Artificial Intelligence and Conscious Attention--Jesse Prinz's AIR theory of Consciousness

Jesse Prinz has argued for that consciousness is best understood as mid-level attention.

Is Attention Necessary and Sufficient for Consciousness?

The Conscious Brain: How Attention Engenders Experience

Consciousness, Prinz argues, is best understood as mid-level attention.  

Low level representers in the brain are neurons that perform simple discrimination tasks such as edge or color detection.  They are activated early on in the process of stimuli from the sensory periphery. 

(a poorly taken, copyright violating picture from Michael Gazzaniga's Cognitive Neuroscience textbook.)

The activation of a horizontal edge detector, by itself, doesn’t constitute organized awareness of the object, or even the edge. 

Neuron complexes in human brains are also capable of very high level, abstract representation.  In a famous study, “Invariant visual representation by single neurons in the humanbrain,” Quiroga, Reddy, Kreiman, Kock, and Fried, they discovered the so-called Halle Berry neuron with some sensitive detectors inserted into different regions of the brains of some test subjects.  This neuron’s activity was correlated with activation patterns for a wide range of Halle Berry images. 

What’s really interesting here is that this neuron became active with quite varied photos and line drawings of Halle Berry, from different angles, in different lighting, in a Cat Woman costume, and even, remarkably, in response to the text “Halle Berry.”   That is, this neuron plays a role in the firing patterns for a highly abstract concept of Halle Berry. 

Prinz is interested in consciousness conceived as mid-level representational attention that lies somewhere between these two extremes.  “Consciousness is intermediate level representation.  Consciousness represents whole objects, rich with surface details, located in depth, and presented from a particular point of view.”  During the real time moments of phenomenal awareness, various representations come to take up our attention in the visual field.  Prinz argues that, “Consciousness arises when we attend, and attention makes information available to working memory. Consciousness does not depend on storage in working memory, and, indeed, the states we are conscious of cannot be adequately stored.”

When you look at a Necker cure, you can first be aware of the lower left square as the leading face.  Then you can switch your awareness to seeing the upper right square as the leading face.  So you attention has shifted from one representation to another. 

That is the level at which Prinz is located the mercurial notion of consciousness, and trying to develop a predictive theory based on the empirical evidence.  And Prinz goes to some lengths to argue that consciousness in this sense is not what’s moved into working memory, it’s not the contents necessarily that have become available to the global workspace such as when they are stored for later access.  These contents may or may not be accessible later for recall.  But at the moment they are the contents of mind, part of the flow and movement of attention. 

Here I’m not interested in the question of whether Prinz provides us with the best theory of human consciousness, but I am interested in what light his view can shed on the AI project.  I’m particularly interesting in Prinz here because it’s arguable that we already have artificial systems that are capable, more or less, of doing the low level and the high level representations described above.  Edge detection, color detection, simple feature detection in a “visual” field are relatively simple tasks for machines.  And processing at a high level of conceptual abstraction has been accomplished in some cases.  IBM’s Jeopardy playing system Watson successfully answered clues such as, “To push one of these paper products is to stretch established limits,” answer:  envelope.  “Tickets aren’t needed for this “event,” a black hole’s boundary from which matter can’t escape,” answer:  event horizon.  “A thief, or the bent part of an arm,” answer:  crook.  Even Google search algorithms do a remarkable job of divining the intentions behind our searches, excluding thousands of possible interpretations of our search strings that would be accurate to the letters, but have nothing to do with what we are interested in.

So think about this.  Simple feature detection isn’t a problem.  And we are on our way to some different kinds of high level conceptual abstraction.  Long term storage for further analysis also isn’t a problem for machines.  That’s one of the things that machines already do better than us.  But what Prinz has put his finger on is the ephemeral movement of attention from moment to moment in awareness.  During the course of writing this piece, I’ve been multi-tasking, which I shouldn’t have.  I’ve been answering emails, sorting out calendar scheduling, making plans to get kids from school, and so on.  And now I’m trying to recall what all I’ve been thinking about over the last hour.  Lots of it is available to me to now.  But there were, no doubt, a lot of mental contents, a lot of random thoughts, that came and went without leaving much of a trace.  I say, “no doubt,” because if they didn’t go into memory, if they didn’t become targets of substantial focus, then even though I had them then I won’t be able to bring them back now.  And I say, “no doubt,” because when I am attending to my conscious experience now, from moment to moment, and I’m really concentrating on just this point, I realize that I’m aware of the feeling of the clicking keyboard keys under my fingers, then I notice the music I’ve got playing in the background, then I glance at my email tab, and so on.  That is, my moments are filled with miscellaneous contents.  I’ve mode those particular ones into a bigger deal in my brain because I just wrote about them in a blog post.  But lots of our conscious lives, maybe most, those contents come and go, like hummingbirds flitting in and out of the scene.  And once they are gone, they are gone.

Now we can ask the questions:  Do we want an AI to have that?  Do we need an AI to have that?  Would it serve any purpose? 

Bottom Up Attention

That capacity in us served an evolutionary purpose.  At any given time, there are countless zombie agents, low level neuronal complexes, that are doing discriminatory work on information from the sensory periphery and from other neural structures.  The outputs of those discriminators may or may not end up being the subject of conscious attention.  In many cases, those contents become the focus of attention from the bottom up.  So lower level system deems the content important enough to call your attention to it, as it were.  So when your car doesn’t sound right when it’s starting up, or when a friend’s face reveals that he’s emotionally troubled it jumps to our attention.  Your brain is adept at scanning your environment for causes for alarm and then thrusting them into the spotlight of attention for action.  

Top Down Attention

But we are able to direct the spotlight as well.  We can focus our attention, sustain mental awareness on a task or some phenomena, to suss out details, make extended plans, anticipate problems, and model out possible future scenarios and so on.  You can go to work finding Waldo:

Chapter 14 of Christof Koch's The Question for Consciousness: A Neurobiological Approach

gives a more detailed account of the evolutionary functions of consciousness. 

Given what we saw above about the difference in Prinz between conscious attention and short and long term memory, we can see conscious attention can be seen as a sort of screening process.  A lot of ordinary phenomenal consciousness is the result of low level monitoring systems crossing a minimal threshold of concern.  This, right here is important enough to take a closer look at.  

  

Part of the reason that the window of our conscious attention is temporally brief and spatially finite is that resources are limited.  Resources were limited when evolution was building the system.  It’s kludged up from parts and systems that we re-adapted from other functions.  There was no long view, or deliberate planning on the process.  Just the slow pruning of mutation branches on the evolutionary tree.  And it modifies the gene pool according to the rates at which organisms, equipped as they are, manage to meet survival challenges. 

Kludge:  Consider to different ways to work on a car.  You could take it apart, analyze the systems, plan, make modifications, build new parts, and then reassemble the car.  While the car is taken apart and while you are building new parts, it doesn’t function.  It’s just a pile of parts on the shop floor. 

But imagine that the car is in a race, and there’s a bin of simple replacement parts on board, some only slightly different than the ones currently in the car, and modifications to the car must be made while the car is racing around the track with the other cars.  The car has to keep going at all times, or it’s out of the race for good.  Furthermore, no one gets to choose which parts get pulled out of the bin and put into the car.  That’s a kludge. 

Resources are also limited because evolution built a system that does triage.  The cognitive systems just have to be good enough to keep the organism alive long enough to bear its young, and possibly make a positive contribution toward their survival.  The monitoring systems that are keeping track of its environment just need to catch the deadly threats, and catch them only far enough in advance to save its ass.  It’s not allowed the luxury of long term, substantial contemplation of one topic or many to the exclusion of all others.  Furthermore, calories are limited.  Only so many can be scrounged up during the course of the day.  So only so many can be dedicated to the relatively costly expenditure of billions of active neural cells. 

The evolutionary functions of consciousness for us give us some insight into whether it might be useful or dangerous in an AI.  First, AIs can be better planned, better designed than evolution’s brains.  An AI need not be confined to triage functions, although we can imagine modeling human brains to some extent and using them to keep watch on bigger, more complex systems where more can go wrong than human operators could keep track of.  An AI might run an airport better, or a subway system, or a power grid, where hundreds or thousands or more subsystems need to be monitored for problems.  The success of self-driving Google cars already suggest what could be possible with wide spread implementation on the street and highway systems.  So bottom up indicated monitoring could clearly be useful in an AI system. 

Top down, executive directed control of the spotlight of attention, and the deliberate investment of processing resources into a representational complex with longer term planning and goal directed activity driving the attention could clearly be useful for an AI system too.  “Hal, we want you to find a cure for cancer.  Here are several hundred thousand journal articles.” 

The looming question, of course, is what about the dangers of building mid-level attention into an AI?  Bostrom’s Superintelligence has been looming in the back of my mind through this whole post.  It’s a big topic.  I’ll save that for a future post, or 3 or 10 or 25.