Predictive Processing Model - Introduction 2023-09-18

predictive-processing-20230918.md

Predictive Processing in Consciousness

ChatGPT 4.0 transcript on 2023-09-18

User

Are you familiar with the cognitive science / neuroscience theory that consciousness is a simulation error-corrected by our senses?

What is this theory called? / In what academic disciplines is it situated? / When was it first proposed? / Please comment on how well it has or has not been adopted.

ChatGPT

Yes, the theory that consciousness is essentially a simulation error-corrected by our senses is often discussed in the context of predictive processing or the Bayesian Brain hypothesis. It is situated primarily within the academic disciplines of cognitive science, neuroscience, and philosophy of mind. The core idea has roots in Helmholtz's notion of unconscious inference from the 19th century, but the predictive processing model as a comprehensive framework began to gain prominence in the early 21st century, particularly with the work of scientists like Karl Friston.

The theory has gained substantial attention but is still a matter of active debate. It has been influential in shaping research agendas in neuroscience and cognitive psychology, providing a unifying framework for understanding perception, action, and learning. However, its applicability to broader questions about consciousness and subjective experience remains contested.

User

1. Who are some key proponents of this theory?

2. What are some good fundamental papers or books to explain it?

3. Who are some key 'opponents' of the theory?

4. Does the theory have some ?description? of what 'thoughts' are? Does the theory account for them somehow?

5. To what degree is this theory falsifiable?

ChatGPT

1. Key proponents include Karl Friston, Andy Clark, and Jakob Hohwy.

2. Fundamental texts:

• "The Predictive Mind" by Jakob Hohwy

• "Surfing Uncertainty" by Andy Clark

• Karl Friston's papers, e.g., "The free-energy principle: a unified brain theory?"

3. Key opponents include those who advocate for alternative theories of consciousness, such as Integrated Information Theory (Giulio Tononi) or Global Workspace Theory (Bernard Baars).

4. In the predictive processing framework, thoughts can be viewed as high-level predictions or hypotheses that the brain uses to interpret sensory data. While not necessarily a full account of "thought," it provides a mechanism by which thoughts could interact with perception and action.

5. The theory is falsifiable to some extent. Empirical tests often involve neural imaging to assess if brain activity corresponds to predictive error minimization. However, broad claims about consciousness pose challenges to direct falsification.

User

Please fact check what you wrote above.

ChatGPT

Certainly:

1. Key proponents: Karl Friston, Andy Clark, and Jakob Hohwy are indeed key proponents of the predictive processing or Bayesian Brain hypothesis.

2. Fundamental texts: The books "The Predictive Mind" by Jakob Hohwy and "Surfing Uncertainty" by Andy Clark are considered seminal works in this area. Karl Friston's papers, such as "The free-energy principle: a unified brain theory?" are also foundational.

3. Key opponents: While it's accurate to state that proponents of alternative theories like Integrated Information Theory and Global Workspace Theory could be considered opponents, they might not actively argue against predictive processing.

4. Description of thoughts: The predictive processing model does offer an account of thoughts as high-level predictions, although this is often more implied than explicitly stated in the literature.

5. Falsifiability: The predictive processing framework has been subject to empirical testing, particularly through neuroimaging studies, making it partially falsifiable. However, like many theories of consciousness, it faces challenges in terms of complete falsification.

Overall, the information provided is accurate.

User

Please lay out the key claims of the Predictive Processing model.

How do people in the field name this theory specifically? How do they abbreviate it?

We've touched on some competing theories. Please explain in particular what they challenge.

ChatGPT

Key Claims of Predictive Processing Model:

1. Hierarchical Organization: The brain is hierarchically organized, with lower-level sensory regions sending predictions to higher-level regions.

2. Prediction Errors: The brain aims to minimize the difference between sensory input and its predictions, known as "prediction errors."

3. Generative Models: The brain uses internal models to generate predictions. These models are updated based on the prediction errors.

4. Action and Perception: Both perception and action are guided by this error-minimization process.

5. Bayesian Inference: The brain operates using a form of Bayesian inference, updating its models based on new evidence.

6. Free Energy Principle: A mathematical formulation, often using free energy as a proxy for prediction error, that aims to describe how the brain (or any adaptive system) resists a state of disorder.

Nomenclature and Abbreviations:

The model is often referred to as the "Predictive Processing Model," "Bayesian Brain Hypothesis," or "Free-Energy Principle." Abbreviations like "PP" for Predictive Processing or "FEP" for Free-Energy Principle are sometimes used.

Competing Theories and Challenges:

1. Integrated Information Theory (IIT): IIT argues that consciousness arises from systems with both differentiation and integration. It challenges PP by offering a quantifiable measure of consciousness that PP lacks.

2. Global Workspace Theory (GWT): GWT posits that consciousness arises from a "global workspace" in the brain where information is broadcast for access by multiple cognitive processes. It challenges PP by focusing on the architecture of the mind and not just the computational mechanisms.

3. Dual-Process Theories: These theories differentiate between unconscious, automatic processing and conscious, deliberate processing. They challenge PP's overarching focus on unconscious prediction error minimization.

Each of these theories tackles consciousness from different perspectives and highlights gaps or limitations in the Predictive Processing model, such as quantifiability, architectural concerns, or the scope of unconscious vs. conscious processing.

See also:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2666703/pdf/rstb20080300.pdf

Predictive coding under the free-energy principle

Karl Friston* and Stefan Kiebel

The Wellcome Trust Centre of Neuroimaging, Institute of Neurology, University College London,
Queen Square, London WC1N 3BG, UK

This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model’s evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.