Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-7479d7b7d-wxhwt Total loading time: 0 Render date: 2024-07-10T22:21:36.427Z Has data issue: false hasContentIssue false

32 - Neurocomputational Methods

From Models of Brain and Cognition to Artificial Intelligence in Education

from Part III - Education and School-Learning Domains

Published online by Cambridge University Press:  24 February 2022

By
Michael S. C. Thomas  and
Kaska Porayska-Pomsta
Edited by
Olivier Houdé  and
Grégoire Borst
Olivier Houdé
Affiliation:
Université de Paris V
Grégoire Borst
Affiliation:
Université de Paris V
Get access

Summary

In this chapter, we consider computational approaches to understanding learning and teaching. We consider the utility of computational methods in two senses, which we address in separate sections. In Section 32.1, we consider the use of computers to build models of cognition, focusing on the one hand on how they allow us to understand the developmental origins of behaviour and the role of experience in shaping behaviour, and on the other hand on how a particular type of model – artificial neural networks – can uncover the way in which the constraints of brain function likely shape the properties of our cognitive systems. In Section 32.2, we consider the use of computers as tools to aid teaching, in particular in the use of artificial intelligence in education.

Type
Chapter
Information
The Cambridge Handbook of Cognitive Development , pp. 662 - 687
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ahr, E., Borst, G., & Houdé, O. (2016a). The learning brain: Neuronal recycling and inhibition. Zeitschrift für Psychologie, 224, 277–285.CrossRefGoogle Scholar
Ahr, E., Houdé, O., & Borst, G. (2016b). Inhibition of the mirror-generalization process in reading in school-aged children. Journal of Experimental Child Psychology, 145 , 157-165.CrossRefGoogle ScholarPubMed
Ahr, E., Houdé, O., & Borst, G. (2017). Predominance of lateral over vertical mirror errors in reading: A case for neuronal recycling and inhibition. Brain and Cognition, 116, 1-8.CrossRefGoogle Scholar
Aleven, V., & Koedinger, K. R. (2013). Knowledge component approaches to learner modeling. In Sottilare, R., Graesser, A., Hu, X., & Holden, H. (eds.), Design Recommendations for Adaptive Intelligent Tutoring Systems (Vol 1 of Learner Modeling, pp. 165–182). Orlando, FL: US Army Research Laboratory.Google Scholar
Anderson, J. R., Boyle, C. F., Corbett, A. T., & Lewis, M. W. (1990). Cognitive modelling and intelligent tutoring. Artificial Intelligence, 42, 7–49.CrossRefGoogle Scholar
Azevedo, R., & Aleven, V. (eds.) (2013). International Handbook of Metacognition and Learning Technologies. Berlin: Springer International Handbooks of Education.CrossRefGoogle Scholar
Baker, R. (2010). Data mining for education, International Encyclopedia of Education, 7, 112–118.CrossRefGoogle Scholar
Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1, 3–17.Google Scholar
Basu, S., Biswas, G., & Kinnebrew, J. S. (2017). Learner modeling for adaptive scaffolding in a computational thinking-based science learning environment. User Modeling and User-Adapted Interaction, 27, 5–53.CrossRefGoogle Scholar
Baylor, A., & Kim, Y. (2004). Pedagogical agent design: The impact of agent realism, gender, ethnicity, and instructional role. Proceedings of the 7th International Conference on Intelligent Tutoring Systems (pp. 592–603). 30 August–3 September, Maceió, Alagoas, Brazil. Berlin: Springer.Google Scholar
Beal, C. R. (2013). AnimalWatch: An intelligent tutoring system for algebra readiness. In Azevedo, R., & Aleven, V. (eds.), International Handbook of Metacognition and Learning Technologies (Springer International Handbooks of Education, p. 26). Berlin: Springer.Google Scholar
Blackburne, L. K., Eddy, M. D., Kalra, P., Yee, D., Sinha, P., & Gabrieli, J. D. (2014). Neural correlates of letter reversal in children and adults. PLoS ONE, 9, e98386.CrossRefGoogle ScholarPubMed
Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108, 624–652.CrossRefGoogle ScholarPubMed
Botvinick, M. M., & Plaut, D. C. (2004). Doing without schema hierarchies: A recurrent connectionist approach to normal and impaired routine sequential action. Psychological Review, 111, 395–429.CrossRefGoogle ScholarPubMed
Box, G. E. P., & Draper, N. R. (1986). Empirical Model-building and Response Surface. New York: John Wiley & Sons.Google Scholar
Bull, S. (1995). ‘Did I say what I think I said, and do you agree with me?’: Inspecting and questioning the student model. Proceedings of the 7th World Conference on Artificial Intelligence in Education. 16–19 August 1995, Washington, DC.Google Scholar
Bull, S., & Kay, J. (2016). SMILI: A framework for interfaces to learning data in open learner models, learning analytics and related fields. International Journal of Artificial Intelligence in Education, 26, 293–331.CrossRefGoogle Scholar
Campbell, J. I. D. (1994). Architectures for numerical cognition. Cognition, 53, 1–44.CrossRefGoogle ScholarPubMed
Chen, L., Lambon Ralph, M. A., & Rogers, T. T. (2017). A unified model of human semantic knowledge and its disorders. Nature Human Behaviour, 1, 0039.CrossRefGoogle ScholarPubMed
Conati, C., Porayska-Pomsta, K., & Mavrkis, M. (2018). AI in education needs interpretable machine learning: Lessons from open learner modelling. CML 2018 Workshop on Human Interpretability in Machine Learning (WHI 2018). 14 July 2018, Stockholm, Sweden.Google Scholar
Corbett, A. T., Koedinger, K. R., & Anderson, J. R. (1997). Intelligent tutoring systems. In Helander, M. G., Landauer, T. K., & Prabhu, P. (eds.), Handbook of Human-Computer Interaction (pp. 849–874). Amsterdam: Elsevier Science.CrossRefGoogle Scholar
Cukurova, M., Luckin, R., Millán, E., & Mavrikis, M (2018). The NISPI framework: Analysing collaborative problem-solving from students’ physical interactions, Computers and Education, 116, 93–109.CrossRefGoogle Scholar
Davis, R., Shrobe, H., & Szolovits, P. (1993). What is knowledge representation? AI Magazine, 14, 17–33.Google Scholar
Dehaene, S. (2003). The neural basis of the Weber–Fechner law: A logarithmic mental number line. Trends in Cognitive Sciences, 7, 145–147.CrossRefGoogle ScholarPubMed
Dehaene, S. (2005). Evolution of human cortical circuits for reading and arithmetic: The ‘neuronal recycling’ hypothesis. In Dehaene, S., Duhamel, J. R., Hauser, M., & Rizzolatti, G. (eds.), From Monkey Brain to Human Brain (pp. 133–157). Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Dehaene, S., & Cohen, L. (1995). Towards an anatomical and functional model of number processing. Mathematical Cognition, 1, 83–120.Google Scholar
Dias, J., & Paiva, A. (2005). Feeling and reasoning: A computational model for emotional characters. In Bento, C., Cardoso, A., & Dias, G. (eds.), Portuguese Conference on Artificial Intelligence (pp. 127–140). Berlin: Springer.Google Scholar
Elman, J. L., Bates, E. A., Johnson, M. H., Karmiloff-Smith, A., Parisi, D., & Plunkett, K. (1996). Rethinking Innateness: A Connectionist Perspective on Development. Cambridge, MA: MIT Press.Google Scholar
Elman, J. L., & McRae, K. (2017). A model of event knowledge. In Gunzelmann, G., Howes, A., Tenbrink, T., & Davelaar, E. (eds.), Proceedings of the Thirty-Ninth Annual Meeting of the Cognitive Science Society (pp. 337–342). Austin, TX: Cognitive Science Society.Google Scholar
Engelbart, D. C. (1962). Augmenting Human Intellect: A Conceptual Framework. Summary Report AFOSR-3233. Menlo Park, CA: Stanford Research Institute.CrossRefGoogle Scholar
Filippi, R., Karaminis, T., & Thomas, M. S. C. (2014). Language switching in bilingual production: Empirical data and computational modelling. Bilingualism: Language and Cognition, 17, 294–315.CrossRefGoogle Scholar
Gopnik, A., & Bonawitz, E. (2015). Bayesian models of child development. WIREs Cognitive Science, 6, 75–86.CrossRefGoogle ScholarPubMed
Haarmann, H., & Usher, M. (2001). Maintenance of semantic information in capacity-limited item short-term memory. Psychonomic Bulletin & Review, 8, 568–578.CrossRefGoogle ScholarPubMed
Harm, M. W., McCandliss, B. D., & Seidenberg, M. S. (2003). Modeling the successes and failures of interventions for disabled readers. Scientific Studies of Reading, 7, 155–182.CrossRefGoogle Scholar
Harm, M. W., & Seidenberg, M. S. (1999). Phonology, reading acquisition, and dyslexia: Insights from connectionist models. Psychological Review, 106, 491–528.CrossRefGoogle ScholarPubMed
Harm, M. W., & Seidenberg, M. S. (2004). Computing the meanings of words in reading: Cooperative division of labor between visual and phonological processes. Psychological Review, 111, 662–720.CrossRefGoogle ScholarPubMed
Hernandez-Orallo, J., & Vold, K. (2019). AI Extenders: The Ethical and Societal Implications of Humans Cognitively Extended by AI. Palo Alto, CA: Association for the Advancement of Artificial Intelligence.CrossRefGoogle Scholar
Hoffman, P., McClelland, J. L., & Lambon Ralph, M. A. (2018). Concepts, control, and context: A connectionist account of normal and disordered semantic cognition. Psychological Review, 125, 293–328.CrossRefGoogle ScholarPubMed
Houdé, O. (2000). Inhibition and cognitive development: Object, number, categorization, and reasoning. Cognitive Development, 15, 63–73.CrossRefGoogle Scholar
Houdé, O. (2019). 3-System Theory of the Cognitive Brain: A Post-Piagetian Approach. New York: Routledge.CrossRefGoogle Scholar
Howard-Jones, P. (2009). Neuroscience, learning and technology (14–19), BECTA Report.Google Scholar
Krach, S., Hegel, F., Wrede, B., Sagerer, G., Binkofski, F., & Kircher, T. (2008). Can machines think? Interaction and perspective taking with robots investigated via FMRI. PLoS ONE, 3, e2597.CrossRefGoogle ScholarPubMed
Lewandowsky, S. (1993). The rewards and hazards of computer simulations. Psychological Science, 4, 236–243.CrossRefGoogle Scholar
Li, N., Cohen, W. W., Koedinger, K. R., & Matsuda, N. (2011). A machine learning approach for automatic student model discovery. Proceedings of the 4th International Conference on Educational Data Mining (pp. 31–40). 6–8 July 2011, Eindhoven, Netherlands.Google Scholar
Licklider, J. C. (1960). Man-computer symbiosis. IRE Transactions on Human Factors in Electronics, 1, 4–11.CrossRefGoogle Scholar
Long, Y., & Aleven, V. (2013). Supporting students’ self-regulated learning with an open learner model in a linear equation tutor. In Lane, H. C., Yacef, K., Mostow, J., & Pavlik, P. (eds.), Proceedings of the 16th International Conference on Artificial Intelligence in Education, AIED 2013 (pp. 219–228). New York: Springer.Google Scholar
Long, Y., & Aleven, V. (2017). Enhancing learning outcomes through self-regulated learning support with an open learner model. User Modeling and User-Adapted Interaction, 27, 55–88.CrossRefGoogle Scholar
Mabbott, A., & Bull, S. (2006) Student preferences for editing, persuading, and negotiating the open learner model. In Ikeda, M., Ashlay, K., & Chan, T.-W. (eds.), Proceedings of the 8th International Conference on Intelligent Tutoring Systems (ITS’06, pp. 481–490). Berlin: Springer-Verlag.CrossRefGoogle Scholar
Macfdyen, LP., Dawson, S., Pardo, A., & Gasevic, D. (2014). Embracing big data in complex educational systems: The learning analytics imperative and the policy challenge. Research & Practice in Assessment, 9, 17–28.Google Scholar
Mareschal, D., Butterworth, B., & Tolmie, A. (2013). Educational Neuroscience. Oxford: Wiley Blackwell.Google Scholar
Mareschal, D., Johnson, M., Sirios, S., Spratling, M., Thomas, M. S. C., & Westermann, G. (2007). Neuroconstructivism: How the Brain Constructs Cognition. Oxford: Oxford University Press.CrossRefGoogle Scholar
Mareschal, D., & Shultz, T. R. (1999). Development of children’s seriation: A connectionist approach. Connection Science, 11, 149–186.CrossRefGoogle Scholar
Mareschal, D., & Thomas, M. S. C. (2007). Computational modeling in developmental psychology. IEEE Transactions on Evolutionary Computation, 11, 137–150.CrossRefGoogle Scholar
Martinez Maldonado, R., Kay, J., Yacef, K., & Schwendimann, B. (2014). An interactive teachers’ dashboard for monitoring groups in a multi-tabletop learning. International Conference on Intelligent Tutoring Systems (pp. 482–492). 5–9 June 2014, Honolulu, Hawaii.Google Scholar
Mavrikis, M. (2008). Data-driven modelling of students’ interactions in an ILE. Proceedings of the 1st International Conference on Educational Data Mining (pp. 87–96). 20–21 June 2008, Montréal, Canada.Google Scholar
McClelland, J. L., McNaughton, B. L., & O’Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102, 419–457.CrossRefGoogle ScholarPubMed
McCloskey, M. (1991). Networks and theories: The place of connectionism in cognitive science. Psychological Science, 2, 387–395.CrossRefGoogle Scholar
McLeod, P., Plunkett, K., & Rolls, E. T. (1998). Introduction to Connectionist Modelling of Cognitive Processes. New York: Oxford University Press.Google Scholar
Misselhorn, C. (2009) Empathy with inanimate objects and the uncanny valley. Minds & Machines, 19, 345–359.CrossRefGoogle Scholar
Moreno, R., Mayer, R. E., Spires, H. A., & Lester, J. C. (2001), The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents? Cognition and Instruction, 19, 177–213.CrossRefGoogle Scholar
Mori, M. (1970). Bukimi no tani. Energy, 7, 33–35, translated into English by K. F. MacDorman and T. Minato (2005). Proceedings of the Humanoids-2005 workshop: Views of the Uncanny Valley. Tsukuba, Japan.Google Scholar
Mori, M. (2005). On the uncanny valley. Proceedings of the Humanoids-2005 Workshop: Views of the Uncanny Valley. Tsukuba, Japan.Google Scholar
O’Reilly, R. C., Bhattacharyya, R., Howard, M. D., & Ketza, N. (2014). Complementary learning systems. Cognitive Science, 38, 1229–1248.CrossRefGoogle ScholarPubMed
Ohlsson, S., & Mitrovic, A. (2007). Fidelity and efficiency of knowledge representations for intelligent tutoring systems. Technology, Instruction, Cognition and Learning, 5, 101–132.Google Scholar
Pelachaud, C., & Andre, E. (2010). Interacting with embodied conversational agents. In Chen, F., & Jokinen, K. (eds.), Speech Technology (pp. 123–149). New York: Springer Verlag.Google Scholar
Plaut, D. C., McClelland, J. L., Seidenberg, M., & Patterson, K. E. (1996). Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103, 56–115.CrossRefGoogle ScholarPubMed
Porayska-Pomsta, K. (2016). AI as a methodology for supporting educational praxis and teacher metacognition, International Journal of Artificial Intelligence in Education, 26, 679–700.CrossRefGoogle Scholar
Porayska-Pomsta, K., Alcorn, A. M., Avramides, K., Beale, S., Bernardini, S., Foster, M.-E., Frauenberger, C., Pain, H. Good, J., Guldberg, K., Kea-Bright, W., Kossyvaki, L., Lemon, O., Mademtzi, M., Menzies, R., Rajendran, G., Waller, A., Wass, S., & Smith, T. J. (2018). Blending human and artificial intelligence to support autistic children’s social communication skills. ACM Transactions on Human-Computer Interaction (TOCHI), 25, 1–35.CrossRefGoogle Scholar
Porayska-Pomsta, K., & Bernardini, S. (2013). Learner modelled environments. In Price, S., Jewitt, C., & Brown, B. (eds.), The SAGE Handbook of Digital Technology Research (pp. 443–458). Newbury Park, CA: SAGE Publications Ltd.Google Scholar
Porayska-Pomsta, K., & Chryssafidou, E. (2018), Adolescents’ self-regulation during job interviews through an AI coaching environment. In Rosé, C. P., Martínez-Maldonado, R., Hoppe, H. U., Luckin, R., Mavrikis, M., Porayska-Pomsta, K., McLaren, B., & du Boulay, B. (eds.), International Conference on Artificial Intelligence in Education (pp. 281–285). Cham: Springer.Google Scholar
Porayska-Pomsta, K., & Mellish, C. (2013). Modelling human tutors’ feedback to inform natural language interfaces for learning. International Journal of Human–Computer Studies, 71, 703–724.CrossRefGoogle Scholar
Porayska-Pomsta, K., & Rajendran, T. (2019). Accountability in human and artificial intelligence decision-making as the basis for diversity and educational inclusion. In Knox, J., Wang, Y., & Gallagher, M. (eds.), Speculative Futures for Artificial Intelligence and Educational Inclusion (pp. 39–59). Singapore: Springer Nature.CrossRefGoogle Scholar
Porayska-Pomsta, K., Rizzo, P., Damian, I., Baur, T., André, E., Sabouret, N., Jones, H., Anderson, K., & Chryssafidou, E. (2014). Who’s afraid of job interviews? Definitely a question for user modelling. In Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., & Houben, G.-J. (eds.), User Modeling, Adaptation, and Personalization (pp. 411–422). Cham: Springer International Publishing.CrossRefGoogle Scholar
Rajalingham, R., Issa, E. B., Bashivan, P., Kar, K., Schmidt, K., & DiCarlo, J. J. (2018). Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks. The Journal of Neuroscience, 38, 7255–7269.CrossRefGoogle ScholarPubMed
Richardson, F. M., Seghier, M. L., Leff, A. P., Thomas, M. S. C., & Price, C. J. (2011). Multiple routes from occipital to temporal cortices during reading. Journal of Neuroscience, 31, 8239–8247.CrossRefGoogle ScholarPubMed
Ritter, F. E., Tehranchi, F., & Oury, J. D. (2018). ACT‐R: A cognitive architecture for modeling cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 10, e1488.Google ScholarPubMed
Rizzolatti, G., & Craighero, L. (2004), The mirror neuron system. Annual Review of Neuroscience, 27, 169–192.CrossRefGoogle ScholarPubMed
Russell, S., & Norvig, P. (1995). A modern, agent-oriented approach to introductory artificial intelligence. ACM SiGART Bulletin, 6, 24–26.CrossRefGoogle Scholar
Russell, S. J., & Norvig, P. (2003). Artificial Intelligence: A Modern Approach (2nd ed.). Hoboken, NJ: Prentice Hall.Google Scholar
Seidenberg, M. S., & McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523–568.CrossRefGoogle ScholarPubMed
Shultz, T. R. (2003). Computational Developmental Psychology. Cambridge, MA: MIT Press.Google Scholar
Slater, M., Antley, A., Davison, A., Swapp, D., Guger, C., Barker, C., Pistrang, N., & Sanchez-Vives, M. V. (2006). A virtual reprise of the Stanley Milgram obedience experiments. PLoS ONE, 1, e39.CrossRefGoogle ScholarPubMed
Spencer, J. P., Perone, S., & Buss, A. T. (2011). Twenty years and going strong: A dynamic systems revolution in motor and cognitive development. Child Developmental Perspectives, 5, 260–266.CrossRefGoogle Scholar
Spencer, J. P., Thomas, M. S. C., & McClelland, J. L. (2009). Toward a New Unified Theory of Development: Connectionism and Dynamical Systems Theory Re-considered. Oxford: Oxford University Press.CrossRefGoogle Scholar
Storrs, K., Mehrer, J., Walther, A., & Kriegeskorte, N. (2017). Architecture matters: How well neural networks explain IT representation does not depend on depth and performance alone. Poster Presented at the Cognitive Computational Neuroscience Conference. 6–8 September 2017, New York. Available from www2.securecms.com/CCNeuro/docs-0/5928796768ed3f664d8a2560.pdf. Last accessed 17 September 2019.Google Scholar
Thomas, M. S. C., Ansari, D., & Knowland, V. C. P. (2019a). Annual research review: Educational neuroscience: Progress and prospects. Journal of Child Psychology and Psychiatry, 60, 477–492.CrossRefGoogle ScholarPubMed
Thomas, M. S. C., Fedor, A., Davis, R., Yang, J., Alireza, H., Charman, T., Masterson, J., & Best, W. (2019b). Computational modeling of interventions for developmental disorders. Psychological Review, 126, 693–726.CrossRefGoogle ScholarPubMed
Thomas, M. S. C., Forrester, N. A., & Ronald, A. (2013). Modeling socio-economic status effects on language development. Developmental Psychology, 49, 2325–2343.CrossRefGoogle ScholarPubMed
Thomas, M. S. C., Forrester, N. A., & Ronald, A. (2016). Multi-scale modeling of gene–behavior associations in an artificial neural network model of cognitive development. Cognitive Science, 40, 51–99.CrossRefGoogle Scholar
Thomas, M. S. C., Mareschal, D., & Dumontheil, I. (2020). Educational Neuroscience: Development across the Lifespan. London: Psychology Press.CrossRefGoogle Scholar
Thomas, M. S. C., & McClelland, J. L. (2008). Connectionist models of cognition. In Sun, R. (ed.), Cambridge Handbook of Computational Cognitive Modelling. Cambridge: Cambridge University Press.Google Scholar
Tversky, B., & Morrison, J.B. (2002) Animation: Can it facilitate? International Journal of Human–Computer Studies, 57, 247–262.CrossRefGoogle Scholar
Ueno, T., Saito, S., Rogers, T. T., & Lambon, R. (2011). Lichtheim 2: Synthesizing aphasia and the neural basis of language in a neurocomputational model of the dual dorsal-ventral language pathways. Neuron, 72, 385–396.CrossRefGoogle Scholar
Westermann, G., Mareschal, D., Johnson, M. H., Sirois, S., Spratling, M. W., & Thomas, M. S. C. (2007). Neuroconstructivism. Developmental Science, 10, 75–83.CrossRefGoogle ScholarPubMed
Wilkinson, H. R., Smid, C., Morris, S., Farran, E. K., Dumontheil, I., Mayer, S., Tolmie, A., Bell, D., Porayska-Pomsta, K., Holmes, W., Mareschal, D., Thomas, M., & The UnLocke Team (2019). Domain-specific inhibitory control training to improve children’s learning of counterintuitive concepts in mathematics and science. Journal of Cognitive Enhancement, 4, 1–19.Google ScholarPubMed
Woolf, B. (2008). Building Intelligent Tutoring Systems. Burlington, MA: Morgan Kaufman.CrossRefGoogle Scholar
Ziegler, S., Pedersen, M. L., Mowinckel, A. M., & Biele, G. (2016). Modelling ADHD: A review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning. Neuroscience and Biobehavioral Reviews, 71, 633–656.CrossRefGoogle ScholarPubMed
Zorzi, M., Stoianov, I., & Umiltà, C. (2005). Computational modeling of numerical cognition. In Campbell, J. (ed.), Handbook of Mathematical Cognition (pp. 67–84). New York: Psychology Press.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×