The Adaptive Brain – How We Navigate Change
Garnet > Lucid Studios
May 8, 2023
INTRODUCTION
This paper aims to tackle a complex topic and synthesize it down into understandable principles. These principles then serve as guides in the development and application of a brain stimulation process known as Brain Engagement.
The following influential brain models are used to explore the topic. Although at first glance, these seven (7) models may appear to be wide ranging and even unrelated, when diving deeper, together these models yield a surprisingly unified insight into how our brain can navigate and adapt to ongoing Change.
7 BRAIN MODELS
These are seven (7) models that examine the adaptive behaviors of our brain. These basic definitions will provide references throughout this paper.
1) Bayesian Statistics (BS)
Bayesian statistics is named after Thomas Bayes, who formulated a specific case of Bayes' theorem in a paper published in 1763. The Bayesian brain considers the brain as a statistical organ of hierarchical inference that predicts current and future events on the basis of past experience.
SIMPLE MEANING BS: Even without all the current facts, with enough facts from the past, you can make a good calculated guess about the future.
2) Predictive Coding (PC)
Predictive coding was originally proposed by Cutler in 1952. The predictive coding theory holds that our experience of the world comes from within. Our brains generate a model of the world that predicts what we are going to see, hear, touch, smell and taste. The job of our senses is to check our predictions to make sure our inner model does not drift far from reality.
Predictive coding states that the brain continually generates models of the world based on context and information from memory to predict sensory input. In terms of brain processing, a predictive model is created in higher cortical areas and communicated through feedback connections to lower sensory areas.
Legend: (AI) Active Inference; (AIP) Adaptive Information Processing; (BR) Bayesian Statistics; (EPIC) Embodied Predictive Interoception Coding; (EB) Entropic Brain; (FE) Free Energy; (PC) Predictive Coding.
SIMPLE MEANING PC: Similar to BS (above), PC gathers up previous sensory experiences to make predictions about future experiences.
3) Adaptive Information Processing (AIP)
Shapiro’s AIP model (1995, 2001, 2006) posits that a particularly distressing incident may become stored in state-specific form, meaning frozen in time in its own neural network, unable to connect with other memory networks that hold adaptive information. AIP is the foundation of Eye Movement Desensitization & Reprocessing (EMDR) psychological technique used in the treatment of PTSD.
SIMPLE MEANING AIP: Our brain tries to integrate experiences to create resolution but some difficult experiences go unprocessed and so get stored away separately in special networks in the brain.
4) Embodied Predictive Interoception Coding (EPIC)
Lisa Feldman Barrett and W. Kyle Simmons (2015) proposed the Embodied Predictive Interoception Coding model, a framework that unifies Bayesian active inference principles with a physiological framework of corticocortical connections.
SIMPLE MEANING EPIC: Our inside sensations/feelings give us important information that helps us make better predictions about the future.
5) Free Energy (FE)
Variational methods were first deployed in physics, most famously by Feynman (1972). By the 1980s it had become clear that techniques from statistical physics could be adopted in machine learning. By at least 1989 Hinton and colleagues were referring to free energy in a purely statistical sense.
The term ‘variational free energy’ came to mean ‘the function that must be minimized in order to improve your approximation of a system’s statistical properties’, even though physical energy was no longer the feature that determined those statistical properties.
SIMPLE MEANING FE: Our brain tries to achieve the most while doing the least in its efforts to make good predictions.
6) Active Inference (AI)
Active inference is a way of understanding sentient behavior—a theory that characterizes perception, planning, and action in terms of probabilistic inference. Developed by theoretical neuroscientist Karl Friston, active inference provides an integrated perspective on brain, cognition, and behavior that is increasingly used across multiple disciplines including neuroscience, psychology, and philosophy.
Active inference is a “first principles” approach to understanding behavior and the brain, framed in terms of a single imperative to minimize free energy. Active inference puts the action into perception. In active inference, the agent minimizes a variational free energy objective with respect to past experience that causes it to learn a generative model of the world, which allows predicting what will happen in the future in order to avoid surprising states.
Legend: (AI) Active Inference; (AIP) Adaptive Information Processing; (BR) Bayesian Statistics; (EPIC) Embodied Predictive Interoception Coding; (EB) Entropic Brain; (FE) Free Energy; (PC) Predictive Coding.
SIMPLE MEANING AI: Our brain doesn’t like making mistakes in its predictions so it puts a “bias filter” on new information to help confirm its current prediction and avoid making prediction mistakes. Basically, it looks for proof that is already correct and actively attempts to shape new information to fit its existing views.
7) Entropic Brain (EB)
Carhart-Harris (2014) At its core, the entropic brain hypothesis proposes that the quality of any conscious state depends on the system's entropy measured via key parameters of brain function.
Entropy is a powerful explanatory tool for cognitive neuroscience since it provides a quantitative index of a dynamic system's randomness or disorder while simultaneously describing its informational character, i.e., our uncertainty about the system's state if we were to sample it at any given time-point.
When applied in the context of the brain, this allows us to make a translation between mechanistic and qualitative properties. Thus, according to this principle, increased subjective uncertainty or “puzzlement” accompanies states of increased system entropy. These ideas are consistent with Karl Friston's free-energy principle.
SIMPLE MEANING EB: Our brain has the natural and positive ability to experience periods of “uncertainty” as a way of exploring new information that can be useful in building up better predictions about the future.
7 BRAIN MODELS – THE UNIFYING PRINCIPLE
These 7 brain models appear to converge upon the principle that our brain feeds off of a continuous flow of present-moment, sensory-based information that lacks organization and has high degrees of uncertainty.
To reduce the uncertainty, our brain actively catalogues present moment experiences into a growing library of past experiences to use as references in making better predictions about the future.
Some experiences evade the cataloguing process and remain outside of the efforts to predict the future. These unprocessed experiences do not allow new information from the present moment but instead the past pretends to be the present.
THE ADAPTIVE BRAIN – NAVIGATING CHANGE
The brain’s ability to adapt depends upon its capacity to continuously process new information from the present moment and integrate that information with previously stored experiences.
When this process is functioning effectively, the brain can update its predictions as new information becomes available. This allows an individual to respond flexibly to changing circumstances rather than relying exclusively on previously established patterns.
However, when experiences remain unprocessed, the brain may continue to rely on older predictions even when current circumstances have changed. In this sense, the past can continue to influence the present because the information required to update the predictive model has not been fully integrated.
CHANGE AND ADAPTATION
Change introduces uncertainty. New sensory information, unfamiliar situations, unexpected events, and novel experiences challenge the brain’s existing predictions.
From the perspective of the seven brain models described above, this uncertainty is not necessarily a problem. Rather, it can provide the information required for the brain to update its internal models.
The adaptive process can therefore be understood as an ongoing cycle:
- New information enters through present-moment sensory experience.
- The brain compares this information with existing predictions.
- Differences between prediction and experience create uncertainty or prediction error.
- The brain processes and integrates the new information.
- The updated information becomes part of the growing library of experience.
- Future predictions can then be generated from the updated model.
This ongoing process provides the foundation for navigating Change.
WHEN ADAPTATION BECOMES STUCK
The models also provide a framework for understanding what can happen when this adaptive process is disrupted.
If new experiences cannot be effectively integrated, existing predictive models may continue to dominate perception and behavior. Rather than allowing present-moment information to modify the model, the brain may continue to interpret new experiences through the lens of what has already been learned.
In this situation, the past effectively becomes the reference point for interpreting the present. The individual may repeatedly experience similar emotional, cognitive, or behavioral patterns even when the circumstances surrounding them have changed.
The adaptive brain requires an ongoing exchange between what has already been learned and what is happening now.
BRAIN ENGAGEMENT
These principles serve as guides in the development and application of a brain stimulation process known as Brain Engagement.
Brain Engagement can be understood within this framework as a process designed to provide the brain with present-moment sensory information that can challenge existing predictions and encourage the processing of new information.
By introducing information that differs from what the brain expects, the process can create an opportunity for prediction, comparison, and updating.
The underlying principle is not simply to provide stimulation, but to engage the brain’s natural adaptive processes.
THE ROLE OF UNCERTAINTY
Uncertainty plays an important role across several of the models presented in this paper. Bayesian Statistics, Predictive Coding, Active Inference, Free Energy, and the Entropic Brain each provide different perspectives on how the brain deals with uncertainty.
From the perspective of the Entropic Brain model, periods of increased uncertainty can provide an opportunity to explore new information. This exploration can contribute to the development of better predictions about future experiences.
Therefore, uncertainty is not presented solely as something to be eliminated. It can also be part of the process through which the brain encounters information that is different from its existing expectations.
THE ADAPTIVE CYCLE
Taken together, the seven models describe a brain that is continuously attempting to make sense of its environment while minimizing uncertainty and maintaining an effective model of the world.
Past experience provides a foundation for prediction. Present-moment sensory information tests those predictions. Unexpected information creates an opportunity for updating, while successful integration expands the brain’s library of experience.
This creates a continuous adaptive cycle between:
- Past experience
- Present-moment sensory information
- Prediction
- Uncertainty
- Integration
- Updated prediction
The ability to move through this cycle is central to how the brain navigates ongoing Change.