematical Association of America and the National Council of Teachers of Mathematics.
Cai, J. (2000). Mathematical thinking involved in U.S. and Chinese students’ solving of process-constrained and process-open problems. Mathematical Thinking and Learning, 2(4), 309–340.
Campbell, K. J., Collis, K., & Watson, J. (1995). Visual processing during mathematical problem solving. Educational Studies in Mathematics, 28, 177–194.
Cellucci, C. (2008). The nature of mathematical explanation. Studies in the History and Philosophy of Science, 39, 202–210.
Chambers, D. (1993). Images are both depictive and descriptive. In B. Roskos-Ewoldson, M. Intons-Peterson, & R. Anderson (Eds.), Imagery, creativity, and discovery: A cognitive perspective (pp. 77–97). Netherlands: Elsevier.
Chiu, M. M. (2001). Using metaphors to understand and solve arithmetic problems: Novices and experts working with negative numbers. Mathematical Thinking and Learning, 3(2/3), 93–124.
Cordes, S., Williams, C., & Meck, W. (2007). Common representations of abstract quantities. Current Directions in Psychological Science, 16(3), 156–161.
Davis, P. (1993). Visual theorems. Educational Studies in Mathematics, 24, 333–344.
Davydov, V. (1990). Types of generalization in instruction: Logical and psychological problems in the structuring of school curricula. Reston, VA: National Council of Teachers of Mathematics.
Dehaene, S. (1997). The number sense. Oxford, UK: Oxford University Press.
Dehaene, S. (2001). Précis of the number sense. Mind & Language, 16(1), 16–36.
Dickson, S. (2002). Tactile mathematics. In C. Bruter (Ed.), Mathematics and art: Mathematical visualization in art and education (pp. 213–222). Berlin: Springer.
Dörfler, W. (2007). Matrices as Peircean diagrams: A hypothetical learning trajectory. CERME, 5, 852–861.
Duval, R. (2000). Basic issues for research in mathematics education. In T. Nakahara & M. Koyama (Eds.), Proceedings of the 24th annual conference of the international group for the psychology of mathematics education (Vol. 1, 55–69). Hiroshima, Japan.
Dretske, F. (1969). Seeing and knowing. Chicago, IL: Chicago University Press.
Duval, R. (2006). A cognitive analysis of problems of comprehension in a learning of mathematics. Educational Studies in Mathematics, 61(1–2), 103–131.
Ebersbach, M., Van Dooren, W., Van den Noortgate, W., & Resing, W. (2008). Understanding linear and exponential growth: Searching for the roots in 6- to 9-year-olds. Cognitive Development, 23, 237–257.
Feigenson, L., Dehaene, S., & Spelke, E. (2004). Core systems of number. Trends in Cognitive Science, 8(7), 307–314.
Fischbein, E. (1977). Image and concept in learning mathematics. Educational Studies in Mathematics, 8, 153–165.
Fischbein, E. (1993). The theory of figural concepts. Educational Studies in Mathematics, 24, 139–162.
Foucault, M. (1982). The archaelogy of knowledge and the discourse on language (A. M. Sheridan-Smith, Trans.). New York: Pantheon.
Gelman, R., & Gallistel, C. (1978). The child’s understanding of number. Cambridge, MA: Harvard University Press.
Giaquinto, M. (1994). Epistemology of visual thinking in elementary real analysis. The British Journal for the Philosophy of Science, 789–813.
Giaquinto, M. (2005). Mathematical activity. In P. Mancosu, K. Jorgensen, & S. Pedersen (Eds.), Visualization, explanation, and reasoning styles in mathematics (pp. 75–90). Dordrecht, Netherlands: Springer.
Giaquinto, M. (2007). Visual thinking in mathematics: An epistemological study. Oxford, UK: Oxford University Press.
Gibson, J. (1986/1979). The ecological approach to visual perception. Hillsdale, NJ: Lawrence Erlbaum Associates.
Giere, R., & Moffatt, B. (2003). Distributed cognition: Where the cognitive and the social merge. Social Studies of Science, 33(2), 301–310.
Goldin, G. (2002). Representation in mathematical learning and problem solving. In L. English (Ed.), Handbook of International Research in Mathematics Education (pp. 197–218). Mahwah, NJ: Erlbaum.
Grelland, H. H. (2007). Physical concepts and mathematical symbols. In G. Adenier, A. Y. Khrennikov, P. Lahti, V. I. Mak’ko, & T. M. Niewenhuizen (Eds.), CP962: quantum theory, reconsideration of foundations (Vol. 4, pp. 258–260). Washington, DC: American Institute of Physics.
Hoffmann, M. (2007). Learning from people, things, and signs. Studies in Philosophy Education, 26, 185–204.
Houser, N. (1987). The importance of pictures. Sciences, 27(3), 17–18.
Jacob, P., & Jeannerod, M. (2003). Ways of seeing: The scope and limits of visual cognition. Great Clarendon Street, Oxford, UK: Oxford University Press.
Jaffe, A., & Quinn, F. (1993). Theoretical mathematics: Toward a cultural synthesis of mathematics and theoretical physics. Bulletin of the American Mathematical Society, 29(1), 1–13.
Katz, V. (1999). Algebra and its teaching: An historical survey. Journal of Mathematical Behavior, 16(1), 25–38.
Kirshner, D., & Awtry, T. (2004). Visual salience of algebraic transformations. Journal for Research in Mathematics Education, 35(4), 224–257.
Kitcher, P. (1983). The nature of mathematical knowledge. Oxford, MA: Oxford University Press.
Kotsopoulos, D., & Cordy, M. (2009). Investigating imagination as a cognitive space for learning mathematics. Educational Studies in Mathematics, 70, 259–274.
Lee, L. (1996). An initiation into algebraic culture through generalization activities. In N. Bednarz, C. Kieran, & Lee, L. (Eds.), Approaches to algebra: Perspectives for research and teaching (pp. 87–106). Dordrecht, Netherlands: Kluwer.
Leushina, A. (1991). Soviet studies in mathematics education (volume 4): The development of elementary mathematical concepts in preschool children. (J. Teller, Trans.). Reston, VA: National Council of Teachers of Mathematics.
Lithner, J. (2008). A research framework for creative and imitative reasoning. Educational Studies in Mathematics, 67, 255–276.
Livingston, E. (1999). Cultures of proving. Social Studies of Science, 29(6), 867–888.
Magnani, L. (2004). Conjectures and manipulations: Computational modeling and the extra-theoretical dimension of scientific discovery. Minds and Machines, 14, 507–537.
Malone, J., Boase-Jelinek, D., Lamb, M., & Leong, S. (2008). The problem of misperception in mathematical visualization. Paper presented at the 11th international congress in mathematical education, Merida, Mexico.
Mandler, J. (2007). On the origins of the conceptual system. American Psychologist, 62(8), 741–751.
Mandler, J. (2008). On the birth and growth of concepts. Philosophical Psychology, 21(2), 207–230.
McCormick, B., DeFanti, T., & Brown, M. (1987). Definition of visualization. ACM SIGGRAP Computer Graphics, 21(6), 1–3.
Miller, J. (1990). The essence of images. In H. Barlow, C. Blakemore, & M. Weston-Smith (Eds.), Images and understanding (pp. 1–4). New York: Cambridge University Press.
Nemirovsky, R., & Ferrara, F. (2009). Mathematical imagination and embodied cognition. Educational Studies in Mathematics, 70, 159–174.
Nemirovsky, R., & Noble, T. (1997). On mathematical visualization and the place where we live. Educational Studies in Mathematics, 33, 99–131.
Ng, S. F., & Lee, K. (2009). The model method: Singapore children’s tool for representing and solving algebraic word problems. Journal for Research in Mathematics Education, 40(3), 282–313.
Norman, J. (2006). After Euclid: Visual reasoning and the epistemology of diagrams. Stanford, CA: CSLI Publications.
O’Daffer, P., & Clemens, S. (1992). Geometry: An investigative approach. New York: Addison Wesley.
O’Regan, J. K. (2001). The “feel” of seeing: An interview with J. Kevin O’Regan. Trends in Cognitive Sciences, 5(6). 278–279.
Owens, K. (1999). The role of visualization in young students’ learning. In O. Zaslavsky (Ed.), Proceedings of the 23rd international conference of the psychology for mathematics education (Vol. 1., pp. 220–234).
Owens, K., & Outhred, L. (2006). The complexity of learning geometry and measurement. In A. Gutierrez & P. Boero (Eds.), Handbook of research on the psychology of mathematics education: Past, present, and future (pp. 83–115). Rotterdam, Netherlands: Sense Publishers.
Palais, R. (1999). The visualization of mathematics: Towards a mathematical exploratorium. Notices of the AMS, 46(6), 647–658.
Papert, S. (2002). Afterword: After how comes what. In R. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 581–586). New York: Cambridge University Press.
Peirce, C. (1958b). Lessons of the history of science. In P. Wiener (Ed.), Charles S. Peirce: Selected writings (Values in a universe of chance) (pp. 227–232). New York: Dover.
Pica, P., Lemer, C., Izard, V., & Dehaene, S. (2004). Exact and approximate arithmetic in an Amazonian indigene group. Science, 306, 499–503.
Pitta-Pantazi, D., Christou, C., & Zachariades, T. (2007). Secondary school students’ levels of understanding in computing exponents. Journal of Mathematical Behavior, 26(4), 301–311.
Presmeg, N. (1986). Visualization in high school mathematics. For the Learning of Mathematics, 6(3), 42–46.
Presmeg, N. (1992). Prototypes, metaphors, metonymies, and imaginative rationality in high school mathematics. Educational Studies in Mathematics, 23, 595–610.
Pylyshyn, Z. (2007). Things and places: How the mind connects with the world. Cambridge, MA: MIT Press.
Radford, L. (2001). Signs and meanings in students’ emergent algebraic thinking: A semiotic analysis. Educational Studies in Mathematics, 42, 237–268.
Radford, L. (2008). Iconicity and contraction: A semiotic investigation of forms of algebraic generalizations of patterns in different context. ZDM: International Journal in Mathematics Education, 40(1), 83–96.
Rakoczy, H., Tomasello, M., & Striano, T. (2005). How children turn objects into symbols: A cultural learning account. In L. Namy (Ed.), Symbol use and symbolic representation: Developmental and comparative perspectives (pp. 69–97). Mahwah, NJ: Erlbaum.
Reber, A. (1989). Implicit learning and tacit knowledge. Journal of Experimental Psychology: General, 118(3), 219–235.
Resnick, L. (1989). Developing mathematical knowledge. American Psychologist, 44(2), 162–169.
Rival, I. (1987). Picture puzzling: Mathematicians are rediscovering the power of pictorial reasoning. Sciences, 27(1), 41–46.
Rodd, M. (2000). On mathematical warrants: Proof does not always warrant, and a warrant may be other than a proof. Mathematical Thinking and Learning, 2(3), 221–244.
Schnepp, M., & Chazan, D. (2004). Incorporating experiences of motion into a calculus classroom. Educational Studies in Mathematics, 57, 309–313.
Simon, T. (1997). Reconceptualizing the origins of number knowledge: A “non-numerical” account. Cognitive Development, 12, 349–372.
Sophian, C. (2007). The origins of mathematical knowledge in childhood. Mahwah, NJ: Erlbaum.
Soto-Andrade, J. (2008). Mathematics as the art of seeing the invisible. Paper presented at the 11th international congress in mathematical education, Merida, Mexico.
Spelke, E., Breinlinger, K., Macomber, J., & Jacobson, K. (1992). Origins of knowledge. Psychological Review, 99, 605–632.
Taylor, M., Pountney, D., & Malabar, I. (2007). Animation as an aid for the teaching of mathematical concepts. Journal of Further and Higher Education, 31(3), 246–261.
Tversky, B. (2001). Spatial schemas in depictions. In M. Gattis (Ed.), Spatial schemas and abstract thought (pp. 79–112). Cambridge, MA: MIT Press.
Uttal, D., & DeLoache, J. (1997). Manipulatives as symbols: A new perspective on the use of concrete objects to teach mathematics. Journal of Applied Developmental Psychology, 18, 37–54.
Vergnaud, G. (1996). The theory of conceptual fields. In P. Cobb, G. Goldin, & B. Greer (Eds.), Theories of Mathematical Learning (pp. 219–239). Mahwah, NJ: Erlbaum.
Vergnaud, G. (2009). The theory of conceptual fields. Human Development, 52, 83–94.
Weber, K. (2002). Developing students’ understanding of exponents and logarithms. In D. Mewborn, P. Sztajn, D. White, H. Wiegel, R. Bryant, & K. Nooney (Eds.), Proceedings of the 24th annual meeting of the North American chapter of the international group for the psychology of mathematics education (Vol. 4). Athens, GA: University of Georgia.
Zahner, D., & Corter, J. (2010). The process of probability problem solving: Use of external visual representations. Mathematical Thinking and Learning, 12, 177–204.
Rivera, F. (2007a). Accounting for students’ schemes in the development of a graphical process for solving polynomial inequalities in instrumented activity. Educational Studies in Mathematics, 65(3), 281–307.
Dörfler, W. (1991). Meaning: Image schemata and protocols. In F. Furinghetti (Ed.), Proceedings of the 15th annual conference of the psychology of mathematics education (Vol. 1, pp. 17–32). Assisi, Italy: PME.
Brown, D., & Presmeg, N. (1993). Types of imagery used by elementary and secondary school students in mathematical reasoning. In I. Hirabayashi, N. Nohda, K. Shigematsu, & F. Lai-Lin (Eds.), Proceedings of the 17th international conference for the psychology of mathematics education (Vol. 2, pp. 137–145). Tsukuba, Japan: University of Tsukuba.
Eisenberg, T., & Dreyfus, T. (1991). On the reluctance to visualize in mathematics. In W. Zimmermann & S. Cunningham (Eds.), Visualization in teaching and learning mathematics (pp. 25–38). Washington, DC: Mathematical Association of America.
Katz, V. (2007). Stages in the history of algebra with implications for teaching. Educational Studies in Mathematics , 66(2), 185–201.
Zazkis, R., Dubinksky, E., & Dautermann, J. (1996). Coordinating visual and analytic strategies: A study of students’ understanding of the group D4. Journal for Research in Mathematics Education, 27(4), 435–457.
Polya, G. (1988). How to solve it: A new aspect of mathematical method. Princeton, NJ: Princeton University Press.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Rivera, F.D. (2011). Visual Roots of Mathematical Cognitive Activity. In: Toward a Visually-Oriented School Mathematics Curriculum. Mathematics Education Library, vol 49. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0014-7_3
Download citation
DOI: https://doi.org/10.1007/978-94-007-0014-7_3
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-0013-0
Online ISBN: 978-94-007-0014-7
eBook Packages: Humanities, Social Sciences and LawEducation (R0)