1. Introduction 2. Reward-Guided Decision Making 3. Content in the Model 4. How to Deflate a Metarepresentational Reading Proust and Carruthers on metacognitive feelings 5. A Deflationary Treatment of RPEs? 5.1 Dispensing with prediction errors 5.2 What is use of the RPE focused on? 5.3 Alternative explanations—worldly correlates 5.4 Contrast cases 6. Conclusion Appendix: Temporal Difference Learning Algorithms.

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)

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)

Introduction: representationalismMost theorists of cognition endorse some version of representationalism, which I will understand as the view that the human mind is an information-using system, and that human cognitive capacities are representational capacities. Of course, notions such as ‘representation’ and ‘information-using’ are terms of art that require explication. As a first pass, representations are “mediating states of an intelligent system that carry information” (Markman and Dietrich 2001, p. 471). They have two important features: (1) they are physically realized, and so (...) have causal powers; (2) they are intentional, in other words, they have meaning or representational content. This presumes a distinction between a representational vehicle—a physical state or structure that has causal powers and is responsible for producing behavior—and its content. Consider the following characterization of a device that computes the addition functionReaders will recognize the similarity t. (shrink)

How can we think about things in the outside world? There is still no widely accepted theory of how mental representations get their meaning. In light of pioneering research, Nicholas Shea develops a naturalistic account of the nature of mental representation with a firm focus on the subpersonal representations that pervade the cognitive sciences.

There are many different notions of information in logic, epistemology, psychology, biology and cognitive science, which are employed differently in each discipline, often with little overlap. Since our interest here is in biological processes and organisms, we develop a taxonomy of functional information that extends the standard cue/signal distinction (in animal communication theory). Three general, main claims are advanced here. (1) This new taxonomy can be useful in describing learning and communication. (2) It avoids some problems that the natural/non-natural information (...) distinction faces. (3) Functional information is​ ​produced through exploration and stabilisation processes. (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)

The distinction between top-down and bottom-up effects is widely relied on in experimental psychology. However, there is an important problem with the way it is normally defined. Top-down effects are effects of previously-stored information on processing the current input. But on the face of it that includes the information that is implicit in the operation of any psychological process – in its dispositions to transition from some types of representational state to others. This paper suggests a way to distinguish information (...) stored in that way from the kind of influence of prior information that psychologists are concerned to classify as a top-down effect. So-drawn, the distinction is not just of service to theoretical psychology. Asking about the extent of top-down processing is one way to pose some of the questions at issue in philosophical debates about cognitive penetrability – about the extent of the influence of cognitive states on perception. The existence of a theoretically-useful perception-cognition distinction has come under pressure, but even if it has to be abandoned, some of the concerns addressed in the cognitive penetrability literature can be recaptured by asking about the extent of top-down influences on any given psychological process. That formulation is more general, since it can be applied to any psychological process, not just those that are paradigmatically perceptual. (shrink)

The brain is often taken to be a paradigmatic example of a signaling system with semantic and representational properties, in which neurons are senders and receivers of information carried in action potentials. A closer look at this picture shows that it is not as appealing as it might initially seem in explaining the function of the brain. Working from several sender-receiver models within the teleosemantic framework, I will first argue that two requirements must be met for a system to support (...) genuine semantic information: 1. The receiver must be competent —that is, it must be able to extract rewards from its environment on the basis of the signals that it receives. 2. The receiver must have some flexibility of response relative to the signal received. In the second part of the paper, this initial framework will be applied to neural processes, pointing to the surprising conclusion that signaling at the single-neuron level is only weakly semantic at best. Contrary to received views, neurons will have little or no access to semantic information (though their patterns of activity may carry plenty of quantitative, correlational information) about the world outside the organism. Genuine representation of the world requires an organism - level receiver of semantic information, to which any particular set of neurons makes only a small contribution. (shrink)

Sensorimotor psychology studies the mental processes that control goal-directed bodily motion. Recently, sensorimotor psychologists have provided empirically successful Bayesian models of motor control. These models describe how the motor system uses sensory input to select motor commands that promote goals set by high-level cognition. I highlight the impressive explanatory benefits offered by Bayesian models of motor control. I argue that our current best models assign explanatory centrality to a robust notion of mental representation. I deploy my analysis to defend intentional (...) realism, to rebut eliminativism and instrumentalism regarding mental representation, and to explore the relation between intentionality and normativity. (shrink)