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Predictive Processing: Unlocking the Mysteries of Mind & Body (Part VI)

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This is the last post I’m going to write for this particular post-series on Predictive Processing (PP).  Here’s the links to parts 1, 2, 3, 4, and 5.  I’ve already explored a bit on how a PP framework can account for folk psychological concepts like beliefs, desires, and emotions, how it accounts for action, language and ontology, knowledge, and also perception, imagination, and reasoning.  In this final post for this series, I’m going to explore consciousness itself and how some theories of consciousness fit very nicely within a PP framework.

Consciousness as Prediction (Predicting The Self)

Earlier in this post-series I explored how PP treats perception (whether online or an offline form like imagination) as simply predictions pertaining to incoming visual information with varying degrees of precision weighting assigned to the resulting prediction error.  In a sense then, consciousness just is prediction.  At the very least, it is a subset of the predictions, likely those that are higher up in the predictive hierarchy.  This is all going to depend on which aspect or level of consciousness we are trying to explain, and as philosophers and cognitive scientists well know, consciousness is difficult to pin down and define in any way.

By consciousness, we could mean any kind of awareness at all, or we could limit this term to only apply to a system that is aware of itself.  Either way we have to be careful here if we’re looking to distinguish between consciousness generally speaking (consciousness in any form, which may be unrecognizable to us) and the unique kind of consciousness that we as human beings experience.  If an ant is conscious, it doesn’t likely have any of the richness that we have in our experience nor is it likely to have self-awareness like we do (even though a dolphin, which has a large neocortex and prefrontal cortex, is far more likely to).  So we have to keep these different levels of consciousness in mind in order to properly assess their being explained by any cognitive framework.

Looking through a PP lens, we can see that what we come to know about the world and our own bodily states is a matter of predictive models pertaining to various inferred causal relations.  These inferred causal relations ultimately stem from bottom-up sensory input.  But when this information is abstracted at higher and higher levels, eventually one can (in principle) get to a point where those higher level models begin to predict the existence of a unified prediction engine.  In other words, a subset of the highest-level predictive models may eventually predict itself as a self.  We might describe this process as the emergence of some level of self-awareness, even if higher levels of self-awareness aren’t possible unless particular kinds of higher level models have been generated.

What kinds of predictions might be involved with this kind of emergence?  Well, we might expect that predictions pertaining to our own autobiographical history, which is largely composed of episodic memories of our past experience, would contribute to this process (e.g. “I remember when I went to that amusement park with my friend Mary, and we both vomited!”).  If we begin to infer what is common or continuous between those memories of past experiences (even if only 1 second in the past), we may discover that there is a form of psychological continuity or identity present.  And if this psychological continuity (this type of causal relation) is coincident with an incredibly stable set of predictions pertaining to (especially internal) bodily states, then an embodied subject or self can plausibly emerge from it.

This emergence of an embodied self is also likely fueled by predictions pertaining to other objects that we infer existing as subjects.  For instance, in order to develop a theory of mind about other people, that is, in order to predict how other people will behave, we can’t simply model their external behavior as we can for something like a rock falling down a hill.  This can work up to a point, but eventually it’s just not good enough as behaviors become more complex.  Animal behavior, most especially that of humans, is far more complex than that of inanimate objects and as such it is going to be far more effective to infer some internal hidden causes for that behavior.  Our predictions would work well if they involved some kind of internal intentionality and goal-directedness operating within any animate object, thereby transforming that object into a subject.  This should be no less true for how we model our own behavior.

If this object that we’ve now inferred to be a subject seems to behave in ways that we see ourselves as behaving (especially if it’s another human being), then we can begin to infer some kind of equivalence despite being separate subjects.  We can begin to infer that they too have beliefs, desires, and emotions, and thus that they have an internal perspective that we can’t directly access just as they aren’t able to access ours.  And we can also see ourselves from a different perspective based on how we see those other subjects from an external perspective.  Since I can’t easily see myself from an external perspective, when I look at others and infer that we are similar kinds of beings, then I can begin to see myself as having both an internal and external side.  I can begin to infer that others see me similar to the way that I see them, thus further adding to my concept of self and the boundaries that define that self.

Multiple meta-cognitive predictions can be inferred based on all of these interactions with others, and from the introspective interactions with our brain’s own models.  Once this happens, a cognitive agent like ourselves may begin to think about thinking and think about being a thinking being, and so on and so forth.  All of these cognitive moves would seem to provide varying degrees of self-hood or self-awareness.  And these can all be thought of as the brain’s best guesses that account for its own behavior.  Either way, it seems that the level of consciousness that is intrinsic to an agent’s experience is going to be dependent on what kinds of higher level models and meta-models are operating within the agent.

Consciousness as Integrated Information

One prominent theory of consciousness is the Integrated Information Theory of Consciousness, otherwise known as IIT.  This theory, initially formulated by Giulio Tononi back in 2004 and which has undergone development ever since, posits that consciousness is ultimately dependent on the degree of information integration that is inherent in the causal properties of some system.  Another way of saying this is that the causal system specified is unified such that every part of the system must be able to affect and be affected by the rest of the system.  If you were to physically isolate one part of a system from the rest of it (and if this part was the least significant to the rest of the system), then the resulting change in the cause-effect structure of the system would quantify the degree of integration.  A large change in the cause-effect structure based on this part’s isolation from the system (that is, by having introduced what is called a minimum partition to the system) would imply a high degree of information integration and vice versa.  And again, a high degree of integration implies a high degree of consciousness.

Notice how this information integration axiom in IIT posits that a cognitive system that is entirely feed-forward will not be conscious.  So if our brain processed incoming sensory information from the bottom up and there was no top-down generative model feeding downward through the system, then IIT would predict that our brain wouldn’t be able to produce consciousness.  PP on the other hand, posits a feedback system (as opposed to feed-forward) where the bottom-up sensory information that flows upward is met with a downward flow of top-down predictions trying to explain away that sensory information.  The brain’s predictions cause a change in the resulting prediction error, and this prediction error serves as feedback to modify the brain’s predictions.  Thus, a cognitive architecture like that suggested by PP is predicted to produce consciousness according to the most fundamental axiom of IIT.

Additionally, PP posits cross-modal sensory features and functionality where the brain integrates (especially lower level) predictions spanning various spatio-temporal scales from different sensory modalities, into a unified whole.  For example, if I am looking at and petting a black cat lying on my lap and hearing it purr, PP posits that my perceptual experience and contextual understanding of that experience are based on having integrated the visual, tactile, and auditory expectations that I’ve associated to constitute such an experience of a “black cat”.  It is going to be contingent on a conjunction of predictions that are occurring simultaneously in order to produce a unified experience rather than a barrage of millions or billions of separate causal relations (let alone those which stem from different sensory modalities) or having millions or billions of separate conscious experiences (which would seem to necessitate separate consciousnesses if they are happening at the same time).

Evolution of Consciousness

Since IIT identifies consciousness with integrated information, it can plausibly account for why it evolved in the first place.  The basic idea here is that a brain that is capable of integrating information is more likely to exploit and understand an environment that has a complex causal structure on multiple time scales than a brain that has informationally isolated modules.  This idea has been tested and confirmed to some degree by artificial life simulations (animats) where adaptation and integration are both simulated.  The organism in these simulations was a Braitenberg-like vehicle that had to move through a maze.  After 60,000 generations of simulated brains evolving through natural selection, it was found that there was a monotonic relationship between their ability to get through the maze and the amount of simulated information integration in their brains.

This increase in adaptation was the result of an effective increase in the number of concepts that the organism could make use of given the limited number of elements and connections possible in its cognitive architecture.  In other words, given a limited number of connections in a causal system (such as a group of neurons), you can pack more functions per element if the level of integration with respect to those connections is high, thus giving an evolutionary advantage to those with higher integration.  Therefore, when all else is equal in terms of neural economy and resources, higher integration gives an organism the ability to take advantage of more regularities in their environment.

From a PP perspective, this makes perfect sense because the complex causal structure of the environment is described as being modeled at many different levels of abstraction and at many different spatio-temporal scales.  All of these modeled causal relations are also described as having a hierarchical structure with models contained within models, and with many associations existing between various models.  These associations between models can be accounted for by a cognitive architecture that re-uses certain sets of neurons in multiple models, so the association is effectively instantiated by some literal degree of neuronal overlap.  And of course, these associations between multiply-leveled predictions allows the brain to exploit (and create!) as many regularities in the environment as possible.  In short, both PP and IIT make a lot of practical sense from an evolutionary perspective.

That’s All Folks!

And this concludes my post-series on the Predictive Processing (PP) framework and how I see it as being applicable to a far more broad account of mentality and brain function, than it is generally assumed to be.  If there’s any takeaways from this post-series, I hope you can at least appreciate the parsimony and explanatory scope of predictive processing and viewing the brain as a creative and highly capable prediction engine.

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Predictive Processing: Unlocking the Mysteries of Mind & Body (Part V)

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In the previous post, part 4 in this series on Predictive Processing (PP), I explored some aspects of reasoning and how different forms of reasoning can be built from a foundational bedrock of Bayesian inference (click here for parts 1, 2, or 3).  This has a lot to do with language, but I also claimed that it depends on how the brain is likely generating new models, which I think is likely to involve some kind of natural selection operating on neural networks.  The hierarchical structure of the generative models for these predictions as described within a PP framework, also seems to fit well with the hierarchical structure that we find in the brain’s neural networks.  In this post, I’m going to talk about the relation between memory, imagination, and unconscious and conscious forms of reasoning.

Memory, Imagination, and Reasoning

Memory is of course crucial to the PP framework whether for constructing real-time predictions of incoming sensory information (for perception) or for long-term predictions involving high-level, increasingly abstract generative models that allow us to accomplish complex future goals (like planning to go grocery shopping, or planning for retirement).  Either case requires the brain to have stored some kind of information pertaining to predicted causal relations.  Rather than memories being some kind of exact copy of past experiences (where they’d be stored like data on a computer), research has shown that memory functions more like a reconstruction of those past experiences which are modified by current knowledge and context, and produced by some of the same faculties used in imagination.

This accounts for any false or erroneous aspects of our memories, where the recalled memory can differ substantially from how the original event was experienced.  It also accounts for why our memories become increasingly altered as more time passes.  Over time, we learn new things, continuing to change many of our predictive models about the world, and thus have a more involved reconstructive process the older the memories are.  And the context we find ourselves in when trying to recall certain memories, further affect this reconstruction process, adapting our memories in some sense to better match what we find most salient and relevant in the present moment.

Conscious vs. Unconscious Processing & Intuitive Reasoning (Intuition)

Another attribute of memory is that it is primarily unconscious, where we seem to have this pool of information that is kept out of consciousness until parts of it are needed (during memory recall or or other conscious thought processes).  In fact, within the PP framework we can think of most of our generative models (predictions), especially those operating in the lower levels of the hierarchy, as being out of our conscious awareness as well.  However, since our memories are composed of (or reconstructed with) many higher level predictions, and since only a limited number of them can enter our conscious awareness at any moment, this implies that most of the higher-level predictions are also being maintained or processed unconsciously as well.

It’s worth noting however that when we were first forming these memories, a lot of the information was in our consciousness (the higher-level, more abstract predictions in particular).  Within PP, consciousness plays a special role since our attention modifies what is called the precision weight (or synaptic gain) on any prediction error that flows upward through the predictive hierarchy.  This means that the prediction errors produced from the incoming sensory information or at even higher levels of processing are able to have a greater impact on modifying and updating the predictive models.  This makes sense from an evolutionary perspective, where we can ration our cognitive resources in a more adaptable way, by allowing things that catch our attention (which may be more important to our survival prospects) to have the greatest effect on how we understand the world around us and how we need to act at any given moment.

After repeatedly encountering certain predicted causal relations in a conscious fashion, the more likely those predictions can become automated or unconsciously processed.  And if this has happened with certain rules of inference that govern how we manipulate and process many of our predictive models, it seems reasonable to suspect that this would contribute to what we call our intuitive reasoning (or intuition).  After all, intuition seems to give people the sense of knowing something without knowing how it was acquired and without any present conscious process of reasoning.

This is similar to muscle memory or procedural memory (like learning how to ride a bike) which is consciously processed at first (thus involving many parts of the cerebral cortex), but after enough repetition it becomes a faster and more automated process that is accomplished more economically and efficiently by the basal ganglia and cerebellum, parts of the brain that are believed to handle a great deal of unconscious processing like that needed for procedural memory.  This would mean that the predictions associated with these kinds of causal relations begin to function out of our consciousness, even if the same predictive strategy is still in place.

As mentioned above, one difference between this unconscious intuition and other forms of reasoning that operate within the purview of consciousness is that our intuitions are less likely to be updated or changed based on new experiential evidence since our conscious attention isn’t involved in the updating process. This means that the precision weight of upward flowing prediction errors that encounter downward flowing predictions that are operating unconsciously will have little impact in updating those predictions.  Furthermore, the fact that the most automated predictions are often those that we’ve been using for most of our lives, means that they are also likely to have extremely high Bayesian priors, further isolating them from modification.

Some of these priors may become what are called hyperpriors or priors over priors (many of these believed to be established early in life) where there may be nothing that can overcome them, because they describe an extremely abstract feature of the world.  An example of a possible hyperprior could be one that demands that the brain settle on one generative model even when it’s comparable to several others under consideration.  One could call this a “tie breaker” hyperprior, where if the brain didn’t have this kind of predictive mechanism in place, it may never be able to settle on a model, causing it to see the world (or some aspect of it) as a superposition of equiprobable states rather than simply one determinate state.  We could see the potential problem in an organism’s survival prospects if it didn’t have this kind of hyperprior in place.  Whether or not a hyperprior like this is a form of innate specificity, or acquired in early learning is debatable.

An obvious trade-off with intuition (or any kind of innate biases) is that it provides us with fast, automated predictions that are robust and likely to be reliable much of the time, but at the expense of not being able to adequately handle more novel or complex situations, thereby leading to fallacious inferences.  Our cognitive biases are also likely related to this kind of unconscious reasoning whereby evolution has naturally selected cognitive strategies that work well for the kind of environment we evolved in (African savanna, jungle, etc.) even at the expense of our not being able to adapt as well culturally or in very artificial situations.

Imagination vs. Perception

One large benefit of storing so much perceptual information in our memories (predictive models with different spatio-temporal scales) is our ability to re-create it offline (so to speak).  This is where imagination comes in, where we are able to effectively simulate perceptions without requiring a stream of incoming sensory data that matches it.  Notice however that this is still a form of perception, because we can still see, hear, feel, taste and smell predicted causal relations that have been inferred from past sensory experiences.

The crucial difference, within a PP framework, is the role of precision weighting on the prediction error, just as we saw above in terms of trying to update intuitions.  If precision weighting is set or adjusted to be relatively low with respect to a particular set of predictive models, then prediction error will have little if any impact on the model.  During imagination, we effectively decouple the bottom-up prediction error from the top-down predictions associated with our sensory cortex (by reducing the precision weighting of the prediction error), thus allowing us to intentionally perceive things that aren’t actually in the external world.  We need not decouple the error from the predictions entirely, as we may want our imagination to somehow correlate with what we’re actually perceiving in the external world.  For example, maybe I want to watch a car driving down the street and simply imagine that it is a different color, while still seeing the rest of the scene as I normally would.  In general though, it is this decoupling “knob” that we can turn (precision weighting) that underlies our ability to produce and discriminate between normal perception and our imagination.

So what happens when we lose the ability to control our perception in a normal way (whether consciously or not)?  Well, this usually results in our having some kind of hallucination.  Since perception is often referred to as a form of controlled hallucination (within PP), we could better describe a pathological hallucination (such as that arising from certain psychedelic drugs or a condition like Schizophrenia) as a form of uncontrolled hallucination.  In some cases, even with a perfectly normal/healthy brain, when the prediction error simply can’t be minimized enough, or the brain is continuously switching between models, based on what we’re looking at, we experience perceptual illusions.

Whether it’s illusions, hallucinations, or any other kind of perceptual pathology (like not being able to recognize faces), PP offers a good explanation for why these kinds of experiences can happen to us.  It’s either because the models are poor (their causal structure or priors) or something isn’t being controlled properly, like the delicate balance between precision weighting and prediction error, any of which that could result from an imbalance in neurotransmitters or some kind of brain damage.

Imagination & Conscious Reasoning

While most people would tend to define imagination as that which pertains to visual imagery, I prefer to classify all conscious experiences that are not directly resulting from online perception as imagination.  In other words, any part of our conscious experience that isn’t stemming from an immediate inference of incoming sensory information is what I consider to be imagination.  This is because any kind of conscious thinking is going to involve an experience that could in theory be re-created by an artificial stream of incoming sensory information (along with our top-down generative models that put that information into a particular context of understanding).  As long as the incoming sensory information was a particular way (any way that we can imagine!), even if it could never be that way in the actual external world we live in, it seems to me that it should be able to reproduce any conscious process given the right top-down predictive model.  Another way of saying this is that imagination is simply another word to describe any kind of offline conscious mental simulation.

This also means that I’d classify any and all kinds of conscious reasoning processes as yet another form of imagination.  Just as is the case with more standard conceptions of imagination (within PP at least), we are simply taking particular predictive models, manipulating them in certain ways in order to simulate some result with this process decoupled (at least in part) from actual incoming sensory information.  We may for example, apply a rule of inference that we’ve picked up on and manipulate several predictive models of causal relations using that rule.  As mentioned in the previous post and in the post from part 2 of this series, language is also likely to play a special role here where we’ll likely be using it to help guide this conceptual manipulation process by organizing and further representing the causal relations in a linguistic form, and then determining the resulting inference (which will more than likely be in a linguistic form as well).  In doing so, we are able to take highly abstract properties of causal relations and apply rules to them to extract new information.

If I imagine a purple elephant trumpeting and flying in the air over my house, even though I’ve never experienced such a thing, it seems clear that I’m manipulating several different types of predicted causal relations at varying levels of abstraction and experiencing the result of that manipulation.  This involves inferred causal relations like those pertaining to visual aspects of elephants, the color purple, flying objects, motion in general, houses, the air, and inferred causal relations pertaining to auditory aspects like trumpeting sounds and so forth.

Specific instances of these kinds of experienced causal relations have led to my inferring them as an abstract probabilistically-defined property (e.g. elephantness, purpleness, flyingness, etc.) that can be reused and modified to some degree to produce an infinite number of possible recreated perceptual scenes.  These may not be physically possible perceptual scenes (since elephants don’t have wings to fly, for example) but regardless I’m able to add or subtract, mix and match, and ultimately manipulate properties in countless ways, only limited really by what is logically possible (so I can’t possibly imagine what a square circle would look like).

What if I’m performing a mathematical calculation, like “adding 9 + 9”, or some other similar problem?  This appears (upon first glance at least) to be very qualitatively different than simply imagining things that we tend to perceive in the world like elephants, books, music, and other things, even if they are imagined in some phantasmagorical way.  As crazy as those imagined things may be, they still contain things like shapes, colors, sounds, etc., and a mathematical calculation seems to lack this.  I think the key thing to realize here is the fundamental process of imagination as being able to add or subtract and manipulate abstract properties in any way that is logically possible (given our current set of predictive models).  This means that we can imagine properties or abstractions that lack all the richness of a typical visual/auditory perceptual scene.

In the case of a mathematical calculation, I would be manipulating previously acquired predicted causal relations that pertain to quantity and changes in quantity.  Once I was old enough to infer that separate objects existed in the world, then I could infer an abstraction of how many objects there were in some space at some particular time.  Eventually, I could abstract the property of how many objects without applying it to any particular object at all.  Using language to associate a linguistic symbol for each and every specific quantity would lay the groundwork for a system of “numbers” (where numbers are just quantities pertaining to no particular object at all).  Once this was done, then my brain could use the abstraction of quantity and manipulate it by following certain inferred rules of how quantities can change by adding to or subtracting from them.  After some practice and experience I would now be in a reasonable position to consciously think about “adding 9 + 9”, and either do it by following a manual iterative rule of addition that I’ve learned to do with real or imagined visual objects (like adding up some number of apples or dots/points in a row or grid), or I can simply use a memorized addition table and search/recall the sum I’m interested in (9 + 9 = 18).

Whether we consider imagining a purple elephant, mentally adding up numbers, thinking about what I’m going to say to my wife when I see her next, or trying to explicitly apply logical rules to some set of concepts, all of these forms of conscious thought or reasoning are all simply different sets of predictive models that I’m simply manipulating in mental simulations until I arrive at a perception that’s understood in the desired context and that has minimal prediction error.

Putting it all together

In summary, I think we can gain a lot of insight by looking at all the different aspects of brain function through a PP framework.  Imagination, perception, memory, intuition, and conscious reasoning fit together very well when viewed as different aspects of hierarchical predictive models that are manipulated and altered in ways that give us a much more firm grip on the world we live in and its inferred causal structure.  Not only that, but this kind of cognitive architecture also provides us with an enormous potential for creativity and intelligence.  In the next post in this series, I’m going to talk about consciousness, specifically theories of consciousness and how they may be viewed through a PP framework.

Predictive Processing: Unlocking the Mysteries of Mind & Body (Part IV)

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In the previous post which was part 3 in this series (click here for parts 1 and 2) on Predictive Processing (PP), I discussed how the PP framework can be used to adequately account for traditional and scientific notions of knowledge, by treating knowledge as a subset of all the predicted causal relations currently at our brain’s disposal.  This subset of predictions that we tend to call knowledge has the special quality of especially high confidence levels (high Bayesian priors).  Within a scientific context, knowledge tends to have an even stricter definition (and even higher confidence levels) and so we end up with a smaller subset of predictions which have been further verified through comparing them with the inferred predictions of others and by testing them with external means of instrumentation and some agreed upon conventions for analysis.

However, no amount of testing or verification is going to give us direct access to any knowledge per se.  Rather, the creation or discovery of knowledge has to involve the application of some kind of reasoning to explain the causal inputs, and only after this reasoning process can the resulting predicted causal relations be validated to varying degrees by testing it (through raw sensory data, external instruments, etc.).  So getting an adequate account of reasoning within any theory or framework of overall brain function is going to be absolutely crucial and I think that the PP framework is well-suited for the job.  As has already been mentioned throughout this post-series, this framework fundamentally relies on a form of Bayesian inference (or some approximation) which is a type of reasoning.  It is this inferential strategy then, combined with a hierarchical neurological structure for it to work upon, that would allow our knowledge to be created in the first place.

Rules of Inference & Reasoning Based on Hierarchical-Bayesian Prediction Structure, Neuronal Selection, Associations, and Abstraction

While PP tends to focus on perception and action in particular, I’ve mentioned that I see the same general framework as being able to account for not only the folk psychological concepts of beliefs, desires, and emotions, but also that the hierarchical predictive structure it entails should plausibly be able to account for language and ontology and help explain the relationship between the two.  It seems reasonable to me that the associations between all of these hierarchically structured beliefs or predicted causal relations at varying levels of abstraction, can provide a foundation for our reasoning as well, whether intuitive or logical forms of reasoning.

To illustrate some of the importance of associations between beliefs, consider an example like the belief in object permanence (i.e. that objects persist or continue to exist even when I can no longer see them).  This belief of ours has an extremely high prior because our entire life experience has only served to support this prediction in a large number of ways.  This means that it’s become embedded or implicit in a number of other beliefs.  If I didn’t predict that object permanence was a feature of my reality, then an enormous number of everyday tasks would become difficult if not impossible to do because objects would be treated as if they are blinking into and out of existence.

We have a large number of beliefs that require object permanence (and which are thus associated with object permanence), and so it is a more fundamental lower-level prediction (though not as low level as sensory information entering the visual cortex) and we use this lower-level prediction to build upon into any number of higher-level predictions in the overall conceptual/predictive hierarchy.  When I put money in a bank, I expect to be able to spend it even if I can’t see it anymore (such as with a check or debit card).  This is only possible if my money continues to exist even when out of view (regardless of if the money is in a paper/coin or electronic form).  This is just one of many countless everyday tasks that depend on this belief.  So it’s no surprise that this belief (this set of predictions) would have an incredibly high Bayesian prior, and therefore I would treat it as a non-negotiable fact about reality.

On the other hand, when I was a newborn infant, I didn’t have this belief of object permanence (or at best, it was a very weak belief).  Most psychologists estimate that our belief in object permanence isn’t acquired until after several months of brain development and experience.  This would translate to our having a relatively low Bayesian prior for this belief early on in our lives, and only once a person begins to form predictions based on these kinds of recognized causal relations can we begin to increase that prior and perhaps eventually reach a point that results in a subjective experience of a high degree in certainty for this particular belief.  From that point on, we are likely to simply take that belief for granted, no longer questioning it.  The most important thing to note here is that the more associations made between beliefs, the higher their effective weighting (their priors), and thus the higher our confidence in those beliefs becomes.

Neural Implementation, Spontaneous or Random Neural Activity & Generative Model Selection

This all seems pretty reasonable if a neuronal implementation worked to strengthen Bayesian priors as a function of the neuronal/synaptic connectivity (among other factors), where neurons that fire together are more likely to wire together.  And connectivity strength will increase the more often this happens.  On the flip-side, the less often this happens or if it isn’t happening at all then the connectivity is likely to be weakened or non-existent.  So if a concept (or a belief composed of many conceptual relations) is represented by some cluster of interconnected neurons and their activity, then it’s applicability to other concepts increases its chances of not only firing but also increasing the strength of wiring with those other clusters of neurons, thus plausibly increasing the Bayesian priors for the overlapping concept or belief.

Another likely important factor in the Bayesian inferential process, in terms of the brain forming new generative models or predictive hypotheses to test, is the role of spontaneous or random neural activity and neural cluster generation.  This random neural activity could plausibly provide a means for some randomly generated predictions or random changes in the pool of predictive models that our brain is able to select from.  Similar to the role of random mutation in gene pools which allows for differential reproductive rates and relative fitness of offspring, some amount of randomness in neural activity and the generative models that result would allow for improved models to be naturally selected based on those which best minimize prediction error.  The ability to minimize prediction error could be seen as a direct measure of the fitness of the generative model, within this evolutionary landscape.

This idea is related to the late Gerald Edelman’s Theory of Neuronal Group Selection (NGS), also known as Neural Darwinism, which I briefly explored in a post I wrote long ago.  I’ve long believed that this kind of natural selection process is applicable to a number of different domains (aside from genetics), and I think any viable version of PP is going to depend on it to at least some degree.  This random neural activity (and the naturally selected products derived from them) could be thought of as contributing to a steady supply of new generative models to choose from and thus contributing to our overall human creativity as well whether for reasoning and problem solving strategies or simply for artistic expression.

Increasing Abstraction, Language, & New Rules of Inference

This kind of use it or lose it property of brain plasticity combined with dynamic associations between concepts or beliefs and their underlying predictive structure, would allow for the brain to accommodate learning by extracting statistical inferences (at increasing levels of abstraction) as they occur and modifying or eliminating those inferences by changing their hierarchical associative structure as prediction error is encountered.  While some form of Bayesian inference (or an approximation to it) underlies this process, once lower-level inferences about certain causal relations have been made, I believe that new rules of inference can be derived from this basic Bayesian foundation.

To see how this might work, consider how we acquire a skill like learning how to speak and write in some particular language.  The rules of grammar, the syntactic structure and so forth which underlie any particular language are learned through use.  We begin to associate words with certain conceptual structures (see part 2 of this post-series for more details on language and ontology) and then we build up the length and complexity of our linguistic expressions by adding concepts built on higher levels of abstraction.  To maximize the productivity and specificity of our expressions, we also learn more complex rules pertaining to the order in which we speak or write various combinations of words (which varies from language to language).

These grammatical rules can be thought of as just another higher-level abstraction, another higher-level causal relation that we predict will convey more specific information to whomever we are speaking to.  If it doesn’t seem to do so, then we either modify what we have mistakenly inferred to be those grammatical rules, or depending on the context, we may simply assume that the person we’re talking to hasn’t conformed to the language or grammar that my community seems to be using.

Just like with grammar (which provides a kind of logical structure to our language), we can begin to learn new rules of inference built on the same probabilistic predictive bedrock of Bayesian inference.  We can learn some of these rules explicitly by studying logic, induction, deduction, etc., and consciously applying those rules to infer some new piece of knowledge, or we can learn these kinds of rules implicitly based on successful predictions (pertaining to behaviors of varying complexity) that happen to result from stumbling upon this method of processing causal relations within various contexts.  As mentioned earlier, this would be accomplished in part by the natural selection of randomly-generated neural network changes that best reduce the incoming prediction error.

However, language and grammar are interesting examples of an acquired set of rules because they also happen to be the primary tool that we use to learn other rules (along with anything we learn through verbal or written instruction), including (as far as I can tell) various rules of inference.  The logical structure of language (though it need not have an exclusively logical structure), its ability to be used for a number of cognitive short-cuts, and it’s influence on our thought complexity and structure, means that we are likely dependent on it during our reasoning processes as well.

When we perform any kind of conscious reasoning process, we are effectively running various mental simulations where we can intentionally manipulate our various generative models to test new predictions (new models) at varying levels of abstraction, and thus we also manipulate the linguistic structure associated with those generative models as well.  Since I have a lot more to say on reasoning as it relates to PP, including more on intuitive reasoning in particular, I’m going to expand on this further in my next post in this series, part 5.  I’ll also be exploring imagination and memory including how they relate to the processes of reasoning.

Conscious Realism & The Interface Theory of Perception

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A few months ago I was reading an interesting article in The Atlantic about Donald Hoffman’s Interface Theory of Perception.  As a person highly interested in consciousness studies, cognitive science, and the mind-body problem, I found the basic concepts of his theory quite fascinating.  What was most interesting to me was the counter-intuitive connection between evolution and perception that Hoffman has proposed.  Now it is certainly reasonable and intuitive to assume that evolutionary natural selection would favor perceptions that are closer to “the truth” or closer to the objective reality that exists independent of our minds, simply because of the idea that perceptions that are more accurate will be more likely to lead to survival than perceptions that are not accurate.  As an example, if I were to perceive lions as inert objects like trees, I would be more likely to be naturally selected against and eaten by a lion when compared to one who perceives lions as a mobile predator that could kill them.

While this is intuitive and reasonable to some degree, what Hoffman actually shows, using evolutionary game theory, is that with respect to organisms with comparable complexity, those with perceptions that are closer to reality are never going to be selected for nearly as much as those with perceptions that are tuned to fitness instead.  More so, truth in this case will be driven to extinction when it is up against perceptual models that are tuned to fitness.  That is to say, evolution will select for organisms that perceive the world in a way that is less accurate (in terms of the underlying reality) as long as the perception is tuned for survival benefits.  The bottom line is that given some specific level of complexity, it is more costly to process more information (costing more time and resources), and so if a “heuristic” method for perception can evolve instead, one that “hides” all the complex information underlying reality and instead provides us with a species-specific guide to adaptive behavior, that will always be the preferred choice.

To see this point more clearly, let’s consider an example.  Let’s imagine there’s an animal that regularly eats some kind of insect, such as a beetle, but it needs to eat a particular sized beetle or else it has a relatively high probability of eating the wrong kind of beetle (and we can assume that the “wrong” kind of beetle would be deadly to eat).  Now let’s imagine two possible types of evolved perception: it could have really accurate perceptions about the various sizes of beetles that it encounters so it can distinguish many different sizes from one another (and then choose the proper size range to eat), or it could evolve less accurate perceptions such that all beetles that are either too small or too large appear as indistinguishable from one another (maybe all the wrong-sized beetles whether too large or too small look like indistinguishable red-colored blobs) and perhaps all the beetles that are in the ideal size range for eating appear as green-colored blobs (that are again, indistinguishable from one another).  So the only discrimination in this latter case of perception is between red and green colored blobs.

Both types of perception would solve the problem of which beetles to eat or not eat, but the latter type (even if much less accurate) would bestow a fitness advantage over the former type, by allowing the animal to process much less information about the environment by not focusing on relatively useless information (like specific beetle size).  In this case, with beetle size as the only variable under consideration for survival, evolution would select for the organism that knows less total information about beetle size, as long as it knows what is most important about distinguishing the edible beetles from the poisonous beetles.  Now we can imagine that in some cases, the fitness function could align with the true structure of reality, but this is not what we ever expect to see generically in the world.  At best we may see some kind of overlap between the two but if there doesn’t have to be any then truth will go extinct.

Perception is Analogous to a Desktop Computer Interface

Hoffman analogizes this concept of a “perception interface” with the desktop interface of a personal computer.  When we see icons of folders on the desktop and drag one of those icons to the trash bin, we shouldn’t take that interface literally, because there isn’t literally a folder being moved to a literal trash bin but rather it is simply an interface that hides most if not all of what is really going on in the background — all those various diodes, resistors and transistors that are manipulated in order to modify stored information that is represented in binary code.

The desktop interface ultimately provides us with an easy and intuitive way of accomplishing these various information processing tasks because trying to do so in the most “truthful” way — by literally manually manipulating every diode, resistor, and transistor to accomplish the same task — would be far more cumbersome and less effective than using the interface.  Therefore the interface, by hiding this truth from us, allows us to “navigate” through that computational world with more fitness.  In this case, having more fitness simply means being able to accomplish information processing goals more easily, with less resources, etc.

Hoffman goes on to say that even though we shouldn’t take the desktop interface literally, obviously we should still take it seriously, because moving that folder to the trash bin can have direct implications on our lives, by potentially destroying months worth of valuable work on a manuscript that is contained in that folder.  Likewise we should take our perceptions seriously, even if we don’t take them literally.  We know that stepping in front of a moving train will likely end our conscious experience even if it is for causal reasons that we have no epistemic access to via our perception, given the species-specific “desktop interface” that evolution has endowed us with.

Relevance to the Mind-body Problem

The crucial point with this analogy is the fact that if our knowledge was confined to the desktop interface of the computer, we’d never be able to ascertain the underlying reality of the “computer”, because all that information that we don’t need to know about that underlying reality is hidden from us.  The same would apply to our perception, where it would be epistemically isolated from the underlying objective reality that exists.  I want to add to this point that even though it appears that we have found the underlying guts of our consciousness, i.e., the findings in neuroscience, it would be mistaken to think that this approach will conclusively answer the mind-body problem because the interface that we’ve used to discover our brains’ underlying neurobiology is still the “desktop” interface.

So while we may think we’ve found the underlying guts of “the computer”, this is far from certain, given the possibility of and support for this theory.  This may end up being the reason why many philosophers claim there is a “hard problem” of consciousness and one that can’t be solved.  It could be that we simply are stuck in the desktop interface and there’s no way to find out about the underlying reality that gives rise to that interface.  All we can do is maximize our knowledge of the interface itself and that would be our epistemic boundary.

Predictions of the Theory

Now if this was just a fancy idea put forward by Hoffman, that would be interesting in its own right, but the fact that it is supported by evolutionary game theory and genetic algorithm simulations shows that the theory is more than plausible.  Even better, the theory is actually a scientific theory (and not just a hypothesis), because it has made falsifiable predictions as well.  It predicts that “each species has its own interface (with some similarities between phylogenetically related species), almost surely no interface performs reconstructions (read the second link for more details on this), each interface is tailored to guide adaptive behavior in the relevant niche, much of the competition between and within species exploits strengths and limitations of interfaces, and such competition can lead to arms races between interfaces that critically influence their adaptive evolution.”  The theory predicts that interfaces are essential to understanding evolution and the competition between organisms, whereas the reconstruction theory makes such understanding impossible.  Thus, evidence of interfaces should be widespread throughout nature.

In his paper, he mentions the Jewel beetle as a case in point.  This beetle has a perceptual category, desirable females, which works well in its niche, and it uses it to choose larger females because they are the best mates.  According to the reconstructionist thesis, the male’s perception of desirable females should incorporate a statistical estimate of the true sizes of the most fertile females, but it doesn’t do this.  Instead, it has a category based on “bigger is better” and although this bestows a high fitness behavior for the male beetle in its evolutionary niche, if it comes into contact with a “stubbie” beer bottle, it falls into an infinite loop by being drawn to this supernormal stimuli since it is smooth, brown, and extremely large.  We can see that the “bigger is better” perceptual category relies on less information about the true nature of reality and instead chooses an “informational shortcut”.  The evidence of supernormal stimuli which have been found with many species further supports the theory and is evidence against the reconstructionist claim that perceptual categories estimate the statistical structure of the world.

More on Conscious Realism (Consciousness is all there is?)

This last link provided here shows the mathematical formalism of Hoffman’s conscious realist theory as proved by Chetan Prakash.  It contains a thorough explanation of the conscious realist theory (which goes above and beyond the interface theory of perception) and it also provides answers to common objections put forward by other scientists and philosophers on this theory.

Transcendental Argument For God’s Existence: A Critique

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Theist apologists and theologians have presented many arguments for the existence of God throughout history including the Ontological Argument, Cosmological Argument, Fine-Tuning Argument, the Argument from Morality, and many others — all of which having been refuted with various counter arguments.  I’ve written about a few of these arguments in the past (1, 2, 3), but one that I haven’t yet touched on is that of the Transcendental Argument for God (or simply TAG).  Not long ago I heard the Christian apologist Matt Slick conversing/debating with the well renowned atheist Matt Dillahunty on this topic and then I decided to look deeper into the argument as Slick presents it on his website.  I have found a number of problems with his argument, so I decided to iterate them in this post.

Slick’s basic argument goes as follows:

  1. The Laws of Logic exist.
    1. Law of Identity: Something (A) is what it is and is not what it is not (i.e. A is A and A is not not-A).
    2. Law of Non-contradiction: A cannot be both A and not-A, or in other words, something cannot be both true and false at the same time.
    3. Law of the Excluded Middle: Something must either be A or not-A without a middle ground, or in other words, something must be either true or false without a middle ground.
  2. The Laws of Logic are conceptual by nature — are not dependent on space, time, physical properties, or human nature.
  3. They are not the product of the physical universe (space, time, matter) because if the physical universe were to disappear, The Laws of Logic would still be true.
  4. The Laws of Logic are not the product of human minds because human minds are different — not absolute.
  5. But, since the Laws of Logic are always true everywhere and not dependent on human minds, it must be an absolute transcendent mind that is authoring them.  This mind is called God.
  6. Furthermore, if there are only two options to account for something, i.e., God and no God, and one of them is negated, then by default the other position is validated.
  7. Therefore, part of the argument is that the atheist position cannot account for the existence of The Laws of Logic from its worldview.
  8. Therefore God exists.

Concepts are Dependent on and the Product of Physical Brains

Let’s begin with number 2, 3, and 4 from above:

The Laws of Logic are conceptual by nature — are not dependent on space, time, physical properties, or human nature.  They are not the product of the physical universe (space, time, matter) because if the physical universe were to disappear, The Laws of Logic would still be true.  The Laws of Logic are not the product of human minds because human minds are different — not absolute.

Now I’d like to first mention that Matt Dillahunty actually rejected the first part of Slick’s premise here, as Dillahunty explained that while logic (the concept, our application of it, etc.) may in fact be conceptual in nature, the logical absolutes themselves (i.e. the laws of logic) which logic is based on are in fact neither conceptual nor physical.  My understanding of what Dillahunty was getting at here is that he was basically saying that just as the concept of an apple points to or refers to something real (i.e. a real apple) which is not equivalent to the concept of an apple, so also does the concept of the logical absolutes refer to something that is not the same as the concept itself.  However, what it points to, Dillahunty asserted, is something that isn’t physical either.  Therefore, the logical absolutes themselves are neither physical nor conceptual (as a result, Dillahunty later labeled “the essence” of the LOL as transcendent).  When Dillahunty was pressed by Slick to answer the question, “then what caused the LOL to exist?”, Dillahunty responded by saying that nothing caused them (or we have no reason to believe so) because they are transcendent and are thus not a product of anything physical nor conceptual.

If this is truly the case, then Dillahunty’s point here does undermine the validity of the logical structure of Slick’s argument, because Slick would then be beginning his argument by referencing the content and the truth of the logical absolutes themselves, and then later on switching to the concept of the LOL (i.e. their being conceptual in their nature, etc.).  For the purposes of this post, I’m going to simply accept Slick’s dichotomy that the logical absolutes (i.e. the laws of logic) are in fact either physical or conceptual by nature and then I will attempt to refute the argument anyway.  This way, if “conceptual or physical” is actually a true dichotomy (i.e. if there are no other options), despite the fact that Slick hasn’t proven this to be the case, his argument will be undermined anyway.  If Dillahunty is correct and “conceptual or physical” isn’t a true dichotomy, then even if my refutation here fails, Slick’s argument will still be logically invalid based on the points Dillahunty raised.

I will say however that I don’t think I agree with the point that Dillahunty made that the LOL are neither physical nor conceptual, and for a few reasons (not least of all because I am a physicalist).  My reasons for this will become more clear throughout the rest of this post, but in a nutshell, I hold that concepts are ultimately based on and thus are a subset of the physical, and the LOL would be no exception to this.  Beyond the issue of concepts, I believe that the LOL are physical in their nature for a number of other reasons as well which I’ll get to in a moment.

So why does Slick think that the LOL can’t be dependent on space?  Slick mentions in the expanded form of his argument that:

They do not stop being true dependent on location. If we travel a million light years in a direction, The Laws of Logic are still true.

Sure, the LOL don’t depend on a specific location in space, but that doesn’t mean that they aren’t dependent on space in general.  I would actually argue that concepts are abstractions that are dependent on the brains that create them based on those brains having recognized properties of space, time, and matter/energy.  That is to say that any concept such as the number 3 or the concept of redness is in fact dependent on a brain having recognized, for example, a quantity of discrete objects (which when generalized leads to the concept of numbers) or having recognized the color red in various objects (which when generalized leads to the concept of red or redness).  Since a quantity of discrete objects or a color must be located in some kind of space — even if three points on a one dimensional line (in the case of the number 3), or a two-dimensional red-colored plane (in the case of redness), then we can see that these concepts are ultimately dependent on space and matter/energy (of some kind).  Even if we say that concepts such as the color red or the number 3 do not literally exist in actual space nor are made of actual matter, they do have to exist in a mental space as mental objects, just as our conception of an apple floating in empty space doesn’t actually lie in space nor is made of matter, it nevertheless exists as a mental/perceptual representation of real space and real matter/energy that has been experienced by interaction with the physical universe.

Slick also mentions in the expanded form of his argument that the LOL can’t be dependent on time because:

They do not stop being true dependent on time. If we travel a billion years in the future or past, The Laws of Logic are still true.

Once again, sure, the LOL do not depend on a specific time, but rather they are dependent on time in general, because minds depend on time in order to have any experience of said concepts at all.  So not only are concepts only able to be formed by a brain that has created abstractions from physically interacting with space, matter, and energy within time (so far as we know), but the mind/concept-generating brain itself is also made of actual matter/energy, lying in real space, and operating/functioning within time.  So concepts are in fact not only dependent on space, time, and matter/energy (so far as we know), but are in fact also the product of space, time, and matter/energy, since it is only certain configurations of such that in fact produce a brain and the mind that results from said brain.  Thus, if the LOL are conceptual, then they are ultimately the product of and dependent on the physical.

Can Truth Exist Without Brains and a Universe?  Can Identities Exist Without a Universe?  I Don’t Think So…

Since Slick himself even claims that The Laws of Logic (LOL) are conceptual by nature, then that would mean that they are in fact also dependent on and the product of the physical universe, and more specifically are dependent on and the product of the human mind (or natural minds in general which are produced by a physical brain).  Slick goes on to say that the LOL can’t be dependent on the physical universe (which contains the brains needed to think or produce those concepts) because “…if the physical universe were to disappear, The Laws of Logic would still be true.”  It seems to me that without a physical universe, there wouldn’t be any “somethings” with any identities at all and so the Law of Identity which is the root of the other LOL wouldn’t apply to anything because there wouldn’t be anything and thus no existing identities.  Therefore, to say that the LOL are true sans a physical universe would be meaningless because identities themselves wouldn’t exist without a physical universe.  One might argue that abstract identities would still exist (like numbers or properties), but abstractions are products of a mind and thus need a brain to exist (so far as we know).  If one argued that supernatural identities would still exist without a physical universe, this would be nothing more than an ad hoc metaphysical assertion about the existence of the supernatural which carries a large burden of proof that can’t be (or at least hasn’t been) met.  Beyond that, if at least one of the supernatural identities was claimed to be God, this would also be begging the question.  This leads me to believe that the LOL are in fact a property of the physical universe (and appear to be a necessary one at that).

And if truth is itself just another concept, it too is dependent on minds and by extension the physical brains that produce those minds (as mentioned earlier).  In fact, the LOL seem to be required for any rational thought at all (hence why they are often referred to as the Laws of Thought), including the determination of any truth value at all.  So our ability to establish the truth value of the LOL (or the truth of anything for that matter) is also contingent on our presupposing the LOL in the first place.  So if there were no minds to presuppose the very LOL that are needed to establish its truth value, then could one say that they would be true anyway?  Wouldn’t this be analogous to saying that 1 + 1 = 2 would still be true even if numbers and addition (constructs of the mind) didn’t exist?  I’m just not sure that truth can exist in the absence of any minds and any physical universe.  I think that just as physical laws are descriptions of how the universe changes over time, these Laws of Thought are descriptions that underlie what our rational thought is based on, and thus how we arrive at the concept of truth at all.  If rational thought ceases to exist in the absence of a physical universe (since there are no longer any brains/minds), then the descriptions that underlie that rational thought (as well as their truth value) also cease to exist.

Can Two Different Things Have Something Fundamental in Common?

Slick then erroneously claims that the LOL can’t be the product of human minds because human minds are different and thus aren’t absolute, apparently not realizing that even though human minds are different from one another in many ways, they also have a lot fundamentally in common, such as how they process information and how they form concepts about the reality they interact with generally.  Even though our minds differ from one another in a number of ways, we nevertheless only have evidence to support the claim that human brains produce concepts and process information in the same general way at the most fundamental neurological level.  For example, the evidence suggests that the concept of the color red is based on the neurological processing of a certain range of wavelengths of electromagnetic radiation that have been absorbed by the eye’s retinal cells at some point in the past.  However, in Slick’s defense, I’ll admit that it could be the case that what I experience as the color red may be what you would call the color blue, and this would in fact suggest that concepts that we think we mutually understand are actually understood or experienced differently in a way that we can’t currently verify (since I can’t get in your mind and compare it to my own experience, and vice versa).

Nevertheless, just because our minds may experience color differently from one another or just because we may differ slightly in terms of what range of shades/tints of color we’d like to label as red, this does not mean that our brains/minds (or natural minds in general) are not responsible for producing the concept of red, nor does it mean that we don’t all produce that concept in the same general way.  The number 3 is perhaps a better example of a concept that is actually shared by humans in an absolute sense, because it is a concept that isn’t dependent on specific qualia (like the color red is).  The concept of the number 3 has a universal meaning in the human mind since it is derived from the generalization of a quantity of three discrete objects (which is independent of how any three specific objects are experienced in terms of their respective qualia).

Human Brains Have an Absolute Fundamental Neurology Which Encompasses the LOL

So I see no reason to believe that human minds differ at all in their conception of the LOL, especially if this is the foundation for rational thought (and thus any coherent concept formed by our brains).  In fact, I also believe that the evidence within the neurosciences suggests that the way the brain recognizes different patterns and thus forms different/unique concepts and such is dependent on the fact that the brain uses a hardware configuration schema that encompasses the logical absolutes.  In a previous post, my contention was that:

Additionally, if the brain’s wiring has evolved in order to see dimensions of difference in the world (unique sensory/perceptual patterns that is, such as quantity, colors, sounds, tastes, smells, etc.), then it would make sense that the brain can give any particular pattern an identity by having a unique schema of hardware or unique use of said hardware to perceive such a pattern and distinguish it from other patterns.  After the brain does this, the patterns are then arguably organized by the logical absolutes.  For example, if the hardware scheme or process used to detect a particular pattern “A” exists and all other patterns we perceive have or are given their own unique hardware-based identity (i.e. “not-A” a.k.a. B, C, D, etc.), then the brain would effectively be wired such that pattern “A” = pattern “A” (law of identity), any other pattern which we can call “not-A” does not equal pattern “A” (law of non-contradiction), and any pattern must either be “A” or some other pattern even if brand new, which we can also call “not-A” (law of the excluded middle).  So by the brain giving a pattern a physical identity (i.e. a specific type of hardware configuration in our brain that when activated, represents a detection of one specific pattern), our brains effectively produce the logical absolutes by nature of the brain’s innate wiring strategy which it uses to distinguish one pattern from another.  So although it may be true that there can’t be any patterns stored in the brain until after learning begins (through sensory experience), the fact that the DNA-mediated brain wiring strategy inherently involves eventually giving a particular learned pattern a unique neurological hardware identity to distinguish it from other stored patterns, suggests that the logical absolutes themselves are an innate and implicit property of how the brain stores recognized patterns.

So I believe that our brain produces and distinguishes these different “object” identities by having a neurological scheme that represents each perceived identity (each object) with a unique set of neurons that function in a unique way and thus which have their own unique identity.  Therefore, it would seem that the absolute nature of the LOL can easily be explained by how the brain naturally encompasses them through its fundamental hardware schema.  In other words, my contention is that our brain uses this wiring schema because it is the only way that it can be wired to make any discriminations at all and validly distinguish one identity from another in perception and thought, and this ability to discriminate various aspects of reality would be evolutionarily naturally-selected for based on the brain accurately modeling properties of the universe (in this case different identities/objects/causal-interactions existing) as it interacts with that environment via our sensory organs.  Which would imply that the existence of discrete identities is a property of the physical universe, and the LOL would simply be a description of what identities are.  This would explain why we see the LOL as absolute and fundamental and presuppose them.  Our brains simply encompass them in the most fundamental aspect of our neurology as it is a fundamental physical property of the universe that our brains model.

I believe that this is one of the reasons that Dillahunty and others believe that the LOL are transcendent (neither physical nor conceptual), because natural brains/minds are neurologically incapable of imagining a world existing without them.  The problem then only occurs because Dillahunty is abstracting a hypothetical non-physical world or mode of existence, yet doesn’t realize that he is unable to remove every physical property from any abstracted world or imagined mode of existence.  In this case, the physical property that he is unable to remove from his hypothetical non-physical world is his own neurological foundation, the very foundation that underlies all concepts (including that of existential identities) and which underlies all rational thought.  I may be incorrect about Dillahunty’s position here, but this is what I’ve inferred anyway based on what I’ve heard him say while conversing with Slick about this topic.

Human (Natural) Minds Can’t Account for the LOL, But Disembodied Minds Can?

Slick even goes on to say in point 5 that:

But, since the Laws of Logic are always true everywhere and not dependent on human minds, it must be an absolute transcendent mind that is authoring them.  This mind is called God.

We can see here that he concedes that the LOL is in fact the product of a mind, only he rejects the possibility that it could be a human mind (and by implication any kind of natural mind).  Rather, he insists that it must be a transcendent mind of some kind, which he calls God.  The problem with this conclusion is that we have no evidence or argument that demonstrates that minds can exist without physical brains existing in physical space within time.  To assume so is simply to beg the question.  Thus, he is throwing in an ontological/metaphysical assumption of substance dualism as well as that of disembodied minds, not only claiming that there must exist some kind of supernatural substance, but that this mysterious “non-physical” substance also has the ability to constitute a mind, and somehow do so without any dependence on time (even though mental function and thinking is itself a temporal process).  He assumes all of this of course without providing any explanation of how this mind could work even in principle without being made of any kind of stuff, without being located in any kind of space, and without existing in any kind of time.  As I’ve mentioned elsewhere concerning the ad hoc concept of disembodied minds:

…the only concept of a mind that makes any sense at all is that which involves the properties of causality, time, change, space, and material, because minds result from particular physical processes involving a very complex configuration of physical materials.  That is, minds appear to be necessarily complex in terms of their physical structure (i.e. brains), and so trying to conceive of a mind that doesn’t have any physical parts at all, let alone a complex arrangement of said parts, is simply absurd (let alone a mind that can function without time, change, space, etc.).  At best, we are left with an ad hoc, unintelligible combination of properties without any underlying machinery or mechanism.

In summary, I believe Slick has made several errors in his reasoning, with the most egregious being his unfounded assumption that natural minds aren’t capable of producing an absolute concept such as the LOL simply because natural minds have differences between one another (not realizing that all minds have fundamental commonalities), and also his argument’s reliance on the assumption that an ad hoc disembodied mind not only exists (whatever that could possibly mean) but that this mind can somehow account for the LOL in a way that natural minds can not, which is nothing more than an argument from ignorance, a logical fallacy.  He also insists that the Laws of Logic would be true without any physical universe, not realizing that the truth value of the Laws of Logic can only be determined by presupposing the Laws of Logic in the first place, which is circular, thus showing that the truth value of the Laws of Logic can’t be used to prove that they are metaphysically transcendent in any way (even if they actually happen to be metaphysically transcendent).  Lastly, without a physical universe of any kind, I don’t see how identities themselves can exist, and identities seem to be required in order for the LOL to be meaningful at all.

Darwin’s Big Idea May Be The Biggest Yet

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Back in 1859, Charles Darwin released his famous theory of evolution by natural selection whereby inherent variations in the individual members of some population of organisms under consideration would eventually lead to speciation events due to those variations producing a differential in survival and reproductive success and thus leading to the natural selection of some subset of organisms within that population.  As Darwin explained in his On The Origin of Species:

If during the long course of ages and under varying conditions of life, organic beings vary at all in the several parts of their organisation, and I think this cannot be disputed; if there be, owing to the high geometrical powers of increase of each species, at some age, season, or year, a severe struggle for life, and this certainly cannot be disputed; then, considering the infinite complexity of the relations of all organic beings to each other and to their conditions of existence, causing an infinite diversity in structure, constitution, and habits, to be advantageous to them, I think it would be a most extraordinary fact if no variation ever had occurred useful to each being’s own welfare, in the same way as so many variations have occurred useful to man. But if variations useful to any organic being do occur, assuredly individuals thus characterised will have the best chance of being preserved in the struggle for life; and from the strong principle of inheritance they will tend to produce offspring similarly characterised. This principle of preservation, I have called, for the sake of brevity, Natural Selection.

While Darwin’s big idea completely transformed biology in terms of it providing (for the first time in history) an incredibly robust explanation for the origin of the diversity of life on this planet, his idea has since inspired other theories pertaining to perhaps the three largest mysteries that humans have ever explored: the origin of life itself (not just the diversity of life after it had begun, which was the intended scope of Darwin’s theory), the origin of the universe (most notably, why the universe is the way it is and not some other way), and also the origin of consciousness.

Origin of Life

In order to solve the first mystery (the origin of life itself), geologists, biologists, and biochemists are searching for plausible models of abiogenesis, whereby the general scheme of these models would involve chemical reactions (pertaining to geology) that would have begun to incorporate certain kinds of energetically favorable organic chemistries such that organic, self-replicating molecules eventually resulted.  Now, where Darwin’s idea of natural selection comes into play with life’s origin is in regard to the origin and evolution of these self-replicating molecules.  First of all, in order for any molecule at all to build up in concentration requires a set of conditions such that the reaction leading to the production of the molecule in question is more favorable than the reverse reaction where the product transforms back into the initial starting materials.  If merely one chemical reaction (out of a countless number of reactions occurring on the early earth) led to a self-replicating product, this would increasingly favor the production of that product, and thus self-replicating molecules themselves would be naturally selected for.  Once one of them was produced, there would have been a cascade effect of exponential growth, at least up to the limit set by the availability of the starting materials and energy sources present.

Now if we assume that at least some subset of these self-replicating molecules (if not all of them) had an imperfect fidelity in the copying process (which is highly likely) and/or underwent even a slight change after replication by reacting with other neighboring molecules (also likely), this would provide them with a means of mutation.  Mutations would inevitably lead to some molecules becoming more effective self-replicators than others, and then evolution through natural selection would take off, eventually leading to modern RNA/DNA.  So not only does Darwin’s big idea account for the evolution of diversity of life on this planet, but the basic underlying principle of natural selection would also account for the origin of self-replicating molecules in the first place, and subsequently the origin of RNA and DNA.

Origin of the Universe

Another grand idea that is gaining heavy traction in cosmology is that of inflationary cosmology, where this theory posits that the early universe underwent a period of rapid expansion, and due to quantum mechanical fluctuations in the microscopically sized inflationary region, seed universes would have resulted with each one having slightly different properties, one of which that would have expanded to be the universe that we live in.  Inflationary cosmology is currently heavily supported because it has led to a number of predictions, many of which that have already been confirmed by observation (it explains many large-scale features of our universe such as its homogeneity, isotropy, flatness, and other features).  What I find most interesting with inflationary theory is that it predicts the existence of a multiverse, whereby we are but one of an extremely large number of other universes (predicted to be on the order of 10^500, if not an infinite number), with each one having slightly different constants and so forth.

Once again, Darwin’s big idea, when applied to inflationary cosmology, would lead to the conclusion that our universe is the way it is because it was naturally selected to be that way.  The fact that its constants are within a very narrow range such that matter can even form, would make perfect sense, because even if an infinite number of universes exist with different constants, we would only expect to find ourselves in one that has the constants within the necessary range in order for matter, let alone life to exist.  So any universe that harbors matter, let alone life, would be naturally selected for against all the other universes that didn’t have the right properties to do so, including for example, universes that had too high or too low of a cosmological constant (such as those that would have instantly collapsed into a Big Crunch or expanded into a heat death far too quickly for any matter or life to have formed), or even universes that didn’t have the proper strong nuclear force to hold atomic nuclei together, or any other number of combinations that wouldn’t work.  So any universe that contains intelligent life capable of even asking the question of their origins, must necessarily have its properties within the required range (often referred to as the anthropic principle).

After our universe formed, the same principle would also apply to each galaxy and each solar system within those galaxies, whereby because variations exist in each galaxy and within each substituent solar system (differential properties analogous to different genes in a gene pool), then only those that have an acceptable range of conditions are capable of harboring life.  With over 10^22 stars in the observable universe (an unfathomably large number), and billions of years to evolve different conditions within each solar system surrounding those many stars, it isn’t surprising that eventually the temperature and other conditions would be acceptable for liquid water and organic chemistries to occur in many of those solar systems.  Even if there was only one life permitting planet per galaxy (on average), that would add up to over 100 billion life permitting planets in the observable universe alone (with many orders of magnitude more life permitting planets in the non-observable universe).  So given enough time, and given some mechanism of variation (in this case, differences in star composition and dynamics), natural selection in a sense can also account for the evolution of some solar systems that do in fact have life permitting conditions in a universe such as our own.

Origin of Consciousness

The last significant mystery I’d like to discuss involves the origin of consciousness.  While there are many current theories pertaining to different aspects of consciousness, and while there has been much research performed in the neurosciences, cognitive sciences, psychology, etc., pertaining to how the brain works and how it correlates to various aspects of the mind and consciousness, the brain sciences (though neuroscience in particular) are in their relative infancy and so there are still many questions that haven’t been answered yet.  One promising theory that has already been shown to account for many aspects of consciousness is Gerald Edelman’s theory of neuronal group selection (NGS) otherwise known as neural Darwinism (ND), which is a large scale theory of brain function.  As one might expect from the name, the mechanism of natural selection is integral to this theory.  In ND, the basic idea consists of three parts as read on the Wiki:

  1. Anatomical connectivity in the brain occurs via selective mechanochemical events that take place epigenetically during development.  This creates a diverse primary neurological repertoire by differential reproduction.
  2. Once structural diversity is established anatomically, a second selective process occurs during postnatal behavioral experience through epigenetic modifications in the strength of synaptic connections between neuronal groups.  This creates a diverse secondary repertoire by differential amplification.
  3. Re-entrant signaling between neuronal groups allows for spatiotemporal continuity in response to real-world interactions.  Edelman argues that thalamocortical and corticocortical re-entrant signaling are critical to generating and maintaining conscious states in mammals.

In a nutshell, the basic differentiated structure of the brain that forms in early development is accomplished through cellular proliferation, migration, distribution, and branching processes that involve selection processes operating on random differences in the adhesion molecules that these processes use to bind one neuronal cell to another.  These crude selection processes result in a rough initial configuration that is for the most part fixed.  However, because there are a diverse number of sets of different hierarchical arrangements of neurons in various neuronal groups, there are bound to be functionally equivalent groups of neurons that are not equivalent in structure, but are all capable of responding to the same types of sensory input.  Because some of these groups should in theory be better than others at responding to some particular type of sensory stimuli, this creates a form of neuronal/synaptic competition in the brain, whereby those groups of neurons that happen to have the best synaptic efficiency for the stimuli in question are naturally selected over the others.  This in turn leads to an increased probability that the same network will respond to similar or identical signals in the future.  Each time this occurs, synaptic strengths increase in the most efficient networks for each particular type of stimuli, and this would account for a relatively quick level of neural plasticity in the brain.

The last aspect of the theory involves what Edelman called re-entrant signaling whereby a sampling of the stimuli from functionally different groups of neurons occurring at the same time leads to a form of self-organizing intelligence.  This would provide a means for explaining how we experience spatiotemporal consistency in our experience of sensory stimuli.  Basically, we would have functionally different parts of the brain, such as various maps in the visual centers that pertain to color versus others that pertain to orientation or shape, that would effectively amalgamate the two (previously segregated) regions such that they can function in parallel and thus correlate with one another producing an amalgamation of the two types of neural maps.  Once this re-entrant signaling is accomplished between higher order or higher complexity maps in the brain, such as those pertaining to value-dependent memory storage centers, language centers, and perhaps back to various sensory cortical regions, this would create an even richer level of synchronization, possibly leading to consciousness (according to the theory).  In all of the aspects of the theory, the natural selection of differentiated neuronal structures, synaptic connections and strengths and eventually that of larger re-entrant connections would be responsible for creating the parallel and correlated processes in the brain believed to be required for consciousness.  There’s been an increasing amount of support for this theory, and more evidence continues to accumulate in support of it.  In any case, it is a brilliant idea and one with a lot of promise in potentially explaining one of the most fundamental aspects of our existence.

Darwin’s Big Idea May Be the Biggest Yet

In my opinion, Darwin’s theory of evolution through natural selection was perhaps the most profound theory ever discovered.  I’d even say that it beats Einstein’s theory of Relativity because of its massive explanatory scope and carryover to other disciplines, such as cosmology, neuroscience, and even the immune system (see Edelman’s Nobel work on the immune system, where he showed how the immune system works through natural selection as well, as opposed to some type of re-programming/learning).  Based on the basic idea of natural selection, we have been able to provide a number of robust explanations pertaining to many aspects of why the universe is likely to be the way it is, how life likely began, how it evolved afterward, and it may possibly be the answer to how life eventually evolved brains capable of being conscious.  It is truly one of the most fascinating principles I’ve ever learned about and I’m honestly awe struck by its beauty, simplicity, and explanatory power.

DNA & Information: A Response to an Old ID Myth

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A common myth that goes around in Intelligent Design (creationist) circles is the idea that DNA can only degrade over time, and thus any and all mutations are claimed to be harmful and only serve to reduce “information” stored in that DNA.  The claim is specifically meant to suggest that evolution from a common ancestor is impossible by naturalistic processes because DNA wouldn’t have been able to form in the first place and/or it wouldn’t be able to grow or change to allow for speciation.  Thus, the claim implies that either an intelligent designer had to intervene and guide evolution every step of the way (by creating DNA, fixing mutations as they occurred or preventing them from happening, and then ceasing this intervention as soon as scientists began studying genetics), or it implies that all organisms must have been created all at once by an intelligent designer with DNA that was “intelligently” designed to fail and degrade over time (thus questioning the intelligence of this designer).

These claims have been refuted a number of times over the years by the scientific community with a consensus that’s been drawn from years of research in evolutionary biology among other disciplines, and the claims seem to be mostly a result of fundamental misunderstandings of biology (or intentional misrepresentations of the facts) and also the result of an improper application of information theory to biological processes.  What’s unfortunate is that these claims are still circulating around, largely because the propagators aren’t interested in reason, evidence, or anything that may threaten their beliefs in the supernatural, and so they simply repeat this non-sense to others without fact checking them and without any consideration as to whether the claims even appear to be rational or logically sound at all.

After having recently engaged in a discussion with a Christian that made this very claim (among many other unsubstantiated, faith-based assertions), I figured it would be useful to demonstrate why this claim is so easily refutable based on some simple thought experiments as well as some explanations and evidence found in the actual biological sciences.  First, let’s consider a strand of DNA with the following 12 nucleotide sequence (split into triplets for convenience):

ACT-GAC-TGA-CAG

If a random mutation occurs in this strand during replication, say, at the end of the strand, thus turning Guanine (G) to Adenine (A), then we’d have:

ACT-GAC-TGA-CAA

If another random mutation occurs in this string during replication, say, at the end of the string once again, thus turning Adenine (A) back to Guanine (G), then we’d have the original nucleotide sequence once again.  This shows how two random mutations could lead to the same original strand of genetic information, thus showing how it can lose its original information and have it re-created once again.  It’s also relevant to note that because there are 64 possible codons produced from the four available nucleotides (4^3 = 64), and since only 20 amino acids are needed to make proteins, there are actually several codons that code for any individual amino acid.

In the case given above, the complementary RNA sequence produced for the two sequences (before and after mutation) would be:

UGA-CUG-ACU-GUC (before mutation)
UGA-CUG-ACU-GUU (after mutation)

It turns out that GUC and GUU (the last triplets in these sequences) are both codons that code for the same amino acid (Valine), thus showing how a silent mutation can occur as well, where a silent mutation is one in which there are no changes to the amino acids or subsequent proteins that the sequence codes for (and thus no functional change in the organism at all).  The fact that silent mutations even exist also shows how mutations don’t necessarily result in a loss or change of information at all.  So in this case, as a result of the two mutations, the end result was no change in the information at all.  Had the two strands been different such that they actually coded for different proteins after the initial mutation, then the second mutation would have reversed this problem anyway thus re-creating the original information that was lost.  So this demonstration in itself already refutes the claim that DNA can only lose information over time, or that mutations necessarily lead to a loss of information.  All one needs are random mutations, and there will always be a chance that some information is lost and then re-created.  Furthermore, if we had started with a strand that didn’t code for any amino acid at all in the last triplet, and then the random mutation changed it such that it did code for an amino acid (such as Valine), this would be an increase in information regardless (since a new amino acid was expressed that was previously absent), although this depends on how we define information (more on that in a minute).

Now we could ask, is the mutation valuable, that is, conducive to the survival of the organism?  That would entirely depend on the internal/external environment of that organism.  If we changed the diet of the organism or the other conditions in which it lived, we could arrive at opposite conclusions.  Which goes to show that of the mutations that aren’t neutral (most mutations are neutral), those that are harmful or beneficial are often so because of the specific internal/external environment under consideration. If an organism is able to digest lactose exclusively and it undergoes a mutation that provides some novel ability of digesting sucrose at the expense of digesting lactose a little less effectively than before, this would be a harmful mutation if the organism lived in an environment with lactose as the only available sugar.  If however, the organism was already in an environment that had more sucrose than lactose available, then the mutation would obviously be beneficial for now the organism could exploit the most available food source.  This would likely lead to that mutation being naturally selected for and increasing its frequency in the gene pool of that organism’s local population.

Another thing that is often glossed over with the Intelligent Design (ID) claims about genetic information being lost is the fact that they first have to define what exactly information is necessarily before presenting the rest of their argument.  Whether or not information is gained or lost requires knowing how to measure information in the first place.  This is where other problems begin to surface with ID claims like these because they tend to leave this definition either poorly defined, ambiguous or conveniently malleable to serve the interests of their argument.  What we need is a clear and consistent definition of information, and then we need to check that the particular definition given is actually applicable to biological systems, and then we can check to see if the claim is true.  I have yet to see this actually demonstrated successfully.  I was able to avoid this problem in my example above, because no matter how information is defined, it was shown that two mutations can lead to the original nucleotide sequence (whatever amount of genetic “information” that may have been).  If the information had been lost, it was recreated, and if it wasn’t technically lost at all during the mutation, then it shows that not all mutations lead to a loss of information.

I would argue that a fairly useful and consistent way to define information in terms of its application to describing the evolving genetics of biological organisms would be to describe it as any positive correlation between the functionality that the genetic sequences code for and the attributes of the environment that the organism is contained in.  This is useful because it represents the relationship between the genes and the environment and it seems to fit in line with the most well-established models in evolutionary biology, including the fundamental concept of natural selection leading to favored genotypes.

If an organism has a genetic sequence such that it can digest lactose (as per my previous example), and it is within an environment that has a supply of lactose available, then whatever genes are responsible for that functionality are effectively a form of information that describes or represents some real aspects of the organism’s environment (sources of energy, chemical composition, etc.).  The more genes that do this, that is, the more complex and specific the correlation, the more information there is in the organism’s genome.  So for example, if we consider the aforementioned mutation that caused the organism to develop a novel ability to digest sucrose in addition to lactose, then if it is in an environment that has both lactose and sucrose, this genome has even more environmental information stored within it because of the increased correlation between that genome and the environment.  If the organism can most efficiently digest a certain proportion of lactose versus sucrose, then if this optimized proportion evolves to approach the actual proportion of sugars in the environment around that organism (e.g. 30% lactose, 70% sucrose), then once again we have an increase in the amount of environmental information contained within its genome due to the increase in specificity.

Defining information in this way allows us to measure degrees of how well-adapted a particular organism is (even if only one trait or attribute at a time) to its current environment as well as its past environment (based on what the convergent evidence suggests) and it also provides at least one way to measure how genetically complex the organism is.

So not only are the ID claims about genetic information easily refuted with the inherent nature of random mutations and natural selection, but we can also see that the claims are further refuted once we define genetic information such that it encompasses the fundamental relationship between genes and the environment they evolve in.