A great deal of human unhappiness and ineffectiveness is rooted in what Buddhists call "attachment"… roughly definable as an exaggerated desire not to be separated from someone, something, some idea, some feeling, etc.
Buddhists view attachment as ensuing largely from a lack of recognition of the oneness of all things. If all things are one, then they can't really be separated anyway, so there's no reason to actively resist separation from some person or thing.
Zen teacher John Daido Loori put it as follows: "[A]ccording to the Buddhist point of view, nonattachment is exactly the opposite of separation. You need two things in order to have attachment: the thing you’re attaching to, and the person who’s attaching. In nonattachment, on the other hand, there’s unity. There’s unity because there’s nothing to attach to. If you have unified with the whole universe, there’s nothing outside of you, so the notion of attachment becomes absurd. Who will attach to what?"
That way of thinking makes plenty of sense to me (in a trans-sensible sort of way!). However, I think one can also take a more prosaic and less cosmic, but quite compatible, approach to the attachment phenomenon...
In this blog post I will present a simple neural and cognitive model of attachment and its opposite.
I want to clarify that I'm not positing that the subjective experiences of attachment or non-attachment "reduce" to the neural/cognitive mechanisms I'll describe here -- I am not: not in a physics sense nor in a basic ontological sense. I prefer to think about the ideas presented here as pertaining to the "neural/cognitive correlates of the experiences of attachment and non-attachment."
After presenting my model of attachment and non-attachment, I will dig into AGI theory for a bit, and explain why I think advanced AGI systems would suffer from the attachment phenomenon far less than human beings. Or in other words:
- Enlightening human minds is, in practice, a chancy and difficult matter ...
- Enlightening AGI minds may merely be a matter of reasonable cognitive architecture design...
Hebbian LearningI will start with some quasi-biological speculation. What might be the neural roots of attachment?
Let's begin with the concept of Hebbian learning, an idea from neural network theory. Hebbian learning has to do with a network in which neurons are joined by weighted synapses. The larger the positive weight on the synapse between neuron N1 and neuron N2, the more of N1's activity will spill over to N2. The larger the negative weight on the synapse between neuron N1 and neuron N2, the more strongly N1's activity will inhibit activity in N2.
In basic Hebbian learning the following two rules obtain:
- If N1 and N2 are active at the same time, the link (synapse) between N1 and N2 has its weight increased
- If N1 is active but N2 is not, or N2 is active but N1 is not, the link between N1 and N2 has its weight decreased
- pairs of neurons that are frequently simultaneously active will be joined by synapses with high positive weights (so when one of them becomes active, the other will tend to be)
- pairs of neurons that are generally active at different times, will be joined by synapses with very negative weights (so when one of them becomes active, the other will tend not to be active)
One of the interesting consequences of Hebbian learning is the formation of "cell assemblies" -- groups of neurons that are richly interconnected via high-positive-weight synapses, and hence tend to become activated as a whole. Donald Hebb, who came up with the idea of Hebbian learning in the late 1940s, suggested that ideas in the mind are represented by neuronal cell assemblies in the brain. 60-odd years later, this still seems a sensible idea, and there is significant evidence in its favor. The emergence of nonlinear dynamics has deepened the theory somewhat; it now seems likely that the cell assemblies representing ideas, memories and feelings in the human mind are associated with complex dynamical phenomena like strange attractors and strange transients.
Hebbian learning is a conceptual and mathematical model, but the basic idea is reflected in the brain in the form of long-term potentiation of synapses. It may be found to be reflected in the brain in other ways as well, e.g. as our understanding of the roles of glia in memory increases.
So what does all this have to do with attachment?
Let's explore this via a simple example....
Suppose that Bob's girlfriend has left him. He misses her.
While his girlfriend was with him, he woke up every morning, found her in the bed next to him, and put his arm around her. He liked that. The association between "wake up" and "put arm around girlfriend" become strong. In Hebbian learning terms, the neurons in the "wake up" cell assembly got strongly positively weighted synapses to the neurons in the "put arm around girlfriend" cell assembly. A larger assembly of the form " wake up and put arm around girlfriend" formed, linking together the two smaller assemblies.
Now, after the girlfriend left, what happens in Bob's brain?
According to straightforward Hebbian learning, the association between "wake up" and "put arm around girlfriend" should gradually decrease, until eventually there is no longer a positive weight between the two cell assemblies. The larger assembly should fragment, leaving the "wake up" and "put arm around girlfriend" assemblies separate; and at the same time the "put arm around girlfriend assembly should start to dissipate, as it no longer gets reinforcement via experience.
But this may not actually be what happens. Suppose, for example, that Bob spends a lot of time thinking about his girlfriend (now his ex-girlfriend). Suppose he lies awake at night in bed and dwells on the fact that he's the only one there. In that case, the "wake up" cell assembly and the "put arm around girlfriend" assembly will be activated simultaneously a lot, and will retain their positive association.
What's happening here is that Bob's emotions are causing a cell assembly to remain highly active -- in a case where the external world, in the absence of these emotions, would drive the assembly to dwindle.
This, I suggest, is the key neural correlate of the psychological phenomenon of attachment. Attachment occurs -- neurally speaking -- when there is a circuit binding a cell assembly to the brain's emotional center, in such a way that emotion keeps the circuit whole and flourishing even though otherwise it would dissipate.
Ideally, a mind with amazing powers of self-control would delete the association between "wake up" and "put arm around girlfriend" as soon as the relationship with the girlfriend ended. However, a mind without emotional interference in its Hebbian network dynamics would do the next best thing: the association would gradually dwindle over time. For a typical human mind, on the other hand, the coupling of the "wake up and put arm around girl" network with the mind's emotional centers, will cause this association to persist a long time after simple Hebbian dynamics would have caused it to dwindle.
The example of Bob and his girlfriend is somewhat simplistic of course, and I chose it largely because of its simplicity. A more pernicious example is when a mind becomes attached to an aspect of its model of itself. For example, someone who derives pleasure from being correct (say, because someone praises them for being correct), may then become emotionally attached to the idea of themselves as someone who knows the right answer. They may then have trouble letting go of this idea, even in contexts where the genuinely do not know the answer, and would be better off to admit this to themselves as well as to others. Becoming attached to inaccurate models of oneself causes all sorts of problems, including the creation of compoundedly, increasingly inaccurate self-models, as well as self-defeating behaviors.
A Semantic Network PerspectiveNow let's take a leap from modeling brain to modeling mind. I've been talking here about neural networks and brains -- but the core idea presented above could actually be relevant to minds with very different biological underpinnings. It could also be relevant if Hebbian learning turns out to be a terrible model of the brain.
Regardless of how the brain works, one can model the mind as a network of nodes, connected by weighted links. The nodes represent concepts, actions, and perceptions in the mind; the links represent relationships between these, including associative relationships. The "semantic networks" often used in AI are a simplistic version of this kind of model, but one can articulate much richer versions, capable of capturing all documented aspects of human cognition.
This sort of model of the mind has been instrumental in my own thinking about AI and cognitive science. I have articulated a specific network model of minds called SMEPH, Self-Modifying Evolving Probabilistic Hypergraphs. I won't go into the details of that here, though -- I mention it only to point out that the model of attachment and non-attachment here may be interpreted two ways: as a neural model, and as a cognitive model. These interpretations are related but far from identical.
COEX SystemsThe model of attachment presented here relates closely to Stanislav Grof's notion of a "COEX (Condensed Experience) system." Roughly, a COEX is a set of related experiences organized around a powerful emotional center. The emotional center is generally one or a few highly emotionally impactful experiences. The various experiences in the COEX, all reinforce each other, keeping each other energetic and relevant to the mind.
In a Hebbian perspective, a COEX system would be modeled as a system of cell assemblies, each representing a certain episodic memory, linked together via positive, reinforcing connections. The memories in the COEX stimulate powerful emotions, and these emotions reinforce the memories -- thus maintaining a powerful, ongoing attachment to the memories.
But Why?I have said that "Attachment occurs -- neurally speaking -- when there is a circuit binding a cell assembly to the brain's emotional center, in such a way that emotion keeps the circuit whole and flourishing even though otherwise it would dissipate."
But why would the human mind be that way?
Emotions, basically, are system-wide (body and mind inclusive) reactions to events regarding system goals/desires/aspirations. We are happy when we are achieving goals better and better; especially happy when we're doing so better than expected. We are sad when we're making progress worse than expected. We're angry when someone or something stands in the way of our goal fulfillment. We feel pity when we use our mind's power of analogy to feel someone ELSE's frustration at their inability to fulfill their goals….
So, it's only natural that the emotion-bearing cell assemblies and attractors, wind up getting richly interlinked with other cell assemblies and attractors.
Let's say the "wake up", "put arm around girlfriend" and "happy emotion" assemblies all get richly interlinked. Then there are multiple reverberating circuits joining all these assemblies. So even when the girlfriend goes away, these circuits will keep on cycling.
This won't be such a problem for an animal like a dog -- because in a dog, the associational cortex is not such a big part of its neural processing -- immediate perceptions and actions tend to hold sway. But a larger and more complex associational cortex brings all sort of new possibilities with it, including the possibilities for more complex and persistent forms of attachment!
The Brains of the EnlightenedIn recent years there has been an increasing amount of work studying the brains of experienced meditators, and of people capable of various "enlightened" states of consciousness. One of the interesting findings here is that such individuals have unusual patterns activity in a part of the brain called the posterior cingulate cortex (PCC).
The PCC does many different things, so the significance of this finding is not fully clear, and may be multidimensional. However, it is noteworthy that ONE thing the PCC does is to regulate the interaction between memory and emotion.
The neural/cognitive theory presented above leads directly to the prediction that, if there's a key difference between the brains of attachment-prone versus non-attached people, it should indeed have to do with the interaction between memory and emotion.
I thus submit the hypothesis that ... ONE of the significant factors the neurodynamics of enlightened states is: A change in the function of the PCC, so that in relatively non-attached people, emotion plays a significantly lesser role in the maintenance and dissolution of cell assemblies and associated attractors representing memories.
Toward Enlightened Digital MindsThis line of thinking, if correct, suggests that it may be relatively straightforward to create digital minds without the persistent phenomenon of attachment that characterizes ordinary human minds.
First of all, a digital mind -- if its design is not slavishly tied to that of the human brain -- may be able to explicitly remove associations and other inferences that are no longer rationally judged as relevant. In other words, when a well-designed robot's girlfriend leaves him, he will just be able to remove any newly irrelevant associations from his brain, so his post-breakup malaise will be brief or nonexistent.
Secondly, even if a digital mind lacks this level of deliberative, rational self-modification, there is no reason it needs to have the same level of coupling of emotion and memory as human beings have. From an AI software design perspective, it is quite simple to make the coupling of memory and emotion optional, to a much greater degree than the human brain does…
The interaction between memory and emotion is valuable for many purposes. There is intelligence in emotional response, sometimes. But there is no need, from a cognitive architecture perspective, for the formation and dissolution of memory attractors to be so inextricably tied to emotion.
Attachment in OpenCogTo explore the notion of attachment in digital minds more concretely, let's take a specific AGI design and muse on it in detail. This exercise will also help us better understand why human minds get so extremely wrapped up in attachment as they do.
What if Bob's mind were a mature, fully functional OpenCog AGI engine, instead of a human?
(NOTE: to understand this example more thoroughly, take an hour or two and read the overview of the CogPrime cognitive architecture being gradually implemented in the OpenCog open-source AI framework.... Or, if you don't have time for that, just skim through the following instead, and you'll probably grok something!)
Then there would be an explicit link in OpenCog's Atomspace knowledge store, such as
(NOTE: the actual nodes in the OpenCog knowledge base probably wouldn't have such evocative names, as they would be learned via experience -- but the basic structure would be like this.)
There would also be a bunch of HebbianLinks, similar to synapses in a neural network with Hebbian learning, going between various nodes related to wake_up and put_arm_around_girlfriend, and various nodes related to Happy.
When the girlfriend left, human-like attachment dynamics would likely be present, related to the HebbianLinks involved. But the probabilistic truth value on the PredictiveImplicationLink would decrease. It would decrease gradually via experience; or might be decreased very rapidly via reasoning (i.e. the AI could rationally infer that since the girlfriend is gone, putting its arm around her is not likely to be associated with happiness anymore).
The question then is: How rapidly and thoroughgoingly would this change in the OpenCog system's explicit knowledge (the PredictiveImplicationLink) cause a corresponding change in the system's implicit knowledge (the HebbianLinks between the assemblies or "maps" of nodes corresponding to "wake-up", "put_arm_around_girlfriend", and "Happy")?
Suppose the OpenCog system has a process that: Whenever the truth value of a link changes dramatically, puts the link in the system's AttentionalFocus (the latter being the set of nodes and links in the system's memory that have the highest Short Term Importance (STI) values, and thus get the most attention from the system's cognitive processes). Putting the link in the AttentionalFocus, will cause STI to be spread to the nodes that the link connects, and to other nodes related to these. This will then cause the HebbianLinks among these nodes to have their weights updated. And this will gradually get rid of assemblies and attractors that are no longer relevant.
So this process that triggers attention based on truth value change, will serve directly to combat attachment.
Why Human Brains Get More Attached than a Smart OpenCog WouldIn the human mind/brain, explicit knowledge is purely emergent from implicit knowledge -- different from the situation with OpenCog where the two kinds of knowledge exist in parallel, dynamically coupled together. Obviously, given this, there must be neural mechanisms for changes in emergent explicit knowledge (derived via reasoning, for example) to cause changes in the corresponding underlying implicit knowledge. But these mechanisms are apparently more complex and harder to control than the corresponding ones in OpenCog.
Evolutionarily, the reason for the difficulty the human brain has in coordinating explicit and implicit knowledge, seems to be that the brain's mechanisms mostly evolved in the context of brains with a lot less associational cortex than the human brain has. In the context of a dog or ape brain, a sloppy mechanism for coordinating explicit and implicit knowledge may not be so troublesome. In the context of a human brain, this sloppy mechanism leads to various problems, such as excessive attachment to ideas, people, feelings, etc. And these problems can be worked around, to a large extent, via difficult and time-consuming practices like meditation, psychotherapy, etc. Perhaps future technologies like brain implants will enable the circumvention of excessive attachment and other problematic aspects of the human mind/brain architecture, without the need for as much effort as uncertainty as is involved in current mind-improving disciplines....