Curve and lattice

Daniel Dennett gave a great lecture at the recent Neukom Symposium. I recently had a chance to revisit it and check out some of the fascinating papers he cites, especially Tecumseh Fitch’s recent work on what he calls “nano-intentionality”. Today’s post will summarize some of Dennett’s talk and then introduce his attack on intrinsic intentionality. The follow-up post later this week will dig deeper, exploring Fitch’s nano-intentionality and how it brings intrinsic intentionality back from the dead.

Dennett’s talk takes as its starting point the claim that the brain is a computer, and to support this he introduces a distinction between cooperative and competitive computation—corresponding (funnily enough) to a distinction between brains and personal computers of the sort we use every day. Within a personal computer there are a number of processes which are systematically assigned resources used to accomplish goals. The brain is different: we don’t have the same kind of resource allocation protocol. Inside of us numerous, massively parallelized processes engage in heated, vicious, life-or-death competition for resources with which they may or may not accomplish their goals. According to Dennett the resource allocation paradigm of the personal computer underpins our intuitive, “default model” of computation—but the “serious, deadly” competition of cells, neurons, and higher level processes for resources in the brain is computational as well, and of great importance to the understanding of consciousness.

This represents a significant update to Dennett’s multiple-drafts/fame-in-the-brain model of consciousness. Very, very briefly—I can not do this idea justice in a single sentence—there is no one place in the brain where consciousness arises (e.g. Descartes’s pineal gland), but we become conscious of things when useful information about them is shared between various, independently operating cognitive modules (which are somewhat chaotic themselves, a la Selfridge’s pandemonium). The latest addition to this model is that all of these different units, or their component parts, are competing for resources, essentially working to starve competing processes out of existence. Much of Dennett’s talk focuses on competition between high-level, abstract units, like metaphors, turns of phrase, or memes. According to this theory, ideas compete for resources, and the ideas we express are the ones which “win”, starving their competitors into oblivion. This is where I diverge from his talk, which expands upon the computer metaphor, discussing the mind as software, ideas as virtual machines, and the implications of competition in the brain for normal and abnormal psychology, sociology, etc. I’m going to go in the opposite direction, into the low-level, micro-scale, really dirty stuff.

How might this competition in the brain really play out on lower levels, such as that of the cell, the synapse, or the neuron? Dennett refers us to Fitch’s paper on nano-intentionality. In order to make sense of nano-intentionality, we’ve got to understand normal-sized intentionality first, an area in which Dennett has been a major player.

Intentionality is a tricky subject. It is the “aboutness” of things; their directedness. One of the hot areas of contention in modern philosophy is the means by which things acquire directedness, especially when those things are minds or thoughts. There is some consensus that there are at least two basic types of intentionality: derived and intrinsic. For example, there is clearly a difference between how a word acquires its directedness—by definition and consensus, from the intentions of the word’s designers—and the way a thought seems to have a kind of intrinsic, original intentionality. We don’t need to define the meaning of a thought about something, in fact the very idea sounds ridiculous. The target of a thought seems somehow inextricably wrapped up in the form of the thought itself, long before the definitive power of language can get inside and fiddle with it. Another canonical example is the difference between a thermostat and the human heart. The thermostat is designed; it is “about” the ambient temperature by virtue of the intentions of its designer. What is the human heart about? Pumping blood? How? Where does this aboutness come from? Is it derived, or intrinsic? And, further, if there is intrinsic intentionality, how exactly does it arise? That last one is a killer.

In The Intentional Stance Dennett suggests that the whole discussion of derived/intrinsic intentionality is pretty confused, and what we are actually doing when we assess intentionality is using one of three tools: the physical, design, or intentional stances. When we take the physical stance, we are evaluating how something operates in a reductive, low-level way. When we take the design stance, we determine the possible function of an item and the reason for its design. Finally, when we take the intentional stance we are speculating about the conscious state of an item; its goals, desires, emotions, what it knows or does not know. The design stance roughly corresponds to assessment of derived intentionality. But where does intrinsic intentionality go? We can adopt the intentional stance toward a thermostat just as well as we can adopt it toward a person. It turns out we do this all the time, referring to inanimate objects as if they have belief and desire. The thermostat “knows” the ambient temperature and “wants” to keep it within a certain range.

The apparent intrinsic intentionality of mental phenomena, then, is just a useful folk-psychological metaphor we use to understand each other, and doesn’t correspond to a real, extant thing. In Fitch’s words, Dennett wants us to “bite the bullet and accept natural selection (and its products, especially ourselves) for the blind and goal-less process that it is.” (Fitch 2007) If we have some sort of intentionality, it is derived from the process of natural selection, the same kind of intentionality possessed by a thermostat. This doesn’t sit right with people, it either seems to ascribe too little to the mind or too much to the thermostat.

Fitch tries, I think successfully, to resurrect intrinsic intentionality. Dennett says he’s on board with Fitch’s approach. Details later this week.

References:

Dennett, D. How Mindless Algorithms Build Minds. May 9, 2008. Online video clip, accessed on July 21, 2008. http://neukominstitute.com/index.php/site/feature/dennett_talk/symposia/49/symposium08

Dennett, D. (1987) The Intentional Stance. MIT Press.

Fitch, W.T. (2007) Nano-Intentionality: A Defense of Intrinsic Intentionality. Biology & Philosophy, Vol. 23: 157-177.

I remember when Scholarpedia was brand new and completely empty. Today I found Daniel Dennett curates an article there on his Multiple Drafts model of consciousness. Poking around, I found a bunch of other great stuff. Here goes:

• Multiple drafts model of consciousness
• Sine-wave speech
• Synesthesia
• Cybernetics
• Models of emotion
• Magnetic resonance imaging
• Functional magnetic resonance imaging

The fMRI article makes no mention of recent pattern classification/MVPA techniques… but neither does the fMRI Wikipedia article. Still room to grow!

I’ve come to the mind-reading issue at an interesting time. Recently, Nikos Logothetis published an article called What we can do and what we cannot do with fMRI. It’s a fascinating paper, and numerous blogs picked up on it. The best summary comes from Mind Hacks and is ominously entitled The great fMRI smackdown cometh.

About the application of pattern-classification techniques (e.g. MVPA) to fMRI data, Logothetis says the following:

In humans, fMRI is used routinely not just to study sensory processing or control of action, but also to draw provocative conclusions about the neural mechanisms of cognitive capacities, ranging from recognition and memory to pondering ethical dilemmas. Its popular fascination is reflected in countless articles in the press speculating on potential applications, and seeming to indicate that with fMRI we can read minds better than direct tests of behaviour itself. Unsurprisingly, criticism has been just as vigorous, both among scientists and the public. In fact, fMRI is not and will never be a mind reader, as some of the proponents of decoding-based methods suggest, nor is it a worthless and non-informative ‘neophrenology’ that is condemned to fail, as has been occasionally argued.

A lot of this has to do with how one defines “mind-reading,” and whether or not what’s being read is low-level (e.g. the orientation of a visual stimulus) or high level (e.g. the presence of a mental state). There are numerous examples of papers which exploit fMRI data to erroneously support what I would call very high-level conclusions about human behavior, such as whether or not subjects have anxiety regarding Mitt Romney. (This and many other such studies are handily refuted by The Neurocritic, who also had some interesting commentary on Logothetis’s paper.) However, I think the study by Kay et al. on the identification of novel, real-world images suggests that, at lest for low-level, sensory information, fMRI might be quite the mind-reader indeed. There’s a good editorial in Science by Greg Miller which has, I think, a pretty balanced view of things, resorting to neither abject pessimism nor fanciful speculation.

The risk I think Logothetis and others are pointing out is that inferring a mental state from brain imaging data puts one on pretty shakey ground. I think this is especially true for studies which don’t take into account distributed patterns of brain activation, but even pattern-classification based studies need to be very careful about making the leap to state attribution. Just because we can say, in a sense, that there is a hierarchy inside a rhesus macaque’s brain that differentiates between cats and dogs, we can’t necessarily say that the monkey knows what a dog is and that it has a degree of similarity to a cat equal to X. It’s possible that the observed hierarchical organization might be completely unconscious.

Logothetis gets specific about his reservations regarding pattern classification techniques:

Such multivariate analyses or pattern-classification-based techniques (decoding techniques) can often detect small differences between two task or stimulus conditions—differences that are not picked up by conventional univariate methods. However, this is not equivalent to saying that they unequivocally reveal the neural mechanisms underlying the activation patterns.

And he’s right. But the strength of pattern classification of fMRI data is not that it can reveal underlying neural mechanisms, nor that it can simply detect small differences between patterns of activation (cheating past the spatial resolution limit, basically), but that it allows us to evaluate the similarity of patterns of activiation based on a variety of arbitrary criterea. It’s not cool because it gets us closer to some underlying machinery, it’s cool because it lets us talk about similarity and difference of patterns of activation in a quantitative way. This has surprising power.

References:

• Logothetis, N.K. (2008) What we can do and what we cannot do with fMRI. Nature 453:869-78.
• Kay, K.N.; Naselaris, T; Prenger, R.J.; Gallant, J.L. (2008) Identifying natural images from human brain activity. Nature 452: 352-356.

A fantastic YouTube video has been making the rounds. It’s a cockatoo named Snowball dancing to The Backstreet Boys. It’s all kinds of awesome, but it’s also one of, if not the first, totally conclusive demonstration of an animal enthusiastically entraining its movements to rhythmic sound. Here’s the bird:

Aniruddh Patel got on the case, did some experiments and wrote a paper on the cockatoo. He concludes that, while it’s confined to a relatively small range of tempos and isn’t particularly reliable, the dancing bird is basically for real. The paper is a great read. Its final sentence is oddly touching:

It will be interesting to determine whether the range of tempi to which Snowball can synchronize is expanded when dancing with a partner.

Here’s the paper:

• Patel, A.D., Iversen, J.R., Bregman, M.B., Schulz, I., & Schulz, C. (in press). Investigating the human-specificity of synchronization to music. In: Proceedings of the 10th International Conference on Music Perception & Cognition (ICMPC10), August 2008, Sapporo, Japan. M. Adachi et al. (Eds.), Adelaide: Causal Productions.

More videos on Patel’s publications page.

Obviously MVPA has a really interesting future. As neuroimaging technology improves spatial and temporal resolution, we’ll get finer distinctions and more detailed pictures of neural representation. Norman et al. have a fantastic overview of MVPA, including numerous interesting questions and speculations about its future, called Beyond mind-reading: multi-voxel pattern analysis of fMRI data.

Some of my thoughts and questions about MVPA follow.

We can use MVPA to hierarchically cluster patterns of neural activation into groups which seem to match up pretty well with our common-sense understanding of categories in the world. Does the MVPA-based discovery of category structure imply knowledge on the part of the subject? Because we can see that a rhesus macaque’s brain responds with a distinct and recognizable pattern of activation when shown various pictures of cars, can we say that it has some knowledge of what a car is? How well correlated are MVPA-derived categorical structures with the reports of subjects regarding their own ideas about categories?

It may be possible to learn something about the acquisition of category discrimination from MVPA. One can learn to perceive things which initially appear imperceptible. It’d be fascinating to see how the categorical perception of different species evolved in terms of patterns of neural activation in a biologist-in-training; or to watch the ability to discriminate chords and their functional associations develop in a music student. Similarly, MVPA may tell us a lot about neural representation and memory.

Kay et al. suggest that it might be possible to reconstruct what a person is seeing from brain activity data alone (Kay et al. 2008). This may shake up debate about the nature of representation within the brain. All of these MVPA studies seem to vindicate a connectionist model of representation, where distributed patterns of activation across a neural network account for the content of mental states.

Most of the MVPA work I’ve discussed focuses on visual experiences as they occur to the subject in the moment. Presumably the brain contains some ephemeral, basically lossless representation of data being transmitted from the retina. Kay et al. used spatial location, orientation, and spatial frequency of as the dimensions of the analysis space in which they compared the similarity of patterns of activation (Kay et al. 2008). That these variables are useful in reconstructing information about the sensory experience of subjects does not necessarily mean that the subjects have conscious access to them. However, if the project of complete reconstruction of visual input from brain activity data is successful, it would seem likely that the parameterization used was at least a good approximation of the terms in which the part of the brain in question utilizes or perhaps makes available relevant information. If a successful model of the perception of syntax in natural language were created using MVPA techniques, it might provide some significant insight into how the brain acts as an information processor.

I think MVPA may also make some progress towards filling the “explanatory gap” claimed by some philosophers as an obstruction to the scientific understanding of the phenomenal nature of experience. For example, if we can use MVPA to examine the relative similarity of massively distributed—perhaps even whole-brain—activation patterns, and classify them in terms of qualitative, phenomenal reports from subjects, we may find a convincing neural correlate for “what it is like” to experience some fairly tricky things, e.g. what it is like to be in love, or to tell a lie. Having a love detector doesn’t necessarily explain why being in love feels the way it does, but comparing that pattern of activation to others, both similar and drastically dissimilar, might have some more explanatory power. Quite fancifully, for example, perhaps the pattern of activation which correlates with being in love is somewhere on a continuum between being hungry and looking at flowers. This is a little better than “it feels that way because it does.”

References:

• Norman, K.A.; Polyn, S.M.; Detre, G.J.; Haxby, J.V. (2006) Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Science Vol. 10, No. 9: 424-430.
• Kay, K.N.; Naselaris, T; Prenger, R.J.; Gallant, J.L. (2008) Identifying natural images from human brain activity. Nature 452: 352-356.