Book contents
- The Cambridge Handbook of Motivation and Learning
- The Cambridge Handbook of Motivation and Learning
- Copyright page
- Epigraph
- Contents
- Figures
- Tables
- Contributors
- Foreword
- Acknowledgments
- Introduction Motivation and Its Relation to Learning
- Part I The Self and Its Impact
- Part II Rewards, Incentives, and Choice
- Part III Interest and Internal Motivation
- Part IV Curiosity and Boredom
- Part V Goals and Values
- Part VI Methods, Measures, and Perspective
- 26 Motivation and Learning
- 27 Addressing the Challenge of Measuring Student Engagement
- 28 Measuring Motivation in Educational Settings
- 29 An Integrative Perspective for Studying Motivation in Relation to Engagement and Learning
- 30 Affordances and Attention
- Index
- References
30 - Affordances and Attention
Learning and Culture
from Part VI - Methods, Measures, and Perspective
Published online by Cambridge University Press: 15 February 2019
Book contents
- The Cambridge Handbook of Motivation and Learning
- The Cambridge Handbook of Motivation and Learning
- Copyright page
- Epigraph
- Contents
- Figures
- Tables
- Contributors
- Foreword
- Acknowledgments
- Introduction Motivation and Its Relation to Learning
- Part I The Self and Its Impact
- Part II Rewards, Incentives, and Choice
- Part III Interest and Internal Motivation
- Part IV Curiosity and Boredom
- Part V Goals and Values
- Part VI Methods, Measures, and Perspective
- 26 Motivation and Learning
- 27 Addressing the Challenge of Measuring Student Engagement
- 28 Measuring Motivation in Educational Settings
- 29 An Integrative Perspective for Studying Motivation in Relation to Engagement and Learning
- 30 Affordances and Attention
- Index
- References
Summary
In this chapter, we draw on Gibson's (1979) description of affordances to consider cultural differences in motivation and learning. We develop the argument that affordances are at the heart of cultural differences. We address the way culture influences both what and how people learn from the affordances that are available to them in their physical and social environments. Brain processes of neural plasticity and psychological learning mechanisms of repetition and connection drawn from the Unified Learning Model (Shell et al., 2010) are used to explain how our brain and memory store knowledge of affordances, as well as the actions needed to take advantage of these affordances. We then discuss the way attention sits at the intersection of motivation and learning, as well as how motivated attention leads to individual and cultural differences in knowledge and use of affordances, both implicitly and volitionally. Finally, the emergence of cultural differences in attention, learning, knowing, and motivation are discussed, with an emphasis on the impact of culture on learning in school.
- Type
- Chapter
- Information
- The Cambridge Handbook of Motivation and Learning , pp. 759 - 782Publisher: Cambridge University PressPrint publication year: 2019
References
Anderson, B. A. (2016). The attention habit: How reward learning shapes attentional selection. Annals of the New York Academy of Science, 1369, 24–39. doi: 10.1111/nyas.12957.Google Scholar
Anselme, P. (2007). Some conceptual problems with the classical theory of behavior. Behavioural Processes, 75, 259–75.CrossRefGoogle Scholar
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W. H. Freeman/Times Books/ Henry Holt & Co.Google Scholar
Bandura, A. (2016). The power of observational learning through social modeling. In Sternberg, R. J., Fisk, S. T., & Foss, D. J. (Eds.), Scientists making a difference: One hundred eminent behavioral and brain scientists talk about their most important contributions (pp. 235–9). New York, NY: Cambridge University Press.Google Scholar
Bereiter, C. & Scardamalia, M. (2014). Knowledge building and knowledge creation: One concept, two hills to climb. In Tan, S. C., So, H. Y., & Yeo, J. (Eds.), Knowledge creation in education (Education Innovation Series). Singapore: Springer. doi: 10.1007/978-981-287-047-6_3.Google Scholar
Berridge, K. C. & Kringelbach, M. L. (2008). Affective neuroscience of pleasure: Reward in humans and animals. Psychopharmacology (Berl), 199(3): 457–80. doi: 10.1007/s00213-008-1099-6.CrossRefGoogle ScholarPubMed
Blumenfeld-Katzir, T., Pasternak, O., Dagan, M., & Assaf, Y. (2011). Diffusion MRI of structural brain plasticity induced by a learning and memory task. PLoS ONE 6(6): e20678. doi: 10.1371/journal.pone.0020678.Google Scholar
Boekaerts, M. & Cascallar, E. (2006). How far have we moved toward the integration of theory and practice in self-regulation? Educational Psychology Review, 18(3), 199–210.CrossRefGoogle Scholar
Bourgeois, A., Chelazzi, L., & Vuilleumier, P. (2016). How motivation and reward learning modulate selective attention. Progress in Brain Research, 229, 325–42. doi: 10.1016/bs.pbr.2016.06.004.CrossRefGoogle ScholarPubMed
Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010). Dopamine in motivational control: Rewarding, aversive, and alerting. Neuron, 68(5), 815–34. doi: 10.1016/j.neuron.2010.11.022.CrossRefGoogle ScholarPubMed
Brooks, D. W. & Shell, D. F. (2006). Working memory, motivation, and teacher-initiated learning. Journal of Science Education and Technology, 15(1), 17–30.CrossRefGoogle Scholar
Brown, A. L., Bransford, J. D., Ferrara, R. A., & Campione, J. C. (1983). Learning, remembering, and understanding. In Flavell, J. H. & Markman, E. M. (Eds.), Handbook of child psychology (4th ed., Vol. 3). New York, NY: Wiley.Google Scholar
Cajete, G. A. (2015). Indigenous community: Rekindling the teaching of the seventh fire. St. Paul, MN: Living Justice Press.Google Scholar
Caporale, N. & Dan, Y. (2009). Spike timing-dependent plasticity: A Hebbian learning rule. Annual Review of Neuroscience, 31, 25–46.CrossRefGoogle Scholar
Chemero, A. (2003). An outline of a theory of affordances. Ecological Psychology, 15(2), 181–95.CrossRefGoogle Scholar
Corbetta, M. (2012). Functional connectivity and neurological recovery. Developmental Psychobiology, 54, 239–53.CrossRefGoogle ScholarPubMed
Cowan, N. (2010). The magical mystery four: How is working memory capacity limited, and Why? Current Directions in Psychological Science, 19(1): 51–7. doi: 10.1177/0963721409359277.Google Scholar
Deco, G., Rolls, E. T., Albantakis, L., & Romo, R. (2013). Brain mechanisms for perceptual and reward-related decision making. Progress in Neurobiology, 103, 194–213.CrossRefGoogle ScholarPubMed
Deloria, V. Jr. & Wildcat, D. (2001). Power and place: Indian education in America. Golden, CO: Fulcrum Resources.Google Scholar
Driemeyer, J., Boyke, J., Gaser, C., Buchel, C., & May, A. (2008) Changes in gray matter induced by learning – Revisited. PLoS ONE 3(7): e2669. doi: 10.1371/journal.pone.0002669.CrossRefGoogle ScholarPubMed
Eccles, J. S. & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–32.Google Scholar
Eck, A., Soh, L.-K., & Shell, D. F. (2016). Investigating differences in wiki-based collaborative activities between student engagement profiles in CS1. Proceedings of the 47th ACM Technical Symposium on Computer Science Education (SIGCSE'2016) (pp. 36–41). New York, NY: ACM. doi: 10.1145/2839509.2844615.CrossRefGoogle Scholar
Erickson, L. C. & Thiessen, E. D. (2015). Statistical learning of language: Theory, validity, and predictions of a statistical learning account of language acquisition. Developmental Review, 37, 66–108.CrossRefGoogle Scholar
Feldman, R., Monakhov, M., Pratt, M., & Ebstein, R. P. (2016). Oxytocin pathway genes: Evolutionary ancient system impacting on human affiliation, sociality, and psychopathology. Biological Psychiatry, 79(3), 174–84. doi: 10.1016/j.biopsych.2015.08.008.CrossRefGoogle ScholarPubMed
Flanigan, A. E., Peteranetz, M. S., Shell, D. F., & Soh, L.-K. (2017). Implicit intelligence beliefs of computer science students: exploring change across the semester. Contemporary Educational Psychology, 48, 179–96. doi: 10.1016/j.cedpsych.2016.10.003.CrossRefGoogle Scholar
Flowerday, T. (2016). Using motivation to teach motivation. In Smith, M. C. & DeFrates-Densch, N. (Eds.), Challenges and innovations in educational psychology teaching and learning (pp. 109–22). Charlotte, NC: Information Age Publishing.Google Scholar
Flowerday, T. & Shell, D. F. (2015). Disentangling the effects of interest and choice on learning, engagement, and attitude. Learning and Individual Differences, 40, 134–40. doi: 10.1016/j.lindif.2015.05.003.CrossRefGoogle Scholar
Flowerday, T., Shell, D. F., & Moreno, R., (2018). Using profiles of motivated strategic self-regulation to understand mathematics achievement of ethnically diverse elementary school students. Manuscript submitted for publication.Google Scholar
Gibson, J. J. (1979). The ecological approach to visual perception. Boston, MA: Houghton Mifflin.Google Scholar
Harmon-Jones, E. & Inzlicht, M. (Eds.) (2016). Social neuroscience: Biological approaches to social psychology. New York, NY: Routledge, Psychology Press.CrossRefGoogle Scholar
Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York, NY: Wiley & Sons.Google Scholar
Hidi, S. (2016). Revisiting the role of rewards in motivation and learning: Implications of neuroscientific research. Educational Psychology Review, 28, 61–93. doi: 10.1007/s10648-015-9307-5.CrossRefGoogle Scholar
Hidi, S. & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41, 111–27.CrossRefGoogle Scholar
Horvitz, J. C. (2000). Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events. Neuroscience, 96(4), 651–6.CrossRefGoogle ScholarPubMed
Husman, J., Brem, S. K., Banegas, S., Duchrow, D. W., & Haque, S. (2015). Learning and future time perspective: The promise of the future–rewarding in the present. In Stolarski, M., Fieulaine, N., & van Beek, W. (Eds.), Time perspective theory; Review, research and application: Essays in Honor of Philip G. Zimbardo (pp. 131–41). Springer International Publishing. doi: 10.1007/978-3-319-07368-2_8.Google Scholar
Husman, J. & Lens, W. (1999). The role of the future in student motivation. Educational Psychologist, 34(2), 113–25.Google Scholar
Husman, J. & Shell, D. F. (2008). Beliefs and perceptions about the future: A measurement of future time perspective. Learning and Individual Differences, 18, 166–75.CrossRefGoogle Scholar
Jack, R. E., Blais, C., Scheepers, C., Schyns, P. G., & Caldara, R. (2009). Cultural confusions show that facial expressions are not universal. Current Biology, 19(18), 1543–8. doi: 10.1016/j.cub.2009.07.051.CrossRefGoogle Scholar
Jairam, D., Kiewra, K. A., Kauffman, D. F., & Zhao, R. (2012). How to study a matrix. Contemporary Educational Psychology, 37, 128–35.CrossRefGoogle Scholar
Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., & Hudspeth, A. J. (2012). Principles of neural science (5th ed.). New York, NY: McGraw-Hill.Google Scholar
Kiani, R. & Shadlen, M. N. (2009). Representation of confidence associated with a decision by neurons in the parietal cortex. Science, 324, 759–64.CrossRefGoogle ScholarPubMed
Kirkham, N. Z., Slemmer, J. A., & Johnson, S. P. (2002). Visual statistical learning in infancy: Evidence for domain-general learning mechanism. Cognition, 83, B35–B42. doi: 10.1016/S0010-0277(02)00004-5.CrossRefGoogle ScholarPubMed
Kóbor, A., Janacsek, K., Takács, A., & Nemeth, D. (2017). Statistical learning leads to persistent memory: Evidence for one-year consolidation. Scientific Reports, 7, Article number: 760. doi: 10.1038/s41598-017-00807-3.Google Scholar
Kolmogorov, A. N. (1950). Foundations of the theory of probability. New York, NY: Chelsea Publishing.Google Scholar
Kuhbandner, C., Lichtenfeld, S., & Pekrun, R. (2011). Always look on the broad side of life: Happiness increases the breadth of sensory memory. Emotion, 11(4), 958–64. doi: 10.1037/a0024075.Google Scholar
Knudsen, E. I. (2007). Fundamental components of attention. Annual Review of Neuroscience, 30, 57–78. doi: 10.1146/annurev.neuro.30.051606.094256.Google Scholar
Matzel, L. D., Hel, F. P., & Miller, R. R. (1988). Information and expression of simultaneous and backward associations: Implications for contiguity theory. Learning and Motivation, 19, 317–44.Google Scholar
Mayer, R. E. & Fiorella, L. (2014). Principles for reducing extraneous processing in multimedia learning: Coherence, signaling, redundancy, special contiguity, and temporal contiguity principles. In Mayer, R. E. (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 279–315). New York, NY: Cambridge University Press.CrossRefGoogle Scholar
McInerney, D. M. & Flowerday, T. (2016). Indigenous issues in education and research: Looking forward. Contemporary Educational Psychology, 47, 1–3.CrossRefGoogle Scholar
Miendlarzewska, E. A., Bavelier, D., & Schwartz, S. (2016). Influence of reward motivation on human declarative memory. Neuroscience and Biobehavioral Reviews, 61, 156–76.CrossRefGoogle ScholarPubMed
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information, Psychological Review, 63, 81–97.Google Scholar
Mitchel, A. D. & Weiss, D. J. (2011). Learning across senses: Cross-modal effects in multisensory statistical learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 37, 1081–91.Google ScholarPubMed
Mooney, J., Seaton, M., Kaur, G., Marsh, H. W., & Yeung, A. S. (2016). Cultural perspectives on Indigenous and non-indigenous Australian students’ school motivation and engagement. Contemporary Educational Psychology, 47, 11–23.CrossRefGoogle Scholar
Na, J., Grossmann, I., Varnum, M. E. W., Kitayama, S., Gonzalez, R., & Nisbett, R. E. (2010). Cultural differences are not always reducible to individual differences. Proceedings of the National Academy of Sciences of the United States of America, 107(14), 6192–7. doi: 10.1073/pnas.1001911107.Google Scholar
Nelson, K. G., Shell, D. F., Husman, J., Fishman, E. J., & Soh, L. K. (2015). Motivational and self-regulated learning profiles of students taking a foundational engineering course. Journal of Engineering Education, 104(1), 74–100. doi: 10.1002/jee.20066.Google Scholar
Numan, M. (2014). Neurobiology of social behavior: Toward an understanding of the prosocial and antisocial brain. Oxford: Academic Press.Google Scholar
Park, D. C. & Huang, C. M. (2010). Culture wires the brain: A cognitive neuroscience perspective. Perspectives on Psychological Science, 5(4), 391–400.CrossRefGoogle Scholar
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review 18(4), 315–41.Google Scholar
Peteranetz, M. S., Flanigan, A. E., Shell, D. F., & Soh, L.-K. (2017). Computational creativity exercises: An avenue for promoting learning in computer science. IEEE Transactions on Education, 60(4), 305–13. doi: 10.1109/TE.2017.2705152.CrossRefGoogle Scholar
Peteranetz, M. S., Flanigan, A. E., Shell, D. F., & Soh, L.-K. (2018). Career aspirations, perceived instrumentality, and achievement in undergraduate computer science courses. Contemporary Educational Psychology, 53, 27–44. doi: 10.1016/j.cedpsych.2018.01.006.CrossRefGoogle Scholar
Renninger, K. A. & Hidi, S. (2016). The power of interest for motivation and engagement. New York, NY: Routledge.Google Scholar
Rotter, J. B. (1966), Generalized expectancies for internal versus external control of reinforcement. Psychology Monographs, 80, 1–28.Google Scholar
Rubie-Davis, C. M. & Peterson, E. R. (2016). Relations between teachers’ achievement over- and underestimation and students’ beliefs for Maori and Pakeha students. Contemporary Educational Psychology, 47, 72–83.CrossRefGoogle Scholar
Sarter, M., Givens, B., & Bruno, J. P. (2001). The cognitive neuroscience of sustained attention: Where top-down meets bottom-up. Brain Research Reviews, 35, 146–60.CrossRefGoogle ScholarPubMed
Schapiro, A. C., Turk-Browne, N. B., Norman, K. A., & Botvinick, M. M. (2016). Statistical learning of temporal community structure in the hippocampus. Hippocampus, 26(1), 3–8. doi: 10.1002/hipo.22523.CrossRefGoogle ScholarPubMed
Schultz, W. (2015). Neuronal reward and decision signals: From theories to data. Physiological Review, 95, 853–951. doi: 10.1152/physrev.00023.Google Scholar
Schunk, D. H. & Zimmerman, B. J. (2013). Self-regulation and learning. In Reynolds, W. M., Miller, G. E., & Weiner, I. B. (Eds.), Handbook of psychology (Vol. 7, pp. 45–68). Hoboken, NJ: John Wiley & Sons.Google Scholar
Seyfartha, R. M. & Cheney, D. L. (2013). Affiliation, empathy, and the origins of theory of mind. Proceedings of the National Academy of Sciences of the United States of America, 110 (Supplement 2), 10349–56. doi: 10.1073/pnas.1301223110.Google Scholar
Shell, D. F., Brooks, D. W., Trainin, G., Wilson, K., Kauffman, D. F., & Herr, L. (2010). The unified learning model: How motivational, cognitive, and neurobiological sciences inform best teaching practices. Dordrecht, Netherlands: Springer.Google Scholar
Shell, D. F., Colvin, C., & Bruning, R. H. (1995). Self-efficacy, attribution, and outcome expectancy mechanisms in reading and writing achievement: Grade level and achievement level differences. Journal of Educational Psychology, 87, 386–98. doi: 10.1037/0022-0663.87.3.386.CrossRefGoogle Scholar
Shell, D. F. & Husman, J. (2008). Control, motivation, affect, and strategic self-regulation in the college classroom: A multidimensional phenomenon. Journal of Educational Psychology, 100(2) 443–59.CrossRefGoogle Scholar
Shell, D. F., Murphy, C. C., & Bruning, R. H. (1989). Self efficacy and outcome expectancy mechanisms in reading and writing achievement. Journal of Educational Psychology, 81, 91, 100. doi: 10.1037/0022-0663.81.1.91.Google Scholar
Shell, D. F. & Soh, L.-K. (2013). Profiles of motivated self-regulation in college computer science courses: Differences in major versus required non-major courses. Journal of Science Education and Technology, 22(6), 899–913.CrossRefGoogle Scholar
Shell, D. F., Soh, L.-K., & Chiriacescu, V. (2015). Modeling self-efficacy as a dynamic cognitive process with the Computational-Unified Learning Model (C-ULM): Implications for cognitive informatics and cognitive computing. International Journal of Cognitive Informatics and Natural Intelligence, 9(3), 1–24. doi: 10.4018/IJCINI.2015070101.CrossRefGoogle Scholar
Shors, T. J. (2014). The adult brain makes new neurons, and effortful learning keeps them alive. Current Directions in Psychological Science, 23(5) 311–18.Google Scholar
Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 50, 1–13, doi: 10.1080/00461520.2014.1002924.CrossRefGoogle Scholar
Skinner, E. A. (1996). A guide to constructs of control. Journal of Personality and Social Psychology, 71, 549–70.CrossRefGoogle ScholarPubMed
Stoffregen, T. A. (2003). Affordances as properties of the animal–environment system. Ecological Psychology, 15(2), 115–34.CrossRefGoogle Scholar
Striepens, N., Matusch, A., Kendrick, K. M., Mihov, Y., Elmenhorst, D., Becker, B., ... Bauer, A. (2014). Oxytocin enhances attractiveness of unfamiliar female faces independent of the dopamine reward system. Psychoneuroendocrinology, 39, 74–87.CrossRefGoogle ScholarPubMed
Stuchlik, A. (2014). Dynamic learning and memory, synaptic plasticity and neurogenesis: An update. Frontiers in Behavioral Neuroscience, 8(Article 106), 1–6. doi: 10.3389/fnbeh.2014.00106.Google Scholar
Sweller, J., Ayres, P. L., & Kalyuga, S. (2011), Cognitive load theory. New York, NY: Springer. doi: 10.1007/978-1-4419-8126-4.CrossRefGoogle Scholar
Tolman, E. C. (1932). Purposive behavior in animal and men. New York, NY: The Century Company.Google Scholar
Turk-Browne, N. B., Scholl, B. J., Chun, M. M., & Johnson, M. K. (2008). Neural evidence of statistical learning: Efficient detection of visual regularities without awareness. Journal of Cognitive Neuroscience, 21, 1934–45.Google Scholar
Vuilleumier, P. (2005). How brains beware: Neural mechanisms of emotional attention. Trends in Cognitive Science, 9, 585–94.Google Scholar
Vuilleumier, P. (2015). Affective and motivational control of vision. Current Opinion in Neurology, 28, 29–35.CrossRefGoogle ScholarPubMed
Vygotsky, L. S. (1980). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Wang, Y., Fariello, G., Gavrilova, M. L., Kinsner, W., Mizoguchi, F., Patel, S., ... Tsumoto, S. (2013). Perspectives on cognitive computers and knowledge processors. International Journal of Cognitive Informatics and Natural Intelligence, 7(3), 1–24. doi: 10.4018/ijcini.2013070101.CrossRefGoogle Scholar
Warner, L. S. (2006). Native ways of knowing: Let me count the ways. Canadian Journal of Native Education, 29(2), 149.Google Scholar
Weinstein, C. E., Husman, J., & Dierking, D. R. (2000). Interventions with a focus on learning strategies. In Boekaerts, M., Pintrich, P. R., & Zeidner, M. (Eds.), Handbook of self-regulation (pp. 727–47). San Diego, CA: Academic Press.Google Scholar
Weisz, J. R. & Stipek, D. J. (1982). Competence, contingency, and the development of perceived control. Human Development, 25, 250–81.Google Scholar
Yin, H. H., Ostlund, S. B., & Balleine, B. W. (2008). Reward-guided learning beyond dopamine in the nucleus accumbens: The integrative functions of cortico-basal ganglia networks. European Journal of Neuroscience, 28(8), 1437–48. doi: 10.1111/j.1460-9568.2008.06422.x.CrossRefGoogle ScholarPubMed
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