Elmarie Venter | Ruhr-Universität Bochum (original) (raw)

Papers by Elmarie Venter

Research paper thumbnail of Getting the World Right: Perceptual Accuracy and the Role of the Perceiver in Predictive Processing Models

Journal of Consciousness Studies, 2019

Predictive processing is often presented as a unifying framework for perception, action, and cogn... more Predictive processing is often presented as a unifying framework for perception, action, and cognition, being able to explain most (if not all) mental phenomena (Hohwy, 2013; Clark, 2016): with regard to perception, the brain harbours a generative model issuing top-down expectations that are matched against bottom-up sensory feedback. Mismatches lead to error messages and model updates until the brain is 'getting it right'. The core notion of prediction error minimization commits the framework to a specification of accuracy conditions. We therefore turn to issues related to the determination of satisfaction (or accuracy) conditions discussed in the philosophy of perception. In particular, we rely on work by Recanati (2007) who shows that the accuracy conditions of perceptual content are partly determined by the intentional mode and the perceiver (or self). It is important to notice that the self can enter the specification of accuracy conditions in two ways, namely as subject or as object. Even if we do not perceive ourselves explicitly as an object, we always implicitly represent ourselves as subject. We discuss whether and how these two ways of self-representation can be respected in the predictive processing framework.

Research paper thumbnail of Toward an Embodied, Embedded Predictive Processing Account

In this paper, I argue for an embodied, embedded approach to predictive processing and thus align... more In this paper, I argue for an embodied, embedded approach to predictive processing and thus align the framework with situated cognition. The recent popularity of theories conceiving of the brain as a predictive organ has given rise to two broad camps in the literature that I call free energy enactivism and cognitivist predictive processing. The two approaches vary in scope and methodology. The scope of cognitivist predictive processing is narrow and restricts cognition to brain processes and structures; it does not consider the body-beyond-brain and the environment as constituents of cognitive processes. Free energy enactivism, on the other hand, includes all self-organizing systems that minimize free energy (including non-living systems) and thus does not offer any unique explanations for more complex cognitive phenomena that are unique to human cognition. Furthermore, because of its strong commitment to the mind-life continuity thesis, it does not provide an explanation of what distinguishes more sophisticated cognitive systems from simple systems. The account that I develop in this paper rejects both of these radical extremes. Instead, I propose a compromise that highlights the necessary components of predictive processing by making use of a mechanistic methodology of explanation. The starting point of the argument in this paper is that despite the interchangeable use of the terms, prediction error minimization and the free energy principle are not identical. But this distinction does not need to disrupt the status quo of the literature if we consider an alternative approach: Embodied, Embedded Predictive Processing (EEPP). EEPP accommodates the free energy principle, as argued for by free energy enactivism, but it also allows for mental representations in its explanation of cognition. Furthermore, EEPP explains how prediction error minimization is realized but, unlike cognitivist PP, it allocates a constitutive role to the body in cognition. Despite highlighting concerns regarding cognitivist PP, I do not wish to discredit the role of the neural domain or representations as free energy enactivism does. Neural structures and processes undeniably contribute to the minimization of prediction error but the role of the body is equally important. On my account, prediction error minimization and free energy minimization are deeply dependent on the body of an agent, such that the body-beyond-brain plays a constitutive role in cognitive processing. I suggest that the body plays three constitutive roles in prediction error minimization: The body regulates cognitive activity, ensuring that cognition and action are intricately linked. The body acts as distributor in the sense that it carries some of the cognitive load by fulfilling the function of minimizing prediction error. Finally, the body serves to constrain the information that is processed by an agent. In fulfilling these three roles, the agent and environment enter into a bidirectional relation through influencing and modeling the structure of the other. This connects EEPP to the Frontiers in Psychology | www.frontiersin.org 1

Research paper thumbnail of How and why actions are selected: action selection and the dark room problem

In this paper, I examine an evolutionary approach to the action selection problem and illustrate ... more In this paper, I examine an evolutionary approach to the action selection problem and illustrate how it helps raise an objection to the predictive processing account. Clark examines the predictive processing account as a theory of brain function that aims to unify perception, action, and cognition, but-despite this aim-fails to consider action selection overtly. He off ers an account of action control with the implication that minimizing prediction error is an imperative of living organisms because, according to the predictive processing account, action is employed to ful" ll expectations and reduce prediction error. One way in which this can be achieved is by seeking out the least stimulating environment and staying there (Friston et al. 2012: 2). Bayesian, neuroscienti" c, and machine learning approaches into a single framework whose overarching principle is the minimization of surprise (or, equivalently , the maximization of expectation. But, most living organisms do not " nd, and stay in, surprise free environments. This paper explores this objection, also called the " dark room problem " , and examines Clark's response to the problem. Finally, I recommend that if supplemented with an account of action selection, Clark's account will avoid the dark room problem.

Research paper thumbnail of Getting the World Right: Perceptual Accuracy and the Role of the Perceiver in Predictive Processing Models

Journal of Consciousness Studies, 2019

Predictive processing is often presented as a unifying framework for perception, action, and cogn... more Predictive processing is often presented as a unifying framework for perception, action, and cognition, being able to explain most (if not all) mental phenomena (Hohwy, 2013; Clark, 2016): with regard to perception, the brain harbours a generative model issuing top-down expectations that are matched against bottom-up sensory feedback. Mismatches lead to error messages and model updates until the brain is 'getting it right'. The core notion of prediction error minimization commits the framework to a specification of accuracy conditions. We therefore turn to issues related to the determination of satisfaction (or accuracy) conditions discussed in the philosophy of perception. In particular, we rely on work by Recanati (2007) who shows that the accuracy conditions of perceptual content are partly determined by the intentional mode and the perceiver (or self). It is important to notice that the self can enter the specification of accuracy conditions in two ways, namely as subject or as object. Even if we do not perceive ourselves explicitly as an object, we always implicitly represent ourselves as subject. We discuss whether and how these two ways of self-representation can be respected in the predictive processing framework.

Research paper thumbnail of Toward an Embodied, Embedded Predictive Processing Account

In this paper, I argue for an embodied, embedded approach to predictive processing and thus align... more In this paper, I argue for an embodied, embedded approach to predictive processing and thus align the framework with situated cognition. The recent popularity of theories conceiving of the brain as a predictive organ has given rise to two broad camps in the literature that I call free energy enactivism and cognitivist predictive processing. The two approaches vary in scope and methodology. The scope of cognitivist predictive processing is narrow and restricts cognition to brain processes and structures; it does not consider the body-beyond-brain and the environment as constituents of cognitive processes. Free energy enactivism, on the other hand, includes all self-organizing systems that minimize free energy (including non-living systems) and thus does not offer any unique explanations for more complex cognitive phenomena that are unique to human cognition. Furthermore, because of its strong commitment to the mind-life continuity thesis, it does not provide an explanation of what distinguishes more sophisticated cognitive systems from simple systems. The account that I develop in this paper rejects both of these radical extremes. Instead, I propose a compromise that highlights the necessary components of predictive processing by making use of a mechanistic methodology of explanation. The starting point of the argument in this paper is that despite the interchangeable use of the terms, prediction error minimization and the free energy principle are not identical. But this distinction does not need to disrupt the status quo of the literature if we consider an alternative approach: Embodied, Embedded Predictive Processing (EEPP). EEPP accommodates the free energy principle, as argued for by free energy enactivism, but it also allows for mental representations in its explanation of cognition. Furthermore, EEPP explains how prediction error minimization is realized but, unlike cognitivist PP, it allocates a constitutive role to the body in cognition. Despite highlighting concerns regarding cognitivist PP, I do not wish to discredit the role of the neural domain or representations as free energy enactivism does. Neural structures and processes undeniably contribute to the minimization of prediction error but the role of the body is equally important. On my account, prediction error minimization and free energy minimization are deeply dependent on the body of an agent, such that the body-beyond-brain plays a constitutive role in cognitive processing. I suggest that the body plays three constitutive roles in prediction error minimization: The body regulates cognitive activity, ensuring that cognition and action are intricately linked. The body acts as distributor in the sense that it carries some of the cognitive load by fulfilling the function of minimizing prediction error. Finally, the body serves to constrain the information that is processed by an agent. In fulfilling these three roles, the agent and environment enter into a bidirectional relation through influencing and modeling the structure of the other. This connects EEPP to the Frontiers in Psychology | www.frontiersin.org 1

Research paper thumbnail of How and why actions are selected: action selection and the dark room problem

In this paper, I examine an evolutionary approach to the action selection problem and illustrate ... more In this paper, I examine an evolutionary approach to the action selection problem and illustrate how it helps raise an objection to the predictive processing account. Clark examines the predictive processing account as a theory of brain function that aims to unify perception, action, and cognition, but-despite this aim-fails to consider action selection overtly. He off ers an account of action control with the implication that minimizing prediction error is an imperative of living organisms because, according to the predictive processing account, action is employed to ful" ll expectations and reduce prediction error. One way in which this can be achieved is by seeking out the least stimulating environment and staying there (Friston et al. 2012: 2). Bayesian, neuroscienti" c, and machine learning approaches into a single framework whose overarching principle is the minimization of surprise (or, equivalently , the maximization of expectation. But, most living organisms do not " nd, and stay in, surprise free environments. This paper explores this objection, also called the " dark room problem " , and examines Clark's response to the problem. Finally, I recommend that if supplemented with an account of action selection, Clark's account will avoid the dark room problem.