Skip to main content

Advertisement

SpringerLink
Log in

Beyond cognitive myopia: a patchwork approach to the concept of neural function

  • S.I.: Neuroscience and Its Philosophy
  • Published:
Synthese Aims and scope Submit manuscript

We must actively frame semantic pictures if we hope to improve our usage through other means than brute trial and error, but it is easily possible to lean upon portraits that are quite badly mistaken or shortsighted.

—— Mark Wilson

Abstract

In this paper, I argue that looking at the concept of neural function through the lens of cognition alone risks cognitive myopia: it leads neuroscientists to focus only on mechanisms with cognitive functions that process behaviorally relevant information when conceptualizing “neural function”. Cognitive myopia tempts researchers to neglect neural mechanisms with noncognitive functions which do not process behaviorally relevant information but maintain and repair neural and other systems of the body. Cognitive myopia similarly affects philosophy of neuroscience because scholars overlook noncognitive functions when analyzing issues surrounding e.g., functional decomposition or the multifunctionality of neural structures. I argue that we can overcome cognitive myopia by adopting a patchwork approach that articulates cognitive and noncognitive “patches” of the concept of neural function. Cognitive patches describe mechanisms with causally specific effects on cognition and behavior which are likely operative in transforming sensory or other inputs into motor outputs. Noncognitive patches describe mechanisms that lack such specific effects; these mechanisms are enabling conditions for cognitive functions to occur. I use these distinctions to characterize two noncognitive functions at the mesoscale of neural circuits: subsistence functions like breathing are implemented by central pattern generators and are necessary to maintain the life of the organism. Infrastructural functions like gain control are implemented by canonical microcircuits and prevent neural system damage while cognitive processing occurs. By adding conceptual patches that describe these functions, a patchwork approach can overcome cognitive myopia and help us explain how the brain’s capacities as an information processing device are constrained by its ability to maintain and repair itself as a physiological apparatus.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Adapted from Douglas and Martin (Douglas and Martin 1991, Fig. 5)

Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. Perhaps cognitive myopia is more problematic in philosophy of neuroscience than in neuroscientific practice as a whole. Philosophers often narrowly focus on cognitive, computational and behavioral neuroscience research. An exception is Craver and Robins (2009) and Bechtel and Abrahamsen (2009) on biological clocks.

  2. Patchwork approaches share this broadly naturalistic approach to concepts with a growing number of philosophers that focus on the practical roles that concepts play in scientific measurement (Chang 2004), problem solving (Nersessian 2008), experimentation and modeling (see Feest and Steinle 2012; Rouse 2015).

  3. The historical analyses of the information concept in neuroscience by Garson (2003) and Christen (2006, ch. 3) show that this emphasis on functional value both preceded and remained more important than the purely quantitative and non-semantic notion of “information” from Shannon’s information theory.

  4. I thank Daniel Burnston and an anonymous reviewer for pressing me to clarify this point.

  5. I restrict this claim to environmental information here because I focus on clear cases of noncognitive functions. Neural structures representing information about the organism itself may present interesting intermediate cases. For example: in the hypothalamus, neural circuits maintain energy homeostasis by representing the energy requirements of the body based on gastrointestinal signals with different time-scales (Beutler et al. 2017).

  6. Neuromodulators are signaling molecules that influence the conductance properties of many neurons over long periods of time. In the intact animal, some combination of neuromodulators is necessary to initiate CPG rhythm generation (Marder 2012).

  7. Sometimes the term “gain control” appears in explanations of how neurons adapt their responses to variations in sensory input (e.g. contrast or luminance in the visual system, cf. Heeger 1992). As will become clear below, this multiplicative effect to enhance narrow information processing is better described as neural gain modulation, whereas gain control refers to the divisive and switch-like effect to prevent circuit damage.

  8. Further constraints can be added by including other infrastructural functions such as the role of microglia cells to remove amyloid plaques when stimulated in the gamma frequency (Iaccarino et al. 2016). Plaque removal enables and constrains learning and memory mechanisms because it prevents onset of neurodegenerative diseases.

  9. I thank an anonymous reviewer for pressing me to clarify why causal role theories are insufficient to overcome cognitive myopia.

  10. Patchwork approaches thus differ from holist theories which posit a unified theory or essentialist theories that posit a universal referential relation to a natural kind to explain how concepts acquire their meaning. Patchwork approaches are better suited to analyse how concepts behave in scientific practice, where exploratory concept formation and changes of reference occur frequently (see Haueis 2018, chs. 2 and 3 for historical examples).

  11. Both properties have been suggested as additions to a bare bones causal role theory of neural function (Garson 2011; McCaffrey 2015). I aim to use both as constraints on the patchwork structure of “neural function”, since each may be useful for functional analysis in different research contexts.

  12. The fact that cortical microcircuits as a whole execute both cognitive and infrastructural functions makes them multifunctional structures (McCaffrey 2015). I discuss the issue of multifunctionality further in the conclusion.

  13. An example is the effect of global changes of neural gain on attention and learning (Eldar et al. 2013). Unlike the enabling condition of local gain control, it has a graded influence on task performance. Unlike operative conditions such as FEF eye gaze control, however, it affects many cognitive tasks at once.

References

  • Abbott, L. F., & Chance, F. S. (2005). Drivers and modulators. Progress in Brain Research, 149, 147–155.

    Article  Google Scholar 

  • Akagi, M. (2017). Rethinking the problem of cognition. Synthese. https://doi.org/10.1007/s11229-017-1383-2.

    Google Scholar 

  • Appelhans, B. M., & Luecken, L. J. (2006). Heart rate variability as an index of regulated emotional responding. Reviews General Psychology, 10(3), 229–240.

    Article  Google Scholar 

  • Bartos, M., Vida, I., & Jonas, P. (2007). Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks. Nature Reviews Neuroscience, 8(1), 45–56.

    Article  Google Scholar 

  • Bechtel, W. (2008). Mental mechanisms. Philosophical perspectives on cognitive neuroscience. New York: Routledge.

    Google Scholar 

  • Bechtel, W. (2013). The endogenously active brain: The need for an alternative cognitive architecture. Philosophia Scientiae, 17, 3–30.

    Article  Google Scholar 

  • Bechtel, W., & Abrahamsen, A. (2009). Decomposing, recomposing, and situating circadian mechanisms: Three tasks in developing mechanistic explanations. In H. Leitgeb & A. Hieke (Eds.), Reduction and elimination in philosophy of mind and philosophy of neuroscience (pp. 173–186). Frankfurt: Ontos Verlag.

    Google Scholar 

  • Bechtel, W., & Richardson, B. (2010). Discovering complexity. Decomposition and localization as strategies in scientific research (2nd ed.). Cambridge, MA: MIT Press.

    Google Scholar 

  • Beutler, L., Chen, Y., Ahn, J. S., Lin, Y.-C., Essner, R. A., & Knight, Z. A. (2017). Dynamics of gut-brain communication underlying hunger. Neuron, 96(2), 461–475.

    Article  Google Scholar 

  • Bickle, J. (2003). Philosophy and neuroscience: A ruthlessly reductionist account. Dordrecht: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Briggmann, K. L., & Kristan, W. B. (2008). Multifunctional pattern-generating circuits. Annual Reviews Neuroscience, 31, 271–294.

    Article  Google Scholar 

  • Burnston, D. (2016). A contextualist approach to functional localization in the brain. Biology and Philosophy, 31(4), 527–550.

    Article  Google Scholar 

  • Bursten, J. (2016). Smaller than a breadbox: Scale and natural kinds. British Journal of Philosophy of Science. https://doi.org/10.1093/bjps/axw022.

    Google Scholar 

  • Cao, R. (2011). A teleosemantic approach to information in the brain. Biology and Philosophy. https://doi.org/10.1007/s10539-011-9292-0.

    Google Scholar 

  • Cao, R. (2014). Signaling in the brain: In search of functional units. Philosophy of Science, 81(5), 891–901.

    Article  Google Scholar 

  • Chang, H. (2004). Inventing temperature. Measurement and scientific progress. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Chang, K., Leopold, D. A., Schölvinck, M. L., Mandelkow, H., Picchioni, D., Liu, X., et al. (2016). Tracking brain arousal with fMRI. PNAS, 113(16), 4518–4523.

    Article  Google Scholar 

  • Christen, M. (2006). The role of spike patterns in neuronal information processing: A historically embedded conceptual clarification. Dissertation, ETH Zürich.

  • Cramer, N. P., Li, Y., & Keller, A. (2007). The whisking rhythm generator: A novel mammalian network for the generation of movement. Journal of Neurophysiology, 97(3), 2148–2158.

    Article  Google Scholar 

  • Craver, C. F. (2007). Explaining the brain. Mechanisms and the mosaic unity neuroscience. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Craver, C. F. (2016). Optogenetics and maker’s knowledge: Foundations for a theory of translational progress. In Presented paper at the 25th biennial meeting of the Philosophy of Science Association, November 3–5 2016, Atlanta, USA.

  • Craver, C. F. & Robins, S. K. (2009). Biological mlocks: Explaining with models of mechanisms. In Bickle, J. (Ed.), Oxford Handbook of Philosophy and Neuroscience, (pp. 41–67). Oxford: Oxford University Press.

    Google Scholar 

  • Cummins, R. (1975). Functional analysis. Journal of Philosophy, 72(20), 741–765.

    Article  Google Scholar 

  • De Felipe, J. (1999). Chandelier cells and epilepsy. Brain, 122(10), 1807–1822.

    Article  Google Scholar 

  • deCharms, R. C., & Zador, A. (2000). Neural representation and the cortical code. Annual Reviews Neuroscience, 23, 613–647.

    Article  Google Scholar 

  • Douglas, R. D., & Martin, K. A. (1991). A functional microcircuit for cat visual cortex. Journal of Physiology, 440, 735–769.

    Article  Google Scholar 

  • Ebbesen, C. L., Doron, G., Lenschow, C., & Brecht, M. (2017). Vibrissa motor cortex activity suppresses contralateral whisking behavior. Nature Reviews Neuroscience, 20(1), 82–89.

    Article  Google Scholar 

  • Eldar, E., Cohen, J. D., & Niv, Y. (2013). The effects of neural gain on attention and learning. Nature Neuroscience, 16(8), 1146–1153.

    Article  Google Scholar 

  • Engl, E., & Atwell, D. (2015). Non-signalling energy use in the brain. Journal of Physiology, 593(16), 3417–3429.

    Article  Google Scholar 

  • Feest, U., & Steinle, F. (Eds.). (2012). Concepts in investigative practice. Berlin: de Gruyter.

    Google Scholar 

  • Fox, M. D., Snyder, A. Z., Vincent, J. L., & Raichle, M. E. (2007). Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior. Neuron, 56, 171–184.

    Article  Google Scholar 

  • Garson, J. (2003). The introduction of information into neurobiology. Philosophy of Science, 70, 926–936.

    Article  Google Scholar 

  • Garson, J. (2011). Selected effects and causal role functions in the brain: The case for an etiological approach to cognitive neuroscience. Biology and Philosophy, 26, 547–565.

    Article  Google Scholar 

  • Ghose, G. M., & Freeman, R. D. (1992). Oscillatory discharge in the visual system: Does it have a functional role? Journal of Neurophysiology, 68(5), 1558–1578.

    Article  Google Scholar 

  • Gould, S. J., & Vrba, E. S. (1982). Exaptation—A missing term in the science of form. Paleobiology, 8(1), 4–15.

    Article  Google Scholar 

  • Grassmann, M., Vlemincx, E., von Leupoldt, A., Mittelstädt, J. M., & Van Der Bergh, O. (2016). Respiratory changes in response to cognitive load: A systematic review. Neural Plasticity. https://doi.org/10.1155/2016/8146809.

    Google Scholar 

  • Grillner, S., Markram, H., De Schutter, E., Silberberg, G., & LeBeau, F. E. (2005). Microcircuits in action–from CPGs to neocortex. Trends in Neuroscience, 28(10), 525–533.

    Article  Google Scholar 

  • Haueis, P. (2014). Meeting the brain on its own terms. Frontiers in Human Neuroscience, 8, 815. https://doi.org/10.3389/fnhum.2014.00815.

    Article  Google Scholar 

  • Haueis, P. (2018). Meeting the brain on its own terms. Exploratory concept formation and noncognitive functions in neuroscience. Dissertation, Otto-von-Guericke University Magdeburg.

  • Heeger, D. J. (1992). Normalization of cell responses in cat striate cortex. Visual Neuroscience, 9, 181–197.

    Article  Google Scholar 

  • Heinzle, J., Hepp, K., & Martin, K. A. (2007). A microcircuit model of the frontal eye fields. Journal of Neuroscience, 27(5), 9341–9353.

    Article  Google Scholar 

  • Hermes, D., Miller, K., Wandell, B. A., & Winawer, J. (2015). Stimulus dependence of gamma oscillations in human visual cortex. Cerebral Cortex, 25(9), 2951–2959.

    Article  Google Scholar 

  • Homma, I., & Masoaka, Y. (2008). Breathing rhythms and emotion. Experimental Physiology, 93(3), 1011–1021.

    Article  Google Scholar 

  • Iaccarino, H. F., Singer, A. C., Martorell, A. J., Rudenko, A., Gao, F., Gillingham, T. Z., et al. (2016). Gamma frequency entrainment attenuates amyloid load and modifies microglia. Nature, 540(7632), 230–235.

    Article  Google Scholar 

  • Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A., & Hudspeth, A. J. (Eds.). (2000). Principles of neural science (4th ed.). New York: McGraw-Hill.

    Google Scholar 

  • Kirschfeld, K. (1992). Oscillations in the insect brain: Do they correspond to the cortical gamma waves of vertebrates? PNAS, 89(10), 4764–4768.

    Article  Google Scholar 

  • Kiviniemi, V., Wang, X., Korhonen, V., Keinänen, T., Tuovinen, T., Autio, J., et al. (2016). Ultra-fast magnetic resonance encephalography of physiological brain activity—Glymphatic pulsation mechanisms? Journal of Cerebral Blood Flow and Metabolism, 36(6), 1033–1045.

    Article  Google Scholar 

  • Klein, C. (2014). The brain at rest: What it’s doing and why that matters. Philosophy of Science, 81(5), 974–985.

    Article  Google Scholar 

  • Klein, C. (2017). Brain regions as difference makers. Philosophical Psychology, 30(1–2), 1–20.

    Article  Google Scholar 

  • Llinas , R. (2001). I of the vortex. From neurons to self. Cambridge: MIT Press.

    Google Scholar 

  • Love, A. C. (2013). Theory is as theory does: Scientific practice and theory structure in biology. Biological Theory, 7(4), 325–337.

    Article  Google Scholar 

  • Luque-Casado, A., Zabala, M., Morales, E., Mateo-March, M., & Sanabria, D. (2013). Cognitive performance and heart rate variability: The influence of fitness level. PLoS One, 8(2), e0056935. https://doi.org/10.1371/journal.pone.0056935.

    Article  Google Scholar 

  • Marder, E. (2012). Neuromodulation of neuronal circuits: Back to the future. Neuron, 76(1), 1–11.

    Article  Google Scholar 

  • Marder, E., & Bucher, D. M. (2007). Understanding circuit dynamics using the stomatogastric nervous system of lobsters and crabs. Annual Reviews Neuroscience, 69, 291–316.

    Google Scholar 

  • Marder, E., & Goaillard, J.-M. (2006). Variability, compensation and homeostasis in neuron and network function. Nature Reviews Neuroscience, 7(7), 563–574.

    Article  Google Scholar 

  • Martin, C. B., & Deutscher, M. (1966). Remembering. Philosophical Review, 75(2), 161–196.

    Article  Google Scholar 

  • McCaffrey, J. (2015). The brain’s heterogenous functional landscape. Philosophy of Science, 82(5), 1010–1022.

    Article  Google Scholar 

  • McCaffrey, J., & Danks, D. (2017). Mixtures and psychological inference with resting state fMRI. British Journal for Philosophy of Science. https://doi.org/10.1093/bjps/axx053.

    Google Scholar 

  • Merker, B. (2013). Cortical gamma oscillations: The functional key is activation, not cognition. Neuroscience Biobehavioral Reviews, 37(3), 401–417.

    Article  Google Scholar 

  • Mesulam, M. M. (1998). From sensation to cognition. Brain, 121(6), 1013–1052.

    Article  Google Scholar 

  • Moore, J. D., Deschênes, M., Furuta, T., Huber, D., Smear, D. C., Demers, M., et al. (2013). Hierarchy of orofacial behaviors revealed through whisking and breathing. Nature, 497(7448), 205–210.

    Article  Google Scholar 

  • Nersessian, N. (2008). Creating scientific concepts. Cambridge, MA: MIT Press.

    Google Scholar 

  • Ni, J., Wunderle, T., Lewis, C. M., Desimone, R., Diester, I., & Fries, P. (2016). Gamma-rhythmic gain modulation. Neuron, 92(1), 240–251.

    Article  Google Scholar 

  • Novick, A. (2018). The fine structure of ‘homology’. Biology & Philosophy, 33, 6. https://doi.org/10.1007/s10539-018-9617-3.

    Article  Google Scholar 

  • O’Leary, T., Williams, A. H., Caplan, J. C., & Marder, E. (2014). Cell types, network homeostasis, and pathological compensation from a biologically plausible ion channel expression model. Neuron, 82(4), 809–821.

    Article  Google Scholar 

  • Panzeri, S., Harvey, C. D., Piasini, E., Latham, P. E., & Fellin, T. (2017). Cracking the neural code for sensory perception by combining statistics, intervention, and behavior. Neuron, 93(3), 491–507.

    Article  Google Scholar 

  • Piccinini, G., & Bahar, S. (2013). Neural computation and the computational theory of cognition. Cognitive Science, 37(3), 453–488.

    Article  Google Scholar 

  • Raichle, M. E. (2015). The Restless brain: How intrinsic activity organizes brain function. Philosophical Transactions of the Royal Society London. https://doi.org/10.1098/rstb.2014.0172.

    Google Scholar 

  • Rathkopf, C. (2013). Intrinsic function and localization. Philosophy of Science, 80(1), 1–21.

    Article  Google Scholar 

  • Rathkopf, C. (2017). Neural information and the problem of objectivity. Biology and Philosophy, 32, 321–336.

    Article  Google Scholar 

  • Rieke, F., Warland, D., de Ruyter van Steveninck, R. R., & Bialek, W. (1999). Spikes: Exploring the neural code (2nd ed.). Cambridge, MA: MIT Press.

    Google Scholar 

  • Rouse, J. (2015). Articulating the world. Conceptual understanding and the scientific image. Chicago: Chicago University Press.

    Book  Google Scholar 

  • Selverston, A. I. (2010). Invertebrate central pattern generator circuits. Philosophical Transactions of the Royal Society London B, 365(1551), 2329–2345.

    Article  Google Scholar 

  • Silva, A. J., Landreth, A., & Bickle, J. (2013). Engineering the next revolution in neuroscience. The new science of experiment planning. Oxford: Oxford University Press.

    Book  Google Scholar 

  • Smallwood, J., Karapanagiotidis, T., Ruby, F., Medea, B., de Caso, I., Konishi, M., et al. (2016). Representing representation: Integration between the temporal lobe and the posterior cingulate influences the content and form of spontaneous thought. PLoS ONE, 11(4), e0152272. https://doi.org/10.1371/journal.pone.0152272.

    Article  Google Scholar 

  • Squire, L. R., Berg, D., Bloom, F. E., du Lac, S., Ghosh, A., & Spitzer, N. C. (Eds.). (2012). Fundamental neuroscience (4th ed.). Amsterdam: Academic Press.

    Google Scholar 

  • Sreenivasan, V., Karmakar, K., Rijli, F. M., & Petersen, C. C. H. (2014). Parallel pathways from motor and somatosensory cortex for controlling whisker movements in mice. European Journal of Neuroscience, 41(3), 354–367.

    Article  Google Scholar 

  • Stevens, C. F. (1996). Spatial memory: The beginning of a dream. Cell, 87(7), 1147–1148.

    Article  Google Scholar 

  • Šustar, P. (2007). Neo-functional analysis: Phylogenetic restrictions on causal role functions. Philosophy of Science, 74(5), 601–615.

    Article  Google Scholar 

  • Wilson, M. (2006). Wandering significance. An essay in conceptual behavior. Oxford: Clarendon Press.

    Book  Google Scholar 

  • Woodruff, A. R., Anderson, S. A., & Yuste, R. (2010). The enigmatic function of chandelier cells. Frontiers in Neuroscience, 4, 201. https://doi.org/10.3389/fnins.2010.00201.

    Article  Google Scholar 

  • Woodward, J. (2010). Causation in biology: Stability, specificity and the choice of levels of explanation. Biology and Philosophy, 25(3), 287–318.

    Article  Google Scholar 

  • Yuste, R., MacLean, J., Smith, J., & Lansner, A. (2005). The cortex as a central pattern generator. Nature Reviews Neuroscience, 6(6), 477–483.

    Article  Google Scholar 

  • Zhu, Y., Stornetta, R. L., & Zhu, J. J. (2004). Chandelier cells control excessive cortical excitation: Characteristics of whisker-evoked synaptic responses of layer 2/3 Nonpyramidal and pyramidal neurons. Journal of Neuroscience, 24(22), 5101–5108.

    Article  Google Scholar 

Download references

Acknowledgements

I would like to thank Daniel Burnston, Mark Oliver Casper, David Colaço, Carl Craver, Lena Kästner and Joseph McCaffrey as well as two anonymous reviewers for helping me to clearly articulate the ideas in this paper. I thank participants of the Neural Mechanism Online Seminar, the ISHBB conference in Sao Paolo, the OHBM conference in Vancouver and members of the Research Group for Neuroanatomy and Connectivity at the MPI for Cognitive and Brain Sciences Leipzig for valuable feedback on presentations of this material. An earlier version of this paper appeared as chapter 4 of my PhD dissertation “Meeting the Brain on its own Terms. Exploratory Concept Formation and Noncognitive Functions in Neuroscience” (Otto-von-Guericke University Madgeburg). I thank Holger Lyre and Henrik Walter for their excellent supervision of this project.

Author information

Authors and Affiliations

  1. Abteilung Philosophie, Universität Bielefeld, Postfach 100131, 33501, Bielefeld, Germany

    Philipp Haueis

  2. Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Luisenstr. 56, 10117, Berlin, Germany

    Philipp Haueis

Authors
  1. Philipp Haueis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Philipp Haueis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Haueis, P. Beyond cognitive myopia: a patchwork approach to the concept of neural function. Synthese 195, 5373–5402 (2018). https://doi.org/10.1007/s11229-018-01991-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11229-018-01991-z

Keywords

Search

Navigation