The mathematical theory of communication.

The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Role of the entrepreneur in a distinct role of profit.

Nobel laureate Erwin Schrödinger's What is Life?, one of the great science classics of the twentieth century appears here together with Mind and Matter.

I identify three lessons from Kenneth Craik’s landmark book “The Nature of Explanation” for contemporary debates surrounding the existence, extent, and nature of mental representation: first, an account of mental representations as neural structures that function analogously to public models; second, an appreciation of prediction as the central component of intelligence in demand of such models; and third, a metaphor for understanding the brain as an engineer, not a scientist. I then relate these insights to discussions surrounding the representational status (...) of predictive processing – which, I argue, provides a contemporary vindication of Craik’s extremely prescient “hypothesis on the nature of thought.”. (shrink)

We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini :1509–1534. doi: 10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves, and from a greater appreciation of the problems (...) solved by bio-cognitive systems, chiefly regulation and prediction. We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of dynamical models as a central postulate in neurocognitive research. (shrink)

According to active inference, action is enabled by a top-down modulation of sensory signals. Computational models of this mechanism complement ideomotor theories of action representation. Such theories postulate common neural representations for action and perception, without specifying how action is enabled by such representations. In active inference, motor commands are replaced by proprioceptive predictions. In order to initiate action through such predictions, sensory prediction errors have to be attenuated. This paper argues that such top-down modulation involves systematic misrepresentations. More specifically, (...) the paper first argues for the following conditional claim. If active inference provides an accurate computational description of how action is enabled in the brain, then action is enabled by systematic misrepresentations. Furthermore, it is argued that an inference to the best explanation provides reason for believing the antecedent is true: Firstly, active inference provides a crucial extension to ideomotor theories. Secondly, active inference explains otherwise puzzling phenomena related to sensory attenuation, e.g. in force-matching or self-tickling paradigms. Taken together, these reasons support the claim that action is indeed enabled by systematic misrepresentations. The claim casts doubt on the assumption that representations are systematically beneficial to the extent that they are true: if the argument in this paper is sound, systematically beneficial misrepresentations may lie at the heart of our neural architecture. (shrink)

In this paper I will argue that Professor Goodman was correct in thinking that there is a problem concerning counterfactual conditionals, but that it is somewhat different from the problem he thought it to be, and is one that is even more basic. I will also try to show that this problem is distinct from Hume's "problem" of induction, and that additional assumptions have to be made for counterfactual induction beyond those required for other kinds of induction.

In a dynamic world, mechanisms allowing prediction of future situations can provide a selective advantage. We suggest that memory systems differ in the degree of flexibility they offer for anticipatory behavior and put forward a corresponding taxonomy of prospection. The adaptive advantage of any memory system can only lie in what it contributes for future survival. The most flexible is episodic memory, which we suggest is part of a more general faculty of mental time travel that allows us not only (...) to go back in time, but also to foresee, plan, and shape virtually any specific future event. We review comparative studies and find that, in spite of increased research in the area, there is as yet no convincing evidence for mental time travel in nonhuman animals. We submit that mental time travel is not an encapsulated cognitive system, but instead comprises several subsidiary mechanisms. A theater metaphor serves as an analogy for the kind of mechanisms required for effective mental time travel. We propose that future research should consider these mechanisms in addition to direct evidence of future-directed action. We maintain that the emergence of mental time travel in evolution was a crucial step towards our current success. (shrink)

Counterfactual thinking involves imagining hypothetical alternatives to reality. Philosopher David Lewis argued that people estimate the subjective plausibility that a counterfactual event might have occurred by comparing an imagined possible world in which the counterfactual statement is true against the current, actual world in which the counterfactual statement is false. Accordingly, counterfactuals considered to be true in possible worlds comparatively more similar to ours are judged as more plausible than counterfactuals deemed true in possible worlds comparatively less similar. Although Lewis (...) did not originally develop his notion of comparative similarity to be investigated as a psychological construct, this study builds upon his idea to empirically investigate comparative similarity as a possible psychological strategy for evaluating the perceived plausibility of counterfactual events. More specifically, we evaluate judgments of comparative similarity between episodic memories and episodic counterfactual events as a factor influencing people's judgments of plausibility in counterfactual simulations, and we also compare it against other factors thought to influence judgments of counterfactual plausibility, such as ease of simulation and prior simulation. Our results suggest that the greater the perceived similarity between the original memory and the episodic counterfactual event, the greater the perceived plausibility that the counterfactual event might have occurred. While similarity between actual and counterfactual events, ease of imagining, and prior simulation of the counterfactual event were all significantly related to counterfactual plausibility, comparative similarity best captured the variance in ratings of counterfactual plausibility. Implications for existing theories on the determinants of counterfactual plausibility are discussed. (shrink)

According to F. Adams [this journal, vol. 68, 2018] cognition cannot be realized in plants or bacteria. In his view, plants and bacteria respond to the here-and-now in a hardwired, inflexible manner, and are therefore incapable of cognitive activity. This article takes issue with the pursuit of plant cognition from the perspective of an empirically informed philosophy of plant neurobiology. As we argue, empirical evidence shows, contra Adams, that plant behavior is in many ways analogous to animal behavior. This renders (...) plants suitable to be described as cognitive agents in a non-metaphorical way. Sections two to four review the arguments offered by Adams in light of scientific evidence on plant adaptive behavior, decision-making, anticipation, as well as learning and memory. Section five introduces the ‘phyto-nervous’ system of plants. To conclude, section six resituates the quest for plant cognition into a broader approach in cognitive science, as represented by enactive and ecological schools of thought. Overall, we aim to motivate the idea that plants may be considered genuine cognitive agents. Our hope is to help propel public awareness and discussion of plant intelligence once appropriately stripped of anthropocentric preconceptions of the sort that Adams' position appears to exemplify. (shrink)

Humans and other animals are able not only to coordinate their actions with their current sensorimotor state, but also to imagine, plan and act in view of the future, and to realize distal goals. In this paper we discuss whether or not their future-oriented conducts imply (future-oriented) representations. We illustrate the role played by anticipatory mechanisms in natural and artificial agents, and we propose a notion of representation that is grounded in the agent’s predictive capabilities. Therefore, we argue that the (...) ability that characterizes and defines a true cognitive mind, as opposed to a merely adaptive system, is that of building representations of the non-existent, of what is not currently (yet) true or perceivable, of what is desired. A real mental activity begins when the organism is able to endogenously (i.e. not as the consequence of current perceptual stimuli) produce an internal representation of the world in order to select and guide its conduct goal-directed: the mind serves to coordinate with the future. (shrink)

Philosophers have traditionally held that propositions mark the domain of rational thought and inference. Many philosophers have held that only conceptually sophisticated creatures like us could have propositional attitudes. But in recent decades, philosophers have adopted increasingly liberal views of propositional attitudes that encompass the mental states of various non-human animals. These views now sit alongside more traditional views within the philosophical mainstream. In this paper I argue that liberalized views of propositional attitudes are so liberal that they encompass states (...) of all sorts of apparently mindless systems like circadian clocks in plants. I begin by arguing that on the most well-developed and widely endorsed theories of underived representation in philosophy, circadian clocks qualify as representations. I then argue that standard reasons for thinking that perceptual states and pictures have propositional content carry over to circadian clocks. Finally, I argue that circadian representations in plants play the kind of functional role that is widely taken to be partly constitutive of belief-like attitudes. So according to mainstream theories of representation, propositions, and attitudes, plants have propositional attitudes. Yet on other more traditional views, this conclusion would seem absurd. So, contrary to appearances, there is no shared, stable understanding of what propositional attitudes are in contemporary philosophy. (shrink)

Philosophers have traditionally held that propositions mark the domain of rational thought and inference. Many philosophers have held that only conceptually sophisticated creatures like us could have propositional attitudes. But in recent decades, philosophers have adopted increasingly liberal views of propositional attitudes that encompass the mental states of various non-human animals. These views now sit alongside more traditional views within the philosophical mainstream. In this paper I argue that liberalized views of propositional attitudes are so liberal that they encompass states (...) of all sorts of apparently mindless systems like circadian clocks in plants. I begin by arguing that on the most well-developed and widely endorsed theories of underived representation in philosophy, circadian clocks qualify as representations. I then argue that standard reasons for thinking that perceptual states and pictures have propositional content carry over to circadian clocks. Finally, I argue that circadian representations in plants play the kind of functional role that is widely taken to be partly constitutive of belief-like attitudes. So according to mainstream theories of representation, propositions, and attitudes, plants have propositional attitudes. Yet on other more traditional views, this conclusion would seem absurd. So, contrary to appearances, there is no shared, stable understanding of what propositional attitudes are in contemporary philosophy. (shrink)

It is widely held in contemporary philosophy of mind that states with underived representational content are ipso facto psychological states. This view—the Content View—underlies a number of interesting philosophical projects, such as the attempt to pick out a psychological level of explanation, to demarcate genuinely psychological from non-psychological states, and to limn the class of states with phenomenal character. The most detailed and influential theories of underived representation in philosophy are the tracking theories developed by Fodor, Dretske, Millikan and others. (...) Tracking theorists initially hoped to ‘naturalize’ underived representation by showing that although it is distinctively psychological it is not irreducibly so, yet they ended up developing theories of representation that by their own lights don’t pick out a distinctively psychological phenomenon at all. Burge sets out to develop a theory of underived representation that does pick out a distinctively psychological phenomenon. His theory promises to vindicate the Content View and the various philosophical projects that depend on it. In this paper I argue that Burge’s theory dementalizes representation for the same reason tracking theories do: These theories hold that representations are states with underived accuracy conditions, yet such states are found in all sorts of mindless systems, like plants. (shrink)

ABSTRACTWhile over the last few decades AI research has largely focused on building tools and applications, recent technological developments have prompted a resurgence of interest in building a genuinely intelligent artificial agent – one that has a mind in the same sense that humans and animals do. In this paper, I offer a theoretical and methodological framework for this project of investigating “artificial minded intelligence” that can help to unify existing approaches and provide new avenues for research. I first outline (...) three desiderata that a framework for AMI research should satisfy. In Section 1, I further motivate the desiderata as well as the need for a new framework. Section 2 develops a general methodological approach, the “generative methodology,” and Section 3 develops a version of this methodology, the “Competence Framework for AI research”. (shrink)

The emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents—who shape and are shaped by their environment—offers a golden opportunity to revisit and revise ideas about the physical and information-theoretic underpinnings of life, mind, and consciousness itself. In particular, the active inference framework makes it possible to bridge connections from computational neuroscience and robotics/AI to ecological psychology and phenomenology, revealing common underpinnings and overcoming key limitations. AIF opposes the mechanistic to the reductive, while staying fully grounded in a (...) naturalistic and information-theoretic foundation, using the principle of free energy minimization. The latter provides a theoretical basis for a unified treatment of particles, organisms, and interactive machines, spanning from the inorganic to organic, non-life to life, and natural to artificial agents. We provide a brief introduction to AIF, then explore its implications for evolutionary theory, ecological psychology, embodied phenomenology, and robotics/AI research. We conclude the paper by considering implications for machine consciousness. (shrink)

Counterfactuals is David Lewis' forceful presentation of and sustained argument for a particular view about propositions which express contrary to fact conditionals, including his famous defense of realism about possible worlds and his theory of laws of nature.

Despite the ubiquity of uncertainty, scientific attention has focused primarily on probabilistic approaches, which predominantly rely on the assumption that uncertainty can be measured and expressed numerically. At the same time, the increasing amount of research from a range of areas including psychology, economics, and sociology testify that in the real world, people’s understanding of risky and uncertain situations cannot be satisfactorily explained in probabilistic and decision-theoretical terms. In this article, we offer a theoretical overview of an alternative approach to (...) uncertainty developed in the framework of the ecological rationality research program. We first trace the origins of the ecological approach to uncertainty in Simon’s bounded rationality and Brunswik’s lens model framework and then proceed by outlining a theoretical view of uncertainty that ensues from the ecological rationality approach. We argue that the ecological concept of uncertainty relies on a systemic view of uncertainty that features it as a property of the organism–environment system. We also show how simple heuristics can deal with unmeasurable uncertainty and in what cases ignoring probabilities emerges as a proper response to uncertainty. (shrink)

An exciting theory in neuroscience is that the brain is an organ for prediction error minimization. This theory is rapidly gaining influence and is set to dominate the science of mind and brain in the years to come. PEM has extreme explanatory ambition, and profound philosophical implications. Here, I assume the theory, briefly explain it, and then I argue that PEM implies that the brain is essentially self-evidencing. This means it is imperative to identify an evidentiary boundary between the brain (...) and its environment. This boundary defines the mind-world relation, opens the door to skepticism, and makes the mind transpire as more inferentially secluded and neurocentrically skull-bound than many would nowadays think. Therefore, PEM somewhat deflates contemporary hypotheses that cognition is extended, embodied and enactive; however, it can nevertheless accommodate the kinds of cases that fuel these hypotheses. (shrink)

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in (...) parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language. Key Words: efference copies; emulation theory of representation; forward models; Kalman filters; motor control; motor imagery; perception; visual imagery. (shrink)

I discuss the bearing on the mind-body problem of some general characteristics of living systems, including the physical basis of metabolism and the relation between living activity and cognitive capacities in simple organisms. I then attempt to describe stages in the history of animal life important to the evolution of subjective experience. Features of the biological basis of cognition are used to criticize arguments against materialism that draw on the conceivability of a separation between mental and physical. I also argue (...) against commonly held views about the "multiple realizability" of mental states of the kind seen in humans. The aim of the paper is to reconfigure and narrow the "explanatory gap" between mental and physical. (shrink)

The paper links discussions of two topics: biological individuality and the simplest forms of mentality. I discuss several attempts to locate the boundary between metabolic activity and ‘minimal cognition.’ I then look at differences between the kinds of individuality present in unicellular life, multicellular life in general, and animals of several kinds. Nervous systems, which are clearly relevant to cognition and subjectivity, also play an important role in the form of individuality seen in animals. The last part of the paper (...) links these biological transitions to the evolutionary history of subjective experience. (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)

Through the use of such "folk" concepts as belief, desire, intention, and expectation, Daniel Dennett asserts in this first full scale presentation of...

What is the function of cognition? On one influential account, cognition evolved to co-ordinate behaviour with environmental change or complexity. Liberal interpretations of this view ascribe cognition to an extraordinarily broad set of biological systems—even bacteria, which modulate their activity in response to salient external cues, would seem to qualify as cognitive agents. However, equating cognition with adaptive flexibility per se glosses over important distinctions in the way biological organisms deal with environmental complexity. Drawing on contemporary advances in theoretical biology (...) and computational neuroscience, we cash these distinctions out in terms of different kinds of generative models, and the representational and uncertainty-resolving capacities they afford. This analysis leads us to propose a formal criterion for delineating cognition from other, more pervasive forms of adaptive plasticity. On this view, biological cognition is rooted in a particular kind of functional organisation; namely, that which enables the agent to detach from the present and engage in counterfactual inference. (shrink)

Recent work in computational and cognitive neuroscience depicts the brain as an ever‐active prediction machine: an inner engine continuously striving to anticipate the incoming sensory barrage. I briefly introduce this class of models before contrasting two ways of understanding the implied vision of mind. One way (Conservative Predictive Processing) depicts the predictive mind as an insulated inner arena populated by representations so rich and reconstructive as to enable the organism to ‘throw away the world’. The other (Radical Predictive Processing) stresses (...) the use of fast and frugal, action‐involving solutions of the kind highlighted by much work in robotics and embodied cognition. But it goes further, by showing how predictive schemes can combine frugal and more knowledge‐intensive strategies, switching between them fluently and continuously as task and context dictate. I end by exploring some parallels with work in enactivism, and by noting a certain ambivalence concerning internal representations and their role in the predictive mind. (shrink)

Recent work in cognitive and computational neuroscience depicts human brains as devices that minimize prediction error signals: signals that encode the difference between actual and expected sensory stimulations. This raises a series of puzzles whose common theme concerns a potential misfit between this bedrock informationtheoretic vision and familiar facts about the attractions of the unexpected. We humans often seem to actively seek out surprising events, deliberately harvesting novel and exciting streams of sensory stimulation. Conversely, we often experience some wellexpected sensations (...) as unpleasant and to-be-avoided. In this paper, I explore several core and variant forms of this puzzle, using them to display multiple interacting elements that together deliver a satisfying solution. That solution requires us to go beyond the discussion of simple information-theoretic imperatives and to recognize the essential role of species-specific prestructuring, epistemic foraging, and cultural practices in shaping the restless, curious, novelty-seeking human mind. (shrink)

We distinguish three qualitatively different types of uncertainty—ethical, option and state space uncertainty—that are distinct from state uncertainty, the empirical uncertainty that is typically measured by a probability function on states of the world. Ethical uncertainty arises if the agent cannot assign precise utilities to consequences. Option uncertainty arises when the agent does not know what precise consequence an act has at every state. Finally, state space uncertainty exists when the agent is unsure how to construct an exhaustive state space. (...) These types of uncertainty are characterised along three dimensions—nature, object and severity—and the relationship between them is examined. We conclude that these different forms of uncertainty cannot be reduced to empirical uncertainty about the state of the world without inducing an increase in its severity. (shrink)