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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Sufficient statistics (Friston et al. 2009).
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For an early version of such hierarchical prediction, see Tani and Nolfi (1999).
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E.g., Locke, Hume, Russell (in some incarnations), Fodor, much of contemporary literature, and even Aristotle’s signet ring impressing its form into wax.
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“Transduction, remember, is the function that Descartes assigned to the pineal gland” (Haugeland 1998, p. 223).
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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).
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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).
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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).
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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.
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But training has to be with respect to some normative criteria, and there are none other than what is built-in to the hyperpriors.
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In spite of brief mention of such architectures in Adams et al. (2012).
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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).
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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.
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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.
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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.
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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.
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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.
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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).
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Certainly not via some set of fixed innate hyperpriors.
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There is, of course, no guarantee that such self-organization will (fully) succeed at any particular time or in any particular situation.
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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).
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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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