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Toward a Model of Functional Brain Processes II: Central Nervous System Functional Macro-architecture

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Abstract

The first paper in this pair (Bickhard in Axiomathes, 2015) developed a model of the nature of representation and cognition, and argued for a model of the micro-functioning of the brain on the basis of that model. In this sequel paper, starting with part III, this model is extended to address macro-functioning in the CNS. In part IV, I offer a discussion of an approach to brain functioning that has some similarities with, as well as differences from, the model presented here: sometimes called the Predictive Brain approach.

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Notes

  1. In the first paper—on micro-level processes—of this pair of papers.

  2. Further, the dynamics of learning enable the dynamics of functional self-organization.

  3. Constructive processes of various kinds have been postulated in multiple models, but it has not been often recognized that action based models force a constructivism.

  4. Generalizing the model of variation and selection beyond biological evolution to epistemological phenomena is the core of evolutionary epistemology (Campbell 1974). Selection principles can, in fact, be generalized even further to almost any kind of non-accidental and non-designed fit to criteria (Bickhard and Campbell 2003). In such broad form, models that make selection phenomena central are sometimes known as forms of Universal Darwinism (Dennett 1995). It should be noted, however, that, just as for any other explanatory principle, variation and selection principles can be used in incorrect models as well as correct models.

  5. For discussions of emergence, see (Bickhard 2009a; Bickhard and Campbell 2003; Clayton and Davies 2006; Deacon 2012; Thompson 2007).

  6. The circularity arises if I claim to model the emergence of representation out of anticipation, but then it turns out that anticipation (or the checking of environmental anticipation) requires representation in turn. So representation would be being defined in terms of representation: a circularity.

  7. And local anticipatory success and failure constitute truth and falsity of the anticipatory set-ups, thus ground representation. See Part II (in the first paper of this pair, on micro-level processes) and Bickhard (2009a).

  8. See later discussion for a comparison with ‘predictive encoding’ models.

  9. It should be noted that this quick model is at best a first approximation. There are multiple delays in eating and blood sugar feedback, that have resulted in multiple forms of detection and feedforward and feedback processes to regulate eating (Carlson 2013). These complexities, however, do not alter the basic point in the text.

  10. E.g., too complex for any non-foresighted constructive process to happen to hit upon it directly (Simon 1969; Campbell 1974). See also discussions of functional scaffolding (Bickhard 1992, 2005b).

  11. In this model, learning and development involve the same underlying constructive processes. They focus on differing aspects of that process: learning focuses on short term properties of such constructions, while development focuses on longer term properties. Enabling and constraint relationships among possible constructions were within the core of, for example, Piagetian models, but their study has diminished as non-action based models (e.g., innatist and other maturational models) have proliferated (Allen and Bickhard 2011a, b, 2013). Often, learning and development are considered (at least implicitly, though often explicitly) to involve distinct processes.

  12. That the processes by which variations are generated are themselves aspects of adaptability follows directly from the nature of variation and selection processes (Bickhard and Campbell 2003). But it is an aspect that has often been overlooked in the focus on selection effects per se (for related discussions, see Brooks and Wiley 1988; Brooks et al. 1989; Weber et al. 1988; Kauffman 1993).

  13. That is, such differentiations and specializations have been retained in macro-evolution because of their adaptability in organism-environment interaction and because of the enabling of further specialization in further evolution. Thus, they constitute one among multiple macro-evolutionary trends.

  14. Generally referred to as sensory and motor systems. But “sensory” at least carries strong and unwanted encoding connotations. Gibson’s term “perceptual systems” is much more congenial to the interactivist model (Gibson 1966).

  15. In abstract machine theoretic terms, this is roughly equivalent to an undefined state transition, but here there is no “halt” condition.

  16. Uncertainty here is an internal functional (microgenetic) condition, not a stimulus.

  17. The identification of such processes as ‘emotion’ requires further elaboration and argument (Bickhard 2000b, 2007a, in preparation). The central point for current purposes is that the ability to interact with microgenetic uncertainty constitutes a powerful adaptive possibility, and, therefore, that it is a plausible phase in the macro-evolution of the central nervous system.

  18. Perhaps dynamically constituted in chaotic processes.

  19. Emotion, thus, doesn’t necessarily reduce uncertainty directly: it yields special kinds of interactions with situations that evoke uncertainty. If successful, such interactions will then reduce uncertainty.

  20. E.g., it could do X if it first did (conditional on first doing) Y and then Z.

  21. Indications of the possibilities of interacting with a toy block in front of a toddler would be an example of part of the situation knowledge for that toddler.

  22. Indications of potentiality are modal and can functionally connect in counterfactual ways. So, memory can be what could be anticipated if the organism were to return to the situation (or if some counterfactual conditions obtained). Note that one of the adaptive advantages of episodic memory is that it does permit recreating past trajectories of experience for, e.g., the purpose of figuring out anticipations that were not made explicit originally. E.g., the group was headed for water, but, now that we’ve been able to drink, it would be advantageous to mentally re-trace our route to see if there were any indications of available food along the way.

  23. Episodic memory, construed as transduced images or videos, constitutes the ground of standard encoding models. It poses a challenge to action based models: how can episodic memory be modeled within an action based, anticipatory, model—a non-image, non-video, non-encodingist model? The model outlined above of exploring trajectories of situation knowledge apperceptive processes provides a frame of an answer.

  24. “Re-entrant” is a common term for this kind of architecture, but it carries the connotation that what is being “re-entered” is semantic information, and that is false.

  25. This is much more complex than I will expressly take into account: the striatum is itself differentiated into rough architectural components, the loop in some respects is a four (or more) node loop, not just three, and there are within-nuclei differentiations as well as between-nuclei differentiations. One example of the latter point is that the head of the caudate is more specialized for cognitive processes, while the tail of the caudate is involved in more traditional motor processes (Koziol and Budding 2009).

  26. For a model of language within this framework, see Bickhard (2007b, 2009a, in preparation). For perception, see Bickhard and Richie (1983) and Bickhard (2009a, b, in preparation). In the standard information processing framework, perception is construed as an input flow into cognition, and cognition, in turn, generates an output flow into action or language. These presumed input and output flows are among the most seductive pulls into an information processing view. This seductive power fades, however, if it is recognized that perception, cognition, action, and language (not to mention emotion, etc.) are all interactive processes, not semantic-information flows.

  27. Variants on models of CNS self-organization can be found in, for example, Arbib (1972) and Juarrero (1999).

  28. Shannon information is often taken to be inherently “semantic” because it is framed in terms of meaningful messages (meaningful to senders and receivers), but all that the mathematics does is to quantify amount of modulation of one process on another. If that second process is being modulated with respect to “problem or conceptual spaces”, then this will involve meaning for the people who interpret things into such spaces. But the mathematics applies equally as well to, for example, the control of a factory of refinery process, in which there is much less, or no, temptation to conclude that the control is “semantic”. “Information” is a measure of control theoretic correlation, and such correlation is, more broadly, what constitutes technical information.

  29. For a more detailed critique of predictive-Bayesian-free energy models, see Bickhard (in press).

  30. Sometimes called feed-forward models.

  31. Though they are strictly inconsistent with Gibson’s claim that perceiving is not based on intermediate representational sensing (Gibson 1979; Bickhard and Richie 1983).

  32. Note that the spaces over which these parameters ‘parameterize’—spaces of functional forms for prediction—must themselves be already available (Friston et al. 2009 on switching between functional forms)—presumably innate. The highest level innate prior probability distributions, over whatever spaces they are distributed, are the highest level instance of this point.

  33. Another framework that emphasizes prediction and anticipation is that of Rosen (1985, 1991). Rosen, however, focuses on prediction based on representational models and does not address the (emergent) nature of representation.

  34. Note that this makes the Bayesian layers, insofar as they exist at all, innate in the architecture of the brain, i.e., not learned.

  35. Note that such reciprocal projections would be ideal for engaging in oscillatory processes that could modulate connected such oscillatory processes.

  36. This might be considered, in an extended analogical sense, as a kind of homeostasis. But it is a homeostasis in a probabilistic sense (ergodicity) rather than a maintenance of set-point sense. Also, it doesn’t necessarily involve feedback.

  37. Note that the assumption of ergodicity is not new in this paper, but has been central to the general model’s conceptual and mathematical framework, e.g., Friston (2012), Friston et al. (2012a, b).

  38. And in the first of this pair of papers.

  39. Regarding the possibility of correlational information constituting representation in itself, consider: information is a factual relationship, not a semantic relationship. It either exists or it does not; there is no way for it to exist but be false. This is a basic Brentano point, one that has been and still is pervasively ignored. For discussion of some recent ingenious but ultimately failed attempts to address it, see Bickhard (1993, 2009a).

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  1. Lehigh University, 17 Memorial Drive East, Bethlehem, PA, 18015, USA

    Mark H. Bickhard

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  1. Mark H. Bickhard
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Correspondence to Mark H. Bickhard.

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Bickhard, M.H. Toward a Model of Functional Brain Processes II: Central Nervous System Functional Macro-architecture. Axiomathes 25, 377–407 (2015). https://doi.org/10.1007/s10516-015-9276-9

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  • DOI: https://doi.org/10.1007/s10516-015-9276-9

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