AUTOORGANIZACIÓN Y EMERGENCIA DE PATRONES DE CONDUCTAS EN EL RAZONAMIENTO Y EL APRENDIZAJE DESDE LA PERSPECTIVA DE LOS SISTEMAS DINÁMICOS
Palabras clave:
Cognición, Sistemas Dinámicos, Entropía, Emergencia, AutoorganizaciónResumen
En este artículo tres supuestos son descritos para fundamentar la idea que la cognición puede ser entendida como patrones de res- puestas dinámicos no representacionales. El primer supuesto señala que las estructuras se autoorganizan para disipar eficientemente energía e incertidumbre. El segundo supuesto propone que detrás de estos patrones subyacen mecanismos de acoplamiento entre acción y percepción que dan regularidad y flexibilidad a la conducta. Finalmente, el tercer supuesto propone que para transitar flexiblemente de un patrón de respuesta a otro, el cerebro y el cuerpo deben poseer ensamblajes funcionales suaves en vez de ser un sistema rígidamente cableado. Posteriormente es analizada la evidencia empírica que permite afirmar que la aparición de nuevos patrones de conducta en tareas de razonamiento tienen las características de los sistemas dinámicos. En la última parte se discute la diferencia entre emergencia y autoorganización desde una perspectiva informacional y es analizado cómo dos modelos, extraídos de la ecología y la física estadística, pueden ser aplicados en el estudio de razonamiento y el aprendizaje.
Citas
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Bak, P. (1996). How nature works: The science of self-organized criticality. New York: Copernicus-Springer-Verlag.
Bak, P., Tang, C. & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of the 1/f noise. Physical Review Letters, 59(4), 381-384.
Bernstein, N. A. (1967). The co-ordination and regulation of movements. Oxford: Pergamon Press.
Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence 47(1-3), 139-159. doi:10.1016/0004-3702(91)90053-M
Castillo, R. D. & Kloos, H. (2013). Can a Flow-Network Approach Shed Light on Children’s Problem Solving? EcologicalPsychology, 25(3) 281-292.
Castillo, R. D., Van Orden, G. & Kloos, H. (2011). The Embodiment of Time Estimation. En A. Vatakis, A. Esposito, M. Giagkou, F. Cummins & G. Papadelis (Eds.), Time and Time Perception 2010, Lecture Notes in Computer Science, 6789 (pp. 196-206). Berlin Heidelberg: Springer-Verlag
Castillo, R. D. (2014). The Emergence of Cognitive Patterns in Learning: Implementation of an Ecodynamic Approach (Disertación doctoral). University of Cincinnati, OH, USA. Recuperada de http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396531855
Chronicle, E. P., MacGregor, J. N. & Ormerod, T. C. (2004). What makes an insight problem? The role of heuristics, goal conception, and solution recoding in knowledge-lean problems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(1), 14-27.
Dixon, J. A., Stephen, D. G., Boncoddo, R. A., & Anastas, J. (2010). The self-organization of cognitive structure. En B. H. Ross (Ed.), The psychology of learning & motivation, vol. 52 (pp. 343-384). Burlington, MA: Academic Press.
Gershenson, C. & Fernandez, N. (2012). Complexity and information: Measuring emergence, self-organization, and homeostasis at multiple scales. Complexity, 18, 29-44. doi: 10.1002/cplx.21424
Goldfield, E. C. (1991). Soft assembly of an infant locomotor action system. Advances in Psychology, 81, 213-229. doi: 10.1016/S0166-4115(08)60767-0
Gopnik, A., Sobel, D. M., Schulz, L. E. & Glymour, C. (2001). Causal learning mechanism in very young children: Two-, Three-, and four-year-olds infer causal relations from patterns of variation and covariation. Development Psychology, 37(5), 620-629.
Hagmayer, Y., & Waldmann, M. R. (2000). Simulating causal models: The way to structural sensitivity. En L. R. Gleitman & A. K. Joshi (Eds.), Proceedings of the Twenty-Second Annual Conference of the Cognitive Science Society (pp. 214-219). Mahwah, NJ: Erlbaum.
Holden, J. G. (2005). Gauging the fractal dimension of response times from cognitive tasks. En M. A. Riley & G. C. Van Orden (Eds.), Contemporary nonlinear methods for behavioral scientists: A web book tutorial (pp. 267-318). Recuperado de http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp
Holyoak, K. J. & Cheng, P. W. (2011). Causal learning and inferenceas a rational process: the new synthesis. Annual Review of Psychology, 62, 135-163.
Johnson, J. S., Spencer, J. P. & Schöner, G. (2008). Moving to higher ground: The dynamic field theory and the dynamics of visual cognition. En F. Garzón, A. Laakso & T. Gomila (Eds.), Dynamics and Psychology [special issue]. New Ideas in Psychology, 26, 227-251.
Jensen, H. J. (1998). Self-Organised Criticality. Cambridge University Press.
Kello, C. T. & Van Orden, G. (2009). Soft-assembly of sensorimotor function. Nonlinear Dynamics, Psychology, and Life Sciences, 13(1), 57-78.
Kello, C. T., Beltz, B. C., Holden, J. G. & Van Orden, G. (2007). The emergent coordination of cognitive function. Journal of Experimental Psychology: General, 136, 551-568.
Kelso, J. A. S. (1995). Dynamic Patterns: The Self-Organization of Brain and Behavior. Cambridge, MA: The MIT Press
Kloos, H. & Van Orden, G. C. (2009). Soft-assembled mechanisms for the grand theory. En J. P. Spencer, M. Thomas & J. McClelland (Eds.), Toward a New Grand Theory of Development? Connectionism and Dynamics Systems Theory Reconsidered (pp. 253-267). Oxford University Press.
Kloos, H. & Van Orden, G. C. (2010). Voluntary behavior in cognitive and motor tasks. Mind and Matter, 8(1), 19-43.
Knoblich, G., Ohlsson, S. & Raney, G. E. (2001). An eye movement study of insight problem solving. Memory and Cognition, 29, 1000-1009. doi: 10.3758/BF03195762
López-Ruiz, R., Mancini, H. L. & Calbet, X. (1995). A statistical measure of complexity. Physics Letters A, 209, 321-326.
López-Ruiz, R. (2001). Complexity in some physical systems. International Journal of Bifurcation and Chaos, 11, 2669. doi: 10.1142/S0218127401003711
Malone, M., Castillo, R. D., Kloos, H., Holden, J. G. & Richardson, M. J. (2014). Dynamic Structure of Joint-Action Stimulus-Response Activity. PLOS ONE, 9(2), e89032. doi:10.1371/journal.pone.0089032
Ohlsson, S. (1992). Information-processing explanations of insight and related phenomena. En M. Keane & K. Gilhooly (Eds.), Advances in the psychology of thinking (pp. 1-44). London: Harvester-Wheatsheaf.
Ohlsson, S. (2011). Deep learning: How the mind overrides experience. Cambridge: Cambridge University Press.
Öllinger, M., Jones, G., Faber, A. H. & Knoblich, G. (2012). Cognitive Mechanisms of Insight: The Role of Heuristics and Representational Change in Solving the Eight-Coin Problem. Journal of Experimental Psychology: Learning, Memory, and Cognition. Publicación anticipada en línea. doi: 10.1037/a0029194
Prokopenko, M., Boschetti, F. & Ryan, A. J. (2008). An information-Theoretic primer on complexity, self-organization, and emergence. Complexity, 15(1), 11-28. doi: 10.1002/cplx.20249
Richardson, M. J., Shockley, K., Riley, M. R., Fajen, B. R. & Turvey, M. T. (2008). Ecological psychology: Six principles for an embodied-embedded approach to behavior. En P. Calvo & T. Gomila (Eds.), Elsevier handbook of new directions in cognitive science (Section I. The embodied architecture of cognition: Conceptual issues) (pp. 161-190). San Diego, CA, USA: Elsevier.
Riley, M. A. & Turvey, M. T. (2002). Variability and determinism in motor behavior. Journal of Motor Behavior, 34, 99-125.
Robbins, P. & Aydede, M. (2009). A short primer on situated cognition. En P. Robbins & M. Aydede (Eds.), The Cambridge Handbook of Situated Cognition (pp. 3-10). Cambridge: Cambridge University Press.
Smith, L. B. (2005). Cognition as a dynamic system: Principles from embodiment. Developmental Review, 25(3-4), 278-298. doi:10.1016/S1364-6613(03)00156-6
Smith, L. B., & Breazeal, C. (2007). The dynamic lift of developmental process. Developmental Science, 10(1), 61-68.
Smith, L. B. & Jones, S. S. (1993). Cognition without concepts. Cognitive Development, 8, 181-188.
Smith, L.B. & Thelen, E. (2003). Development as a dynamic system. Trends in Cognitive Science, 7, 343-348.
Spencer, J. P., Austin, A. & Schutte, A. R. (2012). Contributions of dynamic systems theory to cognitive development. Cognitive Development, 27, 401-418.
Sporns, O., Gally, J. A., Reeke Jr., G. N. & Edelman, G. M. (1989). Reentrant signaling among simulated neuronal groups leads to coherency in their oscillatory activity. Proceedings of the National Academy of Sciences USA, 86, 7265-7269.
Sporns, O., Tononi, G. & Edelman, G. M. (1991). Modeling perceptual grouping and figure-ground segregation by means of active reentrant circuits. Proceedings of the National Academy of Sciences USA, 88, 129-133.
Sporns, O., Tononi, G. & Edelman, G. M. (2000). Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex, 10, 127-141.
Stephen, D. G., Boncoddo, R. A., Magnuson, J. S. & Dixon, J. A. (2009). The dynamics of insight: Mathematical discovery as a phase transition. Memory & Cognition, 37, 1132-1149.
Stephen, D. G. & Dixon, J. A. (2009). The self-organization of insight: Entropy and power laws in problem solving. Journal of Problem Solving, 2, 72-101.
Stephen, D. G., Dixon, J. A. & Isenhower, R. W. (2009). Dynamics of representational change: Entropy, action, and cognition. Journal of Experimental Psychology: Human Perception and Performance, 35, 1811-1822.
Tenenbaum, J. B., Kemp, C., Griffiths, T. L. & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279-1285. doi: 10.1126/science.1192788
Turvey, M. T. (1990). Coordination. American Psychologist, 45, 938-953.
Turvey, M. T. (2007). Action and perception at the level of synergies. Human Movement Science, 26, 657-697.
Turvey, M. T. & Carello, C. (1996). Dynamics of Bernstein’s level of synergies. En M. Latash. & M. T. Turvey (Eds.), Dexterity and its development (pp. 339-376). Mahwah, NJ: Erlbaum.
Ulanowicz, R. E. (1986). Growth and Development: Ecosystems Phenomenology. Lincoln, NE: iUniverse.com press.
Ulanowicz, R. E. (1998). A phenomenology of evolving networks. Systems Research and Behavioral Science, 15, 373-383.
Ulanowicz, R. E. (2000). Ascendency: A measure of ecosystem performance. En S.E. Joergensen & F. Mueller (Eds.), Handbook of Ecosystem Theories and Management (pp. 303-315). Boca Raton, FL: Lewis Publications.
Ulanowicz, R .E. (2009). The dual nature of ecosystem dynamics. Ecological Modelling, 220, 1886-1892.
Ulanowicz, R. E. (2011). Quantitative Methods for Ecological Network Analysis and Its Application to Coastal Ecosystems. En E. Wolanski & D. S. McLusky (Eds.), Treatise on Estuarine and Coastal Science Vol. 9 (pp. 35-57). Waltham, MA: Academic Press.
Van Geert, P. & Steenbeek, H. (2008). A complexity and dynamic systems approach to developmental assessment, modeling and research. En A. M. Battro, K. W. Fischer & P. Léna (Eds.), The educated brain: essays in neuro-education (pp. 71-94). Cambridge: Cambridge University Press.
Van Orden, G. C., Holden, J. G. & Turvey, M. T. (2003). Selforganization of cognitive performance. Journal of Experimental Psychology: General, 132, 331-350.
Van Orden, G. C., Holden, J. G. & Turvey, M. (2005). Human cognition and 1/ƒ scaling. Journal of Experimental Psychology: General, 134, 117-123.
Waldmann, M. R. (2001). Predictive versus diagnostic causal learning: Evidence from an overshadowing paradigm. Psychological Bulletin and Review, 8, 600-608.
Wilson, R. A. & Clark, A. (2009). How to Situate Cognition: Letting Nature Take its Course. En M. Aydede & P. Robbins (Eds.), The Cambridge Handbook of Situated Cognition (pp. 55-77). Cambridge: Cambridge University Press.
Bak, P. (1996). How nature works: The science of self-organized criticality. New York: Copernicus-Springer-Verlag.
Bak, P., Tang, C. & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of the 1/f noise. Physical Review Letters, 59(4), 381-384.
Bernstein, N. A. (1967). The co-ordination and regulation of movements. Oxford: Pergamon Press.
Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence 47(1-3), 139-159. doi:10.1016/0004-3702(91)90053-M
Castillo, R. D. & Kloos, H. (2013). Can a Flow-Network Approach Shed Light on Children’s Problem Solving? EcologicalPsychology, 25(3) 281-292.
Castillo, R. D., Van Orden, G. & Kloos, H. (2011). The Embodiment of Time Estimation. En A. Vatakis, A. Esposito, M. Giagkou, F. Cummins & G. Papadelis (Eds.), Time and Time Perception 2010, Lecture Notes in Computer Science, 6789 (pp. 196-206). Berlin Heidelberg: Springer-Verlag
Castillo, R. D. (2014). The Emergence of Cognitive Patterns in Learning: Implementation of an Ecodynamic Approach (Disertación doctoral). University of Cincinnati, OH, USA. Recuperada de http://rave.ohiolink.edu/etdc/view?acc_num=ucin1396531855
Chronicle, E. P., MacGregor, J. N. & Ormerod, T. C. (2004). What makes an insight problem? The role of heuristics, goal conception, and solution recoding in knowledge-lean problems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(1), 14-27.
Dixon, J. A., Stephen, D. G., Boncoddo, R. A., & Anastas, J. (2010). The self-organization of cognitive structure. En B. H. Ross (Ed.), The psychology of learning & motivation, vol. 52 (pp. 343-384). Burlington, MA: Academic Press.
Gershenson, C. & Fernandez, N. (2012). Complexity and information: Measuring emergence, self-organization, and homeostasis at multiple scales. Complexity, 18, 29-44. doi: 10.1002/cplx.21424
Goldfield, E. C. (1991). Soft assembly of an infant locomotor action system. Advances in Psychology, 81, 213-229. doi: 10.1016/S0166-4115(08)60767-0
Gopnik, A., Sobel, D. M., Schulz, L. E. & Glymour, C. (2001). Causal learning mechanism in very young children: Two-, Three-, and four-year-olds infer causal relations from patterns of variation and covariation. Development Psychology, 37(5), 620-629.
Hagmayer, Y., & Waldmann, M. R. (2000). Simulating causal models: The way to structural sensitivity. En L. R. Gleitman & A. K. Joshi (Eds.), Proceedings of the Twenty-Second Annual Conference of the Cognitive Science Society (pp. 214-219). Mahwah, NJ: Erlbaum.
Holden, J. G. (2005). Gauging the fractal dimension of response times from cognitive tasks. En M. A. Riley & G. C. Van Orden (Eds.), Contemporary nonlinear methods for behavioral scientists: A web book tutorial (pp. 267-318). Recuperado de http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp
Holyoak, K. J. & Cheng, P. W. (2011). Causal learning and inferenceas a rational process: the new synthesis. Annual Review of Psychology, 62, 135-163.
Johnson, J. S., Spencer, J. P. & Schöner, G. (2008). Moving to higher ground: The dynamic field theory and the dynamics of visual cognition. En F. Garzón, A. Laakso & T. Gomila (Eds.), Dynamics and Psychology [special issue]. New Ideas in Psychology, 26, 227-251.
Jensen, H. J. (1998). Self-Organised Criticality. Cambridge University Press.
Kello, C. T. & Van Orden, G. (2009). Soft-assembly of sensorimotor function. Nonlinear Dynamics, Psychology, and Life Sciences, 13(1), 57-78.
Kello, C. T., Beltz, B. C., Holden, J. G. & Van Orden, G. (2007). The emergent coordination of cognitive function. Journal of Experimental Psychology: General, 136, 551-568.
Kelso, J. A. S. (1995). Dynamic Patterns: The Self-Organization of Brain and Behavior. Cambridge, MA: The MIT Press
Kloos, H. & Van Orden, G. C. (2009). Soft-assembled mechanisms for the grand theory. En J. P. Spencer, M. Thomas & J. McClelland (Eds.), Toward a New Grand Theory of Development? Connectionism and Dynamics Systems Theory Reconsidered (pp. 253-267). Oxford University Press.
Kloos, H. & Van Orden, G. C. (2010). Voluntary behavior in cognitive and motor tasks. Mind and Matter, 8(1), 19-43.
Knoblich, G., Ohlsson, S. & Raney, G. E. (2001). An eye movement study of insight problem solving. Memory and Cognition, 29, 1000-1009. doi: 10.3758/BF03195762
López-Ruiz, R., Mancini, H. L. & Calbet, X. (1995). A statistical measure of complexity. Physics Letters A, 209, 321-326.
López-Ruiz, R. (2001). Complexity in some physical systems. International Journal of Bifurcation and Chaos, 11, 2669. doi: 10.1142/S0218127401003711
Malone, M., Castillo, R. D., Kloos, H., Holden, J. G. & Richardson, M. J. (2014). Dynamic Structure of Joint-Action Stimulus-Response Activity. PLOS ONE, 9(2), e89032. doi:10.1371/journal.pone.0089032
Ohlsson, S. (1992). Information-processing explanations of insight and related phenomena. En M. Keane & K. Gilhooly (Eds.), Advances in the psychology of thinking (pp. 1-44). London: Harvester-Wheatsheaf.
Ohlsson, S. (2011). Deep learning: How the mind overrides experience. Cambridge: Cambridge University Press.
Öllinger, M., Jones, G., Faber, A. H. & Knoblich, G. (2012). Cognitive Mechanisms of Insight: The Role of Heuristics and Representational Change in Solving the Eight-Coin Problem. Journal of Experimental Psychology: Learning, Memory, and Cognition. Publicación anticipada en línea. doi: 10.1037/a0029194
Prokopenko, M., Boschetti, F. & Ryan, A. J. (2008). An information-Theoretic primer on complexity, self-organization, and emergence. Complexity, 15(1), 11-28. doi: 10.1002/cplx.20249
Richardson, M. J., Shockley, K., Riley, M. R., Fajen, B. R. & Turvey, M. T. (2008). Ecological psychology: Six principles for an embodied-embedded approach to behavior. En P. Calvo & T. Gomila (Eds.), Elsevier handbook of new directions in cognitive science (Section I. The embodied architecture of cognition: Conceptual issues) (pp. 161-190). San Diego, CA, USA: Elsevier.
Riley, M. A. & Turvey, M. T. (2002). Variability and determinism in motor behavior. Journal of Motor Behavior, 34, 99-125.
Robbins, P. & Aydede, M. (2009). A short primer on situated cognition. En P. Robbins & M. Aydede (Eds.), The Cambridge Handbook of Situated Cognition (pp. 3-10). Cambridge: Cambridge University Press.
Smith, L. B. (2005). Cognition as a dynamic system: Principles from embodiment. Developmental Review, 25(3-4), 278-298. doi:10.1016/S1364-6613(03)00156-6
Smith, L. B., & Breazeal, C. (2007). The dynamic lift of developmental process. Developmental Science, 10(1), 61-68.
Smith, L. B. & Jones, S. S. (1993). Cognition without concepts. Cognitive Development, 8, 181-188.
Smith, L.B. & Thelen, E. (2003). Development as a dynamic system. Trends in Cognitive Science, 7, 343-348.
Spencer, J. P., Austin, A. & Schutte, A. R. (2012). Contributions of dynamic systems theory to cognitive development. Cognitive Development, 27, 401-418.
Sporns, O., Gally, J. A., Reeke Jr., G. N. & Edelman, G. M. (1989). Reentrant signaling among simulated neuronal groups leads to coherency in their oscillatory activity. Proceedings of the National Academy of Sciences USA, 86, 7265-7269.
Sporns, O., Tononi, G. & Edelman, G. M. (1991). Modeling perceptual grouping and figure-ground segregation by means of active reentrant circuits. Proceedings of the National Academy of Sciences USA, 88, 129-133.
Sporns, O., Tononi, G. & Edelman, G. M. (2000). Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex, 10, 127-141.
Stephen, D. G., Boncoddo, R. A., Magnuson, J. S. & Dixon, J. A. (2009). The dynamics of insight: Mathematical discovery as a phase transition. Memory & Cognition, 37, 1132-1149.
Stephen, D. G. & Dixon, J. A. (2009). The self-organization of insight: Entropy and power laws in problem solving. Journal of Problem Solving, 2, 72-101.
Stephen, D. G., Dixon, J. A. & Isenhower, R. W. (2009). Dynamics of representational change: Entropy, action, and cognition. Journal of Experimental Psychology: Human Perception and Performance, 35, 1811-1822.
Tenenbaum, J. B., Kemp, C., Griffiths, T. L. & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279-1285. doi: 10.1126/science.1192788
Turvey, M. T. (1990). Coordination. American Psychologist, 45, 938-953.
Turvey, M. T. (2007). Action and perception at the level of synergies. Human Movement Science, 26, 657-697.
Turvey, M. T. & Carello, C. (1996). Dynamics of Bernstein’s level of synergies. En M. Latash. & M. T. Turvey (Eds.), Dexterity and its development (pp. 339-376). Mahwah, NJ: Erlbaum.
Ulanowicz, R. E. (1986). Growth and Development: Ecosystems Phenomenology. Lincoln, NE: iUniverse.com press.
Ulanowicz, R. E. (1998). A phenomenology of evolving networks. Systems Research and Behavioral Science, 15, 373-383.
Ulanowicz, R. E. (2000). Ascendency: A measure of ecosystem performance. En S.E. Joergensen & F. Mueller (Eds.), Handbook of Ecosystem Theories and Management (pp. 303-315). Boca Raton, FL: Lewis Publications.
Ulanowicz, R .E. (2009). The dual nature of ecosystem dynamics. Ecological Modelling, 220, 1886-1892.
Ulanowicz, R. E. (2011). Quantitative Methods for Ecological Network Analysis and Its Application to Coastal Ecosystems. En E. Wolanski & D. S. McLusky (Eds.), Treatise on Estuarine and Coastal Science Vol. 9 (pp. 35-57). Waltham, MA: Academic Press.
Van Geert, P. & Steenbeek, H. (2008). A complexity and dynamic systems approach to developmental assessment, modeling and research. En A. M. Battro, K. W. Fischer & P. Léna (Eds.), The educated brain: essays in neuro-education (pp. 71-94). Cambridge: Cambridge University Press.
Van Orden, G. C., Holden, J. G. & Turvey, M. T. (2003). Selforganization of cognitive performance. Journal of Experimental Psychology: General, 132, 331-350.
Van Orden, G. C., Holden, J. G. & Turvey, M. (2005). Human cognition and 1/ƒ scaling. Journal of Experimental Psychology: General, 134, 117-123.
Waldmann, M. R. (2001). Predictive versus diagnostic causal learning: Evidence from an overshadowing paradigm. Psychological Bulletin and Review, 8, 600-608.
Wilson, R. A. & Clark, A. (2009). How to Situate Cognition: Letting Nature Take its Course. En M. Aydede & P. Robbins (Eds.), The Cambridge Handbook of Situated Cognition (pp. 55-77). Cambridge: Cambridge University Press.
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19-03-2015
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D. Castillo, R., & Kloos, H. (2015). AUTOORGANIZACIÓN Y EMERGENCIA DE PATRONES DE CONDUCTAS EN EL RAZONAMIENTO Y EL APRENDIZAJE DESDE LA PERSPECTIVA DE LOS SISTEMAS DINÁMICOS. LÍMITE Revista Interdisciplinaria De Filosofía Y Psicología, 10(34). Recuperado a partir de https://revistalimite.uta.cl/index.php/limite/article/view/46
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