Skip to main content
SpringerLink
Log in

Neither Noise nor Signal

The Role of Context in Memory Models

  • Conference paper
  • First Online:
Modeling and Using Context (CONTEXT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9405))

  • 908 Accesses

Abstract

Context plays a crucial role in learning and memory, but a satisfactory characterization of this role in models of memory remains elusive. Classical and recent studies show that context cannot be meaningfully treated as either the figure or the ground, the noise or the signal, in memory models. This impasse belies certain cognitivist assumptions common to traditional cognitive science. A number of postcognitivist movements in philosophy and cognitive science have offered effective critiques of the basic framework, often borrowed in memory science, that depicts the human cognitive system as a dimensionless executive control unit receiving and transforming signals as input from the environment. These revisionary movements have also offered up alternative dynamic approaches to cognitive modeling and explanation, which can and should be deployed in memory science in order to resolve the impasse surrounding the modeling of context and memory.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    It is true that there has been some revisionary language in memory science lately: ‘persistence’ rather than ‘storage’, reconsolidation instead of ‘consolidation’, but I have argued elsewhere that these changes–although indicative of an admirable spirit–have so far been little other than cosmetic [19].

  2. 2.

    Matthew Sanders and colleagues make this explicit, writing: “In order for context conditioning to proceed, the various stimuli in the environment must be associated with one another as the context. Therefore, we have proposed that one function of the hippocampus is to assemble a contextual representation...” [6, pp. 220].

  3. 3.

    Connectionism, of the medium-strength variety, is the endorsement of neural network architectures as robustly informative for those who would study minds. That is, there are few who doubt that neural network architectures have something to do with cognition, a least at the “subsymbolic” level, and there are few who think that neural network architectures have already solved all of our puzzles about cognition, but many among the postcognitivist vanguard stand on the middle ground here, urging that cognitive scientists and philosophers of mind have real and revisionary lessons to learn by paying close attention to the way that neural networks actually function.

  4. 4.

    Although I am here taking my philosophical cue from Wittgenstein, this point brings to mind some particularly Heideggerian critiques of standard cognitive science, given Heidegger’s insistence that we are always already involved in the world, as Dreyfus often points out [23]. An excellent treatment of this Heideggerian/Wittgensteinian point is a recent monograph by Lee Braver [16].

  5. 5.

    This is not to say that informational encapsulation has no place in models, or that a given explanans may not fruitfully be broken up into component “systems”–rather it is something like a shift in the burden of proof, a consistently suspicious attitude toward over-modularizing that which can be done in continuous and dynamic terms.

  6. 6.

    Indeed, adequately characterizing just what explanation consists in is a notorious enough difficulty that I take it that any identification of explanation and information processing-style models can only be a tacit, uncritical move.

  7. 7.

    This is not to suggest that language-involving cognitive phenomena are necessarily beyond the ken of dynamic systems modeling techniques, only that there is good reason to expect successful models of these to be among the late-comers to the dynamic systems table.

  8. 8.

    As characterized above, much of these experiments take the form of tracking the help and hindrance of “context cues” for declarative memory–simple context cue experiments such as those that study positive or negative intereference in semantic memory may be amenable, for example, in the same way that “cognitive control” was found to be in a recent study by Todd Braver [35].

References

  1. Fanselow, M.S.: Context: whats so special about it. In: Science of Memory: Concepts, pp. 101–105. Oxford University Press, New York (2007)

    Google Scholar 

  2. Bouton, M.E.: The multiple forms of context in associative learning theory. In: The Mind in Context, pp. 233–258. Guilford Press, New York (2010)

    Google Scholar 

  3. Smith, S.M.: Context: a reference for focal experience. In: Science of Memory: Concepts, pp. 111–114. Oxford University Press, New York (2007)

    Google Scholar 

  4. Myers, C.E., Gluck, M.A.: Context, conditioning, and hippocampal rerepresentation in animal learning. Behav. Neurosci. 108(5), 835 (1994)

    Article  Google Scholar 

  5. Matus-Amat, P., Higgins, E.A., Barrientos, R.M., Rudy, J.W.: The role of the dorsal hippocampus in the acquisition and retrieval of context memory representations. J. Neurosci. 24(10), 2431–2439 (2004)

    Article  Google Scholar 

  6. Sanders, M.J., Wiltgen, B.J., Fanselow, M.S.: The place of the hippocampus in fear conditioning. Eur. J. Pharmacol. 463(1), 217–223 (2003)

    Article  Google Scholar 

  7. Spencer, W.D., Raz, N.: Differential effects of aging on memory for content and context: a meta-analysis. Psychol. Aging 10(4), 527 (1995)

    Article  Google Scholar 

  8. Fanselow, M.S.: Contextual fear, gestalt memories, and the hippocampus. Behav. Brain Res. 110(1), 73–81 (2000)

    Article  Google Scholar 

  9. De Bot, K., Lowie, W., Verspoor, M.: A dynamic systems theory approach to second language acquisition. Bilingualism Lang. Cogn. 10(1), 7 (2007)

    Article  Google Scholar 

  10. Spencer, J.P., Thomas, M.S.C., McClelland, J.L.: Toward a unified theory of development. In: Spencer, J.P., Thomas, M.S.C., McClelland, J.L. (Eds.) Connectionism and Dynamic Systems Theory Re-Considered, pp. 86–118. Oxford University Press, Oxford, England (2009)

    Google Scholar 

  11. Hutchins, E.: Cognition in the Wild. MIT press, Cambridge (1995)

    Google Scholar 

  12. Shapiro, L.: Embodied Cognition. Routledge, London (2010)

    Google Scholar 

  13. Michaelian, K., Sutton, J.: Distributed cognition and memory research: history and current directions. Rev. Philos. Psychol. 4(1), 1–24 (2013)

    Article  Google Scholar 

  14. Bringsjord, S.: Computationalism is dead; now what? J. Exp. Theor. Artif. Intell. 10(4), 393–402 (1998)

    Article  Google Scholar 

  15. Chemero, A.: Radical Embodied Cognitive Science. MIT press, Cambridge (2011)

    Google Scholar 

  16. Braver, L.: Groundless grounds: a study of Wittgenstein and Heidegger. MIT Press, Cambridge (2012)

    Book  Google Scholar 

  17. Stern, D.G.: Models of memory: Wittgenstein and cognitive science. Philos. Psychol. 4(2), 203–218 (1991)

    Article  Google Scholar 

  18. Brockmeier, J.: After the archive: remapping memory. Cult. Psychol. 16(1), 5–35 (2010)

    Article  Google Scholar 

  19. OLoughlin, I.: The persistence of storage: language and concepts in memory research. In: European Perspectives on Cognitive Science (2011)

    Google Scholar 

  20. Houghton, G., Hartley, T., Glasspool, D.W.: The representation of words and nonwords in short-term memory: Serial order and syllable structure. In: Models of Short-term Memory, pp. 101–127 (1996)

    Google Scholar 

  21. Slotnick, S.D., Dodson, C.S.: Support for a continuous (single-process) model of recognition memory and source memory. Mem. Cogn. 33(1), 151–170 (2005)

    Article  Google Scholar 

  22. Michaelian, K.: Generative memory. Philos. Psychol. 24(3), 323–342 (2011)

    Article  MathSciNet  Google Scholar 

  23. Dreyfus, H.L.: What Computers Still Can’t Do: A Critique of Artificial Reason. MIT Press, Cambridge (1992)

    Google Scholar 

  24. Noë, A.: Action in Perception. MIT press, Cambridge (2004)

    Google Scholar 

  25. Hutto, D.: Folk psychology as narrative practice. J. Conscious. Stud. 16(6–8), 9–39 (2009)

    Google Scholar 

  26. Bennett, M.R., Hacker, P.M.S.: Philosophical Foundations of Neuroscience. Blackwell Publishing, Oxford (2003)

    Google Scholar 

  27. Clark, A.: Mindware: An Introduction to the Philosophy of Cognitive Science. Oxford University Press Inc, Oxford/New York (2013)

    Google Scholar 

  28. Wittgenstein, L.: Philosophical Investigations. John Wiley & Sons, New York (2010)

    MATH  Google Scholar 

  29. Van Geert, P.L.C., Fischer, K.W., Spencer, J.P., Thomas, M.S.C., McClelland, J.: Dynamic systems and the quest for individual-based models of change and development. In: Toward a Unified Theory of Development: Connectionism and Dynamic Systems Theory Reconsidered, pp. 313–336 (2009)

    Google Scholar 

  30. Eich, E.: Context: mood, memory, and the concept of context. In: Science of Memory: Concepts, pp. 107–110. Oxford University Press, New York 2007

    Google Scholar 

  31. Newell, K.M., Liu, Y.T., Mayer-Kress, G.: Time scales in motor learning and development. Psychol. Rev. 108(1), 57 (2001)

    Article  Google Scholar 

  32. Brunel, N.: Hebbian learning of context in recurrent neural networks. Neural comput. 8(8), 1677–1710 (1996)

    Article  Google Scholar 

  33. Wills, T.J., Lever, C., Cacucci, F., Burgess, N., O’Keefe, J.: Attractor dynamics in the hippocampal representation of the local environment. Science 308(5723), 873–876 (2005)

    Article  Google Scholar 

  34. Li, G., Ramanathan, K., Ning, N., Shi, L., Wen, C.: Memory dynamics in attractor networks. Comput. Intell. Neurosci. 2015, 1–7 (2015)

    Google Scholar 

  35. Braver, T.S.: The variable nature of cognitive control: a dual mechanisms framework. Trends Cogn. Sci. 16(2), 106–113 (2012)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Pacific University, Forest Grove, OR, USA

    Ian O’Loughlin

Authors
  1. Ian O’Loughlin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ian O’Loughlin .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Roskilde University, Roskilde, Denmark

    Henning Christiansen

  2. ENS-Pavillon Jardin, CNRS-Institut Jean-Nicod, Paris, France

    Isidora Stojanovic

  3. Department of Computer Science, University of Cyprus, Aglantzia, Cyprus

    George A. Papadopoulos

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

O’Loughlin, I. (2015). Neither Noise nor Signal. In: Christiansen, H., Stojanovic, I., Papadopoulos, G. (eds) Modeling and Using Context. CONTEXT 2015. Lecture Notes in Computer Science(), vol 9405. Springer, Cham. https://doi.org/10.1007/978-3-319-25591-0_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25591-0_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25590-3

  • Online ISBN: 978-3-319-25591-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation