In the first post in this series I re-introduced the basic concepts underling the Predictive Processing (PP) theory of perception, in particular how the brain uses a form of active Bayesian inference to form predictive models that effectively account for perception as well as action. I also mentioned how the folk psychological concepts of beliefs, desires and emotions can fit within the PP framework. In the second post in this series I expanded the domain of PP a bit to show how it relates to language and ontology, and how we perceive the world to be structured as discrete objects, objects with “fuzzy boundaries”, and other more complex concepts all stemming from particular predicted causal relations.
It’s important to note that this PP framework differs from classical computational frameworks for brain function in a very big way, because the processing and learning steps are no longer considered separate stages within the PP framework, but rather work at the same time (learning is effectively occurring all the time). Furthermore, classical computational frameworks for brain function treat the brain more or less like a computer which I think is very misguided, and the PP framework offers a much better alternative that is far more creative, economical, efficient, parsimonious and pragmatic. The PP framework holds the brain to be more of a probabilistic system rather than a deterministic computational system as held in classical computationalist views. Furthermore, the PP framework puts a far greater emphasis on the brain using feed-back loops whereas traditional computational approaches tend to suggest that the brain is primarily a feed-forward information processing system.
Rather than treating the brain like a generic information processing system that passively waits for new incoming data from the outside world, it stays one step ahead of the game, by having formed predictions about the incoming sensory data through a very active and creative learning process built-up by past experiences through a form of active Bayesian inference. Rather than utilizing some kind of serial processing scheme, this involves primarily parallel neuronal processing schemes resulting in predictions that have a deeply embedded hierarchical structure and relationship with one another. In this post, I’d like to explore how traditional and scientific notions of knowledge can fit within a PP framework as well.
Knowledge as a Subset of Predicted Causal Relations
It has already been mentioned that, within a PP lens, our ontology can be seen as basically composed of different sets of highly differentiated, probabilistic causal relations. This allows us to discriminate one object or concept from another, as they are each “composed of” or understood by the brain as causes that can be “explained away” by different sets of predictions. Beliefs are just another word for predictions, with higher-level beliefs (including those that pertain to very specific contexts) consisting of a conjunction of a number of different lower level predictions. When we come to “know” something then, it really is just a matter of inferring some causal relation or set of causal relations about a particular aspect of our experience.
Knowledge is often described by philosophers using various forms of the Platonic definition: Knowledge = Justified True Belief. I’ve talked about this to some degree in previous posts (here, here) and I came up with what I think is a much better working definition for knowledge which could be defined as such:
Knowledge consists of recognized patterns of causality that are stored into memory for later recall and use, that positively and consistently correlate with reality, and for which that correlation has been validated by empirical evidence (i.e. successful predictions made and/or goals accomplished through the use of said recalled patterns).
We should see right off the bat how this particular view of knowledge fits right in to the PP framework, where knowledge consists of predictions of causal relations, and the brain is in the very business of making predictions of causal relations. Notice my little caveat however, where these causal relations should positively and consistently correlate with “reality” and be supported by empirical evidence. This is because I wanted to distinguish all predicted causal relations (including those that stem from hallucinations or unreliable inferences) from the subset of predicted causal relations that we have a relatively high degree of certainty in.
In other words, if we are in the game of trying to establish a more reliable epistemology, we want to distinguish between all beliefs and the subset of beliefs that have a very high likelihood of being true. This distinction however is only useful for organizing our thoughts and claims based on our level of confidence in their truth status. And for all beliefs, regardless of the level of certainty, the “empirical evidence” requirement in my definition given above is still going to be met in some sense because the incoming sensory data is the empirical evidence (the causes) that support the brain’s predictions (of those causes).
Objective or Scientific Knowledge
Within a domain like science however, where we want to increase the reliability or objectivity of our predictions pertaining to any number of inferred causal relations in the world, we need to take this same internalized strategy of modifying the confidence levels of our predictions by comparing them to the predictions of others (third-party verification) and by testing these predictions with externally accessible instrumentation using some set of conventions or standards (including the scientific method).
Knowledge then, is largely dependent on or related to our confidence levels in our various beliefs. And our confidence level in the truth status of a belief is just another way of saying how probable such a belief is to explain away a certain set of causal relations, which is equivalent to our brain’s Bayesian prior probabilities (our “priors”) that characterize any particular set of predictions. This means that I would need a lot of strong sensory evidence to overcome a belief with high Bayesian priors, not least because it is likely to be associated with a large number of other beliefs. This association between different predictions seems to me to be a crucial component not only for knowledge generally (or ontology as mentioned in the last post), but also for our reasoning processes. In the next post of this series, I’m going to expand a bit on reasoning and how I view it through a PP framework.