Accepted Manuscript Foundations of anticipatory logic in biology and physics Jesse S. Bettinger, Timothy E. Eastman PII:

S0079-6107(17)30122-0

DOI:

10.1016/j.pbiomolbio.2017.09.009

Reference:

JPBM 1270

To appear in:

Progress in Biophysics and Molecular Biology

Received Date: 31 May 2017 Revised Date:

1 September 2017

Accepted Date: 4 September 2017

Please cite this article as: Bettinger, J.S., Eastman, T.E., Foundations of anticipatory logic in biology and physics, Progress in Biophysics and Molecular Biology (2017), doi: 10.1016/j.pbiomolbio.2017.09.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Foundations of Anticipatory Logic in Biology and Physics by Jesse S. Bettinger and Timothy E. Eastman

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KEYWORDS: complex systems; predictive coding; anticipatory systems; causal modeling; abduction; entropy and syntropy; perception; neuroscience; Bayesian modeling; quantum interpretations; homeostasis and allostasis; interoception; autopoietic dynamics; biosemiotics

Abstract

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Recent advances in modern physics and biology reveal several scenarios in which top-down effects (Ellis, 2016) and anticipatory systems (Rosen, 1980) indicate processes at work enabling active modeling and inference such that anticipated effects project onto potential causes. We extrapolate a broad landscape of anticipatory systems in the natural sciences extending to computational neuroscience of perception in the capacity of Bayesian inferential models of predictive processing. This line of reasoning also comes with philosophical foundations, which we develop in terms of counterfactual reasoning and possibility space, Whitehead’s process thought, and correlations with Eastern wisdom traditions.

Introduction

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Taking a survey of natural science, we are reminded of a characteristic causality in closed thermodynamical systems whereby over time they tend to disorder. This “classical” causality is formalized by the 2nd law of thermodynamics and describes system evolution from causes-to-effects, or from past-to-present; thus, it goes forward in a progressive and accumulative manner. This classical causality is often presumed to be the dominant mode of causal relation for both physics and biology; however—as the 1937 Nobel Prize winner in Physiology for the discovery of Vitamin C—Albert Szent-Györgyi realized: “it is impossible to explain the qualities of organization and order of living systems starting from the entropic laws of the macrocosm. This is one of the paradoxes of modern biology: the properties of living systems are opposed to the law of entropy that governs the macrocosm” (Synthesis, v1, 1977).

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In the study of complex systems, the theoretical biologist, Robert Rosen, found evidence for anticipatory systems in examples from biology, chemistry, physics, mathematics, and the larger (logical) context of his “relational complexity” (Kineman, 2012). In contrast to classical, forward-oriented, causality that approaches the future directly, anticipatory systems approach the future indirectly through generative models. To embrace such systems reflects an expansion of causal logic to include an anticipatory, or predictive process that enables complex systems with sources of free energy to counteract the second-law of thermodynamics and maintain order. As Rosen explains: "systems that behave as true anticipatory systems, systems in which the present state changes according to future states, violate the law of classical causality according to which changes depend solely on past or present causes" (1985). Rosen follows this by explaining how none of the systems he identifies conform with such classical causality and yet they do not violate any temporal causality. The only way we could imagine it would work as a "true" anticipatory

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system would be if there were two different time scales active in the same process, and one of them was (perhaps considerably) faster than the other. For instance, like von Economo neurons, capable in theory of sending signals quicker than other signaling cells in the same medium. In this case, it might suggest something like a faster signal that becomes an influence on the slower one. It would be like two types of signals racing in an obstacle course to a target, but one signal is much faster, such that when it arrives first, it then turns around and goes back to meet the other racer (signal), and influences/helps guide it to finish the race—perhaps leading to a change of strategy given the influence of the faster feedback signals having already seen the finish line. Von Economo neurons compress a multimodal stream of information into a simple signal and, due to a longer and wider axon, can deliver that signal quicker (than neighboring pyramidal neurons), such that diverse brain regions receive advance influence. This would be like a cellular “nonlocality.”

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Considering a broad panorama of scientific thought over the last 150 years, we discover, in particular, the convergent evolution of so-called retro-causal behavior in certain systems appearing to act from the future onto the present, as anticipatory or predictive. This appears in several particular incarnations: in Fantappie's syntropy (1941); in Einstein's dual-solutions to the mass-energy-momentum equation (1905; see also Wheeler et al, 1973), plus the d'Alembert and D'Broglie equations; in Rosen's definition and collection of qualified anticipatory systems (1980); and in inferential, Bayesian predictive coding (Bayes 1764; Friston, 2003) qua perception and criticality, among others. Given that none of these examples reference the others, they are, in a sense, truly convergent. Thus, they can be taken each as harbingers of a complimentary causality at work in living systems. In some cases, these authors suggest a strong teleology that treats a future state as somehow already actualized; in contrast, such actualism is here denied. Alternative future possibilities may be anticipated and may, in some cases, be inevitable, but such future states are not actualized as in Lewisian possible worlds. The study of complex systems and anticipatory logic is entirely consistent with an open future; any suggestion of telos is limited to anticipations of future possibilities, not to some pre-given actualizations of future states within a block universe.

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Extrapolating the particular mechanisms driving these systems, the purpose of this article is to coalesce the logic underwriting all of these recent scientific developments into a unified philosophical framework. Combining them, we provide a logico-causal scenario in which a syntropic causality (Fantappie, 1941; Vannini, 2005) manifests in top-down effects (Ellis, 2016) qua anticipatory systems (Rosen, 2012), indicating a process at work that enables the active modeling and inferential generation of predictions such that anticipated effects project onto potential causes. We consider the evolutionary advantage of such systems in providing regulatory oversight for the organism, and the ability to keep operations at-or-near certain parameters required for survival and optimal functioning, as in criticality and homeostasis. We reflect on whether this logic can be revised or updated into the logic of physics to reflect the same dynamics at work in corresponding models.

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This line of reasoning also comes with philosophical foundations; in particular, we look at counterfactual reasoning, possibility space, Peirce's kainopythagorean categories, synaptic weights of predictive models, and Whitehead’s process thought—before turning to a discussion on the role of anticipatory, predictive systems as regulatory and based on maintaining the homeostasis of some environment (or "minimizing the free-energy" of a system). We show how this additionally inspires a tidy conjunction with the language and concepts used to describe Tao and other principles/tenets in Taoism & Buddhism.

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Brief History of Anticipatory Logic and Systems Anticipation became part of the vocabulary of science and philosophy at the end of the last century; however, the notion of an inferential and predictive process underwriting perception has been slowly emerging for the last three-hundred years. Beginning with Rev. Thomas Bayes', "An Essay Towards Solving a Problem in the Doctrine of Chances" (1764) arguing in defense of an a priori uniform distribution for an unknown probability (see Stigler, 1982), Helmholtz later rekindled the notion in 1867, putting forward that visual perception is mediated by unconscious inferences. That same year, somewhere in the woods of Pennsylvania, Peirce forwarded the notion of abductive logic, which only recently has been linked to the actual mechanics by which Bayesian reduction takes place (Hohwy, 2013; Douven & Schupbach, 2015). After Peirce, the language of anticipation appears in the science writings of: (Whitehead, 1929, 1938; Feynman, 1949; Svoboda, 1960; Bennett, 1976; Klein, 1989; Hinton, 1995; Xinqing et al., 1996; Nadin, 2000, 2010; Edelman, 2001; Gregory, 1980; Friston, 2010; Seth, 2011, 2013). Perhaps the hallmark text was Rosen's "Anticipatory Systems" (1980), proceeded by “Organisms as Causal Systems which are Not Mechanisms: an Essay into the Nature of Complexity” (1985). Other early contributions providing a foundation include: "Biological systems theory: descriptive and constructive complementarity" (Pattee, 1978); “Categorical System Theory” (Louie, 1983); Burgers’ development of the principle of "choice" and axiom for the "preservation of freedom" as (both) coextensive with anticipation (1975); and Ekdahl’s “Classification of Anticipatory Systems” (1997).

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We begin this paper by introducing Rosen’s concept of anticipatory systems. We show that Rosen’s development, in terms of a philosophical, mathematical and methodological foundation, demonstrates a forerunner to models of predictive coding and active inference that were largely developed in the new millennium, after his passing. This provides an essential foundation for present approaches to perception within computational neuroscience, Friston’s predictive coding process and the free-energy principle. After this, we shift gears to develop anticipatory logic in certain results of 20th century mathematical physics where we encounter advanced signaling such as with apparent “backward-in-time” causation in the context of the D’Alembert, D'Broglie and Einstein equations; and phenomenologically in terms of tachyonic (bosonic) strings. Fantappie nominates this a "syntropic causality." We suggest interpreting these effects as anticipatory signals qua nature’s ecological and organic feedback: as a connection linking us to alternative possibilities for the natural world, cosmos and each other. In a final set of discussions, we turn our attention to a small set of corresponding philosophical indications and applications of anticipatory logic developed in the context of: counterfactual systems and possibility space; Whitehead’s “propositions,” and in the context of Taoism qua Tao (plus Yin/Yang) as a regulatory principle qua homeostasis, free-energy minimization and optimization.

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1. Anticipatory Systems

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The notion that causal processing in biological systems should be something other than a feed-forward progression of data into concrete facts, may feel like a foreign concept when first encountered. For the theoretical biologist, Robert Rosen, discovering "the ubiquity of anticipatory behavior lying at the heart of natural science and mathematics" (1980) provided the sense that he had "arrived upon the threshold of some entirely new perspective in the theory of natural systems, and of biological systems in particular" (1980). However, as such insights go, institutional acclaim is not usually the first impulse. As he describes, "the failure to recognize and understand the nature of anticipatory behavior has not simply been an oversight, but is the necessary consequence of the entire thrust of theoretical science since earliest times" (Rosen, 1980). For this reason, "anticipation has routinely been excluded from any kind of systematic study on the grounds that it violates the causal foundation on which all of theoretical science must rest" (ibid).

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Still, "biology is replete with situations in which organisms can generate and maintain internal predictive models of themselves and their environments, and utilize the predictions of these models about the future for purpose of control in the present" (Rosen, 1980). This led Rosen to the conclusion that "an entirely new approach was needed in which the capability for anticipatory behavior was present from the outset" (1980), and where the modeling relation between a system and its analog was central, stating that "such an approach would necessarily include, as its most important component, a comprehensive theory of models and of modeling" (Rosen, 1980).

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The concept of a system with an internal predictive model offers a rigorous means to study an anticipatory system through hypotheses about future behavior. As Rosen explains, “Organisms describe those systems which can make predictive models (of themselves, and of their environments) and use these models to direct their present actions” (1980). A system containing a predictive model uses the predictions of that model to generate its behavior.

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To begin, "anticipation concerns the capacity exhibited by some systems to tune their behaviour according to a model of the future evolution of themselves or the environment in which they are embedded” (Poli, 2014). "The agency through which the prediction is made must be, in the broadest sense, a model" (Rosen, 1980)—or, "an encoding of qualities, or observables, of a natural system into formal mathematical objects" (ibid). Biologically, these encodings serve "as an interactive set of models – of self, of environment, and of relations between the two”—and dynamical models represent the vehicles through which temporal predictions are made (Rosen, 1980). "The model behaves as the real system would if it possessed only those interactive capabilities which are explicitly encoded into the model" (Rosen, 1980).

Rosen considers the foremost function of a predictive model as pulling "the future into the present; a system with a good model thus behaves in many ways like a true anticipatory

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system" (Rosen, 1980). The means for anticipating the future is through effective recalling of the past. Interestingly, a change of state in the generative model occurs instantaneously "as a function of its predictions” (Rosen, 1980), suggesting a type of biological nonlocality. As Rosen explains, “a modeling relation between systems is established through an encoding of qualities pertaining to one of them into corresponding qualities of the other, in such a way that the linkages between these qualities are preserved [...allowing] for an instantaneous change of state based on predictions of the model pertaining to a future state” (Rosen, 1980). A change in one should, therefore, be correlated with a change in the other. "These models have the capacity to predict next behavior (of self and/or of native environment) based on current behavior" (Rosen, 1980)— as in the example of the frog catching a fly by predicting its flight trajectory a few steps into the future.

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Rosen says that such systems “will behave as if [they] were a true anticipatory system; i.e. a system in which present change of state does depend on future states" (Rosen, 1980). However, this same feature also makes predictive models vulnerable to ‘quick’ and ‘radical’ changes (Rosen, 1980); "as a result, they are often incapable of dealing with or surviving change that is unprecedented. But, as long as environmental changes are slow enough and [...] congruent with what the model entails, the system is robust and remains stable" (Coffman & Mikulecky, 2015).

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In terms of dynamics, this describes an active and evolving, generative model against which new predictions are constantly weighed and adapted qua optimized. Such a model can be understood as an organic process unfolding within the confluential nexus of two distinct causalities converging in a dual, reactive and anticipatory modality. Incoming values have the ability to alarm or beckon the system to take note of outlying values. This occurs via the salience network. In addition, "expectation, as a particular form of anticipation, is connected to future contributions to defining uncertainty” (Rosen, 1980). Such systems are qualified as ergodic (Breakspear, 2004; Friston, Breakspear, Deco, 2012).

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Rosen’s formal models of “anticipatory systems” provide insight into how living systems and organisms are able to maintain homeostasis in a changing environment. To do this, the system must contain a model of its environment, wherein "entailments of the model are congruent with causation outside the system" (1980). The system is anticipatory to the extent that “the model creates entailments more rapidly than the corresponding causation occurs in the world at large" (Coffman & Mikulecky, 2015). Thus, we consider the purpose as primarily to maintain regulation of internal optimal parameters, and in providing predictions of future behavior, as in the case of predictor/prey encounters. Here, predictions of next-behavior represent an active process running through the individual’s mind, linked to differential activation of the supplementary motor cortex (Nachev, 2008). Overall, we see ample room for rolling back teleological claims given that the ergodic property still allows for probabilities about the future based on the ability of complex systems in the present (and predicated on the past) to act in a genuinely anticipatory

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2. Perception, Inference and Neuroscience

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fashion. To address this, Dubois (2000) distinguishes between weak anticipation–along Rosen’s idea of a model-based process–and strong anticipation “when the system uses itself for the construction of its future states” (Dubois & Leydesdorff 2008). The latter case is reinforced by Seligman's notion of prospection, involving "no backward causation; rather, it is guidance not by the future itself but by present, evaluative representations of possible future states" (Seligman et al., 2013). These latter two definitions come nearest to the sense of predictive coding.

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The initial thrust of our paper entails an update to Rosen’s exposition of anticipatory systems into a neuroscientific process that only gained significant notice and formality in the years after the publication of his seminal work "Anticipatory Systems" (1985, 2012). Were Robert Rosen alive today we feel confident that he would agree with this addendum; namely, the notion of the Bayesian brain, which proposes that neural processing in perception is dually-predicated on an inferential process of hierarchical models descending onto the data of a classical, feed-forward sensory (interoceptive) process (Seth, 2013). This logic reverses the intuitive causality of perception as a progressive accumulation (enumeration) of sensory data, and instead describes a predictive process driven by inferential and generative models of the system.

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The first traces of an inferential logic underwriting perception were formally developed in 1763 by Rev. Thomas Bayes, in the context of statistics and probability with the work, “An Essay towards solving a Problem in the Doctrine of Chances;” and later, with Laplace’s “Théorie analytique des probabilités” (1812); Peirce’s “Logic of Science” qua abduction (1865); Helmholtz’s “unconscious inference” (1867); and Ashby’s “Every Good Regulator” hypothesis (1947). Upon this basis, even if tacitly, several applied methods took shape in computational neuroscience including: Bayesian brain models (Jaynes, 1988; Battaglia, Jacobs & Aslin, 2003; Knill & Pouget, 2004), predictive-coding (Mumford, 1992; Rao & Ballard, 1999; Grossburg, 2009; Seth & Critchley, 2013; Seth, 2013; Sel, 2014; Lupyan & Clark, 2015), active inference (Friston et al., 2012; Adams, Shipp & Friston, 2013; Seth, 2013; Gu & FitzGerald, 2014; Mnih et al, 2016; Friston et al., 2016), and the free-energy principle (Friston, Kilner & Harrison, 2006; Friston, 2009). Even though Bayesian coding has been articulated in vision, proprioception and exteroception, the best sensory application appears by virtue of a role underwriting interoceptive affect and emotional awareness in the neurovisceral axis (Seth et al, 2011; Seth, 2013; FitzGerald & Gu, 2014; Quattrocki & Friston, 2014). This logic gains additional conceptual stability when read through the corresponding format articulated in Whitehead’s modes of perception (Bettinger, 2015). The Bayesian Brain The Bayesian-brain hypothesis states that the neural processing of perception can be prodigiously described under the rubric of a probabilistic model. Here, probability distributions (known as posterior beliefs) are encoded by the activity of neural

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Predictive Coding and the Free-Energy Principle

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populations encoding predictions (Friston, 2013). This indicates that "a probabilistic representation is induced by biophysical states of the brain” (Friston, 2013). The likelihood, or confidence of these predictions is updated in light of sensory signals following Bayes’ rule, as more information becomes available. "The insight here was that the same problems that Richard Feynman (1972) had solved in statistical physics, using path integral formulations and variational calculus could be applied to the problem of Bayesian inference, namely, how to evaluate the evidence for a model" (Friston, 2011). As such, Bayesian inference provides a method for combining new evidence with prior beliefs through the active generation of sensory predictions about the “hidden causes in the external world” (Friston, 2012). In so doing, the brain tries to explain what is happening in the outside world. As (Gregory, 1980) and (Dayan & Hinton, 1995) explain, this suggests that perception is hypothesis-driven.

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The (pure, ideal) Bayesian brain model takes shape in an approximated computational capacity through a method known as predictive coding: a method entwined with the freeenergy principle and active inference. Predictive-coding offers a neurobiological framework for inferentially-acquiring knowledge about the world through a process wherein bottom-up driven sensory signals are largely constrained (systematically suppressed) by converging, top-down predictions designed to minimize the surprise (or deviation from expectation/norm) that correlates with a measure of free-energy.

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According to the free-energy principle, autopoietic (self-organizing) systems maintaining an equilibrium with their environment operate in order to minimize their free-energy (Friston, Kilner & Harrison, 2006). Mathematically, this explains how adaptive biological systems resist the second law of thermodynamics through the provision of "a principled explanation for self-organization in the face of a natural tendency to disorder" (Friston, 2013; see also Ashby, 1947; Nicolis & Prigogine, 1977; Haken, 1983; Kaufmann, 1993). Applied to Bayesian models of perception, free-energy represents “the difference between what is predicted, given the current estimates of hidden states, and the sensory inputs actually sampled” (Friston, Breakspear & Deco, 2012). The prediction-error of predictive coding is an information theoretic quantity that is effectively equal to free-energy in statistical thermodynamics such that to minimize one is to minimize both. From an operational perspective, maximizing model-evidence provides the Yin to the Yang of minimizing (variational) free-energy—thus affording two outcomes for one process. As Seth and Friston explain, “placing predictive coding in an embodied or enactive framework in which both action and perception are in the game of minimizing the same prediction error is known as “active inference” (Friston et al., 2010; Seth and Friston, 2016). Active inference refers to when prediction errors are minimized through action, or allostasis. In an active inferential model, the brain is modeled as a “statistical organ” that contextualizes the world through predictions. While the concept may seem foreign, it is actually most intuitive. Not just humans, but all organisms update models as novel

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experiences offer new precedents. Interestingly, an optimization of values applies not only for the difference between (top-down) predictions and (bottom-up) sensory data: that is, the prediction-error—but also to the precision (or reliability) for future sensorydata of that type (Friston, 2013; Dayan & Hinton, 1995), and as a “confidence” measure based on previously-established synaptic-weighting values (Friston, 2003; Barrett & Simmons, 2015; Chanes & Barrett, 2016).

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Free-energy can be used to model neural simulations of perception and action (Friston et al., 2009) using "internal hierarchical models to predict its sensory input and suggests that prediction-errors are constantly minimized by neural activity and synaptic connections” (Friston, 2008; Carhart-Harris & Friston, 2010). Methodologically, this is achieved by suppressing successfully-predicted values (Friston, 2010) for the purpose of identifying remaining outliers, each to be appended a salience signature and brought to the attention of that organism, both in terms of affect and physiological vigilance (Goulden, 2014). The principle driving this operation involves a constantly-adapting push towards “optimization,” or the fine-tuning of models.

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Developed independently in 1867, Peirce articulated a form of logical inference—moving from an observation to a theory accounting for that observation—termed abduction, or "inference to the best explanation” (Hohwy, 2013).1 Unlike in deductive reasoning, the premises do not guarantee the conclusion. Functionally, this describes a process where solutions are asymptotically obtained by eliminating all other values until only the remainder(s) are left. Taken together in terms of a Bayesian brain, this nominates an (active) inferential predictive coding process to serve as a sieve for information designed to abductively identify outliers and differences, to be made salient in attention for the sake of maintaining balance via possible allostatic action. This ‘elimination’ or constraining element also implies Dennett’s notion of ‘explaining away’ phenomena (2001) as a process underwriting consciousness insofar as predicted values are ‘negatively prehended’ (Whitehead, 1929), leaving only input data with more entropy (surprise) to be attenuated for conscious awareness via salience and physiological vigilance.

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In the context of interoception and the neurovisceral axis, predictions descend onto incoming sensory afferent fibers arriving in the posterior insular cortex—while prediction errors register through differential activations of the anterior cingulate and anterior insular cortices, made salient in perceptual and physiological awareness. As such, predictive coding and active inference provide regulatory oversight of activity in the internal milieu aimed at maintaining homeostasis through the minimizations of freeenergy qua prediction errors. This is where active inference and allostasis come into play: when presented with errors, either the model adapts or the operator (of the organism, the agent) performs an action to restore balance, as in eating food to address hunger pangs.

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Notably, Peirce’s abduction includes a function of ‘musing’ as well, which provides space for novelty by not assigning truth values to propositions.

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Friston's work in computational neuroscience and dynamic causal modeling (Friston, 2002; Friston, Harrison, Penny, 2003; Friston, 2009) can be shown to represent an applied result of Rosen's axioms for anticipatory systems, as evidenced in Bayesian models of predictive coding and active inference qua free-energy minimization, involving synaptic gain modulation and adjustments, optimizations of synaptic-weighting models, and laminar-layered hierarchical processing. Perhaps the most succinct link between Rosen’s description of anticipatory systems applied in the context of perception neuroscience is illustrated in the following description:

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The means by which a living system is internally guided and controlled involves encoded information acting as an interactive set of models – of self, of environment, and of relations between the two. . . through time. These models have the capacity to predict proximate behavior (of self and environment) based on current behavior (Rosen, 1980).

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Rosen explains, “we seek to encode natural systems into formal ones [such that] the inferences or theorems we can elicit within such formal systems become predictions about the natural systems we have encoded into them” (Rosen, 1980). Developing this in a neuroscientific sense, information is encoded through synaptic weighting, and predictions can be altered by hierarchical gain modulation operating as generative models of the system regarding incoming sensory data.

4. Mathematical Physics and the Quantum Transactional Approach

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Let us suppose for a moment that virtual tachyonic bosons can be modeled to reflect 'personal probability' waves qua synaptic weighting. In this sense, they would not reflect “faster-than-light” activity, but signaling predictions, or "propositions" (Whitehead, PR) waiting to be contextualized from an anticipatory, or predictive causality. Applied in a neuroscientific context, one might consider whether the tachyons of quantum theory negative solutions can be reframed to reflect synaptic weighting. Currently the most promising realization of this can be linked with Quantum Bayesian modeling (Fuchs and Schack, 2011). In general, what takes the place of “faster-than-light” dynamics, apparently moving from the future to the present, are causal operations based on opposing dynamics to the usual entropic enumeration, as instead, convergent, adaptive and anticipatory. This suggests a convergence of ‘reality’ and an intrinsic model of such reality to converge by way of predictions correlating with actual data, as seen also in predictive coding and active inference (Seth and Friston, 2016). In a related approach, the Transactional Interpretation builds on the fact that, in quantum mechanics, not only are all probability paths traced in the wave function, but past and future are interconnected in a time-symmetric hand-shaking relationship so that the final states of a wave-particle or entangled ensemble, on absorption, are boundary conditions for the interaction, just as the initial states that created them (Cramer, 1986; Kastner, 2010, 2012). This interaction can be portrayed in terms of offer waves from the past emitter/s, and confirmation waves from “future” absorbers, whose wave interference

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becomes the single or entangled particles passing between. When an entangled-pair is created, each “particle” instantaneously “knows the state of the other, and if one is found to be in a given state (polarization or spin), the other is immediately in the complementary state, no matter how far away in space-time” (King, 2015). This describes the ‘spooky action at a distance’ that Einstein disclaimed. Given that quantum reality has been shown to adhere to Bell’s (1966) theorem, such reality is inconsistent with local Einsteinian causality associated with particles communicating only in a progressive capacity. Notably, the physicist Cramer tends to describe the above processes of his Transactional Interpretation in a way that presumes full time symmetry and a block universe model such that past and future events are equally “actual.” In contrast, the philosopher Ruth Kastner is careful to make the distinction of the ‘real’ as comprised of both the ‘actual’ and the ‘possible’, such that reference to future states pertain to possible states, thus a type of anticipation (Kastner, 2013). Independently, and building on recent development in category theory and the decoherence interpretation, Epperson and Zafiris (2013) have laid out a robust theory that also emphasizes distinguishing the ‘actual’ and the ‘possible’ within the ‘real’ to adequately explain various quantum experiments.

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In the context of predictions, a link between quantum transactions and activeinference/predictive-coding can be overlaid. In this sense, transactions are between the 'offer' waves of predictions, and confirmation waves can be taken as the difference between the prediction and the actual data reflected back up to inform confidence (or reliability) measures qua precision-weighting of that prediction. For a biological application to transactional logic, “future states of the system leave their mark on a pattern of related transactions in a coherently excited cell, with a time-scale determined by the lifetime of exchanged excitations. Even a time-scale in milliseconds would provide a critical advantage” (Southwick & Miller, 1998). Consciousness thus becomes entailed in conscious anticipation (Dennett, 1997; King, 2015).

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As discussed in the last section, given the connection between salience, as a product of outlier values within predictive coding, and physiological vigilance, it could be expected that some physiological signals also operate in advance of certain stimuli, providing real anticipatory activity responding to the immediate future of a preprogrammed event. As Di Corpo predicts: "if vital processes feed on syntropy (advanced waves), then the parameters of the autonomic nervous system (skin conductance and heart rate)—which support vital functions—should react in advance to stimuli" (Di Corpo, 1981; 2007). Remarkably, a few labs have independently verified the existence of anticipatory physiological activity in advanced response—in one case—to certain salacious images presented on a computer screen just prior to their frame appearing (Bierman & Radin, 1997, 1998; Radin, 2004). This was also recorded in Libet's Bereitschaft potential: or, the “readiness potential” (1983). By now, several scholars have recorded similar findings in a host of scenarios (Lehman et al., 2000; Bierman & Scholte, 2002; McCraty, 2004; for a solid review, see Mossbridge, 2012).

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5. Philosophical Foundations

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Though we will not develop them here, two additional, key avenues can be brought into the anticipatory domain as syntheses between perception and quantum logic. First, from the perspective of dissipative quantum neurodynamics, Vitiello's "double" (2001), as a model of memory qua vacuum states of quantum fields, extends Ricciardi and Umezawa’s applied quantum field theory (QFT) formalism for modeling brain states and memory (1967), to consider the interaction of a system with its environment. In Vitiello’s approach, “the system-environment interaction causes a doubling of the collective modes of the system in its environment. This yields infinitely many differently coded vacuum states, offering the possibility of many memory contents without overprinting” (Atmanspacher, 2015). Also of note, the emergence of scale-free, powerlaw distributions bears a profound kinship with dissipative quantum coherent states (Vitiello, 2012; Atmanspacher, 2015). We can secondly consider an overarching link between perception and an update to the Copenhagen interpretation of quantum mechanics by way of the recent movement to specify "personalized probabilities" (or "strands" see Chew, 2008; Bettinger, 2015) qua Quantum Bayesianism or QB-ism (Bub, 2010; Fuchs, 2010; Fuchs & Schack., 2011).

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The 20th century scholarship of Whitehead has proven to be prescient in mathematical physics, philosophy, logic, chemistry and neuroscience. In terms of our present topic, we are not surprised to find that Whitehead also suggests a process logic pointing towards anticipatory operations. As Maclachlan explains, “Whitehead’s revolutionary thesis is that causal connection takes place, not in virtue of the activity of the cause, but through the activity of the effect” (1992), with the result of pulling causality ever towards the future. This can be understood as an example of an attractor logic. Whitehead qualifies this as the "subjective aim" that pulls the phases of concrecence towards their "satisfaction," which is, like an attractor, the maximal state of "beauty" or symmetry/coherence and complexity that the group of values strive towards.

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This anticipatory emphasis suggests an attractor logic as applied in computational neuroscience and dynamical systems, and can also be shown to influence current trends in neuroscience and perception. To this latter degree, we explore how predictive processing can be philosophically aligned with Whitehead's “propositions,” as well as in terms of identity and sense-of-self. Whitehead’s “Propositions” and Modes of Perception qua Interoception Symbolic logic…is the symbolic examination of "pattern with the use of real variables" [Whitehead, SP, 140]; in other words, it is the systematic examination of "propositions" (Steinbeck, 1989)

The primary connection to be developed involves Whitehead’s “proposition” as linked to inferential "predictions" in Friston’s model. As Whitehead explains: "the proposition is the potentiality of an assigned predicative pattern finding realization in indicated logical

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subjects" (PR 24/35, 186/283, 257f/393f, 261/398). This makes them anticipatory, as Rosen required, given that propositions depend on incoming information for context, just like predictions require incoming sensory information. As Steinbeck suggests: “the proposition is a "new kind of entity" [that] enjoys a unique status” (Steinbeck, 1989). Such propositions function as inferential predictions—hailing from a generative model—and descending onto incoming, present data. Thus, not really from the future, but certainly convening as a prediction about expectation values. In a larger capacity, we can link "propositions" in the context of Whitehead’s modes of perception, which have been shown (in Bettinger, 2016) to overlay with neurophysiological dynamics underwriting interoception and the neurovisceral axis, to secure a phenomenological basis.

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Throughout his work, Whitehead addresses the notion that "a proposition is not a particular; it transcends brute fact just enough to grant it an air of impartiality” (MS 320; PR 197, 299-300). This situates a proposition within the domain of a prediction, as not the actual, real fact it predicts, but with an air of impartiality qua prediction, as well. This "signals an approach to propositions from the top-down" — “invested with indeterminacy” (Steinbeck, 1989), "as if." Closely allied, Rosen explains how: “in the study of anticipatory systems, we find that ‘ought’ is of the essence” (1980). Again, this implies the expectation inherent in a prediction: that it ought to be as such. Whitehead describes this as an "intermediate universal" (MS 320) bearing the attribute of “patience” (PR 256, 391) for realization. In this sense, as described earlier, a proposition abides patiently inherent within the synaptic weighting of a generative model until applicable data (facts) impel it into action.

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In this sense, we read ‘patience’ to provide the clue that predictions are not the initiators, but the anticipatory responders that require sensory data in order to perform predictions about next behavior. As such they are content to abide in the possibility space of a generative model (via synaptic weights) until suitable sensory data beckons them into active inference. Patience in this sense is a way of indicating that the predictions are contingent on actual sensory data to become active. As such they are seen by Whitehead as ‘intermediate,’ (and we would say:) in the possibility space between the model and actual data. The “indeterminacy” of a proposition again suggests the inferential, and what is more, the abductive (Peirce, 1867) nature of generative predictions. Like deductions, they do not begin from a solid fact, but instead, from hypothetical inferences that include both ‘musings’ with full openness to novelty as well as inferences "to the best explanation" (Hohwy, 2014). As an adaptive measure, predictions are fine-tuned in a continual optimization through modifications entailed in ever-streaming, new data. In a final notion, Steinbeck explains how there are "two subjects inherent in the proposition, namely, the ‘logical subject’ and the ‘percipient’ (or ‘prehending subject’).” In a proposition, the "logical subjects" are not tied down to objectivity; and, as Whitehead contends, their role in actuality—that is, their complete determinateness—is eliminated. Instead, logical subjects function with “hypothetical relevance to a predicative pattern now potentially determinate of these logical subjects” (Steinbeck, 1989), which is to say,

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data creates models that are used to predict new data. “Because the proposition is not given as a finished fact, but presented as a way it could be (the optative quality preserving the indeterminacy unique to eternal objects), a proposition is a real possibility” (ibid). For a neuroscientifc correspondence in terms of interoceptive predictive coding (Seth, 2013), the prehending subject represents sensory afferent fibers reaching the primary insular cortex, while the logical subject refers to the descending predictions onto the sensory data (Friston, 2012, 2015; Seth, 2013). The difference between the proposition (effect), and the actual sensory data (cause), is ‘sent back up’ to inform synaptic-weighted models. Similarly, Whitehead describes how a ‘fact’ is contrasted with a ‘proposition’ in a comparison “between what is and what might be” (Griffin, 2002), in what he calls an “affirmation/negation contrast” (PR, 243).2

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This logic provides an excellent primer for recent approaches in computational and systems neuroscience predicated on Bayesian approaches to modeling the brain in an inferential and predictive capacity. Even just this abbreviated review of propositions clearly points to a notion of inferential, predictive coding. What Friston calls predictive coding and active inference, for Whitehead is the affirmation/negation contrast. What is ‘active’ is the emergent difference between proposition and data, and this is what gets prehended positively or negatively and carries on in the process of signal processing in the interoceptive (insular) cortex. In the case of outlier-hunting (abductive Bayesian inference), successful predictions are suppressed while "surprises" are made salient. This is exactly opposite to the processing of positive feelings in Whitehead’s prehension model, where the negatively-prehended values are the one’s suppressed. This suggests that both processes take place concurrently, attending to different features by way of a simple reversal of the logic for selection-criteria. Anticipatory Logic in Counterfactual Reasoning, Possibility Space, Possible Worlds Semantics and Peirce’s Secondness

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We now address the notion of a landscape and conceptual space for anticipatory, predictive processing and propositions. We begin with the notion of counterfactual logic and reasoning, and in the study of brain signatures, as our minds are a capable mixture of representation and counterfactual logic. Notably, Mackie presented a counterfactual account of the concept of a cause as “what makes the difference in relation to some background or causal field” (1980; see also Menzies, 2014). This background field can be linked to the domain of a predictive model. We consider predictive model space as counterfactual, in that the model is an imperfect model trying to optimize its predictions and learn about the system it is modeling. In this strict sense, it therefore qualifies as a counterfactual model, but uniquely, one that develops towards greater accuracy, such 2

Resolving the affirmation/negation contrast maintains stability and reinforces homeostasis. By contrast, Mahootian (2016) proposes that Whitehead also maintains the active novelty of experience by suspending the affirmation/negation contrast. See Mahootian, F. (2016) “Whitehead on Intuition – Implications for Science and Civilization,” in Desmet, R. (ed.) Intuition in Mathematics and Physics: A Whiteheadian Approach, Process Century Press, pp. 78-80, 83.

ACCEPTED MANUSCRIPT 14 that it seeks to slowly shed its counter-facticity over time. This makes it an inimitable blend of counterfactuals.

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There is much to be said for an abductive process of fine-tuning within a counterfactual domain, especially given the nature of predictions as abductive inferences, therefore, beginning not with facts, like in deduction, but with hypotheses to the best approximation. For this reason, we are dealing in a strict sense with approximate values operating as counterfactuals with the intent of constantly optimizing (Friston, 2011). Thus, generative, anticipatory models operate in a counterfactual logic distinguished by a constant urge towards optimization, as an imperfect model seeking enrichment.

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Turning to the triadic phenomenology (phaneroscopy) of Charles Sanders Peirce, his counterfactual and abductive logic, operating in the mode of “secondness” as a “saltus”— involving a “push-and-pull” (Peirce, 1867)—bears close resemblance to the operations of a model constantly pushing out predictions and pulling in new data for optimization. Whitehead refers to this pulling as “prehension” (PR, 344).

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Menzies explains how counterfactuals refer to “un-actualised possibilities” (2014). In another sense, counter-facticity brings with it the link to a “possible worlds dynamism” (2001) that can be developed in the sense of adjudicating between first and third person modes of being and facticity in a possibility space (Eastman, 2016) akin to the notion of modeling space. As Menzies explains, "the true potential of the counterfactual approach to causation did not become clear until counterfactuals became better understood through the development of possible world semantics in the early 1970's” (Menzies, 2014). To these ends, Lewis famously espouses a realism about possible worlds, placing them in the same class as “real, concrete entities on par with the actual world" (1986).

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Eastman et al. (2016) posit that such “possible worlds” actualism is different from spaces of “possibilia” in quantum physics in which, arguably, there is a critical distinction to be made for the “real” between the “actual” and the “possible.” With this corrective to Lewis’ incomplete framing of the real, we can still incorporate his explanation that “the central notion of a possible world semantics for counterfactuals is a relation of comparative similarity between worlds” (Lewis 1973). This provides a useful basis for predictive models that seek to optimize predictions, given data. Adding (indirect) weight to Whitehead’s model, Menzies characterizes the consensus understanding of possible worlds as “maximally consistent sets of propositions” (Menzies, 2014; emphasis ours). These comparisons indicate a new basis for abductive inferential and predictive models.

6. Discussion As an overview, we now address three issues: the general purpose of anticipatory systems in biology; to what extent predictions are actually about the future; and to what extent this line of reasoning might also be applied to models of self and physics. Rosen nominates the principle characteristic of anticipatory systems by the fact that they

ACCEPTED MANUSCRIPT 15 employ predictive models (1980). But to what ends do these models serve? One clue comes from (Coffman & Mikulecky, 2015), citing how:

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Organisms are anticipatory, insofar as they engage in a modeling relation with their environment that allows them to adaptively anticipate changes in the environment and work toward fulfilling their existential needs before those needs become a crisis.

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Such a modeling relationship proves well suited to the task of maintaining homeostasis (balance) through the optimization of predictive models operating on the minimization of variational free-energy within the environment and internal body-system. We consider that anticipating change requires predication on the regularity of a dynamic and adaptive model-imperative, such that changes reflect a difference to some platform model. Secondly, the modeling relation that enables the organism to ‘fulfill’ its ‘existential needs’ indicates a clear harbinger to the role of the salience network and allostasis. In both cases, the mechanism underwriting these attributes traces to the maintenance and restoration of homeostasis. Computationally, minimizing free-energy (through a predictive model acting on incoming sensory data) provides a functional means for maintaining homeostasis, insofar as errors in prediction are made salient and brought to the awareness of the organism for the purpose of correcting, fulfilling a need, or restoring the conditions back to normal: aka, allostasis.

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In the first case, it can be argued that active inference really provides future predictions. When adaptively anticipating changes in the environment based on present data, anticipation is predicated on past data but applied to the present (using a model) to predict the future. This is because the reactions are based on the likelihood of the future (or next-behavior) of an event: e1, at t1, given this is how e1 behaved in e-1, e-2, e-3, etc. (in historical events). This hinges on the property that the event-sample is seen as approximately ergodic (Breakspear, 2004; Friston, Breakspear, Deco, 2012).

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The term, ergodic, is used to describe a dynamical system that has the same behavior averaged over time as averaged over its states. The celebrated ergodic theorem is due to Birkhoff (1931), and concerns the behavior of systems that have been evolving for a long time: intuitively, an ergodic system forgets its initial states, such that the probability a system is found in any state becomes—for almost every state—the proportion of time that state is occupied. (Friston, Breakspear, Deco, 2012)

In this case, we can read ‘events’ as signs, like somatic markers (Damasio, 1997), or blockheuristics upon which predictions are staked. For a phenomenological example, if you are sitting in the woods writing and suddenly a large spider scurries across your makeshift table, stops near the edge, and rears up on its hind legs with both arms held high in the air—you can immediately predict the parameter of likely ‘next-behaviors’ based on what you know about that type of body language from experience. Several other examples are provided in the extensive biosemiotics literature (Hoffmeyer, 2008; Favareau, 2010). This guides your own decision-making, which is responsive to the possibility or likelihood of a future event. Given past information, you can make actual, genuine predictions about the

ACCEPTED MANUSCRIPT 16 future of present data. In the context of autopoietic computational operations, the statistical parameters for ‘next behavior’ constantly adjust based on present data.

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From a cytoarchitectonic perspective, the long dendrites of evolutionarily-specialized von Economo neurons enable faster and more diffuse signal transmission than the large pyramidal cells with which they share cellular real-estate (Nimchinsky et al., 1997, 1999; Allman, Hakeem, Watson, 2002). As such, these neurons are evolutionarily tailored to synthesize and transmit vast amounts of information into simple signals distributed profusely throughout the brain for fast-acting responses leading to adaptive and intuitive results (Allman et al., 2005). To date, von Economo neurons have only been found in the anterior insular cortex, anterior cingulate cortex, and dorsolateral prefrontal cortex— together making up the salience network, and serving as a responder qua regulatory oversight to any deviations within the internal or external environment. Von Economo neurons have been specifically linked to the advanced processing of social information qua intuition (Allman et al., 2005; Stimpson et al., 2011). In this sense, von Economo neurons can be seen to provide anticipatory signaling qua ‘faster than normal’ processing (Rosen, 1980) that serves to inform decision-making about present data. This aligns with Nadin’s reading where: “both phenomenologically and empirically, an anticipatory system can be considered to have at least two clocks, operating as a syncopated, or countercorrelated pair of processes unfolding at different time-scales” (Nadin, 2009).

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Importantly, von Economo neurons are contextualized within the neurovisceral axis, as the vehicle through which the body communicates (to the individual) the ongoing and evolving conditions of the dynamic, internal milieu—and in terms of the salience network—as error-registry via affect and physiological vigilance. This two-way communication between the brain and the gut takes shape perceptually as the specialized faculty of interoception—or, the ability to sense the internal physiological environment of your body and changes therein (Craig, 2009; Mayer, 2011).

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Locating predictive models in the context of perception-neuroscience, the way-bearing scholarship of Friston (2003, 2014) provides a requisite foundation for developing predictive processing for the internal environment of the organism. Here, internal models serve in the capacity of maintenance procedures qua constant divestiture of generative predictions and active inference schemes designed to minimize the freeenergy (entropy) of the system, and report outliers qua salience effect. This is detailed in terms of adaptive models constantly seeking to optimize models of the internal and external environments through sensory signals. As such, maintaining homeostasis is functionally-based on predictive (generative) models of self and environment, and the relationship between the organism and model (Rosen, 1980). This follows the axiom that "every good regulator is also a model of that system" (Ashby, 1946; Conant, Ibsen & Ashby, 1947). For Rosen, this is equivalent to the fact that “any model-based guidance system will only be as good as the encoded information it uses” (1980). As Coffman and Mikulecky explain:

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Rosen’s formal model of “anticipatory systems” provides insight into how organisms, and more generally, living systems, are able to maintain homeostasis (that is, remain stable) in a changing environment. To do that the system must contain or embody a model of its environment, wherein entailments of the model are congruent with causation outside the system. The system is anticipatory to the extent that the model creates entailments more rapidly than the corresponding causation occurs in the world at large. (2015)

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Closely linked, in terms of external models of the local environment, we take a major cue from the evolutionarily-ancient, first visual and auditory system of the superior and inferior colliculi (Fuchs & Ansorge, 2012; King, 2004)—and their extended network of interregional brain connections linked to emotional salience (Almeida, Soares, & CasteloBranco, 2015; Morris, Öhman, & Dolan, 1999; Pessoa & Adolphs, 2011) and vigilance (Posner & Petersen, 1990; Robinson, 1985)—providing a neurobiological mechanism for local meta-awareness qua self-organized, or autopoietic surveillance mode—and can even move the head and eyes exactly to a place in the local 360-degree visual field to where the possible threat is detected (King, 2004). As such, we submit the evolutionary purpose of predictive models is linked to the regulation and oversight of the internal and local external environments of the organism.

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In a closing note: when you stop to think about it, predictive models really play an important role in our sense of identity, and we tend to feel them as parts or elements of our self. We should expect self-identity to be largely linked to models, per person, and rightfully so, as each model is unique to each person. Both Rosen and Seth feel this way, providing further evidence of the inherent link between these two capacities. As Seth explains: "In humans, self-related predictive coding simultaneously engages multiple levels of self-representation, including physiological homeostasis, physical bodily integrity, morphology and position, and – more speculatively – the metacognitive and narrative ‘I’" (2013).

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We might even say that the models embodied in an anticipatory system are what comprise its individuality; what distinguish it uniquely from other systems. As we have seen, a change in these models is a change of identity; this is perhaps why, for human beings, the preservation of models becomes identical with the preservation of self. (Rosen, 1980)

In a very similar way, Jacob von Uexküll defined Umwelt “as the perceptual world in which an organism exists and acts as a subject.” (Wikipedia entry on von Uexküll), and this Umwelt always incorporates a biologically under-determined modeling system (Deely, 2004, p. 20). In summarizing the concepts of von Uexküll’s Umwelt and of “modeling system” as forwarded by the Moscow-Tartu school of semiotics, Sebeok states that “The notion of a secondary modeling system, in the broad sense, refers to an ideological model of the world where the environment stands in reciprocal relationship with some other system, such as an individual organism, a collectivity, a computer, or the like, and where its reflection functions as a control of this system’s total mode of communication.” (Thomas A. Sebeok, Signs: An Introduction to Semiotics, Toronto: University of Toronto Press, 1994, 2001, p. 139). Clearly, there are substantial overlaps of

ACCEPTED MANUSCRIPT 18 Rosen's analysis of models in complex systems, of predictive models in neuroscience, and of the analysis of Umwelts (models) in biosemiotics; however, further consideration of such overlap is beyond our current scope.

Homeostasis in Buddhism and Taoism

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Shifting gears, we next turn to explore how anticipatory systems, guiding the regulation of internal parameters for survival, can then be linked to motivate the notion of Tao and the operators, Yin and Yang.

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Riding the momentum of the first part, if the purpose and adaptive usefulness of anticipatory systems partially accounts for the maintenance and oversight of homeostasis, then we find ample provision within Taoism for linking the Tao itself to the same regulatory principle underwriting homeostasis, as an optimal and selected state to abide within the system's internal parameters. Our trajectory through this section is to link homeostasis and allostasis to Tao and yin yang and to the autonomic nervous system (ANS); and then to link ANS to anticipatory behavior via Di Corpo and Vannini (2013).

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The whole process follows the principle of homeostasis (balance and order) within a narrative that it does not create or lay claim to, but that is provided by the details of the internal milieu of a particular organism. As a principle, it guides the behavior and has a means to correct it via changes to models and allostasis; thus, homeostasis does not create sensory data, but instead serves the purpose of attractor, by which it is regulated.

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The two operators, yin/yang are used to distinguish and to provide allostatic treatment in order to bring the system back into balance. In the context of interoception and homeostasis of the internal milieu, the two control parameters of Tao, as Yin and Yang, can be taken to correspond with the allostatic, physiological parameters of the autonomic nervous system qua sympathetic (SNS) and parasympathetic (PNS) nervous systems, employed differently in different occasions to restore the system to balance. As such, the dynamic operators maintaining cardiac autonomic balance and flexibility (Cacioppo et al., 1994; Friedman, 2007; Porges, 2007; Bernston, 2008; Cribbet, 2011) naturally align with the Yin and Yang operators maintaining Tao. Taken in this way we recognize a natural connection between the notion of Tao—way, path, route, or key—and homeostasis—as the maintenance of optimal internal parameters of the body (internal milieu) for survival—as maintained primarily through anticipatory models. Tao is the intuitive knowing of life that cannot be grasped fully in concept but is known through actual living experience of an everyday conscious individual qua autopoietic organism with certain intrinsic aims like balance, optimization and the minimization of free-energy. This link is epitomized in the sense that “the Tao can be roughly thought of as the flow of the Universe, or as some essence or pattern behind the natural world that keeps the Universe balanced and ordered” (Cane, 2002). We link this process of contextualizing systems at multiple scales, to internal systems designed to do the same in the body.

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Tao is also related to the idea of qi, or the essential energy of action and existence, both internal and external. However, we focus on internal "in the body" aspects of qi in terms of the dynamical inner values of self and internal milieu qua interception and autonomic activity, sensed on a physiological level and in local, environmental activity.

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The Tao produces all things and nourishes them; it produces them and does not claim them as its own; it does all, and yet does not boast of it; it presides over all, and yet does not control them. This is what is called 'the mysterious Quality'. (Verse 10; Legge, 1994)

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A preliminary attempt to naturalize this comes in terms of Whitehead’s proposition, as aimed overall at maintaining homeostasis by minimizing free-energy. As he explains: a proposition “tells no tale about itself” (PR 256f/391-3); it is purely a prediction about an incoming value. In a similar steed, eastern views explicitly reject a substance interpretation of Tao, and “the Principle that can be enunciated is not the one that always was. The being that can be named is not the one that was at all times” (Bryce, 1999). The Tao is thus, "eternally nameless,” distinguished from “the countless named things” considered its manifestations (Tao Te Ching-32). This description can be taken to underwrite a universal dynamic, manifested biologically in terms of homeostasis. To these ends, homeostasis, and the maintenance procedures through which it is sustained, can also be thought of like Tao, for in maintaining optimal survival parameters, it is not entailed in any of the values that demarcate or underwrite it (neurovisceral, afferent fibers). It is an active and autopoietic principle balancing a system operating in dynamic instability, and according to operations like the minimization of free-energy and optimization, helping keep it within systemically requisite parameters.

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In this way, we suggest thinking of the inner aspects of the Tao in the capacity of homoeostasis and interoception as dynamic processes where homeostasis is neither a state nor an exact value, but rather, a guideline of parameters (an attractor) required to be maintained; and yet, the moment-to-moment new data always keeps the process dynamical and able to be adapted with new information. As such we recognize the bodyinternal driven by procedures designed to maintain and optimize signal processing and internal conditions of the body within certain parameters. This qualifies the process as active and holistic, and thus as a dynamic system. This bears strong camaraderie within a process paradigm. “The thirty spokes unite in the one nave; but it is on the empty space (for the axle), that the use of the wheel depends.” Is it possible to now see this in the same light as the regulatory attractor logic of internal systems, interoception and homeostasis? While motivational, we see clear and positive signals that it takes the East to add basis and context for a renewed science to restore a philosophical and physical (scientific) fabric to our understanding of nature, consciousness and the cosmos as a process and emergent logic of organism and ecology.

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7. Conclusion: Connections and Integration

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If it were necessary to try to characterize in a few words the difference between living organisms and inorganic systems, such a characterization would not involve the presence of DNA, or any other purely structural attributes; but rather that organisms constitute the class of systems which can behave in an anticipatory fashion (Rosen, 1980).

Every indication available to the external senses suggests that causality appertains in a method moving from causes to effects: that is, from the past to the present (or forwardin-time). Such is also shown to form the basis for the second law of thermodynamics qua entropy to the degree that over time systems tend to increase in disorder. Hume explains:

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We may define a cause to be an object followed by another, and where all the objects, similar to the first, are followed by objects similar to the second. Or, in other words, where, if the first object had not been, the second never had existed.” (1748; see also Menzies, 2014).

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However, contrary to Hume, indications of a corresponding causal impulse operating in advance of direct experience have also borne witness to the subtle senses and intellect of a handful of scholars from neuroscience and ecology to quantum physics and an expanding literature base in biosemiotics. Over the last hundred years we trace the convergent evolution of anticipatory logic couched under the heading of prediction, inference, anticipatory systems, and apparent backwards-in-time causality (most often interpretable as operating in some ‘possibility’ space, not future ‘actualizations’ per se). We witness these attributes most vividly in terms of perception neuroscience, mathematical physics and computational biology.

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Logically, anticipatory systems violate the forward-trending causal foundation of theoretical science (from cause to effect). Empirically, anticipatory systems violate the second law of thermodynamics qua minimization of entropy, and causal progression from future-to-past, portending an attractor-logic. Fantappie was prescient to this idea with his formulation of syntropy as opposite to entropy. Aligning syntropy within the harness of anticipatory systems proves natural. From this, we consider the importance of such operations in experience for their role in overseeing the survival of organisms; and how they might come to bear across ecosystems (local/global) at multiple scales. The purpose of this paper has been to provide a structural foundation qua conceptual narrative for phenomena encompassing anticipatory and predictive systems operating in the context of biology, perception neuroscience, physics and philosophy. In addition to Rosen's work, we introduce the Bayesian brain hypothesis, highlighting the notions of: predictive coding, active inference, and the minimization of variational free energy qua synaptic weighting, precision and descending neural gain modulation operating in laminar layers of limbic cortices in interoception, and in visual cortices for environmental assessment. The bulk of this motivation comes from works stalwarted by Karl Friston, Lisa Barrett, Kyle Simmons, and Lorena Chanes.

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Beginning with Rosen’s definition and early enumeration of the logic underwriting anticipatory systems, we are able to trace a smooth course into the bases of Friston’s predictive coding and active inferential schemes—and where Seth applies this to interoception (Seth et al., 2012; Seth, 2013). This provides current approaches to perception in neuroscience with a philosophical foundation. In particular, we have shown how Rosen’s definition of "anticipatory systems" provides the functional basis for a syntropic causality manifest in terms of perception neuroscience, driven by models of predictive coding and active inference (in the capacity of interoception, proprioception and vision) as regulatory oversight operations maintaining homeostasis qua free-energy minimization (Friston, Kilner, Harrison, 2006; Friston, 2010) and abductive logic (Peirce, 1867). Within perception, this insight is couched under the heading of inference, and is functionally-realized in terms of a retro-causal process projecting from predictive attractors onto incoming sensory data.

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We suggest that the purpose of anticipatory systems qua predictive models is for regulatory oversight. The mechanism is via models-of-self and environment: internal and external. In this capacity, models of internal-self lead to interoception while models of environment, to visual and proprioceptive (e.g., movement of others in combat) entailing threat assessment and monitoring of external conditions/environment. Both involve the monitoring of change in the environment in the internal and external. The neural basis for this resides in part with the superior and inferior colliculi and their links to amygdala and emotional salience network and attention network and vigilant physiological arousal. In perception this entails an anticipatory process at work, leveraging data from vision, proprioception, and the model imperative of them all, interoception (Seth, 2013; Ondobaka, Kilner, Friston, 2016). Closely related there is also the principle of homeostasis, whose purpose is to ensure the optimal maintenance of the organism within survival parameters of internal conditions and differences and fluctuating activity therein.

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The existence of impulses operating in advance of perception has lured a handful of thinkers dating back to the earliest known example of Alhazen in 1080, and more notably, to the Reverend Thomas Bayes in 1764, for probability theory of inferring next results from current evidence; and to Hermann von Helmholtz, in 1866, for adducing visual perception as mediated by unconscious inference. We also recognize Charles Sanders Peirce for independently developing abductive logic which can be applied to characterize the type of functional process that Bayesian and Helmholtzian operations follow: that is, by eliminating options until only the right one(s) remain, or in the case of perception and predictive coding, cancelling out expected values to discover all surprise outlier values based on the parameterized model of that system and its expectations. These ideas establish a basis for models of perception that a handful of others like (Hinton, 1995; Edelman, 2001; Gregory, 2003) later brought to light, and applied to the computational neuroscience of perception by Karl Friston, transformed into formalisms like predictive coding, active inference, and the minimization of variational free-energy.

ACCEPTED MANUSCRIPT 22 In logical causality (see also Epperson and Zafiris, 2013), this gives rise to a formalization of anticipatory and predictive phenomena in a category of causal phenomena that Fantappie (1941) called “syntropic.” As Fantappie explained:

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What makes life different is the presence of syntropic qualities: finalities, goals, and attractors. Now as we consider causality the essence of the entropic world, it is natural to consider finality the essence of the syntropic world. It is therefore possible to say that the essence of life are final causes, syntropy. Living means tending to attractors [...] the law of life is not the law of mechanical causes […] the law which dominates life is the law of finalities: the law of syntropy. (Fantappie, 1942)

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Gyorgi explains how “there must be a force that is able to counter the universal tendency of matter towards chaos and energy towards dissipation. Life always shows a decrease in entropy and an increase in complexity, in apparent conflict with the law of entropy. Fantappie’s formalization of the concept: syntropy, as the opposite of entropy, then works as an anticipatory, or predictive operation. We also understand now, from studies of nonequilibrium systems, that sources of free energy (e.g., the Sun) can drive negative entropy, thus the ‘syntropic’ generation of order out of chaos (Prigogine and Stengers, 1984; Kauffman, 1993). This proves notable in light of the fact that Alfred North Whitehead also specifically develops a triadic theory of perception which is itself, found to link directly to a phenomenological description of: interoception, affect/emotion, sense of self/body-ownership, biological intuition—and its cell-fiber substrate in the neurovisceral axis (see Pred, 2005; Weber, 2009; Weber & Weekes, 2010; Bettinger, 2016).

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In biology, the principle regulating the internal conditions or body-milieu, within narrow, optimal parameters for living conditions, is called homeostasis, and it has a very important job for maintaining the survival of the organism over time. Overall, this describes an evolving and adapting (optimizing) process that reverses the logical causality of operations from the usual past-to-present sequence of entropic logic to that of a future-to-present (syntropic, attractor) logic acting to maintain a desired inner-range of state-values, such as in self-organized (neural) criticality (Bonachela, 2010; Hesse & Gross, 2014). Having highlighted the integral role that anticipatory systems play in maintaining the operational integrity of the internal milieu—through generative active-prediction models predicated on models of the internal system and environment—we trace a connection between the similar notions of Tao and homeostasis, given that both represent phenomena that do not take on the identity of their constantly arising constituents. In physical systems, this accords with the anticipatory features of Cramer’s transactional quantum theory; tachyonic (bosonic) strings; in Vitiello’s “double;” and in a Quantum Bayesian modeling method. This permits us to further consider whether the so-called “backwards-in-time” behavior, qualified in terms of a predictive modeling element, can be described as a responsive feedback connection with respect to the local ecosystem, other organisms, planet, others non-local, and cosmos. We often speak of non-local connections, but in generalizing the property, we acquire the sense in which these are not just connections, but describe a primitive responsiveness and local, ecological feedback

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Ultimately, physics and biology represent two pillars of an even more general discipline: the theory of complex systems (Poli, 2014; Bertalanffy, 1968). As we continue to explore the earmarks of anticipatory (predictive) processes, we are drawn closer to complex many-bodied systems operating in a platform of dynamic instability (stochastic multi- or meta-stability) and criticality. At the epistemic core, there is a fundamental shift from a reductive bias to multi-level complex systems that is only just beginning with the renewed recognition that fundamental logic is actually triadic, not dyadic (a la Peirce); that contemporary number systems and mathematics are a small subset of what will be available to us within a few decades; and that recognition of the real richness of complex systems, bio-systems, biosemiotics, nonlinear dynamics—has only just begun; that is, we may be in the middle of a new renaissance in scientific understanding, not close to the “end of science” as some of the advocates of simplistic reductionism have recently claimed.

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