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![(a) The classical conception of observation, standardly carried over into quantum theory. The observer interacts with a system of interest; both are embedded in a surrounding environment. (b) Interactions between observer (O), system of interest (S) and environment (E) enabling environmental decoherence [1,2]. The Hamiltonian H OS transfers outcome information from S to O; H SE and H OE decohere S and O respectively. Adapted from [66] Figure 1.](publication/328203695/figure/fig1/AS:680362569121795@1539222485875/a-The-classical-conception-of-observation-standardly-carried-over-into-quantum-theory.png)
(a) The classical conception of observation, standardly carried over into quantum theory. The observer interacts with a system of interest; both are embedded in a surrounding environment. (b) Interactions between observer (O), system of interest (S) and environment (E) enabling environmental decoherence [1,2]. The Hamiltonian H OS transfers outcome information from S to O; H SE and H OE decohere S and O respectively. Adapted from [66] Figure 1.
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Multiple sciences have converged, in the past two decades, on a hitherto mostly unremarked question: what is observation? Here, I examine this evolution, focusing on three sciences: physics, especially quantum information theory, developmental biology, especially its molecular and “evo-devo” branches, and cognitive science, especially perceptual ps...
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Context 1
... is, as noted above, the objective, observer-independent existence of systems that allows the eigenvalues of their objective, observer-independent interactions with an objective, observer-independent environment to be encoded with objective, observer-independent redundancy in the theory of quantum Darwinism [29][30][31]. An even more extreme example of this assumption of given, a priori systems can be found in Tegmark's [65,66] description of decoherence (Figure 1). Here, the "system" S is defined as comprising only the "pointer" degrees of freedom of interest to the observer O, and O is defined as comprising only the degrees of freedom that record the observed pointer values. ...
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... this, obviously, requires observation. It requires observing not just S, but other things besides S-for example, the tables and chairs that she has to navigate around before she gets to S. These other things are part of the "world" W in which S is embedded; in the notation of Figure 1, W = S ⊕ E. It is W that Alice has to interact with to identify S, which she must do before she can read its pointer value ( Figure 2). This W is, it bears emphasizing, Alice's world: it comprises everything in the universe except Alice. ...
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... W is, it bears emphasizing, Alice's world: it comprises everything in the universe except Alice. Figure 2. (a) Observers are standardly assumed to interact with a priori given systems, as in Figure 1. Here, the observer is equipped with an observable (e.g., a meter reading) with which to interact with the system S. Adapted from [35] Figure 1. ...
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... 2. (a) Observers are standardly assumed to interact with a priori given systems, as in Figure 1. Here, the observer is equipped with an observable (e.g., a meter reading) with which to interact with the system S. Adapted from [35] Figure 1. (b) In practice, observers must look for the system of interest S by probing the "world" W in which it is embedded. ...
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... changes are the "differences that make a difference" [78] for observers. In the traditional picture of Figure 1, the goal is to discover the pre-existing, observer-independent pointer state |P of a given, pre-existing system S. We have seen above, however, that to measure |P, one must first measure the time-invariant reference state |R = |x ...
Citations
... In this view, whether something is computing is not a philosophical question, but one to be settled experimentally by specifying a computational framework and showing empirically what new levels of capability, experiments, and research are enabled by adopting that framework. The only thing left is to enable system subcomponents, not just human scientists, to act as observers [22][23][24][25]. From that perspective, the quality of a computational metaphor in science is evidenced by its degree of productivity in new experimental capabilities, while the quality of a computational stance adopted by a biological subsystem is cashed out by the adaptive advantage that is evinced by it. ...
The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing”—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.
... (Indeed, an organism is, by definition, "the environment" of its environment.) While such statements are sometimes considered "antirealist" or "subjectivist" [173], they are just consequences of modeling physical interaction as information exchange [174,175]. Organisms such as humans that employ technologies to extend their perception and action capabilities are, effectively, extending their Markov blankets to encode the values of additional state variables. It may be partially implemented by a structure (here, a plasma membrane) in a 3D space. ...
One of the most salient features of life is its capacity to handle novelty and namely to thrive and adapt to new circumstances and changes in both the environment and internal components. An understanding of this capacity is central to several fields: the evolution of form and function, the design of effective strategies for biomedicine, and the creation of novel life forms via chimeric and bioengineering technologies. Here, we review instructive examples of living organisms solving diverse problems and propose competent navigation in arbitrary spaces as an invariant for thinking about the scaling of cognition during evolution. We argue that our innate capacity to recognize agency and intelligence in unfamiliar guises lags far behind our ability to detect it in familiar behavioral contexts. The multi-scale competency of life is essential to adaptive function, potentiating evolution and providing strategies for top-down control (not micromanagement) to address complex disease and injury. We propose an observer-focused viewpoint that is agnostic about scale and implementation, illustrating how evolution pivoted similar strategies to explore and exploit metabolic, transcriptional, morphological, and finally 3D motion spaces. By generalizing the concept of behavior, we gain novel perspectives on evolution, strategies for system-level biomedical interventions, and the construction of bioengineered intelligences. This framework is a first step toward relating to intelligence in highly unfamiliar embodiments, which will be essential for progress in artificial intelligence and regenerative medicine and for thriving in a world increasingly populated by synthetic, bio-robotic, and hybrid beings.
... Under these conditions, we can ask how much a living system S can learn, i.e., what information S can obtain by observation, about the internal structure or dynamics of its environment E, and, conversely, how much E can learn about the internal structure or dynamics of S. The answer is that what can be learned at t is strictly limited to the classical information actually exchanged at t. This information is, for any pair of finite, separable physical systems, regardless of their size or complexity, strictly insufficient to fully determine the internal structure or dynamics of either interaction partner [70,71]; see [72,73] for informal discussions of this point. The information obtainable by either party by observation is, therefore, conditionally independent of the internal structure or dynamics of the other party; these can, in principle, vary arbitrarily without affecting the observations obtained at any given t. ...
Meaning has traditionally been regarded as a problem for philosophers and psychologists. Advances in cognitive science since the early 1960s, however, broadened discussions of meaning, or more technically, the semantics of perceptions, representations, and/or actions, into biology and computer science. Here, we review the notion of “meaning” as it applies to living systems, and argue that the question of how living systems create meaning unifies the biological and cognitive sciences across both organizational and temporal scales.
Self-referential cellular homeostasis is maintained by the measured assessment of both internal status and external conditions based within an integrated cellular information field. This cellular field attachment to biologic information space-time coordinates environmental inputs by connecting the cellular senome, as the sum of the sensory experiences of the cell, with its genome and epigenome. In multicellular organisms, individual cellular information fields aggregate into a collective information architectural matrix, termed a N-space Episenome, that enables mutualized organism-wide information management. It is hypothesized that biological organization represents a dual heritable system constituted by both its biological materiality and a conjoining N-space Episenome. It is further proposed that morphogenesis derives from reciprocations between these inter-related facets to yield coordinated multicellular growth and development. The N-space Episenome is conceived as a whole cell informational projection that is heritable, transferable via cell division and essential for the synchronous integration of the diverse self-referential cells that constitute holobionts.
