Andrew Clark

Can what we know change what we see? Does language affect cognition and perception? The last few years have seen increased attention to these seemingly disparate questions, but with little theoretical advance. We argue that substantial clarity can be gained by considering these questions through the lens of predictive processing, a framework in which mental representations—from the perceptual to the cognitive—reflect an interplay between downward-flowing predictions and upward-flowing sensory signals. This framework provides a parsimonious account of how what we know ought to change what we see and helps us understand how a putatively high-level trait such as language can impact putatively low-level processes such as perception. Within this framework, language begins to take on a surprisingly central role in cognition by providing a uniquely focused and flexible means of constructing predictions against which sensory signals can be evaluated. Predictive processing thus provides a plausible mechanism for many of the reported effects of language on perception, thought, and action, and new insights on how and when speakers of different languages construct the same “reality” in alternate ways.

W ostatnich latach odnotowano pojawienie się nowej, ważnej, fundamentalnej teorii funkcji mózgu. Teoria ta łączy podejścia teorii informacji, bayesowskie, neuronaukowe i uczenia maszynowego w jedną ramę, której główną zasadą jest minimalizacja zaskoczenia (czy równoważnie: maksymalizowanie oczekiwania). Najpełniejsze ujęcie tej teorii stanowi sformułowana przez Karla Fristona „minimalizacja energii swobodnej” (zobacz: np. Friston i Stephan, 2007; Friston, 2010a,b – a także: Fiorillo, 2010; Thornton, 2010). Najczęściej podnoszonym przez krytyków tych modeli problemem jest to, że wcale nie wydaje się, żeby systemy biologiczne unikały zaskoczeń. Mówiąc wprost, nie szukamy niezmiennej, ciemnej komory, żeby w niej pozostać. Na tym właśnie polega „problem ciemnego pokoju”. Poniżej opiszemy ten problem oraz rozwiniemy zagadnienia, do których się odnosi. Wykorzystamy do tego formę prologu z książki Space, Time and Gravitation Eddingtona (1920) przedstawiając naszą dyskusję jako rozmowę między teoretykiem informacji (Thorntonem), fizykiem (Fristonem) i filozofem (Clarkiem).

According to an emerging vision in computational cognitive neuroscience, perception (rich, full-blooded, world-presenting perception of the kind we enjoy) depends heavily on prediction. To visually perceive, if this schema is correct, is to meet incoming visual information with a set of matching top-down predictions that track the evolving visual signal across multiple spatial and temporal scales. This chapter first introduces this general explanatory schema, then displays some recent evidence for the schema, and discusses to what extent it marks a radical departure from previous (feature-detection based) models of perception. It ends by exploring some implications of the schema for questions concerning multimodal and crossmodal effects in sensory processing, and for an understanding of the deep and fundamental relations between perception, imagination, and understanding.

One of the most important research programs in contemporary cognitive science is that of extended cognition. In this area of study, features of a subject’s cognitive environment can, in certain conditions, become constituent parts of the cognitive process itself. The aim of this volume is to explore the epistemological ramifications of this idea. The book brings together papers written by a range of distinguished and emerging academics, from a variety of different perspectives, to investigate the very idea of an extended epistemology. The first part of the volume explores foundational issues with regard to an extended epistemology as well as from a critical perspective. The second part of the volume examines the applications of this idea, and the new theoretical directions that it might take us. These include the relevance of Chinese philosophy, the ethical ramifications of extended epistemology, and its import to the epistemology of education, moral ethics, and emerging digital technologies.

This chapter presents an excerpt from Natural Born Cyborgs, which argues that we are already seamlessly interwoven with technologies around us and that the path toward becoming cyborgs does not lead us to become essentially different than we are. Human minds are already both computational and integrated with the larger technological world around us. Such is our cyborg nature. The idea of human cognition as subsisting in a hybrid, extended architecture remains vastly under‐appreciated. We need to understand in detail how brains like ours dovetail their problem‐solving activities to additional resources, and how the larger systems thus created operate, change, and evolve. In addition, we need to understand that the very ideas of minds and persons are not limited to the biological skin‐bag, and that our sense of self, place, and potential are all malleable constructs ready to expand, change, or contract at surprisingly short notice.

In this chapter, the author explains that the brain explains the concept of “functional decomposition” ‐ how it is a blend of different functional subcomponents, each of which computes its own algorithm to carry out a specialized function. The different subcomponents are wired together by evolution and experience to do important tasks. The human is apprised of only the bare minimum of knowledge about the brain's inner activities. What the human (the conscious agent) gets from the brain is rather like what a driver gets from an electronic dashboard display: information pertaining to the few inner and outer parameters to which his/her gross considered activity can make a useful difference.

There is a school of thought which links connectionist models of cognition to eliminativism‐the thesis that the constructs of commonsense psychology (principally, beliefs and desires) do not exist. This way of construing the impact of connectionist modelling is, I argue, deeply mistaken and depends crucially on a shallow analysis of the notion of explanation. I argue that good, higher level descriptions may group together physically heterogenous mechanisms, and that the constructs of folk psychology may fulfil such a grouping function even if they fail to pick out discrete states of individual processing. More speculatively, the paper goes on to suggest that the virtues which recommend such constructs from a theorist's third person perspective may nonetheless have close analogues in individual processing. Various difficulties for what I term a pure distributed connectionism may be solved by systems which utilise both distributed and classical symbolic representations‐the latter exhibiting the discreteness and semantic interpretability which the eliminativist (wrongly) requires to vindicate common‐sense psychology. If human beings turned out to be such mixed systems, then the eliminativist claim would be doubly misguided. It would be false as a conditional since even if pure distributed connectionism were a complete and accurate formal model of individual processing, it would not follow that the other, higher level constructs were not accurate and essential tools for a different kind of explanation. And it would involve a false antecedent since in a mixed system the symbolic descriptions may indeed be incarnate in the system's own individual processing. The paper is in four main sections. Section 1 attempts to systematise a certain received picture of the relative status and accuracy of various levels of description of a (pure distributed) connectionist system. Section 2 then introduces a general model of explanations which aim to group systems into equivalence classes defined for various purposes. Each grouping requires a special vocabulary and the constructs of any given vocabulary are legitimate just insofar as the grouping is interesting and useful. Section 3 goes on to show that, relative to such a model of explanation, the constructs of both symbolic AI and commonsense psychology may have a legitimate role to play in giving psychological explanations. This role is not just that of a useful approximation. The paper ends with a speculative section in which the argument for the theoretical usefulness of such symbolic constructs is extended, in a very natural way, to the domain of individual processing. Here the cognizer, in the process of regulating, debugging and understanding her own representations, creates symbols to stand for sets of distributed activity patterns. The section points out some difficulties for a pure distributed approach which may be eased by the addition of such symbolic constructs and relates the speculations to the current debate over the ‘correct’architecture of cognition. If the speculations are on target, this whole debate turns out to be fundamentally illposed.

How does language (spoken or written) impact thought? One useful way to approach this important but elusive question may be to consider language itself as a cognition-enhancing animal-built structure. To take this perspective is to view language as a kind of self-constructed cognitive niche. These self-constructed cognitive niches play, I suggest, three distinct but deeply interlocking roles in human thought and reason. Working together, these three interlocking routines radically transform the human mind, and mark a genuine discontinuity in the space of anitnal minds.

ABSTRACT How do intelligent agents spawn and exploit integrated processing regimes spanning brain, body, and world? The answer may lie in the ability of the biological brain to select actions and policies in the light of counterfactual predictions—predictions about what kinds of futures will result if such-and-such actions are launched. Appeals to the minimization of ‘counterfactual prediction errors’ (the ones that would result under various scenarios) already play a leading role in attempts to apply the basic toolkit of the neurocomputational theory known as ‘predictive processing’ to higher cognitive functions such as policy selection and planning. In this paper, I show that this also leads naturally to the discovery and use of extended processing regimes defined across heterogeneous mixtures of biological and non-biological resources. This solves a long-standing puzzle concerning the ‘recruitment’ of the right non-neural processing resources at the right time. It reveals how (and why) human brains spawn and maintain extended human minds.

Recent research has relied on the use of fine-tuning techniques to incorporate philosophical knowledge into Large Language Models (LLMs). The present paper outlines an alternative approach to the development of such systems—one that is rooted in a technique known as Retrieval-Augmented Generation (RAG). In contrast to fine-tuning, RAG does not seek to adjust the internal parameters (or internal memory) of an LLM. Instead, RAG relies on the retrieval of information from an externally-situated store, which functions as a form of non-parametric (or external) memory. Applying this technique to the works of the contemporary philosopher Andy Clark yields Digital Andy: an LLM that is able to respond to questions about the extended mind. This serves as a practical demonstration of RAG-based techniques, highlighting how philosophical knowledge can be ‘incorporated’ into an LLM without the need for additional machine learning. But Digital Andy’s reliance on extra-systemic resources also raises questions about the scope of active externalist theorizing, encouraging us to consider Digital Andy’s status as an extended cognitive/computational system. Addressing these questions reveals some interesting points of convergence between the philosophical effort to understand the extended mind and the technological effort to build the next generation of LLMs.

This article applies the thesis of the extended mind to ambient smart environments. These systems are characterised by an environment, such as a home or classroom, infused with multiple, highly networked streams of smart technology working in the background, learning about the user and operating without an explicit interface or any intentional sensorimotor engagement from the user. We analyse these systems in the context of work on the “classical” extended mind, characterised by conditions such as “trust and glue” and phenomenal transparency, and find that these conditions are ill-suited to describing our engagement with ambient smart environments. We then draw from the active inference framework, a theory of brain function which casts cognition as a process of embodied uncertainty minimisation, to develop a version of the extended mind grounded in a process ontology, where the boundaries of mind are understood to be multiple and always shifting. Given this more fluid account of the extended mind, we argue that ambient smart environments should be thought of as extended allostatic control systems, operating more or less invisibly to support an agent’s biological capacity for minimising uncertainty over multiple, interlocking timescales. Thus, we account for the functionality of ambient smart environments as extended systems, and in so doing, utilise a markedly different version of the classical thesis of extended mind.

In the movie, Memento, the hero, Leonard, suffers from a form of anterograde amnesia that results in an inability to lay down new memories. Nonetheless, he sets out on a quest to find his wife’s killer, aided by the use of notes, annotated polaroids, and (for the most important pieces of information obtained) body tattoos. Using these resources he attempts to build up a stock of new beliefs and to thus piece together the puzzle of his wife’s death. At one point in the movie, a character exasperated by Leonard’s lack of biological recall, shouts.