This paper aims to establish several interconnected points. First, a particular interpretation of the mathematical definition of information, known as the causal interpretation, is supported largely by misunderstandings of the engineering context from which it was taken. A better interpretation, which makes the definition and quantification of information relative to the function of its user, is outlined. The first half of the paper is given over to introducing communication theory and its competing interpretations. The second half explores three consequences of (...) the main thesis. First, a popular claim that the quantification of information in a signal is irrelevant for the meaning of that signal is exposed as fallacious. Second, a popular distinction between causal and semantic information is shown to be misleading, and I argue it should be replaced with a related distinction between natural and intentional signs. Finally, I argue that recent empirical work from microbiology and cognitive science drawing on resources of mathematical communication theory is best interpreted by the functional account. Overall, a functional approach is shown to be both theoretically and empirically well-supported. (shrink)

The scientific study of consciousness or subjective experiencing is a rapidly expanding research program engaging philosophers of mind, psychologists, cognitive scientists, neurobiologists, evolutionary biologists and biosemioticians. Here we outline an evolutionary approach that we have developed over the last two decades, focusing on the evolutionary transition from non-conscious to minimally conscious, subjectively experiencing organisms. We propose that the evolution of subjective experiencing was driven by the evolution of learning and we identify an open-ended, representational, generative and recursive form of associative (...) learning, which we call Unlimited Associative Learning (UAL), as an evolutionary transition marker of minimal consciousness. This evolutionary marker provides evidence that the evolutionary transition to consciousness has gone to completion and allows reverse-engineering from this learning capacity to the system that enables it – making possible the construction of a toy model of UAL. The model allows us to identify some of the key processes and structures that constitute minimal consciousness, points its taxonomic distribution and the ecological context in which it first emerged, highlights its function and suggests a framework for exploring developmental and evolutionary modifications of consciousness. We point to ways of experimentally testing the relationship between UAL and consciousness in human and in non-human animals and discuss the theoretical and ethical implications of our approach. The framework we offer allows the exploration of the evolutionary changes in agency, value systems, selective processes and goals that were involved in the transition to subjective experiencing from a perspective that resonates with the approaches of bio-semioticians. (shrink)

A single physical process may often be described equally well as computing several different mathematical functions—none of which is explanatorily privileged. How, then, should the computational identity of a physical system be determined? Some computational mechanists hold that computation is individuated only by either narrow physical or functional properties. Even if some individuative role is attributed to environmental factors, it is rather limited. The computational semanticist holds that computation is individuated, at least in part, by semantic properties. She claims that (...) the mechanistic account lacks the resources to individuate the computations performed by some systems, thereby leaving interesting cases of computational indeterminacy unaddressed. This article examines some of these views, and claims that more cases of computational indeterminacy can be addressed, if the system-environment interaction plays a greater role in individuating computations. A new, long-arm functional strategy for individuating computation is advanced. (shrink)

The claims that “The brain processes information” or “Cognition is information processing” are accepted as truisms in cognitive science. However, it is unclear how to evaluate such claims absent a specification of “information” as it is used by neurocognitive theories. The aim of this article is, thus, to identify the key features of information that information-based neurocognitive theories posit. A systematic identification of these features can reveal the explanatory role that information plays in specific neurocognitive theories, and can, therefore, be (...) both theoretically and practically important. These features can be used, in turn, as desiderata against which candidate theories of information may be evaluated. After discussing some characteristics of explanation in cognitive science and their implications for “information”, three notions are briefly introduced: natural, sensory, and endogenous information. Subsequently, six desiderata are identified and defended based on cognitive scientific practices. The global workspace theory of consciousness is then used as a specific case study that arguably posits either five or six corresponding features of information. (shrink)

The idea that the brain is an information processing system raises some challenging questions about whether information exists independently of brains. Answering these questions is relevant for clarifying the theoretical foundations of the sciences of mind and brain, but also for appropriately interpreting and evaluating the evidence about how brains—and other biological systems—work. This article claims that (1) informational descriptions in the sciences of mind and brain can be genuinely explanatory, despite assuming a mind-dependent notion of information; and (2) that (...) either Popperian objectivity or response-dependence in normal receivers is sufficient for grounding the explanatory role that information often plays in these sciences. Hierarchical predictive processing, which has become a central theoretical framework for neurocognitive research, is used as a case study for supporting these two claims. (shrink)

All information results from a process, intrinsic to living beings, of info-autopoiesis or information self-production; a sensory commensurable, self-referential feedback process immanent to Bateson’s ‘difference which makes a difference’. To highlight and illustrate the fundamental nature of the info-autopoietic process, initially, two simulations based on one-parameter feedback are presented. The first, simulates a homeostatic control mechanism (thermostat) which is representative of a mechanistic, cybernetic system with very predictable dynamics, fully dependent on an external referent. The second, simulates a homeorhetic process, (...) inherent to biological systems, illustrating a self-referenced, autonomous system. Further, the active incorporation/interference of viral particles by prokaryotic cells and the activation of CRISPR-Cas can be understood as info-autopoiesis at the most fundamental cellular level, as well as constituting a planetary network of self-referenced information. Moreover, other examples of the info-autopoietic nature of information are presented to show the generality of its applicability. In short, info-autopoiesis is a recursive process that is sufficiently generic to be the only basis for information in nature: from the single cell, to multi-cellular organisms, to consideration of all types of natural and non-natural phenomena, including tools and artificial constructions. (shrink)

Philosophical proponents of predictive processing cast the novelty of predictive models of perception in terms of differences in the functional role and information content of neural signals. However, they fail to provide constraints on how the crucial semantic mapping from signals to their informational contents is determined. Beyond a novel interpretative gloss on neural signals, they have little new to say about the causal structure of the system, or even what statistical information is carried by the signals. That means that (...) the predictive framework for perception can be relabeled in traditional, non-predictive terms, with no empirical consequences relevant to existing or future data. To the extent that neuroscientific research based on predictive processing is both innovative and productive, it will be due to the framework’s suggestive heuristic effects, or perhaps auxiliary empirical claims about implementation, rather than a difference in the information-processing structure that it describes. (shrink)

In his recent book, Daniel Dennett defends a novel account of semantic information in terms of design worth getting (Dennett, 2017). While this is an interesting proposal in itself, my purpose in this commentary is to challenge several of Dennett’s claims. First, he argues that semantic information can be transferred without encoding and storing it. Second, this lack of encoding is what makes semantic information unmeasurable. However, the argument for both these claims, presented by Dennett as an intuition pump, is (...) invalid. (shrink)