scholarly journals Why should any body have a self?

2017 ◽  
Author(s):  
Jakob Hohwy ◽  
John Michael
Keyword(s):  
Brain Function ◽  
Prediction Error ◽  
The Body ◽  
The Self ◽  
Error Minimization ◽  
Computational Framework ◽  
Key Characteristics ◽  
Prediction Error Minimization ◽  
Hierarchical Bayesian Inference

We use a general computational framework for brain function to develop a theory of the self. The theory is that the self is an inferred model of endogenous, deeply hidden causes of behavior. The general framework for brain function on which we base this theory is that the brain is fundamentally an organ for prediction error minimization.There are three related parts to this project. In the first part (Sections 2-3), we explain how prediction error minimization must lead to the inference of a network of deeply hidden endogenous causes. The key concept here is that prediction error minimization in the long term approximates hierarchical Bayesian inference, where the hierarchy is critical to understand the place of the self, and the body, in the world.In the second part (Sections 4-5), we discuss why such a set of hidden endogenous causes should qualify as a self. We show how a comprehensive prediction error minimization account can accommodate key characteristics of the self. It turns out that, though the modelled endogenous causes are just some among other inferred causes of sensory input, the model is special in being, in a special sense, a model of itself.The third part (Sections 6-7) identifies a threat from such self-modelling: how can a self-model be accurate if it represents itself? We propose that we learn to be who we are through a positive feedback loop: from infancy onward, humans apply agent-models to understand what other agents are up to in their environment, and actively align themselves with those models. Accurate self-models arise and are sustained as a natural consequence of humans’ skill in modeling and interacting with each other. The concluding section situates this inferentialist yet realist theory of the self with respect to narrative conceptions of the self.

Experienced Wholeness

2018 ◽  
Author(s):  
Wanja Wiese
Keyword(s):  
Prediction Error ◽  
Predictive Processing ◽  
Second Step ◽  
Error Minimization ◽  
Gestalt Theory ◽  
Bodily Experiences ◽  
Prediction Error Minimization ◽  
Hierarchical Bayesian Inference ◽  
The Brain ◽  
Phenomenal Unity

The unity of the experienced world and the experienced self have puzzled humanity for centuries. How can we understand this and related types of phenomenal (i.e., experienced) unity? This book develops an interdisciplinary account of phenomenal unity. It focuses on examples of experienced wholes such as perceived objects (chairs and tables, but also groups of objects), bodily experiences, successions of events, and the attentional structure of consciousness. As a first step, the book investigates how the unity of consciousness can be characterized phenomenologically: what is it like to experience wholes, what is the experiential contribution of phenomenal unity? This raises conceptual and empirical questions. In addressing these questions, connections are drawn to phenomenological accounts and research on Gestalt theory. As a second step, the book suggests how phenomenal unity can be analyzed computationally, by drawing on concepts and ideas of the framework of predictive processing. The result is a conceptual framework, as well as an interdisciplinary account of phenomenal unity: the regularity account of phenomenal unity. According to this account, experienced wholes correspond to a hierarchy of connecting regularities. The brain tracks these regularities by hierarchical prediction error minimization, which approximates hierarchical Bayesian inference.

scholarly journals Toward an Embodied, Embedded Predictive Processing Account

2021 ◽  
Vol 12 ◽  
Author(s):  
Elmarie Venter
Keyword(s):  
Free Energy ◽  
Prediction Error ◽  
The Body ◽  
The Other ◽  
Predictive Processing ◽  
Error Minimization ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Prediction Error Minimization

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 free energy principle because the whole embodied agent minimizes free energy in virtue of being a model of its econiche. This grants the body a constitutive role as part of the collection of mechanisms that minimize prediction error and free energy. The body can only fulfill its role when embedded in an environment, of which it is a model. In this sense, EEPP offers the most promising alternative to cognitivist predictive processing and free energy enactivism.

scholarly journals Boredom begets creativity: a solution to the exploitation-exploration trade-off in predictive coding

2017 ◽  
Author(s):  
Jaime Gomez-Ramirez ◽  
Tommaso Costa
Keyword(s):  
Mathematical Model ◽  
Prediction Error ◽  
Predictive Coding ◽  
Error Minimization ◽  
Trade Off ◽  
Black Scholes ◽  
Intelligent Behavior ◽  
Prediction Error Minimization ◽  
Key Drivers ◽  
Technical Systems

AbstractHere, we investigate whether systems that minimize prediction error e.g. predictive coding, can also show creativity, or on the contrary, prediction error minimization unqualifies for the design of systems that respond in creative ways to non recurrent problems. We argue that there is a key ingredient that has been overlooked by researchers that needs to be incorporated to understand intelligent behavior in biological and technical systems. This ingredient is boredom. We propose a mathematical model based on the Black-Scholes-Merton equation which provides mechanistic insights into the interplay between boredom and prediction pleasure as the key drivers of behavior.

scholarly journals ANALISIS PENGARUH INFLASI TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA DENGAN METODE FINAL PREDICTION ERROR

2017 ◽  
Vol 5 (1) ◽  
pp. 42
Author(s):  
Dwi Hartini ◽  
Yuni Prihadi Utomo
Keyword(s):  
Economic Growth ◽  
Prediction Error ◽  
Time Series Data ◽  
Negative Influence ◽  
The Body ◽  
Series Data ◽  
Causality Test ◽  
Equilibrium Relationship ◽  
Final Prediction Error

The purpose of the research is to know whether inflation influences economic growth or economic growth influence inflation and to know the final prediction error of long - term equilibrium relationship between inflation and economic growth.The hypothesis presented in this research is that inflation has negative influence on economic growth and economic growth has negative influence on inflation. It is assumed that the final prediction error of long - term equilibrium relationship between inflation and economic growth has negative influence, and the final prediction error of equilibrium relationship between economic growth and inflation has negative influence.The method used in this research is causality analysis of Final Prediction Error (FPE) by using time series data of 1973 through 2002 taken from the Body of Statistic Center (BPS).It is conducted stationerity and causality test of FPE in this research. This research shows that there is one direction causality in which economic growth influences inflation.

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