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-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-24T21:23:33.641Z Has data issue: false hasContentIssue false

37 - Philosophical Issues in Computational Cognitive Sciences

from Part V - General Discussion

Published online by Cambridge University Press:  21 April 2023

By
Mark Sprevak
Edited by
Ron Sun
Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
Get access

Summary

What counts as a philosophical issue in computational cognitive science? This chapter briefly reviews possible answers before focusing on a specific subset of philosophical issues. These surround challenges that have been raised by philosophers regarding the scope of computational models of cognition. The arguments suggest that there are aspects of human cognition that may, for various reasons, resist explanation or description in terms of computation. The primary targets of these “no go” arguments have been semantic content, phenomenal consciousness, and central reasoning. This chapter reviews the arguments and considers possible replies. It concludes by highlighting the differences between the arguments, their limitations, and how they might contribute to the wider project of estimating the value of ongoing research programs in computational cognitive science.

Keywords

computationcognitionconsciousnessframe problemintentionalityphilosophyrepresentation
Type
Chapter
Information
The Cambridge Handbook of Computational Cognitive Sciences , pp. 1201 - 1227
Publisher: Cambridge University Press
Print publication year: 2023

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

Adams, F., & Aizawa, K. (2021). Causal theories of mental content. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Anderson, J. R. (2007). How Can the Human Mind Occur in a Physical Universe? Oxford: Oxford University Press.Google Scholar
Anderson, M. L. (2014). After Phrenology: Neural Reuse and the Interactive Brain. Cambridge, MA: MIT Press.Google Scholar
Apperly, I. A., & Butterfill, S. A. (2009). Do humans have two systems to track belief and belief-like states? Psychological Review, 116, 953–970.Google Scholar
Baars, B. (1988). A Cognitive Theory of Consciousness. Cambridge: Cambridge University Press.Google Scholar
Baars, B., & Franklin, S. (2003). How conscious experience and working memory interact. Trends in Cognitive Sciences, 7, 166–172.Google Scholar
Barrett, H. C. (2020). Towards a cognitive science of the human: cross-cultural approaches and their urgency. Trends in Cognitive Sciences, 24, 620–638.CrossRefGoogle ScholarPubMed
Barrett, H. C., & Kurzban, R. (2006). Modularity in cognition: framing the debate. Psychological Review, 113, 628–647.Google Scholar
Block, N. (1978). Troubles with functionalism. In Savage, C. W. (Ed.), Perception and Cognition: Issues in the Foundations of Psychology (Minnesota Studies in the Philosophy of Science, Vol. 9, pp. 261–325). Minneapolis, MN: University of Minnesota Press.Google Scholar
Block, N. (1980). What intuitions about homunculi don’t show. Behavioral and Brain Sciences, 3, 425–426.Google Scholar
Block, N. (1986). Advertisement for a semantics for psychology. Midwest Studies in Philosophy, 10, 615–678.Google Scholar
Block, N. (1990). Consciousness and accessibility. Behavioral and Brain Sciences, 13, 596–598.Google Scholar
Block, N. (2007). Consciousness, accessibility, and the mesh between psychology and neuroscience. Behavioral and Brain Sciences, 30, 481–548.Google Scholar
Boden, M. A. (1989). Escaping from the Chinese Room. In Artificial Intelligence in Psychology (pp. 82–100). Cambridge, MA: MIT Press.Google Scholar
Boolos, G., Burgess, J. P., & Jeffrey, R. C. (2002). Computability and Logic (4th ed.). Cambridge: Cambridge University Press.Google Scholar
Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47, 139–159.Google Scholar
Buckner, C. (2021). Black boxes or unflattering mirrors? Comparative bias in the science of machine behaviour. The British Journal for the Philosophy of Science (online). https://doi.org/10.1086/714960Google Scholar
Burge, T. (1986). Individualism and psychology. Philosophical Review, 95, 3–45.CrossRefGoogle Scholar
Carruthers, P. (2006). The Architecture of the Mind. Oxford: Oxford University Press.Google Scholar
Chalmers, D. J. (1996). The Conscious Mind. Oxford: Oxford University Press.Google Scholar
Chalmers, D. J. (2010a). Facing up to the problem of consciousness. In Chalmers, D. J. (Ed.), The Character of Consciousness (pp. 3–4). Oxford: Oxford University Press.CrossRefGoogle Scholar
Chalmers, D. J. (2010b). The two‐dimensional argument against materialism. In Chalmers, D. J. (Ed.), The Character of Consciousness (pp. 141–205). Oxford: Oxford University Press.Google Scholar
Chalmers, D. J. (2010c). Consciousness and its place in nature. In Chalmers, D. J. (Ed.), The Character of Consciousness (pp. 103–139). Oxford: Oxford University Press.CrossRefGoogle Scholar
Chalmers, D. J. (2010d). How can we construct a science of consciousness? In Chalmers, D. J. (Ed.), The Character of Consciousness (pp. 37–58). Oxford: Oxford University Press.Google Scholar
Chalmers, D. J. (2012). A computational foundation for the study of cognition. Journal of Cognitive Science, 12, 323–357.Google Scholar
Chirimuuta, M. (forthcoming). How to Simplify the Brain. Cambridge, MA: MIT Press.Google Scholar
Chomsky, N. (1995). Language and nature. Mind, 104, 1–61.Google Scholar
Chow, S. J. (2013). What’s the problem with the frame problem? Review of Philosophy and Psychology, 4, 309–331.Google Scholar
Clark, A. (2000). A case where access implies qualia? Analysis, 60, 30–38.Google Scholar
Clark, A. (2002). Global abductive inference and authoritative sources, or, how search engines can save cognitive science. Cognitive Science Quarterly, 2, 115–140.Google Scholar
Clark, A. (2016). Surfing Uncertainty: Prediction, Action, and the Embodied Mind. Oxford: Oxford University Press.Google Scholar
Cohen, M. A., & Dennett, D. C. (2011). Consciousness cannot be separated from function. Trends in Cognitive Sciences, 15, 358–364.CrossRefGoogle ScholarPubMed
Cole, D. (2020). The Chinese room argument. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Colombo, M., & Hartmann, S. (2017). Bayesian cognitive science, unification, and explanation. The British Journal for the Philosophy of Science, 68, 451–484.CrossRefGoogle Scholar
Colombo, M., & Seriès, P. (2012). Bayes on the brain – on Bayesian modelling in neuroscience. The British Journal for the Philosophy of Science, 63, 697–723.Google Scholar
Danks, D. (2014). Unifying the Mind: Cognitive Representations as Graphical Models. Cambridge, MA: MIT Press.Google Scholar
Davis, E., & Morgenstern, L. (2004). Introduction: progress in formal commonsense reasoning. Artificial Intelligence, 153, 1–2.Google Scholar
Dehaene, S., & Changeux, J.-P. (2004). Neural mechanisms for access to consciousness. In Gazzaniga, M. (Ed.), The Cognitive Neurosciences III (pp. 1145–1157). Cambridge, MA: MIT Press.Google Scholar
Dehaene, S., & Changeux, J.-P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70, 200–227.CrossRefGoogle ScholarPubMed
Dehaene, S., Changeux, J.-P., Naccache, L., Sackur, J., & Sergent, C. (2006). Conscious, preconscious, and subliminal processing: a testable taxonomy. Trends in Cognitive Sciences, 10, 204–211.Google Scholar
Dennett, D. C. (1978). Why you can’t make a computer that feels pain. Synthese, 38, 415–456.Google Scholar
Dennett, D. C. (1987). The Intentional Stance. Cambridge, MA: MIT Press.Google Scholar
Dennett, D. C. (1991). Consciousness Explained. Boston, MA: Little, Brown & Company.Google Scholar
Dennett, D. C. (1995). The unimagined preposterousness of zombies. Journal of Consciousness Studies, 2, 322–326.Google Scholar
Dennett, D. C. (2001). The zombic hunch: extinction of an intuition? Royal Institute of Philosophy Supplement, 48, 27–43.CrossRefGoogle Scholar
Dennett, D. C. (2013). Intuition Pumps and Other Tools for Thinking. New York, NY: W. W. Norton.Google Scholar
Dennett, D. C. (2017). From Bacteria to Bach and Back: The Evolution of Minds. New York, NY: W. W. Norton.Google Scholar
Dewhurst, J. (2018). Individuation without representation. The British Journal for the Philosophy of Science, 69, 103–116.Google Scholar
Dretske, F. (1981). Knowledge and the Flow of Information. Cambridge, MA: MIT Press.Google Scholar
Dretske, F. (1995). Naturalizing the Mind. Cambridge, MA: MIT Press.Google Scholar
Dreyfus, H. L. (1972). What Computers Can’t Do. New York, NY: Harper & Row.Google Scholar
Dreyfus, H. L. (1991). Being-in-the-World: A Commentary on Heidegger’s Being and Time, Division I. Cambridge, MA: MIT Press.Google Scholar
Dreyfus, H. L. (1992). What Computers Still Can’t Do. Cambridge, MA: MIT Press.Google Scholar
Dreyfus, H. L. (2007). Why Heideggerian AI failed and how fixing it would require making it more Heideggerian. Artificial Intelligence, 171, 1137–1160.CrossRefGoogle Scholar
Dreyfus, H. L., & Dreyfus, S. E. (1988). Making a mind versus modeling the brain: artificial intelligence back at a branchpoint. Daedalus, 117, 15–44.Google Scholar
Egan, F. (2003). Naturalistic inquiry: where does mental representation fit in? In Antony, L. M. & Hornstein, N. (Eds.), Chomsky and His Critics. Oxford: Blackwell.Google Scholar
Egan, F. (2010). Computational models: a modest role for content. Studies in History and Philosophy of Science, 41, 253–259.Google Scholar
Egan, F. (2014). How to think about mental content. Philosophical Studies, 170, 115–135.Google Scholar
Elgin, C. Z. (2017). True Enough. Cambridge, MA: MIT Press.Google Scholar
Eliasmith, C. (2003). Moving beyond metaphors: understanding the mind for what it is. The Journal of Philosophy, 10, 493–520.Google Scholar
Eliasmith, C. (2005). Neurosemantics and categories. In Cohen, H. & Lefebvre, C. (Eds.), Handbook of Categorization in Cognitive Science (pp. 1035–1055). Amsterdam: Elsevier.Google Scholar
Eliasmith, C. (2013). How to Build a Brain: A Neural Architecture for Biological Cognition. Oxford: Oxford University Press.Google Scholar
Fodor, J. A. (1978). Tom Swift and his procedural grandmother. Cognition, 6, 229–247.CrossRefGoogle Scholar
Fodor, J. A. (1980). Searle on what only brains can do. Behavioral and Brain Sciences, 3, 431–432.CrossRefGoogle Scholar
Fodor, J. A. (1983). The Modularity of Mind. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Fodor, J. A. (1990). A Theory of Content and Other Essays. Cambridge, MA: MIT Press.Google Scholar
Fodor, J. A. (1998). Concepts. Oxford: Blackwell.Google Scholar
Fodor, J. A. (2000). The Mind Doesn’t Work That Way. Cambridge, MA: MIT Press.Google Scholar
Fodor, J. A. (2008). LOT2: The Language of Thought Revisited. Oxford: Oxford University Press.Google Scholar
Fodor, J. A., & Lepore, E. (1992). Holism: A Shopper’s Guide. Oxford: Blackwell.Google Scholar
Fodor, J. A., & Pylyshyn, Z. W. (1988). Connectionism and cognitive architecture. Cognition, 28, 3–71.Google Scholar
Ford, K. M., & Pylyshyn, Z. W. (Eds.) (1996). The Robot’s Dilemma Revisited. Norwood, NJ: Ablex.Google Scholar
Frankish, K. (2016). Illusionism as a theory of consciousness. Journal of Consciousness Studies, 23, 11–39.Google Scholar
Freeman, W. J. (2000). How Brains Make Up Their Minds. New York, NY: Columbia University Press.Google Scholar
Gelder, T. van. (1995). What might cognition be, if not computation? The Journal of Philosophy, 91, 345–381.Google Scholar
Gigerenzer, G., Todd, P. M., & ABC Research Group (Eds.) (1999). Simple Heuristics That Make Us Smart. New York, NY: Oxford University Press.Google Scholar
Glymour, C. (2001). The Mind’s Arrows: Bayes Nets and Graphical Causal Models in Psychology. Cambridge, MA: MIT Press.Google Scholar
Godfrey-Smith, P. (2016). Mind, matter, and metabolism. The Journal of Philosophy, 113, 481–506.Google Scholar
Goyal, A., Didolkar, A., Lamb, A., et al. (2021). Coordination among neural modules through a shared global workspace. arXiv:2103.01197.Google Scholar
Graziano, M. S. A. (2016). Consciousness engineered. Journal of Consciousness Studies, 23, 98–115.Google Scholar
Gulick, R. van. (2018). Consciousness. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Harman, G. (1987). (Nonsolipsistic) conceptual role semantics. In Lepore, E. (Ed.), New Directions in Semantics (pp. 55–81). London: Academic Press.Google Scholar
Harnad, S. (1990). The symbol grounding problem. Physica D, 42, 335–346.Google Scholar
Haugeland, J. (1998). Mind embodied and embedded. In Haugeland, J. (Ed.), Having Thought: Essays in the Metaphysics of Mind (pp. 207–240). Cambridge, MA: Harvard University Press.Google Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33, 61–135.Google Scholar
Irvine, E., & Sprevak, M. (2020). Eliminativism about consciousness. In Kriegel, U. (Ed.), The Oxford Handbook of the Philosophy of Consciousness (pp. 348–370). Oxford: Oxford University Press.Google Scholar
Isaac, A. M. C. (2019). The semantics latent in Shannon information. The British Journal for the Philosophy of Science, 70, 103–125.Google Scholar
Johnson-Laird, P. N. (1978). What’s wrong with Grandma’s guide to procedural semantics: a reply to Jerry Fodor. Cognition, 6, 249–261.Google Scholar
Kripke, S. A. (1980). Naming and Necessity. Cambridge, MA: Harvard University Press.Google Scholar
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253.Google Scholar
Lee, J. (2018). Mechanisms, wide functions and content: towards a computational pluralism. The British Journal for the Philosophy of Science (online). https://doi.org/10.1093/bjps/axy061Google Scholar
Lenat, D. B., & Feigenbaum, E. A. (1991). On the thresholds of knowledge. Artificial Intelligence, 47, 185–250.Google Scholar
Lifschitz, V. (2015). The dramatic true story of the frame default. Journal of Philosophical Logic, 44, 163–196.CrossRefGoogle Scholar
Loewer, B. (2017). A guide to naturalizing semantics. In Hale, B., Wright, C., & Miller, A. (Eds.), Companion to the Philosophy of Language (2nd ed., pp. 174–196). New York, NY: John Wiley & Sons.Google Scholar
Lormand, E. (1990). Framing the frame problem. Synthese, 82, 353–374.Google Scholar
Ludwig, K., & Schneider, S. (2008). Fodor’s challenge to the classical computational theory of mind. Mind and Language, 23 (3), 123–143.Google Scholar
Machery, E. (2013). In defense of reverse inference. The British Journal for the Philosophy of Science, 65, 251–267.Google Scholar
Machery, E. (forthcoming). Discovery and confirmation in evolutionary psychology. In Prinz, J. (Ed.), The Oxford Handbook of Philosophy of Psychology. Oxford: Oxford University Press.Google Scholar
Marcus, G., & Davis, E. (2019). Rebooting AI: Building Artificial Intelligence We Can Trust. New York, NY: Penguin Books.Google Scholar
Mashour, G. A., Roelfsema, P. R., Changeux, J.-P., & Dehaene, S. (2020). Conscious processing and the Global Neuronal Workspace hypothesis. Neuron, 105, 776–798.Google Scholar
Maudlin, T. (1989). Computation and consciousness. The Journal of Philosophy, 86, 407–432.Google Scholar
McCarthy, J. (1990). Formalizing Common Sense: Papers by John McCarthy. (Lifschitz, V. L., Ed.). Norwood, NJ: Ablex.Google Scholar
McCarthy, J., & Hayes, P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence. In Meltzer, B. & Michie, D. (Eds.), Machine Intelligence 4 (pp. 463–502). Edinburgh: Edinburgh University Press.Google Scholar
Millikan, R. G. (2004). The Varieties of Meaning. Cambridge, MA: MIT Press.Google Scholar
Mollo, D. C. (2018). Functional individuation, mechanistic implementation: the proper way of seeing the mechanistic view of concrete computation. Synthese, 195, 3477–3497.Google Scholar
Mollo, D. C. (2021). Deflationary realism: representation and idealization in cognitive science. Mind and Language (online). https://doi.org/10.1111/mila.12364CrossRefGoogle Scholar
Morrison, M. (2014). Reconstructing Reality: Models, Mathematics, and Simulations. Oxford: Oxford University Press.Google Scholar
Nagel, T. (1974). What is it like to be a bat? Philosophical Review, 83, 435–450.Google Scholar
Neander, K., & Schulte, P. (2021). Teleological theories of mental content. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Newell, A., & Simon, H. A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Nisbett, R. E. (2003). The Geography of Thought. New York, NY: The Free Press.Google Scholar
Papineau, D. (1987). Reality and Representation. Oxford: Blackwell.Google Scholar
Piccinini, G. (2015). The Nature of Computation. Oxford: Oxford University Press.Google Scholar
Potochnik, A. (2017). Idealization and the Aims of Science. Chicago, IL: University of Chicago Press.Google Scholar
Prinz, J. (2016). Against illusionism. Journal of Consciousness Studies, 23, 186–196.Google Scholar
Putnam, H. (1981). Reason, Truth and History. Cambridge: Cambridge University Press.Google Scholar
Pylyshyn, Z. W. (Ed.). (1987). The Robot’s Dilemma. Norwood, NJ: Ablex.Google Scholar
Ramsey, W. M. (2007). Representation Reconsidered. Cambridge: Cambridge University Press.Google Scholar
Rescorla, M. (2013). Against structuralist theories of computational implementation. The British Journal for the Philosophy of Science, 64, 681–707.Google Scholar
Rescorla, M. (2016). Bayesian sensorimotor psychology. Mind and Language, 31, 3–6.Google Scholar
Rolls, E. T., & Treves, A. (2011). The neural encoding of information in the brain. Progress in Neurobiology, 95, 448–490.Google Scholar
Ryder, D. (2004). SINBAD neurosemantics: a theory of mental representation. Mind and Language, 19, 211–240.Google Scholar
Samuels, R. (1998). Evolutionary psychology and the massive modularity hypothesis. The British Journal for the Philosophy of Science, 49, 575–602.Google Scholar
Samuels, R. (2005). The complexity of cognition: tractability arguments for massive modularity. In Carruthers, P., Laurence, S., & Stich, S. P. (Eds.), The Innate Mind: Vol. I, Structure and Contents (pp. 107–121). Oxford: Oxford University Press.Google Scholar
Samuels, R. (2010). Classical computationalism and the many problems of cognitive relevance. Studies in History and Philosophy of Science, 41, 280–293.Google Scholar
Schank, R. C., & Abelson, R. P. (1977). Scripts, Plans, Goals, and Understanding. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Schneider, S. (2011). The Language of Thought: A New Philosophical Direction. Cambridge, MA: MIT Press.Google Scholar
Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3, 417–424.Google Scholar
Searle, J. R. (1984). Minds, Brains and Science. Cambridge, MA: Harvard University Press.Google Scholar
Searle, J. R. (1990). Is the brain’s mind a computer program? Scientific American, 262, 20–25.Google Scholar
Searle, J. R. (1992). The Rediscovery of the Mind. Cambridge, MA: MIT Press.Google Scholar
Sellars, W. (1962). Philosophy and the scientific image of man. In Colodny, R. (Ed.), Frontiers of Science and Philosophy (pp. 35–78). Pittsburgh, PA: University of Pittsburgh Press.Google Scholar
Shagrir, O. (2012). Structural representations and the brain. The British Journal for the Philosophy of Science, 63, 519–545.Google Scholar
Shagrir, O. (2020). In defense of the semantic view of computation. Synthese, 197, 4083–4108.Google Scholar
Shanahan, M. (1997). Solving the Frame Problem. Cambridge, MA: Bradford Books/MIT Press.Google Scholar
Shanahan, M. (2016). The frame problem. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Shanahan, M., & Baars, B. (2005). Applying global workspace theory to the frame problem. Cognition, 98, 157–176.CrossRefGoogle Scholar
Shea, N. (2013). Naturalising representational content. Philosophy Compass, 8, 496–509.Google Scholar
Shea, N. (2018). Representation in Cognitive Science. Oxford: Oxford University Press.Google Scholar
Shea, N., & Bayne, T. (2010). The vegetative state and the science of consciousness. The British Journal for the Philosophy of Science, 61, 459–484.Google Scholar
Skyrms, B. (2010). Signals. Oxford: Oxford University Press.Google Scholar
Sprevak, M. (2010). Computation, individuation, and the received view on representation. Studies in History and Philosophy of Science, 41, 260–270.Google Scholar
Sprevak, M. (2013). Fictionalism about neural representations. The Monist, 96, 539–560.Google Scholar
Sprevak, M. (2016). Philosophy of the psychological and cognitive sciences. In Humphreys, P. (Ed.), Oxford Handbook for the Philosophy of Science (pp. 92–114). Oxford: Oxford University Press.Google Scholar
Sprevak, M. (2019). Review of Susan Schneider, The Language of Thought: A New Philosophical Direction. Mind, 128, 555–564.Google Scholar
Sterelny, K. (2003). Thought in a Hostile World. Oxford: Blackwell.Google Scholar
Strawson, G. (2010). Mental Reality (2nd ed.). Cambridge, MA: MIT Press.Google Scholar
Strawson, G. (2018). The consciousness deniers. The New York Review of Books.Google Scholar
Sullivan, E. (2019). Understanding from machine learning models. The British Journal for the Philosophy of Science (online). https://doi.org/10.1093/bjps/axz035Google Scholar
Swoyer, C. (1991). Structural representation and surrogative reasoning. Synthese, 87, 449–508.Google Scholar
Tye, M. (2018). Qualia. In Zalta, E. N. (Ed.), The Stanford Encyclopedia of Philosophy. Redwood City, CA: Stanford University Press.Google Scholar
Usher, M. (2001). A statistical referential theory of content: using information theory to account for misrepresentation. Mind and Language, 16, 311–334.CrossRefGoogle Scholar
Wakefield, J. C. (2003). The Chinese room argument reconsidered: essentialism, indeterminacy, and Strong AI. Minds and Machines, 13, 285–319.Google Scholar
Wheeler, M. (2005). Reconstructing the Cognitive World. Cambridge, MA: MIT Press.Google Scholar
Wheeler, M. (2008). Cognition in context: phenomenology, situated robotics and the frame problem. International Journal of Philosophical Studies, 16, 323–349.Google Scholar
Winograd, T. (1972). Understanding natural language. Cognitive Psychology, 3, 1–91.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] 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
×