Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to (...) adaptive success. This target article critically examines this approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency. (shrink)

‘Predictive Processing’ is an emerging paradigm in cognitive neuroscience that depicts the human mind as an uncertainty management system that constructs probabilistic predictions of sensory s...

This paper discusses the relation between intelligence and motivation in artificial agents, developing and briefly arguing for two theses. The first, the orthogonality thesis, holds (with some caveats) that intelligence and final goals (purposes) are orthogonal axes along which possible artificial intellects can freely vary—more or less any level of intelligence could be combined with more or less any final goal. The second, the instrumental convergence thesis, holds that as long as they possess a sufficient level of intelligence, agents having (...) any of a wide range of final goals will pursue similar intermediary goals because they have instrumental reasons to do so. In combination, the two theses help us understand the possible range of behavior of superintelligent agents, and they point to some potential dangers in building such an agent. (shrink)

This is a book about normativity -- where the central normative terms are words like 'ought' and 'should' and their equivalents in other languages. It has three parts: The first part is about the semantics of normative discourse: what it means to talk about what ought to be the case. The second part is about the metaphysics of normative properties and relations: what is the nature of those properties and relations whose pattern of instantiation makes propositions about what ought to (...) be the case true. The third part is about the epistemology of normative beliefs: how we could ever know, or even have rational or justified belief in, propositions about what ought to be the case. (shrink)

According to normative judgment internalism (NJI), normative judgments -- that is, judgments of the form 'I ought to F' and the like -- are "essentially practical", in the sense that they are in some way essentially connected to practical reasoning, or to motivation for action. Many metaethicists believe that if NJI is true, then it would cast grave doubts on any robustly realist (RR) conception of normative judgments. These metaethicists are mistaken. This mistake about the relations between NJI and RR (...) seems to be due to hasty and undefended assumptions about the nature of belief. Any philosophical conception of belief that has the resources to deal with Frege's Puzzle (how to explain the apparent failures of referential transparency in belief ascriptions) will also have the resources to reconcile NJI and RR with respect to normative judgments. (shrink)

What is the Moral Problem? NORMATIVE ETHICS VS. META-ETHICS It is a common fact of everyday life that we appraise each others' behaviour and attitudes from ...

Moral Realism is a systematic defence of the idea that there are objective moral standards. Russ Shafer-Landau argues that there are moral principles that are true independently of what anyone, anywhere, happens to think of them. His central thesis, as well as the many novel supporting arguments used to defend it, will spark much controversy among those concerned with the foundations of ethics.

Just as there are rational requirements on thought, there are rational requirements on action. This book defends a conception of ethics, and a related conception of human nature, according to which altruism is included among the basic rational requirements on desire and action. Altruism itself depends on the recognition of the reality of other persons, and on the equivalent capacity to regard oneself as merely one individual among many.

1. Presumably the point of, say, inculcating a moral outlook lies in a concern with how people live. It may seem that the very idea of a moral outlook makes room for, and requires, the existence of moral theory, conceived as a discipline which seeks to formulate acceptable principles of conduct. It is then natural to think of ethics as a branch of philosophy related to moral theory, so conceived, rather as the philosophy of science is related to science. On (...) this view, the primary topic of ethics is the concept of right conduct, and the nature and justification of principles of behaviour. If there is a place for an interest in the concept of virtue, it is a secondary place. Virtue is a disposition to behave rightly; the nature of virtue is explained, as it were, from the outside in. (shrink)

The so-called “dark room problem” makes vivd the challenges that purely predictive models face in accounting for motivation. I argue that the problem is a serious one. Proposals for solving the dark room problem via predictive coding architectures are either empirically inadequate or computationally intractable. The Free Energy principle might avoid the problem, but only at the cost of setting itself up as a highly idealized model, which is then literally false to the world. I draw at least one optimistic (...) conclusion, however. Real-world, real-time systems may embody motivational states in a variety of ways consistent with idealized principles like FEP, including ways that are intuitively embodied and extended. This may allow predictive coding theorists to reconcile their account with embodied principles, even if it ultimately undermines loftier ambitions. (shrink)

This paper starts by considering an argument for thinking that predictive processing (PP) is representational. This argument suggests that the Kullback–Leibler (KL)-divergence provides an accessible measure of misrepresentation, and therefore, a measure of representational content in hierarchical Bayesian inference. The paper then argues that while the KL-divergence is a measure of information, it does not establish a sufficient measure of representational content. We argue that this follows from the fact that the KL-divergence is a measure of relative entropy, which can (...) be shown to be the same as covariance (through a set of additional steps). It is well known that facts about covariance do not entail facts about representational content. So there is no reason to think that the KL-divergence is a measure of (mis-)representational content. This paper thus provides an enactive, non-representational account of Bayesian belief optimisation in hierarchical PP. (shrink)

If one formulates Helmholtz's ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on (...) Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure. (shrink)

If one formulates Helmholtz’s ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on (...) Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain’s organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system’s state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure. (shrink)

Why do brains have so many connections? The principles exposed by Andy Clark provide answers to questions like this by appealing to the notion that brains distil causal regularities in the sensorium and embody them in models of their world. For example, connections embody the fact that causes have particular consequences. This commentary considers the imperatives for this form of embodiment.

In this paper I argue that, by combining eliminativist and fictionalist approaches toward the sub-personal representational posits of predictive processing, we arrive at an empirically robust and yet metaphysically innocuous cognitive scientific framework. I begin the paper by providing a non-representational account of the five key posits of predictive processing. Then, I motivate a fictionalist approach toward the remaining indispensable representational posits of predictive processing, and explain how representation can play an epistemologically indispensable role within predictive processing explanations without thereby (...) requiring that representation metaphysically exists. Finally, I outline four consequences of accepting this approach and explain why they are beneficial: we arrive at a victory for metaphysical eliminativism in the ‘representation wars’; my account fits with extant empirical practice; my account provides guidance for future research; and, my account provides the beginnings of a response to Mark Sprevak’s IBE problem for fictionalist approaches toward sub-personal representation. (shrink)

How is it that thoroughly physical material beings such as ourselves can think, dream, feel, create and understand ideas, theories and concepts? How does mere matter give rise to all these non-material mental states, including consciousness itself? An answer to this central question of our existence is emerging at the busy intersection of neuroscience, psychology, artificial intelligence, and robotics.In this groundbreaking work, philosopher and cognitive scientist Andy Clark explores exciting new theories from these fields that reveal minds like ours to (...) be prediction machines - devices that have evolved to anticipate the incoming streams of sensory stimulation before they arrive. These predictions then initiate actions that structure our worlds and alter the very things we need to engage and predict. Clark takes us on a journey in discovering the circular causal flows and the self-structuring of the environment that define "the predictive brain." What emerges is a bold, new, cutting-edge vision that reveals the brain as our driving force in the daily surf through the waves of sensory stimulation. (shrink)

A new theory is taking hold in neuroscience. It is the theory that the brain is essentially a hypothesis-testing mechanism, one that attempts to minimise the error of its predictions about the sensory input it receives from the world. It is an attractive theory because powerful theoretical arguments support it, and yet it is at heart stunningly simple. Jakob Hohwy explains and explores this theory from the perspective of cognitive science and philosophy. The key argument throughout The Predictive Mind is (...) that the mechanism explains the rich, deep, and multifaceted character of our conscious perception. It also gives a unified account of how perception is sculpted by attention, and how it depends on action. The mind is revealed as having a fragile and indirect relation to the world. Though we are deeply in tune with the world we are also strangely distanced from it. (shrink)

Agency and identity -- Necessitation -- Acts and actions -- Aristotle and Kant -- Agency and practical identity -- The metaphysics of normativity -- Constitutive standards -- The constitution of life -- In defense of teleology -- The paradox of self-constitution -- Formal and substantive principles of reason -- Formal versus substantive -- Testing versus weighing -- Maximizing and prudence -- Practical reason and the unity of the will -- The empiricist account of normativity -- The rationalist account of normativity (...) -- Kant on the hypothetical imperative -- Against particularistic willing -- Deciding and predicting -- Autonomy and efficacy -- The function of action -- The possibility of agency -- Non-rational action -- Action -- Attribution -- The psychology of action -- Expulsion from the garden : the transition to humanity -- Instinct, emotion, intelligence, and reason -- The parts of the soul -- Inside or outside -- Pull yourself together -- The constitutional model -- Models of the soul -- The city and the soul -- Platonic virtues -- Justice : substantive, procedural, and platonic -- Kant and the constitutional model -- Defective action -- The problem of bad action -- Being governed by the wrong law -- Or five bad constitutions -- Conceptions of evil -- Degrees of action -- Integrity and interaction -- Deciding to be bad -- The ordinary cases -- Dealing with the disunified -- Kant's theory of interaction -- My reasons -- Deciding to treat someone as an end in himself -- Interacting with yourself -- How to be a person -- What's left of me? (shrink)

Simon Blackburn puts forward a compelling original philosophy of human motivation and morality. He maintains that we cannot get clear about ethics until we get clear about human nature. So these are the sorts of questions he addresses: Why do we behave as we do? Can we improve? Is our ethics at war with our passions, or is it an upshot of those passions? Blackburn seeks the answers in an exploration of guilt, shame, disgust, and other moral emotions; he draws (...) also on game theory and cognitive science in his account of the structures of human motivation. Many philosophers have wanted a naturalistic ethics a theory that integrates our understanding of human morality with the rest of our understanding of the world we live in. What is special about Blackburn's naturalistic ethics is that it does not debunk the ethical by reducing it to the non-ethical. At the same time he banishes the spectres of scepticism and relativism that have haunted recent moral philosophy. Ruling Passions sets ethics in the context of human nature: it offers a solution to the puzzle of how ethics can maintain its authority even though it is rooted in the very emotions and motivations that it exists to control. (shrink)

What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to ever-greater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the “singularity”. The basic argument here was set out by the statistician I.J. Good in his 1965 article “Speculations Concerning the First Ultraintelligent Machine”: Let an ultraintelligent machine be defined as a machine that can far (...) surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an “intelligence explosion”, and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make. The key idea is that a machine that is more intelligent than humans will be better than humans at designing machines. So it will be capable of designing a machine more intelligent than the most intelligent machine that humans can design. So if it is itself designed by humans, it will be capable of designing a machine more intelligent than itself. By similar reasoning, this next machine will also be capable of designing a machine more intelligent than itself. If every machine in turn does what it is capable of, we should expect a sequence of ever more intelligent machines. This intelligence explosion is sometimes combined with another idea, which we might call the “speed explosion”. The argument for a speed explosion starts from the familiar observation that computer processing speed doubles at regular intervals. Suppose that speed doubles every two years and will do so indefinitely. Now suppose that we have human-level artificial intelligence 1 designing new processors. Then faster processing will lead to faster designers and an ever-faster design cycle, leading to a limit point soon afterwards. The argument for a speed explosion was set out by the artificial intelligence researcher Ray Solomonoff in his 1985 article “The Time Scale of Artificial Intelligence”.1 Eliezer Yudkowsky gives a succinct version of the argument in his 1996 article “Staring at the Singularity”: “Computing speed doubles every two subjective years of work.. (shrink)