Abstract
It is becoming increasingly accepted that some form of anticipation is central to the functioning of the brain. But modeling such anticipation has been in several forms concerning what is anticipated, whether and how such ‘anticipation’ can be normative in the sense of possibly being wrong, the nature of the anticipatory processes and how they are realized in the brain, etc. Here I outline two such approaches – the Predictive Brain approach and the Interactivist approach – and undertake a critical comparison and contrast.
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Notes
- 1.
Sufficient statistics (Friston et al. 2009).
- 2.
For an early version of such hierarchical prediction, see Tani and Nolfi (1999).
- 3.
E.g., Locke, Hume, Russell (in some incarnations), Fodor, much of contemporary literature, and even Aristotle’s signet ring impressing its form into wax.
- 4.
“Transduction, remember, is the function that Descartes assigned to the pineal gland” (Haugeland 1998, p. 223).
- 5.
Or perhaps they’re independently innate? This issue is alive and well in contemporary work: in child development, for example, a fundamental question is whether or not it is possible to construct, say, object encodings or number representations, out of sensory encodings. Some say yes, and some say that such higher-level encoding representations must be innate. Ultimately, neither stance is successful (Allen and Bickhard 2013a, b).
- 6.
This literature proceeds within a background assumption of semantic information models, conflating technical covariation information with representational (about) information, without ever addressing this assumption. It is, nevertheless, evident everywhere, including Clark (2013).
- 7.
With respect to some underlying metric on the underlying space.
- 8.
Block and Siegel (2013) suggest that a better term than “error” might be “discrepancy”, but this too suggests a normative standard from which the “predictions” are “discrepant”. “Difference” is more neutral in this regard: overall, the dynamics of such a system settles into a minimization of such differences. There are no “errors” (Bickhard and Terveen 1995). There are multiple similar abuses of language in this literature, such as “error”, “representation”, “cause”, “belief”, “expectation”, “describe”, etc. none of which (in this literature) refer to anything like the phenomena that such words are generally taken to refer to. Nevertheless, they leave the suggestion, without argument, that they do constitute models for the phenomena at issue (McDermott 1981).
- 9.
This connection between cognition and life was at the center of the interactivist model from its inception, e.g., “knowing as explicated above is an intrinsic characteristic of any living system” (Bickhard 1973, p. 8; also Bickhard 1980a, p. 68). This is also a strong intuition in the enactivist framework, but, so I argue, is not so well captured by the definition of autopoiesis.
- 10.
But training has to be with respect to some normative criteria, and there are none other than what is built-in to the hyperpriors.
- 11.
In spite of brief mention of such architectures in Adams et al. (2012).
- 12.
- 13.
Hunger and eating is much more complex than this, with multiple feedforward and feedback processes, but this captures the basic organization of the phenomenon (Carlson 2013).
- 14.
This paper is not the occasion to attempt to present the entire model. I have addressed only enough to be able to make some comparisons with predictive brain models.
- 15.
Note that “steepest descent” processes are not nearly as general as an evolutionary epistemology. This is another aspect of the fact that the Bayesian models require pre-given spaces, metrics on those spaces, and innate distributions (expectations) at least at the highest “hyperprior” level.
- 16.
And such forms of interaction – e.g., with peppers – will not evoke negative emotional processes, such as fear and anxiety. I will not present the interactivist model of emotions here, but wish to point out that they too are involved in successful microgenetic anticipation (Bickhard 2000).
- 17.
Insofar as the highest level hyperpriors are “built-in” to the whole organism, not just into the nervous system, it might be claimed that such properties of pain inputs are what constitute the relevant hyperprior(s) for pain. But the interactivist model for pain and for learning with respect to pain is a selection model, a cost or utility or normative model, which – as mentioned earlier – is precisely what Bayesian hyperpriors are supposed to obviate the need for. Thus, to make such a claim contradicts the supposed ability of the Bayesian hierarchical predictive brain model to do without explicit cost or norm considerations.
- 18.
- 19.
In thus focusing on action and interaction, the interactivist model is in strong convergence with Piaget and with other pragmatist influenced models (Bickhard 2006, 2009a). (There is in fact an intellectual descent from Peirce and James through Baldwin to Piaget.) There are also interesting convergences of this model with Dewey.
- 20.
- 21.
The literature on astrocytes has expanded dramatically in recent years: e.g., Bushong et al. 2004; Chvátal and Syková 2000; Hertz and Zielker 2004; Nedergaard et al. 2003; Newman 2003; Perea and Araque 2007; Ransom et al. 2003; Slezak and Pfreiger 2003; Verkhratsky and Butt 2007; Viggiano et al. 2000.
- 22.
- 23.
- 24.
- 25.
Kiebel et al. (2008) discuss differential times scales involved in the Bayesian hierarchical model, but, in that model, the time scale differences arise because differing sequences being tracked in the environment change on differing time scales, not because of any differences at the neural and glial level (Bickhard 2015a, b).
- 26.
For more specificity concerning such macro-functional considerations, see Bickhard (2015a, b, in preparation). For the general model of perception, apperception, and so on, see Bickhard and Richie (1983; Bickhard 2009a). The model of perceiving offered has strong convergences with Gibson (1966, 1977, 1979), but also some important divergences (Bickhard and Richie 1983). A partial convergence with the model of interactive knowledge of the situation is found in Gross et al. (1999).
- 27.
Certainly not via some set of fixed innate hyperpriors.
- 28.
There is, of course, no guarantee that such self-organization will (fully) succeed at any particular time or in any particular situation.
- 29.
Lack of coherence is certainly possible, and it can also be functional (in several ways) for the brain to engage in chaotic processes. For example, chaotic processes can be a baseline form of process from which functional attractor landscapes can be induced and controlled (Freeman 1995, 2000a, b; Freeman and Barrie 1994; Bickhard 2008).
- 30.
Note also that in the Bayesian brain model, the reciprocal projections among various cortical regions are supposed to be engaging in descent iterations, not oscillations (Friston et al. 2012b).
References
Adams, R. A., Shipp, S., & Friston, K. J. (2012). Predictions not commands: Active inference in the motor system. Brain Structure and Function. doi:10.1007/s00429-012-0475-5.
Agnati, L. F., Bjelke, B., & Fuxe, K. (1992). Volume transmission in the brain. American Scientist, 80(4), 362–373.
Agnati, L. F., Fuxe, K., Nicholson, C., & Syková, E. (2000). Volume transmission revisited (Progress in brain research, Vol. 125). Amsterdam: Elsevier.
Allen, J. W. P., & Bickhard, M. H. (2013a). Stepping off the pendulum: Why only an action-based approach can transcend the nativist-empiricist debate. Cognitive Development, 28, 96–133.
Allen, J. W. P., & Bickhard, M. H. (2013b). The pendulum still swings. Cognitive Development, 28, 164–174.
Bickhard, M. H. (1973). A model of developmental and psychological processes. Ph.D. Dissertation, University of Chicago.
Bickhard, M. H. (1980a). A model of developmental and psychological processes. Genetic Psychology Monographs, 102, 61–116.
Bickhard, M. H. (1980b). Cognition, convention, and communication. New York: Praeger Publishers.
Bickhard, M. H. (1993). Representational content in humans and machines. Journal of Experimental & Theoretical Artificial Intelligence, 5, 285–333.
Bickhard, M. H. (1997). Cognitive representation in the brain. In R. Dulbecco (Ed.), Encyclopedia of human biology (2nd ed., pp. 865–876). San Diego: Academic.
Bickhard, M. H. (2000). Motivation and emotion: An interactive process model. In R. D. Ellis & N. Newton (Eds.), The caldron of consciousness (pp. 161–178). Amsterdam/Philadelphia: J. Benjamins.
Bickhard, M. H. (2006). Developmental normativity and normative development. In L. Smith & J. Voneche (Eds.), Norms in human development (pp. 57–76). Cambridge: Cambridge University Press.
Bickhard, M. H. (2008, May 22–23). The microgenetic dynamics of cortical attractor landscapes. Workshop on Dynamics in and of Attractor Landscapes, Parmenides Foundation, Isola d’Elba, Italy.
Bickhard, M. H. (2009a). The interactivist model. Synthese, 166(3), 547–591.
Bickhard, M. H. (2009b). Interactivism. In J. Symons & P. Calvo (Eds.), The routledge companion to philosophy of psychology (pp. 346–359). London: Routledge.
Bickhard, M. H. (2009c). The biological foundations of cognitive science. New Ideas in Psychology, 27, 75–84.
Bickhard, M. H. (2015a). Toward a model of functional brain processes I: Central nervous system functional micro-architecture. Axiomathes. doi:10.1007/s10516-015-9275-x.
Bickhard, M. H. (2015b). Toward a model of functional brain processes II: Central nervous system functional macro-architecture. Axiomathes. doi:10.1007/s10516-015-9276-9.
Bickhard, M. H. (in preparation). The whole person: Toward a naturalism of persons – Contributions to an ontological psychology.
Bickhard, M. H., & Campbell, R. L. (1996). Topologies of learning and development. New Ideas in Psychology, 14(2), 111–156.
Bickhard, M. H., & Campbell, D. T. (2003). Variations in variation and selection: The ubiquity of the variation-and-selective retention ratchet in emergent organizational complexity. Foundations of Science, 8(3), 215–282.
Bickhard, M. H., & Richie, D. M. (1983). On the nature of representation: A case study of James Gibson’s theory of perception. New York: Praeger Publishers.
Bickhard, M. H., & Terveen, L. (1995). Foundational issues in artificial intelligence and cognitive science: Impasse and solution. Amsterdam: Elsevier Scientific.
Block, N., & Siegel, S. (2013). Attention and perceptual adaptation. Behavioral and Brain Sciences, 36, 205–206.
Brann, D. W., Ganapathy, K. B., Lamar, C. A., & Mahesh, V. B. (1997). Gaseous transmitters and neuroendocrine regulation. Neuroendocrinology, 65, 385–395.
Buisson, J.-C. (2004). A rhythm recognition computer program to advocate interactivist perception. Cognitive Science, 28(1), 75–87.
Bullock, T. H. (1981). Spikeless neurones: Where do we go from here? In A. Roberts & B. M. H. Bush (Eds.), Neurones without impulses (pp. 269–284). Cambridge: Cambridge University Press.
Bushong, E. A., Martone, M. E., & Ellisman, M. H. (2004). Maturation of astrocyte morphology and the establishment of astrocyte domains during postnatal hippocampal development. International Journal of Developmental Neuroscience, 2(2), 73–86.
Campbell, D. T. (1974). Evolutionary epistemology. In P. A. Schilpp (Ed.), The philosophy of Karl Popper (pp. 413–463). LaSalle: Open Court.
Carlson, N. R. (2013). Physiology of behavior (11th ed.). Upper Saddle River: Pearson.
Chvátal, A., & Syková, E. (2000). Glial influence on neuronal signaling. In L. F. Agnati, K. Fuxe, C. Nicholson, & E. Syková (Eds.), Volume transmission revisited (Progress in brain research, Vol. 125, pp. 199–216). Amsterdam: Elsevier.
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36, 181–253.
Di Paolo, E. A. (2005). Autopoiesis, adaptivity, teleology, agency. Phenomenology and the Cognitive Sciences, 4(4), 429–452.
Dowling, J. E. (1992). Neurons and networks. Cambridge, MA: Harvard University Press.
Doya, K. (1999). What are the computations of the cerebellum, the basal ganglia, and the cerebral cortex? Neural Networks, 12, 961–974.
Doya, K. (2002). Metalearning and neuromodulation. Neural Networks, 15, 495–506.
Fodor, J. A., & Pylyshyn, Z. (1981). How direct is visual perception?: Some reflections on Gibson’s ecological approach. Cognition, 9, 139–196.
Freeman, W. J. (1995). Societies of brains. Mahwah: Erlbaum.
Freeman, W. J. (2000a). How brains make up their minds. New York: Columbia.
Freeman, W. J. (2000b). Mesoscopic brain dynamics. London: Springer.
Freeman, W. J. (2005). NDN, volume transmission, and self-organization in brain dynamics. Journal of Integrative Neuroscience, 4(4), 407–421.
Freeman, W. J., & Barrie, J. M. (1994). Chaotic oscillations and the genesis of meaning in cerebral cortex. In G. Buzsaki, R. Llinas, W. Singer, A. Berthoz, & Y. Christen (Eds.), Temporal coding in the brain (pp. 13–37). Berlin: Springer.
Freeman, W. J., Livi, R., Obinata, M., & Vitiello, G. (2012). Cortical phase transitions, non-equilibrium thermodynamics and the time-dependent Ginzburg-Landau equation. International Journal of Modern Physics B, 26(6), 29 p.
Friston, K. J. (2008). Hierarchical models in the brain. PLoS Computational Biology, 4(11), e1000211. doi:10.1371/journal.pcbi.1000211.
Friston, K. J. (2012). A free energy principle for biological systems. Entropy, 14, 2100–2121. doi:10.3390/e14112100.
Friston, K. (2013). Active inference and free energy. Behavioral and Brain Sciences, 36, J.212–J.213.
Friston, K. J., & Stephan, K. E. (2007). Free-energy and the brain. Synthese, 159, 417–458.
Friston, K. J., Daunizeau, J., & Kiebel, S. J. (2009). Reinforcement learning or active inference? PLoS ONE, 4(7), e6421. doi:10.1371/journal.pone.0006421.
Friston, K. J., Adams, R. A., Perrinet, L., & Breakspear, M. (2012a). Perceptions as hypotheses: Saccades as experiments. Frontiers in Psychology, 3, 1–20.
Friston, K. J., Samothrakis, S., & Montague, R. (2012b). Active inference and agency: Optimal control without cost functions. Biological Cybernetics. doi:10.1007/s00422-012-0512-8.
Fuxe, K., & Agnati, L. F. (1991). Two principal modes of electrochemical communication in the brain: Volume versus wiring transmission. In K. Fuxe & L. F. Agnati (Eds.), Volume transmission in the brain: Novel mechanisms for neural transmission (pp. 1–9). New York: Raven.
Gentner, D., & Jeziorski, M. (1993). The shift from metaphor to analogy in western science. In A. Ortony (Ed.), Metaphor and thought (2nd ed., pp. 447–480). New York: Cambridge University Press.
Gentner, D., & Rattermann, M. J. (1991). Language and the career of similarity. In S. A. Gelman & J. P. Byrnes (Eds.), Perspectives on language and thought: Interrelations in development (pp. 225–277). London: Cambridge University Press.
Gershman, S. J., & Daw, N. D. (2012). Perception, action, and utility: The tangled skein. In M. I. Rabinovich, K. J. Friston, & P. Verona (Eds.), Principles of brain dynamics: Global state interactions (pp. 293–312). Cambridge, MA: MIT.
Gibson, J. J. (1966). The senses considered as perceptual systems. Boston: Houghton Mifflin.
Gibson, J. J. (1977). The theory of affordances. In R. Shaw & J. Bransford (Eds.), Perceiving, acting and knowing (pp. 67–82). Hillsdale: Erlbaum.
Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.
Gross, H.-M., Heinze, A., Seiler, T., & Stephan, V. (1999). Generative character of perception: A neural architecture for sensorimotor anticipation. Neural Networks, 12, 1101–1129.
Haag, J., & Borst, A. (1998). Active membrane properties and signal encoding in graded potential neurons. The Journal of Neuroscience, 18(19), 7972–7986.
Hall, Z. W. (1992). Molecular neurobiology. Sunderland: Sinauer.
Haugeland, J. (1998). Having thought. Cambridge, MA: Harvard U. Press.
Hertz, L., & Zielker, H. R. (2004). Astrocytic control of glutamatergic activity: Astrocytes as stars of the show. Trends in Neurosciences, 27(12), 735–743.
Izhikevich, E. M. (2001). Resonate and fire neurons. Neural Networks, 14, 883–894.
Izhikevich, E. M. (2002). Resonance and selective communication via bursts in neurons. Biosystems, 67, 95–102.
Izhikevich, E. M. (2007). Dynamical systems in neuroscience. Cambridge, MA: MIT.
Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008). A hierarchy of time-scales and the brain. PLoS Computational Biology, 4(11), e1000209. doi:10.1371/journal.pcbi.1000209.
Koziol, L. F., & Budding, D. E. (2009). Subcortical structures and cognition. New York: Springer.
MacKay, D. M. (1956). The epistemological problem for automata. In C. E. Shannon & J. McCarthy (Eds.), Automata studies (pp. 235–251). Princeton: Princeton University Press.
MacKay, D. M. (1969). Information, mechanism and meaning. Cambridge, MA: MIT Press.
Marder, E. (2012). Neuromodulation of neuronal circuits: Back to the future. Neuron, 76, 1–11.
Marder, E., & Thirumalai, V. (2002). Cellular, synaptic and network effects of neuromodulation. Neural Networks, 15, 479–493.
Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition. Dordrecht: Reidel.
McDermott, D. (1981). Artificial intelligence meets natural stupidity. In J. Haugeland (Ed.), Mind design (pp. 143–160). Cambridge, MA: MIT Press.
Medin, D. L., Goldstone, R. L., & Gentner, D. (1993). Respects for similarity. Psychological Review, 100, 254–278.
Nauta, W. J. H., & Feirtag, M. (1986). Fundamental neuroanatomy. San Francisco: Freeman.
Nedergaard, M., Ransom, B., & Goldman, S. A. (2003). New roles for astrocytes: Redefining the functional architecture of the brain. Trends in Neurosciences, 26(10), 523–530.
Neisser, U. (1967). Cognitive psychology. New York: Appleton.
Newman, E. A. (2003). New roles for astrocytes: Regulation of synaptic transmission. Trends in Neurosciences, 26(10), 536–542.
Nieuwenhuys, R. (2001). Neocortical macrocircuits. In G. Roth & M. F. Wullimann (Eds.), Brain evolution and cognition (pp. 185–204). New York: Wiley.
Perea, G., & Araque, A. (2007). Astrocytes potentiate transmitter release at single hippocampal synapses. Science, 317, 1083–1086.
Pezzulo, G. (2008). Coordinating with the future: The anticipatory nature of representation. Minds and Machines, 18, 179–225.
Pezzulo, G., Candidi, M., Dindo, H., & Barca, L. (2013). Action simulation in the human brain: Twelve questions. New Ideas in Psychology. http://dx.doi.org/10.1016/j.newideapsych.2013.01.004
Powers, W. T. (1973). Behavior: The control of perception. Chicago: Aldine.
Ransom, B., Behar, T., & Nedergaard, M. (2003). New roles for astrocytes (stars at last). Trends in Neurosciences, 26(10), 520–522.
Roberts, A., & Bush, B. M. H. (Eds.). (1981). Neurones without impulses. Cambridge: Cambridge University Press.
Roesch, E. B., Nasuto, S. J., & Bishop, J. M. (2012). Emotion and anticipation in an enactive framework for cognition (response to Andy Clark). Frontiers in Psychology, 3, 1–2.
Slezak, M., & Pfreiger, F. W. (2003). New roles for astrocytes: Regulation of CNS synaptogenesis. Trends in Neurosciences, 26(10), 531–535.
Sokolov, E. M. (1960). Neuronal models and the orienting reflex. In M. Brazier (Ed.), The central nervous system and behavior (pp. 187–276). New York: Josiah Macy Jr. Foundation.
Tani, J., & Nolfi, S. (1999). Learning to perceive the world as articulated: An approach for hierarchical learning in sensory-motor systems. Neural Networks, 12, 1131–1141.
Varela, F. J. (1979). Principles of biological autonomy. New York: North Holland.
Varela, F. J. (1997). Patterns of life: Intertwining identity and cognition. Brain and Cognition, 34, 72–87.
Verkhratsky, A., & Butt, A. (2007). Glial neurobiology. Chichester: Wiley.
Viggiano, D., Ibrahim, M., & Celio, M. R. (2000). Relationship between glia and the perineuronal nets of extracellular matrix in the rat cerebral cortex: Importance for volume transmission in the brain. In L. F. Agnati, K. Fuxe, C. Nicholson, & E. Syková (Eds.), Volume transmission revisited (Progress in brain research, Vol. 125, pp. 193–198). Amsterdam: Elsevier.
Weber, A., & Varela, F. J. (2002). Life after Kant: Natural purposes and the autopoietic foundations of biological individuality. Phenomenology and the Cognitive Sciences, 1, 97–125.
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Bickhard, M.H. (2016). The Anticipatory Brain: Two Approaches. In: Müller, V.C. (eds) Fundamental Issues of Artificial Intelligence. Synthese Library, vol 376. Springer, Cham. https://doi.org/10.1007/978-3-319-26485-1_16
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