‘Plant neurobiology’ has emerged in recent years as a multidisciplinary endeavor carried out mainly by steady collaboration within the plant sciences. The field proposes a particular approach to the study of plant intelligence by putting forward an integrated view of plant signaling and adaptive behavior. Its objective is to account for the way plants perceive and act in a purposeful manner. But it is not only the plant sciences that constitute plant neurobiology. Resources from philosophy and cognitive science are central (...) to such an interdisciplinary project, if plant neurobiology is to maintain its target well-focused. This manifesto outlines a road map for the establishment and development of a new subject—the Philosophy of Plant Neurobiology—, a new field of research emerging at the intersection of the philosophy of cognitive science and plant neurobiology. The discipline is herewith presented, introducing challenges and novel lines of engagement with the empirical investigation, and providing an explanatory framework and guiding principles that will hopefully ease the integration of research on the quest for plant intelligence. (shrink)

‘Plant neurobiology’ has emerged in recent years as a multidisciplinary endeavor carried out mainly by steady collaboration within the plant sciences. The field proposes a particular approach to the study of plant intelligence by putting forward an integrated view of plant signaling and adaptive behavior. Its objective is to account for the way plants perceive and act in a purposeful manner. But it is not only the plant sciences that constitute plant neurobiology. Resources from philosophy and cognitive science are central (...) to such an interdisciplinary project, if plant neurobiology is to maintain its target well-focused. This manifesto outlines a road map for the establishment and development of a new subject—the Philosophy of Plant Neurobiology—, a new field of research emerging at the intersection of the philosophy of cognitive science and plant neurobiology. The discipline is herewith presented, introducing challenges and novel lines of engagement with the empirical investigation, and providing an explanatory framework and guiding principles that will hopefully ease the integration of research on the quest for plant intelligence. (shrink)

Tyler Burge presents an original study of the most primitive ways in which individuals represent the physical world. By reflecting on the science of perception and related psychological and biological sciences, he gives an account of constitutive conditions for perceiving the physical world, and thus aims to locate origins of representational mind.

In this paper, we argue for a theoretical separation of the free-energy principle from Helmholtzian accounts of the predictive brain. The free-energy principle is a theoretical framework capturing the imperative for biological self-organization in information-theoretic terms. The free-energy principle has typically been connected with a Bayesian theory of predictive coding, and the latter is often taken to support a Helmholtzian theory of perception as unconscious inference. If our interpretation is right, however, a Helmholtzian view of perception is incompatible with Bayesian (...) predictive coding under the free-energy principle. We argue that the free energy principle and the ecological and enactive approach to mind and life make for a much happier marriage of ideas. We make our argument based on three points. First we argue that the free energy principle applies to the whole animal–environment system, and not only to the brain. Second, we show that active inference, as understood by the free-energy principle, is incompatible with unconscious inference understood as analagous to scientific hypothesis-testing, the main tenet of a Helmholtzian view of perception. Third, we argue that the notion of inference at work in Bayesian predictive coding under the free-energy principle is too weak to support a Helmholtzian theory of perception. Taken together these points imply that the free energy principle is best understood in ecological and enactive terms set out in this paper. (shrink)

This paper presents a blueprint for an ecological cognitive architecture. Ecological psychology, I contend, must be complemented with a story about the role of the CNS in perception, action, and cognition. To arrive at such a story while staying true to the tenets of ecological psychology, it will be necessary to flesh out the central metaphor according to which the animal perceives its environment by ‘resonating’ to information in energy patterns: what is needed is a theory of resonance. I offer (...) here the two main elements of such a theory: a framework and a methodology. (shrink)

There is a certain excitement in vision science concerning the idea of applying the tools of bayesian decision theory to explain our perceptual capacities. Bayesian models are thought to be needed to explain how the inverse problem of perception is solved, and to rescue a certain constructivist and Kantian way of understanding the perceptual process. Anticlimactically, I argue both that bayesian outlooks do not constitute good solutions to the inverse problem, and that they are not constructivist in nature. In explaining (...) how visual systems derive a single percept from underdetermined stimulation, orthodox versions of bayesian accounts encounter a problem. The problem shows that such accounts need to be grounded in Natural Scene Statistics (NSS), an approach that takes seriously the Gibsonian insight that studying perception involves studying the statistical regularities of the environment in which we are situated. Additionally, I argue that bayesian frameworks postulate structures that hardly rescue a constructivist way of understanding perception. Except for percepts, the posits of bayesian theory are not representational in nature. bayesian perceptual inferences are not genuine inferences. They are biased processes that operate over nonrepresentational states. (shrink)

Objects are central to perception and our interactions with the world. We perceive the world as parsed into discrete entities that instantiate particular properties, and these items capture our attention and shape how we interact with the environment. Recently there has been some debate about whether the sense of smell allows us to perceive odours as discrete objects, with some suggesting that olfaction is aspatial and doesn’t allow for object-individuation. This paper offers two empirically tractable criteria for assessing whether particular (...) objects are exhibited in perceptual experience— susceptibility to figure-ground segregation and perceptual constancies—and argues that these criteria are fulfilled by olfactory perception, and thus there are olfactory objects. I argue that there are, in fact, two different ways that olfaction allows for figure-ground segregation. First, I look at various Gestalt grouping principles, which are thought to govern when features are perceived as grouped into structured wholes, segregated from everything around them. I argue that these principles apply to olfactory experience, providing evidence of non-spatial figure-ground segregation. Second, I defend the contentious idea that a spatial variety of figure-ground segregation can also occur in olfaction. To see this, however, we need to look to empirical evidence showing that tactile stimulation and bodily movements play a crucial role in olfactory phenomenology. Finally, I draw on empirical evidence and olfactory phenomenology to argue that there are perceptual constancies in olfactory experience, allowing us to perceive odours as coherent objects that survive shifts in our perspectives on the world. (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)