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References

Published online by Cambridge University Press:  23 June 2022

Zygmunt Pizlo
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University of California, Irvine
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Problem Solving
Cognitive Mechanisms and Formal Models
, pp. 183 - 189
Publisher: Cambridge University Press
Print publication year: 2022

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References

Adelson, E. H., Anderson, C. H., Bergen, J. R., Burt, P. J., & Ogden, J. M. (1984) Pyramid methods in image processingRCA Engineer 29(6), 3341.Google Scholar
Agarwala, R., Applegate, D.L., Maglott, D., Schuler, G.F., & Schäffer, A.A. (2000) A fast and scalable radiation hybrid map construction and integration strategy. Genome Research 10, 350–64.Google Scholar
Anderson, J. R. (1980) Cognitive Psychology and Its Implications. New York: W.H. Freeman.Google Scholar
Applegate, D.L., Bixby, R.E., Chvátal, V., & Cook, W.J. (2006) The Traveling Salesman Problem. Princeton University Press.Google Scholar
Arslan, B., Taatgen, N.A., & Verbrugge, R. (2017) Five-year-olds’ systematic errors in second-order false belief tasks are due to first-order Theory of Mind strategy selection: a computational modeling study. Frontiers in Psychology 8, 275.CrossRefGoogle ScholarPubMed
Attneave, F. (1950) Dimensions of similarity. American Journal of Psychology 63, 516–56.Google Scholar
Auersperg, A.M.I., von Bayern, A.M.P., Gajdon, G.K., Huber, L., & Kacelnik, A. (2011) Flexibility in problem solving and tool use of kea and New Caledonian crows in a multi access box paradigm. PLoS ONE 6, 18.CrossRefGoogle Scholar
Baddeley, A. (1986) Working Memory. Oxford: Clarendon Press.Google Scholar
Basdevant, J.-L. (2007) Variational Principles in Physics. New York: Springer.Google Scholar
Batchelder, W.H., & Alexander, G.E. (2012) Insight problem solving: a critical examination of the possibility of formal theory. Journal of Problem Solving 5, 56100.CrossRefGoogle Scholar
Bennett, S. (1979) A History of Control Engineering 1800–1930. London: Peregrinus.CrossRefGoogle Scholar
Bennett, S. (1993) A History of Control Engineering 1930–1955. Stevenage: Peregrinus.Google Scholar
Benson-Amram, S., Dantzer, B., Stricker, G., Swanson, E.M., & Holekamp, K.E. (2016) Brain size predicts problem-solving ability in mammalian carnivores. PNAS 113, 2532–37.Google Scholar
Benson-Amram, S., Weldele, M.L., & Holekamp, K.E. (2013) A comparison of innovative problem-solving abilities between wild and captive spotted hyaenas, Crocuta crocuta. Animal Behavior 85, 349–56.Google Scholar
Bernard, C. (1878) Leçons sur les phénomènes de la vie communs aux animaux et aux végétaux. Paris: Bailliere.Google Scholar
Blaker, J.W., & Tavel, M.A. (1974) The application of Noether’s theorem to optical systems. American Journal of Physics 42, 857–61.CrossRefGoogle Scholar
Bland, R.E., & Shallcross, D.F. (1987) Large Traveling Salesman Problem arising from experiments in x-ray crystallography: a preliminary report on computation. Technical Report No. 730, School of OR/IE, Cornell University, Ithaca, ny.Google Scholar
Brusco, M.J. (2007) Measuring human performance on clustering problems: some potential objective criteria and experimental research opportunities. Journal of Problem Solving 1, 3352.Google Scholar
Burkholder, L. (2012) Book review of Rosenhouse, The Monty Hall Problem. Journal of Problem Solving 4, 4351.Google Scholar
Cannon, W.B. (1932) The Wisdom of the Body. New York: W.W. Norton.CrossRefGoogle Scholar
Carruthers, S., Stege, U., & Masson, M.E.J. (2018) The role of the goal in solving hard computational problems: do people really optimize? Journal of Problem Solving 11, 119.Google Scholar
Chu, Y., Dewald, A. D., & Chronicle, E. P. (2007) Theory-driven hints in the cheap necklace problem: a preliminary investigation. Journal of Problem Solving 1(2), 1832.CrossRefGoogle Scholar
Chu, Y., Li, Z., Su, Y., & Pizlo, Z. (2010) Heuristics in problem solving: the role of direction in controlling search space. Journal of Problem Solving 3, 2751.CrossRefGoogle Scholar
Chu, Y., & MacGregor, J.N. (2011) Human performance on insight problem solving: a review. Journal of Problem Solving 3, 119–50.Google Scholar
Craik, K.J.W. (1943) The Nature of Explanation. Cambridge University Press.Google Scholar
Cramer, A.E., & Gallistel, C.R. (1997) Vervet monkeys as traveling salesmen. Nature 387, 464.CrossRefGoogle Scholar
Curie, P. (1982) On symmetry in physical phenomena, symmetry of an electric field and of a magnetic field. In Rosen, J. (ed.), Symmetry in Physics: Selected Reprints, Stony Brook, ny: American Association of Physics Teachers (pp. 1725). (Original work published 1894).Google Scholar
Dallari, F., Marchet, G., & Ruggeri, R. (2000) Optimisation of man-on-board automated storage/retrieval systems. Integrated Manufacturing Systems 11, 8793.Google Scholar
de Vries, J.V. (1604/1968) Perspective. New York: Dover.Google Scholar
Dry, M., Lee, M.D., Vickers, D., & Hughes, P. (2006) Human performance on visually presented traveling salesperson problems with varying numbers of nodes. Journal of Problem Solving 1, 2032.Google Scholar
Dry, M.J., Preiss, K., & Wagemans, J. (2012) Clustering, randomness, and regularity: spatial distributions and human performance on the traveling salesperson problem and minimum spanning tree problem. Journal of Problem Solving 4, 117.Google Scholar
Duncker, K. (1945) On Problem-Solving. Psychological Monographs 58, Whole No. 270. Washington, DC: American Psychological Association.Google Scholar
Durbin, R., & Willshaw, D. (1987) An analogue approach to the Travelling Salesman Problem using an elastic net method. Nature, 326, 689–91.Google Scholar
Ekman, G. (1954) Dimensions of color vision. Journal of Psychology 38, 467–74.Google Scholar
Fitts, P.M. (1954) The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology 47, 281391.CrossRefGoogle ScholarPubMed
Foster, D. H. (1975) Visual apparent motion and some preferred paths in the rotation group so(3). Biological Cybernetics 18, 8189.Google Scholar
Foster, D. H. (1978) Visual apparent motion and the calculus of variations. In Leeuwenberg, E. & Buffart, H. (eds.), Formal Theories of Visual Perception, Chichester: Wiley (6782).Google Scholar
Gallup, G.G. (1970) Chimpanzees: self-recognition. Science 167, 8687.Google Scholar
Gamow, G., and Stern, M. (1958) Puzzle-Math. London: Viking Press.Google Scholar
Garey, M.R., & Johnson, D.S. (1979) Computers and Intractability: A Guide to the Theory of NP-Completeness. New York: W.H. Freeman.Google Scholar
Gibson, E., & Walk, R. (1960) The “visual cliff.Scientific American 202, 6471.CrossRefGoogle ScholarPubMed
Golden, B., Bodin, L., Doyle, T., & Stewart, W. (1980). Approximate traveling salesman algorithms. Operations Research 28, 694711.Google Scholar
Graham, S.M., Joshi, A., & Pizlo, Z. (2000) The Traveling Salesman Problem: a hierarchical model. Memory & Cognition 28, 1191–204.Google Scholar
Hart, P.E., Nilsson, N.J., & Raphael, B. (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4, 100–07.CrossRefGoogle Scholar
Haxhimusa, Y., Carpenter, E., Catrambone, J., Foldes, D., Stefanov, E., Arns, L., & Pizlo, Z. (2011) 2D and 3D Traveling Salesman Problem. Journal of Problem Solving 3, 167–93.Google Scholar
Hedden, T., & Zhang, J. (2002) What do you think I think you think?: Strategic reasoning in matrix games. Cognition 85, 136.CrossRefGoogle Scholar
Helie, S., & Pizlo, Z. (2021) When is psychology research useful in artificial intelligence? A case for reducing computational complexity in problem solving. Topics in Cognitive Science, 00 (2021), 115.Google Scholar
Hilbert, D., & Cohn-Vossen, S. (1952) Geometry and the Imagination. New York: Chelsea.Google Scholar
Hildebrandt, S., & Tromba, A. (1996) The Parsimonious Universe. New York: Copernicus-Springer.CrossRefGoogle Scholar
Hills, T. T. (2006) Animal foraging and the evolution of goal‐directed cognition. Cognitive Science, 30(1), 341.Google Scholar
Hills, T.T., Jones, M.N., & Todd, P.M. (2012) Optimal foraging in semantic memory. Psychological Review 119, 431–40.Google Scholar
Hills, T.T., Todd, P.M., & Jones, M.N. (2015) Foraging in semantic fields: how we search through memory. Topics in Cognitive Sciences 7, 513–34.Google Scholar
Hochberg, J.E. (1978) Perception. Englewood Cliffs, nj: Prentice-Hall.Google Scholar
Hommel, B. (2017) Goal-directed actions. In Waldmann, M.R. (ed.), The Oxford Handbook of Causal Reasoning, Oxford University Press (265–77).Google Scholar
James, W. (1890/1950) The Principles of Psychology. New York: Dover.Google Scholar
James, W. (1904) Does ‘consciousness’ exist? Journal of Philosophy, Psychology and Scientific Methods 1, 477–91.CrossRefGoogle Scholar
Johnston, R.A., Milne, A.B., Williams, C., & Hosie, J.A. (1997) Do distinctive faces come from outer space? An investigation of the status of a multidimensional face-space. Visual Cognition 4, 5967.Google Scholar
Jolion, J. M., & Rosenfeld, A. (1994) A Pyramid Framework for Early Vision. Boston: Kluwer.Google Scholar
Jones, M. N., Kintsch, W., & Mewhort, D.J.K. (2006) High-dimensional semantic space accounts of priming. Journal of Memory and Language 55, 534–52.Google Scholar
Kaiser, M.K., Proffitt, D.R., Whelan, S.M., & Hecht, H. (1992) Influence of animation on dynamical judgments. Journal of Experimental Psychology: Human Perception and Performance 18(3), 669.Google Scholar
Koffka, K. (1935) Principles of Gestalt Psychology. New York: Harcourt, Brace.Google Scholar
Köhler, W. (1920/1938) Physical gestalten. In Ellis, W.D. (ed.), A Source Book of Gestalt Psychology, New York: Routledge & Kegan Paul (1754).Google Scholar
Köhler, W. (1925) The Mentality of Apes. New York: Harcourt.Google Scholar
Kong, X., & Shunn, C.S. (2007) Global vs. local information processing in visual/spatial problem solving: the case of Traveling Salesman Problem. Cognitive Systems Research 8, 192207.Google Scholar
Korf, R.E. (1985) Depth-first iterative-deepening: an optimal admissible tree search. Artificial Intelligence 27, 97109.CrossRefGoogle Scholar
Kwon, T., Agrawal, K., Li, Y., & Pizlo, Z. (2016) Spatially-global integration of closed, fragmented contours by finding the shortest-path in a log-polar representation. Vision Research 126, 143–63.Google Scholar
Larsen, A., & Bundesen, C. (1978) Size scaling in visual pattern recognition. Journal of Experimental Psychology: Human Perception and Performance 4, 120.Google Scholar
Lee, B.-S., Pizlo, Z., & Allebach, J.P. (2007) Characterization of red-green and blue-yellow opponent channels. Journal of Imaging, Science and Technology 51, 2333.Google Scholar
Lemons, D.S. (1997) Perfect Form: Variational Principles, Methods, and Applications in Elementary Physics. Princeton University Press.CrossRefGoogle Scholar
Levitin, A., & Levitin, M. (2011) Algorithmic Puzzles. Oxford University Press.CrossRefGoogle Scholar
Lewandowsky, S., Brown, G.D.A., & Thomas, J.L. (2009) Traveling economically through memory space: characterizing output order in memory for serial order. Memory & Cognition 37, 181–93.Google Scholar
Li, Y., Sawada, T., Shi, Y., Kwon, T., & Pizlo, Z. (2011) A Bayesian model of binocular perception of 3D mirror symmetric polyhedra. Journal of Vision 11(4), 1–20.Google Scholar
Lin, S., & Kernighan, B.W. (1973) An effective heuristic algorithm for the traveling-salesman problem. Operations Research 21, 498516.Google Scholar
MacGregor, J.N., & Chu, Y. (2011) Human performance on the traveling salesman and related problems: a review. Journal of Problem Solving 3, 129.Google Scholar
MacGregor, J. N., & Ormerod, T. (1996) Human performance on the Traveling Salesman Problem. Perception & Psychophysics 58, 527–39.Google Scholar
Mach, E. (1906/1959) The Analysis of Sensations. New York: Dover. (Original work published in 1886.)Google Scholar
Mach, E. (1919) The Science of Mechanics. Chicago, il: Open Court. (Original work published in 1883.)Google Scholar
Maier, N.R.F. (1930) Reasoning in humans: on direction. Journal of Comparative Psychology 10, 115–43.Google Scholar
Marr, D. (1982) Vision. New York: W.H. Freeman.Google Scholar
Mascalzoni, E., Regolin, L., Vallortigara, G., & Simion, F. (2013) The cradle of causal reasoning: newborns’ preference for physical causality. Developmental Science 16, 327–35.Google Scholar
McCloskey, M. (1983) Intuitive physics. Scientific American 248, 122–31.Google Scholar
McCloskey, M., Washburn, A., & Felch, L. (1983) Intuitive physics: the straight-down belief and its origin. Journal of Experimental Psychology: Learning, Memory, and Cognition 9, 636–49.Google Scholar
Menzel, E.W. (1973) Chimpanzee spatial memory organization. Science 182, 943–45.Google Scholar
Menzel, E.W., Savage-Rumbaugh, E.S., & Lawson, J. (1985) Chimpanzee (Pan troglodytes) spatial problem solving with the use of mirrors and televised equivalents of mirrors. Journal of Comparative Psychology 99, 211–17.Google Scholar
Michaux, V., Jayadevan, V., Delp, E., & Pizlo, Z. (2016) Figure-ground organization based on 3D symmetry. Journal of Electronic Imaging 25(6).Google Scholar
Michelon, P., & Zacks, J.M. (2006) Two kinds of visual perspective taking. Perception & Psychophysics 68, 327–37.Google Scholar
Michotte, A. (1963) The Perception of Causality. London: Methuen. (Original work published in 1946.)Google Scholar
Miller, G.A. (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychological Review 63, 8197.Google Scholar
Miller, G.A., Galanter, E., & Pribram, K.H. (1960) Plans and the Structure of Behavior. New York: Holt.Google Scholar
Moulin, Hervé (1986) Game Theory for Social Sciences. New York University Press.Google Scholar
Nairne, J. S., & Neath, I. (2013) Sensory and working memory. In Healy, A.F. & Proctor, R.W. (eds.), Comprehensive Handbook of Psychology, vol. iv: Experimental Psychology, 2nd edn, New York: Wiley (419–45).Google Scholar
Neath, I., & Surprenant, A.M. (2003) Human Memory: An Introduction to Research, Data, and Theory. 2nd edn. Belmont, ca: Wadsworth.Google Scholar
Neisser, U. (1967) Cognitive Psychology. New York: Appleton.Google Scholar
Newell, A., & Ernst, G. (1965) The search for generality. In Kalenich, W.A. (ed.), Information Processing: Proceedings of IFIP Congress, vol. i, Washington, DC: Spartan (17–24).Google Scholar
Newell, A., & Simon, H. (1956) The logic theory machine: a complex information processing system. IRE Transactions on Information Theory 2, 6179.Google Scholar
Newell, A., & Simon, H. (1972) Human Problem Solving. Englewood Cliffs, nj: Prentice-Hall.Google Scholar
Newton, I. (1704) Opticks. London: Printed for Sam. Smith, and Benj. Walford.Google Scholar
Nilsson, N.J. (1971) Problem-Solving Methods in Artificial Intelligence. New York: McGraw-Hill.Google Scholar
Nishimura, M., Maurer, D., & Gao, X. (2009) Exploring children’s face-space: a multidimensional scaling analysis of the mental representation of facial identity. Journal of Experimental Child Psychology 103, 355–75.Google Scholar
Noether, E. (1918) Invariante variationsprobleme. Nachrichten von der Gesellschaft der Wissenschaften zu Göttingen, Mathematisch-Physikalische Klasse, 1918, 235–57.Google Scholar
Pan, V.Y. (1997) Solving a polynomial equation: some history and recent progress. SIAM Review 39, 187220.Google Scholar
Pedelty, L., Levine, S.C., & Shevell, S.K. (1985) Developmental changes in face processing: results from multidimensional scaling. Journal of Experimental Child Psychology 39, 421–36.Google Scholar
Pizlo, Z. (1991) Shape constancy in human beings and computers based on a perspective invariant. Ph.D. dissertation. University of Maryland at College Park.Google Scholar
Pizlo, Z. (2001) Perception viewed as an inverse problem. Minireview. Vision Research 41, 3145–61.Google Scholar
Pizlo, Z. (2008) 3D Shape: Its Unique Place in Visual Perception. Cambridge, MA: MIT Press.Google Scholar
Pizlo, Z. (2019) Unifying physics and psychophysics on the basis of symmetry, least-action ≈ simplicity principle, and conservation laws ≈ veridicality. American Journal of Psychology 132, 125.Google Scholar
Pizlo, Z. & de Barros, A. (2021) The concept of symmetry and the theory of perception. Submitted.Google Scholar
Pizlo, Z., & Li, Z. (2003) Pyramid algorithms as models of human cognition. Proceedings of IS&T/SPIE Conference on Computational Imaging, 5016, 112.CrossRefGoogle Scholar
Pizlo, Z., & Li, Z. (2005) Solving combinatorial problems: 15-puzzle. Memory & Cognition 33, 1069–84.Google Scholar
Pizlo, Z., Li, Y., Sawada, T., & Steinman, R.M. (2014) Making a Machine that Sees Like Us. Oxford University Press.Google Scholar
Pizlo, Z., Rosenfeld, A., & Epelboim, J. (1995) An exponential pyramid model of the time course of size processing. Vision Research 35, 1089–107.Google Scholar
Pizlo, Z., & Scheessele, M.R. (1998) Perception of 3-D scenes from pictures. Proceedings of IS&T/SPIE Conference on Human Vision and Electronic Imaging 3299, 410–23.Google Scholar
Pizlo, Z., & Stefanov, E. (2013) Solving large problems with a small working memory. Journal of Problem Solving 6(1), 3443.Google Scholar
Poggio, T., & Koch, C. (1985) Ill-posed problems in early vision: from computational theory to analogue networks. Proceedings of the Royal Society of London B 226, 303–23.Google Scholar
Poggio, T., Torre, V., & Koch, C. (1985) Computational vision and regularization theory. Nature 317, 314–19.Google Scholar
Polya, G. (1945) How to Solve It. Princeton University Press.Google Scholar
Polya, G. (1954) Mathematics and Plausible Reasoning, vol. i. Princeton University Press.Google Scholar
Polya, G. (1962) Mathematical Discovery. New York: Wiley.Google Scholar
Polya, G., & Kilpatrick, J. (1974) The Stanford Mathematics Problem Book. New York: Teachers College Press.Google Scholar
Povinelli, D.J. (2000) Folk Physics for Apes: The Chimpanzee’s Theory of How the World Works. Oxford University Press.Google Scholar
Povinelli, D.J., & Cant, J.G.H. (1995) Arboreal clambering and the evolution of self-conception. Quarterly Review of Biology 70, 393421.Google Scholar
Romney, A.K., Brewer, D.D., & Batchelder, W.H. (1993) Predicting clustering from semantic structure. Psychological Science 4, 2834.Google Scholar
Rosen, J. (2008) Symmetry Rules. Berlin: Springer.Google Scholar
Rosenblueth, A., Wiener, N., & Bigelow, J. (1943) Behavior, purpose and teleology. Philosophy of Science 10, 1824.Google Scholar
Rosenfeld, A., & Thurston, M. (1971) Edge and curve detection for visual scene analysis. IEEE Transactions on Computers C-20, 562–69.Google Scholar
Russell, S.J., & Norvig, P. (2018) Artificial Intelligence: A Modern Approach. Uttar Pradesh: Pearson.Google Scholar
Saalweachter, J., & Pizlo, Z. (2008) Non-Euclidean Traveling Salesman Problem. In Kugler, T., Smith, J.C., Sun, Y.-J., & Connolly, T. (eds.), Decision Modeling and Behavior in Complex and Uncertain Environments, New York: Springer (339–58).Google Scholar
Sanborn, A.N., Mansinghka, V.K., & Griffiths, T.L. (2013) Reconciling intuitive physics and Newtonian mechanics for colliding objects. Psychological Review 120, 411–37.Google Scholar
Sangalli, A. (1992) Why sales reps pose a hard problem. New Scientist 12, 2428.Google Scholar
Schank, R.C., & Abelson, R.P. (1977) Scripts, Plans, Goals and Understanding. Hillsdale, NJ: Erlbaum.Google Scholar
Schrijver, A. (2003) Combinatorial Optimization: Polyhedra and Efficiency. Berlin: Springer.Google Scholar
Schroeder, M. (1991) Fractals, Chaos, Power Laws. New York: Dover.Google Scholar
Searle, J.R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences 3(3), 417–24.Google Scholar
Shannon, C.E. (1948) A mathematical theory of communication. The Bell System Technical Journal 27 (623–56), 379423.Google Scholar
Shepard, R.N. (1962) The analysis of proximities: multidimensional scaling with an unknown distance function. II. Psychometrika 27, 219–46.Google Scholar
Shepard, R.N. (1980) Multidimensional scaling, tree-fitting, and clustering. Science 210, 390–98.Google Scholar
Shepard, R.N. (1981) Psychophysical complementarity. In Kubovy, M. & Pomerantz, J.R. (eds.), Perceptual Organization, Hillsdale, NJ: Lawrence Erlbaum (279341).Google Scholar
Shepard, R.N. (2008) The step to rationality: the efficacy of thought experiments in science, ethics and free will. Cognitive Science 32, 335.Google Scholar
Shepard, R. N., & Metzler, J. (1971) Mental rotation of three-dimensional objects. Science 171 (3972), 701–03.Google Scholar
Simon, H.A. (1969) The Sciences of the Artificial. Cambridge, MA: MIT Press.Google Scholar
Spelke, E.S., Katz, G., Purcell, S.E., Erlich, S.M., & Breinlinger, K. (1994) Early knowledge of object motion: continuity and inertia. Cognition 51, 131–76.Google Scholar
Spelke, E.S., & Kinzler, K.D. (2007) Core knowledge. Developmental Science 10, 8996.Google Scholar
Stavrianos, B.K. (1945) The relation of shape perception to explicit judgments of inclination. Archives of Psychology 296, 194.Google Scholar
Sugihara, K. (1986) Machine Interpretation of Line Drawings. Cambridge, MA: MIT Press.Google Scholar
Tanimoto, S., & Pavlidis, T. (1975) A hierarchical data structure for picture processing. Computer Graphics and Image Processing 4, 104–19.Google Scholar
Tavel, M.A. (1971) Milestones in mathematical physics: Noether’s theorem. Transport Theory and Statistical Physics 1(3), 183207.Google Scholar
Teller, D.Y., & Palmer, J. (2022) Linking Vision and the Visual System. Forthcoming.Google Scholar
Thorndike, E. (1899) The instinctive reactions of young chicks. Psychological Review 6, 282–91.Google Scholar
Tolman, E.C. (1948) Cognitive maps in rats and men. Psychological Review 55, 189208.Google Scholar
Troyer, A.K., Moscovitch, M., & Winocur, G. (1997) Clustering and switching as two components of verbal fluency: evidence from younger and older healthy adults. Neuropsychology 11, 138–46.Google Scholar
Turing, A. (1950) Computing machinery and intelligence. Mind 59, 433–60.Google Scholar
Udrescu, S.M., Tan, A., Feng, J., Neto, O., Wu, T., & Tegmark, M. (2020) AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. arXiv preprint arXiv:2006.10782.Google Scholar
vanDrunen, J., & Pizlo, Z. (2019) The effectiveness of multidimensional scaling in TSPs whose metric is not Euclidean. Poster presented at the Annual Meeting of the Society for Mathematical Psychology.Google Scholar
van Rooij, I., Schactman, A., Kadlec, H., & Stege, U. (2006) Perceptual or analytical processing? Evidence from children’s and adult’s performance on the Euclidean traveling salesperson problem. Journal of Problem Solving 1, 4473.Google Scholar
von Bayern, A.M.P., Heathcote, R.J.P., Rutz, C., & Kacelnik, A. (2009) The role of experience in problem solving and innovative tool use in crows. Current Biology 19, 1965–68.Google Scholar
Waldmann, M.R. (2017) Oxford Handbook of Causal Reasoning. Oxford University Press.Google Scholar
Warren, H.C. (1916) A study of purpose. I. Journal of Philosophy, Psychology and Scientific Methods 13, 526.Google Scholar
Wertheimer, M. (1923/1938) Laws of organization in perceptual forms. In Ellis, W.D. (ed.), A Source Book of Gestalt Psychology, London: Routledge (7188).Google Scholar
Wertheimer, M. (1945) Productive Thinking. New York: Harper.Google Scholar
Wertheimer, M. (1961) Psychomotor coordination of auditory and visual space at birth. Science 1961, 1692.Google Scholar
Weyl, H. (1952) Symmetry. Princeton University Press.Google Scholar
White, P.A. (2012) Visual impressions of causality: effects of manipulating the direction of the target object’s motion in a collision event. Visual Cognition 20, 121–42.Google Scholar
White, P.A. (2017) Visual impressions of causality. In Waldmann, M.R. (ed.), Oxford Handbook of Causal Reasoning, Oxford University Press (245–64).Google Scholar
Wiener, N. (1948) Cybernetics. Cambridge, MA: MIT Press.Google Scholar
Wigner, E.P. (1960) The unreasonable effectiveness of mathematics in the natural sciences. Communications on Pure and Applied Mathematics 13(1), 114.Google Scholar
Wigner, E.P. (1967) Symmetries and reflections. Bloomington, IN: Indiana University Press.Google Scholar
Wimmer, H., & Perner, J. (1983) Beliefs about beliefs: representation and constraining function of wrong beliefs in young children’s understanding of deception. Cognition 13, 103–28.Google Scholar

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  • References
  • Zygmunt Pizlo, University of California, Irvine
  • Book: Problem Solving
  • Online publication: 23 June 2022
  • Chapter DOI: https://doi.org/10.1017/9781009205603.013
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  • References
  • Zygmunt Pizlo, University of California, Irvine
  • Book: Problem Solving
  • Online publication: 23 June 2022
  • Chapter DOI: https://doi.org/10.1017/9781009205603.013
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  • References
  • Zygmunt Pizlo, University of California, Irvine
  • Book: Problem Solving
  • Online publication: 23 June 2022
  • Chapter DOI: https://doi.org/10.1017/9781009205603.013
Available formats
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