Introduction
I read an article in issue 4 of Philosophy Now, entitled "Knowledge & Reasons" (read it here), in which the author (Joe Cruz) gives an interesting evolutionary narrative perspective of epistemology (theory of knowledge).
It made me reflect on how beliefs in general might form and how knowledge is used to inform them.
Table of Contents
Model for 'belief'
I think, fundamentally, and simply, that the progenitor of all knowledge must be certainty. For example, any known or learnt cause and effect, predictable pattern or pure function would qualify in my mind as knowledge.
Furthermore, to derive a belief in anything likely requires acting on knowledge and requires an initial set of beliefs to be based upon, which the author exemplifies using axioms or rules in Mathematics. These, I consider, are the origins of belief and upon which any new rational belief may begin. These are apriori beliefs.
A belief may be true/false, good or bad, and so there is an element of subjectivity that influences what one may consider a belief's corresponding quality or correctness, or even appropriateness given the situation.
Indeed, a set of apriori beliefs may be incorrect, misinterpreted or subject to personal and subjective rendering. Either way, any measure of quality derived from a belief system/process that necessitates subjectivity is subjective in and of itself. In this way, a measure of quality must be agreed upon by a shared agreement or consensus that the belief is good/bad/wrong/right, etc. This is (necessarily) how the judicial system has determined that a collective balance of probabilities from a jury is required to inform a good and appropriate measure of agreement/consensus when individual subjectivity is too dangerous to consider solely.
Following a systematic derivation from a priori beliefs that are used in conjunction with known rules of causality (knowledge) and coupled with the subjectivity of the reasoner, an opinion or belief based on the prior components can be modelled:
See A Model for Belief.
The capacity to 'know'
Another interesting thought presented in that article is, "What makes knowledge possible at all?".
For having a capacity to know anything (a model for the capability of knowing), I consider the requirements that provide the capacity to 'know' being Memory and a place to store models of cause and effect (such as the Brain) and the ability to interpret (or process) cause and effect, which I consider to be logic.
The models of cause and effect are representations of what we consider knowledge - they provide us with a clear idea of what causes what. Lastly, there must be a need for knowledge. For example, knowledge might help you survive or provide a reward.
Then you need personality to provide subjectivity to qualify the reasoner as an individual, with freedom and all the unpredictability this brings and which often appears chaotic when externally observed by others. This helps provide subjectivity, a potential for error, misinterpretation, freedom and autonomy, etc.
My feeling is that the mind is either an extension of the brain or an abstraction that the brain creates/uses to make it possible to provide or draw upon the subjectivity above. This, I think, is probably what makes us human and likely drives us to be conscious. Being conscious of things that influence you, I believe, leads to the inherent subjectivity that makes us unique and individual beings. In my model, I represent the engine of subjectivity to be the mind.
With a capacity to store notions of cause and effect (memory) and an ability to interpret them (logic), one can then draw on one's subjective experiences accessed via the mind.
One might consider the brain to be the physical device that the mind uses to produce abstract concepts and ideas to form knowledge that is stored/retrieved in the brain.
Whether it is the mind or the brain that is the playground for deriving beliefs, models of causality are likely developed using abstractions, exploration and theoretical use and planning of these models of causality. They are likely simplified representations of how our brain/mind simulates or represents the world in terms of cause and effect.
Either way, these 'representations' are models of cause and effect that we've learnt to represent in our minds/brains. In this way, I consider learning to be the process of knowing cause -> effect, just like a neural network provides a model for a mapping from cause -> effect by 'learning' based on observation of cause and effect. Similarly, other models like Bayesian networks use statistical averages of such observations to derive this mapping of cause and effect.
It is not certain what actual models exist in the brain/mind; however, our artificial models (Neural networks and Bayesian models, etc.) aim to simulate them.
A model for behaviour
An intriguing idea is how all of this informs behaviour, and an idea presented by Cruz is that different representations inform different behaviours, such that one can "decouple behaviour from stimuli through representations".
It's not explicitly stated that 'representations' in Cruz's mind are what I consider them to be, i.e actual instances of models of cause and effect, but the inherent usefulness in this thinking that that flexible outcomes or behaviours can be seen to be derived from whichever model/representation is triggered by an external stimulus. This provides a useful/interesting model for behaviour, particularly in simulating an artificial agent.
For example, an agent might possess different models of causality, such as neural networks, pattern recognition, and predefined rules, and each provides a different form of behavioural outcome based on the nature and limitations of the model.
The quality of the behavioural response is also then subject to a shared agreement that forms a consensus about its appropriateness given the situation.
The diversity of responses possible is based on which model or representation was triggered/selected, and this provides for flexible or adaptive outcomes and might therefore simulate more closely human behaviour when these stimuli are coupled with subjectivity to predicate the selection of a model of causality to base its behaviour/response on.
Perception and Understanding
To understand or make sense of anything is to be prompted to do so. That is, a stimulation causes us to attend to that stimulation and in attending to it, we might choose to make sense of its nature, or understand why it occured or what it will mean for us etc... This process of attending to stimulation is what is referred to as understanding/perception, and thus, I consider all understanding to start with attention.
My view is that Understanding (is perception), and is the process of finding or matching appropriate existing models of cause -> effect that already exist in the mind (knowledge). Which models match or are found relevant are based on what is being observed/perceived/sensed, etc. The process of finding might in itself update and contribute to the existing models of cause -> effect, in which case, I think this would be a reasonable definition of learning (defining cause -> effect) while observing, for example. Understanding is, therefore, a process.
As an artificial example, a neural network is by definition knowledge by virtue of the fact that it maps cause to effect. If by observing additional evidence for cause and effect externally (maybe through sight), for example, one might supply that that model/representation with information that helps make that neural network (knowledge/mapping) better, for example, you observe more cause -> effect that can be added to the neural network as training data; the neural network, in effect, was located as model of knoweldge and it was updated/improved (learning) based on your observation.
Understanding (perception), generally, in psychology, is said to represent a top-down and a bottom-up process. The bottom is the sensory reality or the stimuli itself that triggered the attention to deal with it. The top is the knowledge of cause-and-effect that is consulted in processing the stimuli for the benefit of gaining and better understanding of the nature of that stimulus and what it will mean once understanding/perception of it has been completed. Interestingly enough, as knowledge is, in my opinion, higher conceptual internal representations of cause-and-effect.
I theorise that expectation, in general, is the prediction of consulting and using our internal cause-and-effect models (our knowledge) to predict/infer the outcome based on what the model suggests based on what it knows about cause-and-effect. Prediction is a part of understanding. A top-down process might be expectations about what will happen or result based on a stimulus. If the expected outcome is not met, this suggests the model needs to improve. A bottom process is creating knowledge (or consulting existing knowledge to understand) a sensory stimulus.
In humans, the brain and the mind and the internal representations of knowledge of cause and effect, are the realm of the cognitive 'top' realm. This draws on and relies on existing knowledge to make sense of and understand what is sensed. You might suggest that a belief is a result of understanding.
Cause and effect
In my mind, knowledge is the mapping of cause to effect. Understanding or perception is finding the model of causality.
A cause is thus in many ways simply an observation. What comes with that observation is data. What do you do with that data to map it to its effect is the process of understanding.
In psychology, the Gestalt theory of perception, in my mind, is simply representing observational data, which may be complex in nature (a scene), into simpler constituent cause-and-effect models.
For example, the idea of grouping similar colours, attributes, shapes, and patterns of some observational/sensory stimuli (data), is simply a very rudimentary means to create a cause-and-effect models that can suggest that those shapes, colours might be in themselves be a means to make a knowledge mapping (i.e it's a bottom-up process) such that colours (cause) might mean and outcome, such as danger (effect). In this way, cause-and-effect is fundamental to how we represent our understanding.
It might be that elementary patterns, groups, similarities, or familiarieties are inherent cause-and-effect models that we build up on in a layered fashion to build up more and more complex, hierarchical cause-and-effect models from observational/sensory data/stimuli. The notion of being round or similar or consistent (Gestalt principles), are simpler cause-and-effect model of observed data that can be used to build higher meaning, cause and effect models, such as the red circles or patterns in the scene of observational data is what means 'dangerous'.