Abstract
How can we best understand human cognitive architectural variability? We believe that the relationships between theories in neurobiology, cognitive science and evolutionary biology posited by evolutionary psychology’s Integrated Causal Model (ICM) has unduly supported various essentialist conceptions of the human cognitive architecture, monomorphic minds (to use Griffiths’ apt phrase), that mask HCA variability, and we propose a different set of relationships between theories in the same domains to support a different, non-essentialist, understanding of HCA variability. To set our case against essentialist theories of HCA variability, we detail the general notion of an ICM and the specific ICM at the heart of evolutionary psychology. We briefly illustrate the type of essentialism fostered by evolutionary psychology’s ICM by showing how it grounds essentialist theories of cognitive gender. We shall not criticize these theories here since the literature is replete with compelling objections to them, but shall instead focus on motivating a replacement ICM to destabilize evolutionary psychology’s ICM wholesale. ICMs usually span larger than the models they support, hence larger than arguments against these models, and one reason the essentialist theories addressed here have the kind of staying power they do is that they are partly supported by the ICM in which they are grounded. In short, we offer “A New Hope” against the essentialist empire. True to the Hollywood trope, this new hope rests on an alliance between a young theory, cognitive network neuroscience, and two older, but still quite young, epistemic rebels: enactive cognitive science and developmental systems theory. Accordingly, we detail and discuss the proposed emerging ICM and test-drive it by sketching the multimorphic view of gender it grounds.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
We say “local” because the goal is not to unify science as a whole, but rather to link up different theories or models to solve particular epistemic or practical problems.
Although the SSSM is a model (or family of models) of culture, as the title of their 1992 paper makes clear, Tooby and Cosmides’ focus is on the “psychological foundations of culture”, and what they end up offering to account for the psychological foundations of culture is a cognitive architecture, a distinctively human cognitive architecture (albeit deeply continuous with non-human cognitive architectures), since they take the cognitive capacities most relevantly responsible for culture to have evolved during hominization. Like Tooby and Cosmides, we focus here on the human cognitive architecture.
We do not claim identity here. We take it, with the semantic conception of theories, that theories imply sets of models, but do not wish to further maintain that any set of model implies a theory.
They are clear about the fact that it is psychology (and not neuroscience) that is to be transformed by its integration with evolutionary biology. In their preface to Baron-Cohen’s (1997) book, Tooby and Cosmides talk about their project as a form of “naturalization of the psychological sciences” (1997, p. xvi). As they see it, an integration of cognitive sciences with evolutionary sciences would potentially correct the “many major wrong turns in the history of the behavioral sciences—for example, many important aspects of the Freudian, Skinnerian, or Piagetian paradigms—[that] would not have been made if their core propositions had been scrutinized for consistency with the kinds of outcomes that natural selection could plausibly have produced” (Sznycer et al. 2011, p. 296). So, the integration of psychology with evolutionary theories is seen as having the potential to produce a more accurate picture of the mind.
“Thirty years ago, except for certain seemingly outdated schools of neurologists, the modular view of the cognitive system that cognitive neuropsychology offers would have seemed as implausible as that provided by Gall. The answers that have been given for a variety of phenomena discovered and documented over many aspects of perception, language, memory and cognition might not survive” (Shallice, From Neuropsychology to Mental Structure, quoted by Uttal (2001) p. xix).
Anderson (2010, 2014) is also an advocate of 4E cognition, and his 2014 book After Phrenology: Neural Reuse and the Interacting Brain comes close to putting its finger on the new ICM, but fails ultimately by staying too closely wedded to neuroscience. Contrary to him, we believe that it is not only brain regions that are reused and that interact, but parts of brain-body-environment systems, and hence it’s the latter that form networks.
Although systems in which all parts are directly interacting (fully connected networks) are a theoretical possibility (and useful object of study) no natural systems are thus connected.
Even more extremely, a node could correspond to a whole brain in a hyperscanning experiment (see below) or to individuals in social networks (Falk and Bassett 2017).
Other distinctions are possible: excitatory versus inhibitory connections, unidirectional connections versus bidirectional connections, types of neurotransmitters connections (dopaminergic, serotonergic, etc.), and so on.
The study of the brain’s functional connectivity is thus limited by the current temporal and spatial resolution of imaging technology. Subvoxel functional connectivity can be studied in formal neural network models (such as Friston’s (2005)).
Park and Friston (2013) seem to agree when they claim that: “This context sensitivity makes direct measurement of effective connectivity unattainable without a very carefully controlled neuronal context and an explicit model of neuronal interactions” (p. 7).
Connectivity is a property of networks (or subnetworks): a connected network (or subnetwork) is one in which all nodes can reach each other through other nodes in the network (or subnetwork).
Density is a property of a network’s subnetworks: a network’s subnetwork is dense when all nodes in the subnetwork have a higher probability to link to other nodes in the subnetwork than to nodes outside the subnetwork.
More precisely the clustering coefficient measures the local density of links in a node’s neighborhood (Barabasi): the ratio between the number of triangles in which a node participates and the number of connected triples in which it participates (Medaglia et al. 2015, p. 7).
To belabor the point, structural connectivity is here out of the question. Barring sci-fi examples, there are no structural connections between the two brains. But as we saw, functional connectivity is mostly what task-oriented cognition and behavior is about (that latter is where functional connectivity and structural connectivity are uncoupled).
Recall that density (a higher probability to link to other nodes in the subnetwork than to nodes outside the subnetwork) is one of the two properties (with connectivity) that define network communities and that an increase in a network’s density can be understood as an increase in its modularity (i.e., modularization).
Autism disorder or “Autism Spectrum Disorder” (as it is now called) is a neurodevelopmental disorder with varying phenotypic expressions and diagnosed based on observable deficits of social communication and social interaction as well as repetitive and stereotyped behaviors (American Psychiatric Association 2013, p. 31).
The classically cited difference is the presence in males of a gene network that contains the SRY gene, present on the Y chromosome, whose expression triggers a cascade of genetic and cellular events, eventually increasing fetal testosterone levels, which causes the masculinization of the fetus and a number of differences in structural and functional brain connectivity in males and females. Although a classic in the field, this story is now believed to be overly simple, both as an account of the development of the male and the female phenotype (Karkanaki et al. 2007).
These results do not depend on the large range on age in the sample, as similar results are obtained for a more restricted age range, and they do not depend on the definition of “male-end” and “female-end”, as different proportions (10–80–10, 20–60–20 and 50–50) show even less internal consistency.
For references to the original works, see: Joel (2011).
References
Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3(2), e17. https://doi.org/10.1371/journal.pcbi.0030017.
Adams, F., & Aizawa, K. (2008). The bounds of cognition. Malden, MA: Blackwell Pub.
American Psychiatric Association. (2013). Autism spectrum disorder. In Diagnostic and statistical manual of mental disorders (5th edn). https://doi.org/10.1176/appi.books.9780890425596.dsm05.
Anderson, M. L. (2010). Neural reuse: A fundamental organizational principle of the brain. Behavioral and Brain Sciences, 33(4), 245.
Anderson, M. L. (2014). After phrenology: Neural reuse and the interactive brain. Cambridge, MA: MIT Press.
Anderson, M. L., & Finlay, B. L. (2014). Allocating structure to function: The strong links between neuroplasticity and natural selection. Frontiers in Human Neuroscience, 7, 918.
Andrews, G. E. (1998). The theory of partitions (1st paperback ed.). Cambridge: Cambridge University Press.
Arnaboldi, V., Conti, M., Passarella, A., & Dunbar, R. (2013). Dynamics of personal social relationships in online social networks: A study on twitter. In COSN '13: Proceedings of the first ACM conference on online social networks (pp. 15–26). https://doi.org/10.1145/2512938.2512949.
Arnaud, S., & Gratton, C. (2018). Les femmes en philo, qu’est-ce que ça mange en hiver? GLAD, 4. Retrieved from https://www.revue-glad.org/1120.
Ball, G., Aljabar, P., Zebari, S., Tusor, N., Arichi, T., Merchant, N., et al. (2014). Rich-club organization of the newborn human brain. Proceedings of National Academy of Sciences, 111(20), 7456–7461. https://doi.org/10.1073/pnas.1324118111.
Barabási, A.-L., & Pósfai, M. (2016). Network science. Cambridge: Cambridge University Press.
Barkow, J., Cosmides, L., & Tooby, J. (1992). The adapted mind: Evolutionary psychology and the generation of culture. Oxford: Oxford University Press.
Baron-Cohen, S. (1997). Mindblindness: An essay on autism and theory of mind. Cambridge: MIT Press.
Baron-Cohen, S. (2002). The extreme male brain theory of autism. Trends in Cognitive Sciences, 6, 248–254.
Baron-Cohen, S. (2003). The essential difference: Men, women and the extreme male brain. London: Penguin.
Baron-Cohen, S. (2005). The empathizing system: a revision of the 1994 model of the mindreading system. In B. Ellis & D. Bjorklund (Eds.), Origins of the social mind. New York: Guilford Publications Inc.
Baron-Cohen, S., & Sweettenham, J. (1996). The relationship between SAM and ToMM: the lock and key hypothesis. In P. Carruthers & P. Smith (Eds.), Theories of theories of mind. Cambridge: Cambridge University Press.
Barrett, H. C., & Kurzban, R. (2006). Modularity in cognition: Framing the debate. Psychological Review, 113(3), 628–647.
Bechtel, W. (2007). Mental mechanisms: Philosophical perspectives on cognitive neuroscience. London: Psychology Press.
Bechtel, W., & Richardson, R. C. (1993). Discovering complexity decomposition and localization as strategies in scientific research. Princeton: Princeton University Press.
Boone, W., & Piccinini, G. (2016). The cognitive neuroscience revolution. Synthese, 193(5), 1509–1534.
Borgatti, S. P., & Everett, M. G. (2000). Models of core/periphery structures. Social Networks, 21(4), 375–395. https://doi.org/10.1016/S0378-8733(99)00019-2.
Brigandt, I. (2010). Beyond reduction and pluralism: Toward an epistemology of explanatory integration in biology. Erkenntnis, 73(3), 295–311.
Buller, D. J. (2005). Adapting minds: Evolutionary psychology and the persistent quest for human nature. Cambridge, MA: MIT Press.
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. https://doi.org/10.1038/nrn2575.
Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews Neuroscience, 13(5), 336–349. https://doi.org/10.1038/nrn3214.
Byrge, L., Sporns, O., & Smith, L. B. (2014). Developmental process emerges from extended brain–body–behavior networks. Trends in Cognitive Sciences, 18(8), 395–403. https://doi.org/10.1016/j.tics.2014.04.010.
Cao, H., Plichta, M. M., Schäfer, A., Haddad, L., Grimm, O., Schneider, M., et al. (2014). Test–retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state. NeuroImage, 84, 888–900. https://doi.org/10.1016/j.neuroimage.2013.09.013.
Carruthers, P. (2006). The architecture of the mind: Massive modularity and the flexibility of thought. Oxford: Oxford University Press.
Chamberland, M., Girard, G., Bernier, M., Fortin, D., Descoteaux, M., & Whittingstall, K. (2017). On the origin of individual functional connectivity variability: The role of white matter architecture. Brain Connectivity. https://doi.org/10.1089/brain.2017.0539.
Chomsky, N., & Ronat, M. (1979). Language and responsibility: Based on conversations with Mitsou Ronat (1 American ed.). New York, NY: Pantheon Books.
Colombo, M. (2013). Moving forward (and beyond) the modularity debate: A network perspective. Philosophy of Science, 80(3), 356–377.
Cosmides, L., & Tooby, J. (1992). Cognitive adaptations for social exchange. In J. Barkow, L. Cosmides, & J. Tooby (Eds.), The adapted mind: Evolutionary psychology and the generation of culture (pp. 163–228). New York: Oxford University Press.
Craig, A. D. (2002). How do you feel? Interoception: The sense of the physiological condition of the body. Nature Reviews Neuroscience, 3, 655–666.
Craver, C. F. (2007). Explaining the brain: Mechanisms and the mosaic unity of neuroscience. Oxford: Oxford University Press, Clarendon Press.
Cummins, R. (1975). Functional analysis. Journal of Philosophy, 72(November), 741–764.
Cummins, R. C. (1983). The nature of psychological explanation (Vol. 19). Cambridge: MIT Press.
Dale, R., Fusaroli, R., Duran, N. D., & Richardson, D. C. (2013). The self-organization of human interaction. The Psychology of Learning and Motivation, 59, 43–95.
Danon, L., Díaz-Guilera, A., Duch, J., & Arenas, A. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09), P09008–P09008. https://doi.org/10.1088/1742-5468/2005/09/p09008.
Darden, L., & Maull, N. (1977). Interfield theories. Philosophy of Science, 44(1), 43–64.
Dehaene, S., & Cohen, L. (2007). Cultural recycling of cortical maps. Neuron, 56(2), 384–398.
Dehaene, S., Duhamel, J.-R., Hauser, M. D., & Rizzolatti, G. (Eds.). (2005). From monkey brain to human brain. Cambridge: MIT Press.
Derényi, I., Palla, G., & Vicsek, T. (2005). Clique percolation in random networks. Physical Review Letters. https://doi.org/10.1103/PhysRevLett.94.160202.
Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., et al. (2017). Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom. Current Biology, 27(9), 1375–1380.
Edelman, G. M., & Gally, J. A. (2001). Degeneracy and complexity in biological systems. Proceedings of the National Academy of Sciences, 98(24), 13763. https://doi.org/10.1073/pnas.231499798.
Falk, E. B., & Bassett, D. S. (2017). Brain and social networks: Fundamental building blocks of human experience. Trends in Cognitive Sciences, 21(9), 674–690. https://doi.org/10.1016/j.tics.2017.06.009.
Faucher, L. (2012). Unity of science and pluralism: Cognitive neurosciences of racial prejudice as a case study. In O. Pombo, J. M. Torres, J. Symons, & S. Rahman (Eds.), Special sciences and the unity of science (pp. 177–204). Berlin: Springer.
Fausto-Sterling, A. (2005). The bare bones of sex: Part 1–Sex and gender. Signs, 30(2), 1491–1527. https://doi.org/10.1086/424932.
Fine, C. (2010). Delusions of gender: How our minds, society and neurosexism create difference. London: Icon Books.
Fine, C., Dupré, J., & Joel, D. (2017). Sex-linked behavior: Evolution, stability, and variability. Trends in Cognitive Sciences, 21(9), 666–673.
Fodor, J. (1974). Special sciences, or the disunity of science as a working hypothesis. Synthese, 28, 77–115.
Fodor, J. A. (1983). The modularity of mind (Vol. 94). Cambridge: MIT Press.
Fodor, J. A. (1985). Precis of the modularity of mind. Behavioral and Brain Sciences, 8(1), 1–42.
Fodor, J. (1998). Concepts where cognitive science went wrong. Oxford: Oxford University Press.
Fornito, A., Zalesky, A., & Bullmore, E. T. (2016). Fundamentals of brain network analysis. Amsterdam, Boston: Elsevier/Academic Press.
Fortunato, S., & Barthelemy, M. (2007). Resolution limit in community detection. Proceedings of the National Academy of Sciences, 104(1), 36–41. https://doi.org/10.1073/pnas.0605965104.
Friston, K. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1456), 815–836. https://doi.org/10.1098/rstb.2005.1622.
Fuchs, T. (2018). Ecology of the brain: The phenomenology and biology of the embodied mind (1st ed.). Oxford: Oxford University Press.
Galison, P., & Stump, D. J. (Eds.). (1996). The disunity of science: Boundaries, contexts, and power. Stanford: Stanford University Press.
Gallese, V., & Lakoff, G. (2005). The Brain’s concepts: The role of the Sensory-motor system in conceptual knowledge. Cognitive Neuropsychology, 22(3), 455–479.
Gilbert, E. N. (1959). Random graphs. The Annals of Mathematical Statistics, 30(4), 1141–1144. https://doi.org/10.1214/aoms/1177706098.
Grantham, T. (2004). Conceptualizing the (dis)unity of science. Philosophy of Science, 71, 133–155.
Grayson, D. S., Ray, S., Carpenter, S., Iyer, S., Dias, T. G. C., Stevens, C., et al. (2014). Structural and functional rich club organization of the brain in children and adults. PLoS ONE, 9(2), e88297. https://doi.org/10.1371/journal.pone.0088297.
Griffiths, P. E. (1998). What emotions really are: The problem of psychological categories. Chicago: The University of Chicago Press.
Hermundstad, A. M., Bassett, D. S., Brown, K. S., Aminoff, E. M., Clewett, D., Freeman, S., et al. (2013). Structural foundations of resting-state and task-based functional connectivity in the human brain. Proceedings of the National Academy of Sciences, 110(15), 6169–6174. https://doi.org/10.1073/pnas.1219562110.
Heyes, C. (2018). Cognitive gadgets: The cultural evolution of thinking. Cambridge: Harvard University Press.
Hofer, M. A. (1994). Hidden regulators in attachment, separation, and loss. Monographs of the Society for Research in Child Development, 59(2–3), 192–207.
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., et al. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences, 106(6), 2035–2040. https://doi.org/10.1073/pnas.0811168106.
Hurley, S. (2008). The shared circuits model (SCM): How control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences, 31, 1–22.
Hutzler, F. (2014). Reverse inference is not a fallacy per se: Cognitive processes can be inferred from functional imaging data. NeuroImage, 84, 1061–1069. https://doi.org/10.1016/j.neuroimage.2012.12.075.
Joel, D. (2011). Male or female? Brains are intersex. Frontiers in Integrative Neuroscience. https://doi.org/10.3389/fnint.2011.00057.
Joel, D., Berman, Z., Tavor, I., Wexler, N., Gaber, O., Stein, Y., et al. (2015). Sex beyond the genitalia: The human brain mosaic. Proceedings of the National Academy of Sciences, 112(50), 15468. https://doi.org/10.1073/pnas.1509654112.
Joel, D., Hänggi, J., & Pool, J. (2016). Reply to Glezerman: Why differences between brains of females and brains of males do not “add up” to create two types of brains. Proceedings of the National Academy of Sciences of the United States of America, 113(14), E1972. https://doi.org/10.1073/pnas.1600791113.
Joel, D., & Vikhanski, L. (2019). Mosaic Brain. Beyond the myth of the male and female brain. London: Endeavour Press.
Jordan-Young, R. (2010). Brain storm: The flaws in the science of sex differences. Cambridge: Harvard University Press.
Karkanaki, A., Praras, N., Katsikis, I., Kita, M., & Panidis, D. (2007). Is the Y chromosome all that is required for sex determination? Hippokratia, 11(3), 120–123.
Kernighan, B. W., & Lin, S. (1970). An efficient heuristic procedure for partitioning graphs. Bell System Technical Journal, 49(2), 291–307. https://doi.org/10.1002/j.1538-7305.1970.tb01770.x.
Kitcher, P. (1999). Unification as a regulative ideal. Perspectives on Science, 7(3), 337–348.
Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters. https://doi.org/10.1103/PhysRevLett.87.198701.
Levins, R., & Lewontin, R. C. (1987). The dialectical biologist. Cambridge, MA: Harvard University Press.
Liu, D., Liu, S., Liu, X., Zhang, C., Li, A., Jin, C., et al. (2018). Interactive brain activity: Review and progress on EEG-based hyperscanning in social interactions. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2018.01862.
Longino, H. E. (2006). Theoretical pluralism and the scientific study of behavior. In S. Kellert, H. E. Longino, & C. K. Waters (Eds.), Scientific pluralism (Vol. 19, pp. 102–131). Minneapolis: University of Minnesota Press.
Longino, H. E. (2013). Studying human behavior: How scientists investigate aggression and sexuality. Chicago: University of Chicago Press.
Machery, E., & Faucher, L. (2005). Social construction and the concept of race. Philosophy of Science, 72(5), 1208–1219.
Marshall, C. R., Hardy, C. J. D., Allen, M., Russell, L. L., Clark, C. N., Bond, R. L., et al. (2018). Cardiac responses to viewing facial emotion differentiate frontotemporal dementias. Annals of Clinical and Translational Neurology, 5(6), 687–696.
Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015). Cognitive network neuroscience. Journal of Cognitive Neuroscience, 27(8), 1471–1491. https://doi.org/10.1162/jocn_a_00810.
Merritt, M. (2013). Instituting impairment: Extended cognition and the construction of female sexual dysfunction. Cognitive Systems Research, 25–26, 47–53. https://doi.org/10.1016/j.cogsys.2013.03.005.
Meunier, D., Fonlupt, P., Saive, A.-L., Plailly, J., Ravel, N., & Royet, J.-P. (2014). Modular structure of functional networks in olfactory memory. NeuroImage, 95, 264–275. https://doi.org/10.1016/j.neuroimage.2014.03.041.
Mitchell, S. D. (2002). Integrative pluralism. Biology and Philosophy, 17, 55–70.
Mitchell, S. D. (2009). Unsimple truths: Science, complexity, and policy. Chicago: Chicago University Press.
Müller, V., & Lindenberger, U. (2011). Cardiac and respiratory patterns synchronize between persons during choir singing. PLoS ONE, 6(9), e24893. https://doi.org/10.1371/journal.pone.0024893.
Nagel, E. (1961). The structure of science. London: Routledge and Kegan Paul.
Newen, A., De Bruin, L., & Gallagher, S. (2018). The oxford handbook 4E cognition. New York: Oxford University Press.
Newman, M. E. J. (2018). Networks (2nd ed.). Oxford: Oxford University Press.
Oppenheim, P., & Putnam, H. (1958). Unity of science as a working hypothesis. Minnesota Studies in the Philosophy of Science, 2, 3–36.
Oyama, S., Griffiths, P. E., & Gray, R. D. (2003). Cycles of contingency: Developmental systems and evolution. Cambridge, MA: MIT.
Palla, G., Derényi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043), 814–818. https://doi.org/10.1038/nature03607.
Park, H.-J., & Friston, K. (2013). Structural and functional brain networks: From connections to cognition. Science, 342(6158), 1238411. https://doi.org/10.1126/science.1238411.
Pascual-Leone, A., Amedi, A., Fregni, F., & Merabet, L. B. (2005). The plastic human brain cortex. Annual Review of Neuroscience, 28, 377–401.
Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect (1st ed.). New York: Basic Books.
Pitts-Taylor, V. (2016). The brain’s body: Neuroscience and corporeal politics. Durham: Duke University Press.
Poirier, P., Faucher, L., & Bourdon, J.-N. (2019). Cultural blankets: Epistemological pluralism in the evolutionary epistemology of mechanisms. Journal for General Philosophy of Science. https://doi.org/10.1007/s10838-019-09472-8.
Poirier, P., Faucher, L., & Lachapelle, J. (2008). The concept of innateness and the destiny of evolutionary psychology. Journal of Mind & Behavior, 29(1), 17–47.
Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10(2), 59–63.
Purves, D. (Ed.). (2013). Principles of cognitive neuroscience (2nd ed.). Sunderland, MA: Sinauer Associates Inc.
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, 98(2), 676–682. https://doi.org/10.1073/pnas.98.2.676.
Richardson, D. C., Street, C. N. H., Tan, J. Y. M., Kirkham, N. Z., Hoover, M. A., & Cavanaugh, A. G. (2012). Joint perception: Gaze and social context. Frontiers in Human Neuroscience, 6, 1–8.
Rippon, G. (2019). The gendered brain: The new neuroscience that shatters the myth of the female brain. London: The Bodley Head.
Rozin, P. (1976). The evolution of intelligence and access to the cognitive unconscious. In J. A. Sprague & N. Epstein (Eds.), Progress in psychobiology and physiological psychology (Vol. 6, pp. 245–280). New York: Academic Press.
Salvia, E., Harvey, M., Nazarian, B., & Grosbras, M. H. (2020). Social perception drives eye-movement related brain activity: Evidence from pro- and anti-saccades to faces. Neuropsychologia. https://doi.org/10.1016/j.neuropsychologia.2020.107360.
Sänger, J., Müller, V., & Lindenberger, U. (2012). Intra- and interbrain synchronization and network properties when playing guitar in duets. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2012.00312.
Schmälzle, R., Brook O’Donnell, M., Garcia, J. O., Cascio, C. N., Bayer, J., Bassett, D. S., et al. (2017). Brain connectivity dynamics during social interaction reflect social network structure. Proceedings of the National Academy of Sciences, 114(20), 5153–5158. https://doi.org/10.1073/pnas.1616130114.
Shallice, T. (1988). From neuropsychology to mental structure. Cambridge: Cambridge University Press.
Shen, K., Bezgin, G., Hutchison, R. M., Gati, J. S., Menon, R. S., Everling, S., et al. (2012). Information processing architecture of functionally defined clusters in the macaque cortex. Journal of Neuroscience, 32(48), 17465–17476. https://doi.org/10.1523/JNEUROSCI.2709-12.2012.
Shors, T. J., Chua, C., & Falduto, J. (2001). Sex differences and opposite effects of stress on dendritic spine density in the male versus female hippocampus. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 21(16), 6292–6297.
Sporns, O. (2012). Discovering the human connectome. Cambridge, MA: MIT Press.
Sporns, O. (2016). Networks of the brain. Cambridge: MIT Press.
Stanley, M. L., Gessell, B., & De Brigard, F. (2019). Network modularity as a foundation for neural reuse. Philosophy of Science, 86(1), 23–46.
Sterelny, K., & Griffiths, P. (1999). Sex and death: An introduction to philosophy of biology. Chicago, IL: University of Chicago Press.
Stevens, A. A., Tappon, S. C., Garg, A., & Fair, D. A. (2012). Functional brain network modularity captures inter- and intra-individual variation in working memory capacity. PLoS ONE, 7(1), e30468. https://doi.org/10.1371/journal.pone.0030468.
Sullivan, J. (2017). Coordinated pluralism as a means to facilitate integrative taxonomies of cognition. Philosophical Explorations, 20, 129–145.
Suppe, F. (Ed.). (1977). The structure of scientific theories. Urbana: University of Illinois Press.
Suppes, P. (1960). A comparison of the meaning and uses of models in mathematics and the empirical sciences. Synthese, 12, 287–301.
Sznycer, D., Tooby, J., & Cosmides, L. (2011). Evolutionary psychology. In P. C. Hogan (Ed.), The Cambridge encyclopedia of the language sciences (pp. 295–299). New York, NY: Cambridge University Press.
Uttal, W. R. (2001). The new phrenology: The limits of localizing cognitive processes in the brain. Cambridge: MIT Press.
Varela, F., Thompson, E., & Rosch, E. (1991). The embodied mind: Cognitive science and human experience. Cambridge, MA: MIT Press.
Vogelstein, B., Lane, D., & Levine, A. J. (2000). Surfing the p53 network. Nature, 408(6810), 307–310. https://doi.org/10.1038/35042675.
Wimsatt, W. C. (1997). Aggregativity: Reductive heuristics for finding emergence. Philosophy of Science, 64(4), 372–384.
Wylie, A. (1999). Rethinking unity as a “working hypothesis” for philosophy: How archaeologists exploit the disunities of science. Perspectives on Science, 7(3), 293–317.
Acknowledgements
We wish to thank Christophe Malaterre and Éric Muszynski who organized the workshop The Biology of Behaviour: Explanatory Pluralism across the Life Sciences, from which this paper is derived, and patience all along the editorial process. We also thank Evi Amanda-Leigh Cox for her skillful linguistic help while preparing the final manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Poirier, P., Faucher, L. A New Hope: A better ICM to understand human cognitive architectural variability. Synthese 199, 871–903 (2021). https://doi.org/10.1007/s11229-020-02739-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11229-020-02739-4