“The Singing Neanderthal”, as its title suggests, is about the role of music in the evolutionary development of the modern human. We all seem to be born with an element of music in our heart, and Mithin seeks to understand why this is so, and how music is related to, and part of the development of, language. Mithin argues that elements of music developed in various later hominids as a form of primitive communication², but separated from language in homo sapiens when music became specialised to the communication of emotion and language to more precise actions and concepts.

The book ‘explains’ various known musical facts, including the universality of music across cultures and the fact that most of us do not have perfect pitch … even though young babies do (p77). The hard facts of how things were for humans or related species tens or hundreds of thousands of years ago are sparse, so there is inevitably an element of speculation in Mithin’s theories, but he shows how many, otherwise disparate pieces of evidence from palaeontology, psychology and musicology make sense given the centrality of music.

Whether or not you accept Mithin’s thesis, the first part of the book provides a wide ranging review of current knowledge about the human psychology of music. Coincidentally, while reading the book, there was an article in the Independent reporting on evidence for the importance of music therapy in dealing with depression and aiding the rehabilitation of stroke victims³, reinforcing messages from Mithin’s review.

The topic of “The Singing Neanderthal” is particularly close to my own heart as my first personal forays into evolutionary psychology (long before I knew the term, or discovered Cosmides and Tooby’s work), was in attempting to make sense of human limits to delays and rhythm.

Those who have been to my lectures on time since the mid 1990s will recall being asked to first clap in time and then swing their legs ever faster … sometimes until they fall over! The reason for this is to demonstrate the fact that we cannot keep beats much slower than one per second⁴, and then explain this in terms of our need for a mental ‘beat keeper’ for walking and running. The leg shaking is to show how our legs, as a simple pendulum, have a natural frequency of around 1Hz, hence determining our slowest walk and hence need for rhythm.

Mithin likewise points to walking and running as crucial in the development of rhythm, in particular the additional demands of bipedal motion (p150). Rhythm, he argues, is not just about music, but also a shared skill needed for turn-taking in conversation (p17), and for emotional bonding.

In just the last few weeks, at the HCI conference in Newcastle, I learnt that entrainment, when we keep time with others, is a rare skill amongst animals, almost uniquely human. Mithin also notes this (p206), with exceptions, in particular one species of frog, where the males gather in groups to sing/croak in synchrony. One suggested reason for this is that the louder sound can attract females from a larger distance. This cooperative behaviour of course acts against each frog’s own interest to ‘get the girl’ so they also seek to out-perform each other when a female frog arrives. Mithin imagines that similar pressures may have sparked early hominid music making. As well as the fact that synchrony makes the frogs louder and so easy to hear, I wonder whether the discerning female frogs also realise that if they go to a frog choir they get to chose amongst them, whereas if they follow a single frog croak they get stuck with the frog they find; a form of frog speed dating?

Mithin also suggests that the human ability to synchronise rhythm is about ‘boundary loss’ seeing oneself less as an individual and more as part of a group, important for early humans about to engage in risky collaborative hunting expeditions. He cites evidence of this from the psychology of music, anthropology, and it is part of many people’s personal experience, for example, in a football crowd, or Last Night at the Proms.

This reminds me of the experiments where a rubber hand is touched in time with touching a person’s real hand; after a while the subject starts to feel as if the rubber hand is his or her own hand. Effectively our brain assumes that this thing that correlates with feeling must be part of oneself⁵. Maybe a similar thing happens in choral singing, I voluntarily make a sound and simultaneously everyone makes the sound, so it is as if the whole choir is an extension of my own body?

Part of the neurological evidence for the importance of group music making concerns the production of oxytocin. In experiments on female prairie voles that have had oxytocin production inhibited, they engage in sex as freely as normal voles, but fail to pair bond (p217). The implication is that oxytocin’s role in bonding applies equally to social groups. While this explains a mechanism by which collaborative rhythmic activities create ‘boundary loss’, it doesn’t explain why oxytocin is created through rhythmic activity in the first place. I wonder if this is perhaps to do with bipedalism and the need for synchronised movement during face-to-face copulation, which would explain why humans can do synchronised rhythms whereas apes cannot. That is, rhythmic movement and oxytocin production become associated for sexual reasons and then this generalises to the social domain. Think again of that chanting football crowd?

I should note that Mithin also discusses at length the use of music in bonding with infants, as anyone who has sung to a baby knows, so this offers an alternative route to rhythm & bonding … but not one that is particular to humans, so I will stick with my hypothesis

Sexual selection is a strong theme in the book, the kind of runaway selection that leads to the peacock tail. Changing lifestyles of early humans, in particular longer periods looking after immature young, led to a greater degree of female control in the selection of partners. As human size came close to the physical limits of the environment (p185), Mithin suggests that other qualities had to be used by females to choose their mate, notably male singing and dance – prehistoric Saturday Night Fever.

As one evidence for female mate choice, Mithin points to the overly symmetric nature of hand axes and imagines hopeful males demonstrating their dexterity by knapping ever more perfect axes in front of admiring females (p188). However, this brings to mind Calvin’s “Ascent of Mind“, which argues that these symmetric, ovoid axes were used like a discus, thrown into the midst of a herd of prey to bring one down. The two theories for axe shape are not incompatible. Calvin suggests that the complex physical coordination required by axe throwing would have driven general brain development. In fact these forms of coordination, are not so far from those needed for musical movement, and indeed expert flint knapping, so maybe it was this skills that were demonstrated by the shaping of axes beyond that immediately necessary for purpose.

Mithin’s description of the musical nature of mother-child interactions also brought to mind Broomhall’s “Eternal Child“. Broomhall ‘s central thesis is that humans are effectively in a sort of arrested development with many features, not least our near nakedness, characteristic of infants. Although it was not one of the points Broomhall makes, his arguments made sense to me in terms of the mental flexibility that characterises childhood, and the way this is necessary for advanced human innovation; I am always encouraging students to think in a more childlike way. If Broomhall’s theories were correct, then this would help explain how some of the music making more characteristic of mother-infant interactions become generalised to adult social interactions.

I do notice an element of mutual debunking amongst those writing about richer cognitive aspects of early human and hominid development. I guess a common trait in disciplines when evidence is thin, and theories have to fill a lot of blanks. So maybe Mithin, Calvin and Broomhall would not welcome me bringing their respective contributions together! However, as in other areas where data is necessarily scant (such as sub-atomic physics), one does feel a developing level of methodological rigour, and the fact that these quite different theoretical approaches have points of connection, does suggest that a deeper understanding of early human cognition, while not yet definitive, is developing.

In summary, and as part of this wider unfolding story, “The Singing Neanderthal” is an engaging and entertaining book to read whether you are interested in the psychological and social impact of music itself, or the development of the human mind.

… and I have another of Mithin’s books in the birthday pile, so looking forward to that too!

1. See particularly my essay on the role of imagination in bringing together our different forms of ‘specialised intelligence’. “The Prehistory of the Mind” highlighted the importance of this ‘cognitive fluidity’, linking social, natural and technological thought, but lays this largely in the realm of language. I would suggest that imagination also has this role, creating a sort of ‘virtual world’ on which different specialised cognitive modules can act (see “imagination and rationality“). [back]
2. He calls this musical communication system Hmmmm in its early form – Holistic, Multiple-Modal, Manipulative and Musical, p138 – and later Hmmmmm – Holistic, Multiple-Modal, Manipulative, Musical and Mimetic, p221. [back]
3. “NHS urged to pay for music therapy to cure depression“, Nina Lakhani, The Independent, Monday, 1 August 2011 [back]
4. Professional conductors say 40 beats per minute is the slowest reliable beat without counting between beats. [back]
5. See also my previous essay on “driving as a cyborg experience“. [back]

Last night I both learnt something new about language and cognition, and also developed a new trick for creativity!

In the dream in question I was in a meeting. I know, a sad topic for a dream, and perhaps even sadder it had started with me filling in forms!  The meeting was clearly one after I’d given a talk somewhere as a person across the table said she’d been wanting to ask me (obviously as a sort of challenge) if there was a relation between … and here I’ll expand later … something like evolutionary and ecological something.  Ever one to think on my feet I said something like “that’s an interesting question”, but it was also clear that the question arose partly because the terms sounded somewhat similar, so had some of the sense of a rhyming riddle “what’s the difference between a jeweller and a jailor”.  So I went on to mention random metaphors as a general creativity technique and then, so as to give practical advice, suggested choosing two words next to each other in a dictionary and then trying to link them.

Starting with the last of these, the two words in a dictionary method is one I have never suggested to anyone before, not even thought about. It was clearly prompted by the specific example where the words had an alliterative nature, and so was a sensible generalisation, and after I woke realised was worth suggesting in future as an exercise.  But it was entirely novel to me, I had effectively done the exactly sort of thinking / problem solving that I would have done in the real life situation, but while dreaming.

One of the reasons I find dreams fascinating is that in some ways they are so normal — we clearly have no or little sensory input, and certain parts of our brain shut down (e.g. motor control to stop us thrashing about too much in our sleep) — but other parts seem to function perfectly as normal.  I have written before about the cognitive nature of dreams (including maybe how to model dreaming) and what we may be able to learn about cognitive function because not everything is working, rather like running an engine when it is out of the car.

In this dream clearly the ‘conscious’ (I know an oxymoron) problem-solving part of the mind was operating just the same as when awake.  Which is an interesting fact about dreaming, but  I was already aware of it from previous dreams.

In this dream it was the language that was interesting, the original conundrum I was given.  The problem came as I woke up and tried to reconstruct exactly what my interlocutor had asked me.  The words clearly *meant* evolutionary and ecological, but in the dream had ‘sounded’ even closer aurally, more like evolution and elocution (interesting to consider, images of God speaking forth creation).

So how had the two words sound more similar in my dream than in real speech?

For this we need the Jabberwocky circuit.

There is a certain neurological condition that arises, I think due to tumours or damage in particular areas of the grain, which disrupts particular functions of language.   The person speaks interminably; the words make sense and the grammar is flawless, but there is no overall sense.  Each small snippet of speech is fine, just there is no larger scale linkage.

When explaining this phenomenon to people I often evoke the Jabberwocky circuit.  Now I should note that this is not a word used by linguists, neurolinguists, or cognitive scientists, and is a gross simplification, but I think captures the essence of what is happening.  Basically there is a part of your mind (the conscious, thinking bit) that knows what to say and it asks another bit, the Jabberwocky circuit, to actually articulate the words.  The Jabberwocky circuit knows about the sound form of words and how to string them together grammatically, but basically does what it is told.  The thinking bit needs to know enough about what can be said, but doesn’t have time to deal with precisely how they are strung together and leaves that to Jabberwocky.

Even without brain damage we can see occasional slips in this process.  For example, if you are talking to someone (and even more if typing) and there is some other speech audible (maybe radio in the background), occasionally a word intrudes into your own speech that isn’t part of what you meant to say, but is linked to the background intruding sound.

Occasionally too, you find yourself stopping in mid sentence when the words don’t quite make sense, for example, when what would be reasonable grammar overlaps with a colloquialism, so that it no longer makes sense.  Or you may simply not be able to say a word that you ‘know’ is there and insert “thingy” or “what’s it called” where you should say “spanner”.

The relationship between the two is rather like a manager and someone doing the job: the manager knows pretty much what is possible and can give general directions, but the person doing the job knows the details.  Occasionally, the instructions get confused (when there is intruding background speech) or the manager thinks something is possible which turns out not to be.

Going back to the dream I thought I ‘heard’ the words, but examining more closely after I woke I realised that no word would actually fit.  I think what is happening is that during dreaming (and maybe during imagined dialogue while awake), the Jabberwocky circuit is not active, or not being attended to.  It is like I am hearing the intentions to speak of the other person, not articulated words.  The pre-Jabberwocky bit of the mind does know that there are two words, and knows what they *mean*.  It also knows that they sound rather similar at the beginning (“eco”, “evo”), but not exactly what they sound like throughout.

I have noticed a similar thing with the written word.  Often in dreams I am reading a book, sheet of paper or poster, and the words make sense, but if I try to look more closely at the precise written form of the text, I cannot focus, and indeed often wake at that point¹.  That is the dream is creating the interpretation of the text, but not the actual sensory form, although if asked I would normally say that I had ‘seen’ the words on the page in the dream, it is more that I ‘see’ that there are words.

Fiona does claim to be able to see actual letters in dreams, so maybe it is possible to recreate more precise sensory images, or maybe this is just the difference between simply writing and reading, and more conscious spelling-out or attending to words, as in the well known:

Paris in the
the spring

Anyway, I am awake now and the wiser.  I know a little more about dreaming, which cognitive functions are working and which are not;  I know a little more about the brain and language; and I know a new creativity technique.

Not bad for a night in bed.

What do you learn from your dreams?

1. The waking is interesting, I have often noticed that if the ‘logic’ of the dream becomes irreconcilable I wake.  This is a long story in itself, but I think similar to the way you get a ‘breakdown’ situation when things don’t work as expected and are forced to think about what you are doing.  It seems like the ‘kick’ that changes your mode of thinking often wakes you up! [back]

We all know “Cogito ergo sum” (or in French, ” Je pense, donc je suis”), “I think, therefore I am”, which comes near the beginning of “Principles”.  This is preceded by an exhortation to a form of radical scepticism casting doubt on every sense, belief and assumption we have about the world.  Only by throwing away everything we took as given, are we able to know what is really true.

However, Descartes regards this as “once in a lifetime” activity for the enquirer, not a position of continuing doubt and uncertainty, but an opportunity through doubt to come to solid bedrock.

He is also very practical, warning:

“… this process of doubt should be restricted to our considering what is true. For as far as the conduct of life is concerned, the moment for action would usually have passed long before we could resolve our doubts. We are often forced to opt for what is only probably right, and sometimes we even have to choose between two equally probable alternatives.” (Part I, Section 7)

In life we often must simply act.

During this once in a lifetime event of radical scepticism, and having come to the position of absolute doubt, the one thing that we can be certain of is the awareness of our own doubt and the surety that there is something/someone, me, who is doubting; hence “cogito ergo sum”.

However, Descartes is then left with the problem of how we can know anything else.  Our senses of the world can be flawed and whilst the fact that we perceive things is further evidence of our own existence (that awareness of perception is part of the ‘cogito’), it gives no reliable evidence of anything else.

Descartes at this point turns to God.  Self examination and a variant of the “ontological argument”² forces him to both believe in an omnipotent and good God, and also that, while our sense are imperfect, God would not give us a fundamentally flawed view of the world.  Our senses are at least partially reliable because of the nature of God; and hence our own personal exploration of the world and the whole scientific endeavour become meaningful.

A constant problem for all non-theistic ethics is how to move from descriptive accounts of particular ethical positions to prescriptive ones of how one should act.  They are morally toothless.  However,  the non-theistic epistemological gap in science is less often discussed.

I recall some years ago, just before I went to university, talking to a mathematics lecturer who was, I believe, an atheist.  He explained to me how he believed the sun would rise every morning, but had no evidence that it would.  Even assuming that our sense are reliable (which Descartes would doubt), we can observe that the laws of physics stay the same and have been the same, but in the end have no reason to believe that this will continue to be the case.  The “law of uniformitarianism” can be observed to have held in the past, but only by reflexively applying the ‘law’ to itself can we assume it will continue to do so.

Science without God is ultimately blind faith.

I was also fascinated by Descartes’ exposition of the nature of emotion and senses (part IV section 190) and surprised at the sophistication of the model of sensory stimulation of nerves (assumed to be mechanical motions), including discussion of phantom limbs, which recur in modern texts.  He talks about our five external senses and two internal ones.  The list of external senses has been fairly stable since Greek times and one of his internal ones would also feature in ‘modern’ lists³, that is the sense we have of stomach, bladder etc., which Descartes relates to our appetites.

The other internal sense is perhaps more interesting, he suggests that motions of the “little nerves” of the heart give rise to emotion.  If the blood stretches in the heart, the expansion is felt as joy; if it is sluggish the lack of expansion of the ventricles is felt as sadness.  Although the exact physical model differs, the general flavour of this is surprisingly modern; current models of complex emotion usually involve some form of interpretation of body, lower brain activity, and hormones and other chemicals in the blood stream and brain.  Descartes describes how calling an event to mind can lead to a bodily reaction which is then felt as the emotion related to the event.  This cycle of thought leading to bodily emotion leading back to feelings is exactly what one would find in a modern account; a holistic view that sees body and mind both distinct in some ways but also intimately connected.

So, if anyone ever describes your work as too Cartesian … be proud.

1. René Descartes, 1644, Principles of Philosophy, trans. George MacDonald Ross, 1998–1999 [back]
2. I have never found the ontological argument for the existence of God particularly convincing, but it continues to cause debate even now nearly 1000 years after Anslem’s first formulation. [back]
3. Now-a-days the list of internal senses would be longer including proprioception (sense of body position) and equilibrioception (sense of balance). [back]

One talk was by Stuart Hameroff on A New Marriage of Brain and Computer.  He is the guy that works with Penrose on the possibility that quantum effects in microtubules may be the source of consciousness.  I notice that he used calculations for computational capacity based on traditional neuron-based models that are very similar to my own calculations some years ago in “the brain and the web” when I worked out that the memory and computational capacity of a single human brain is very similar to those of the entire web. Hameroff then went on to say that there are an order of magnitude more microtubules (sub-cellular structures, with many per neuron), so the traditional calculations do not hold!

Microtubules are fascinating things, they are like little mechano sets inside each cell.  It is these microtubules that during cell division stretch out straight the chromosomes, which are normally tangled up the nucleus.  Even stranger those fluid  movements of amoeba gradually pushing out pseudopodia, are actually made by mechanical structures composed of microtubules, only looking so organic because of the cell membrane – rather like a robot covered in latex.

The main reason for going to the text talks was one by Steve Souders “Life’s Too Short – Write Fast Code” that has lots of tips for on speeding up web pages including allowing Javascript files to download in parallel.  I was particularly impressed by the quantification of costs of delays on web pages down to 100ms!

This is great.  Partly because of my long interest in time and delays in HCI. Partly because I want my own web scripts to be faster and I’ve already downloaded the Yahoo! YSlow plugin for FireFox that helps diagnose causes of slow pages.  And partly  because I get so frustrated waiting for things to happen, both on the web and on the desktop … and why oh why does it take a good minute to get a WiFi connection ….  and why doesn’t YouTube introduce better controls for skimming videos.

… and finally, because I’d already spent too much time skimming the tech talks, I looked at one last talk: David Levy, “No Time To Think” … how we are all so rushed that we have no time to really think about problems, not to mention life¹.  At least that’s what I think it said, because I skimmed it rather fast.

1. see also my own discussion of Slow Time [back]

I had a great time in St Andrews and was well looked after by some I knew already Ian, Gordan, John and Russell, and also met many new people. Ate good food and stayed in a lovely hotel overlooking the sea (and golf course) and full of pictures of golfers (well what do you expect in St Andrews).

For the lectures, I was told the general pattern was one lecture about the general academic area, one ‘state of the art’ and one about my own stuff … hence the three parts of the title!  Ever for cutesy titles I then called the individual lectures “Whose Computer Is It Anyway”, “The Great Escape” and “Connected, but Under Control, Big, but Brainy?”.

The first lecture was about the fact that computers are always ultimately for people (surprise surprise!) and I used Ian’s slight car accident on the evening before the lecture as a running example (sorry Ian).

The second lecture was about the way computers have escaped the office desktop and found their way into the physical world of ubiquitous computing, the digital world of the web ad into our everyday lives in out homes and increasingly the hub of our social lives too.  Matt Oppenheim did some great cartoons for this and I’m going to use them again in a few weeks when I visit Dublin to do the inaugural lecture for SIGCHI Ireland.

(© Matt Oppenheim)

(© Matt Oppenheim)

The last lecture was about intelligent internet stuff, similar to the lecture I gave at Aveiro a couple of weeks back … mentioning again the fact that the web now has the same information storage and processing capacity as a human brain¹ … always makes people think … well at least it always makes ME think about what it means to be human.

… and now … in Tiree … sun, wild wind, horizontal hail, and paddling in the (rather chilly) sea at dawn

1. see the brain and the web [back]

The people there included someone who studied people coding about DNA, someone interested in text, anthropologosts, artists and an ex-AI man. We talked about embodied computation¹, the human body as part of computation, the physical nature of code, the role of the social and physical environment in computation … and briefly over lunch I even strayed onto the modeling of regret … but actually a little off topic.

physicality – Played a little with sticks and stones while talking about properties of physical objects: locality of effect, simplicity of state, proportionality and continuity of effect².

physical interaction – Also talked about the DEPtH project and previous work with Masitah on natural interaction. Based on the piccie I may have acted out driving when talking about natural inverse actions

ubiquity of computation – I asked the question I often do “How many computers do you have in your house” … one person admitted to over 10 … and she meant real computers³. However, as soon as you count the computer in the TV and HiFi, the washing machine and microwave, central heating and sewing machine the count gets bigger and bigger. Then there is the number you carry with you: mobile phone, camera, USB memory stick, car keys (security codes), chips on credit cards.

However at the Firefly demo later in the morning they got to see what may be the greatest concentration of computers in the UK … and all on a Christmas Tree. Behind each tiny light (over 1000 of them) is a tiny computer, each as powerful as the first PC I owned allowing them to act together as a single three dimensional display.

embodiment of computation – Real computation always happens in the physical world: electrons zipping across circuit boards and transistors routing signals in silicon. For computation to happen the code (the instruction of what needs to happen) and the data (what it needs to happen with and to) need to be physically together.

The Turing Machine, Alan Turing’s thought experiment, is a lovely example of this. Traditionally the tape in the Turing machine is thought of as being dragged across a read-write head on the little machine itself.

However … if you were really to build one … the tape would get harder and harder to move as you used longer and longer tapes. In fact it makes much more sense to think of the little machine as moving over the tape … the Turing machine is really a touring machine (ouch!). Whichever way it goes, the machine that knows what to do and the tape that it must do it to are brought physically together⁴.

This is also of crucial importance in real computers and one of the major limits on fast computers is the length of the copper tracks on circuit boards – the data must come to the processor, and the longer the track the longer it takes … 10 cm of PCB is a long distance for an electron in a hurry.
brain as a computer - We talked about the way each age reinvents humanity in terms of its own technology: Pygmalion in stone, clockwork figures, pneumatic theories of the nervous system, steam robots, electricity in Shelley’s Frankenstein and now seeing all life through the lens of computation.

This withstanding … I did sort of mention the weird fact (or is it a factoid) that the human brain has similar memory capacity to the web⁵ … this is always a good point to start discussion

While on the topic I did just sort of mention the socio-organisational Church-Turing hyphothesis … but that is another story

more … I recall counting the number of pairs of people and the number of seat orderings to see quadratic (n squared) and exponential effects, the importance of interpretation, why computers are more than and less than numbers, the Java Virtual Machine, and more, more, more, … it was very full hour

1. I just found notes I’d made for web page in embodied computation 5 years ago … so have put the notes online [back]
2. see preface to Physicality 2006 proceedings [back]
3. I just found an online survey on How many computers in your house [back]
4. Yep I know that Universal Turing machine has the code on the tape, but there the ‘instructions’ to be executed are basically temporarily encoded into the UTM’s state while it zips off to the data part of the tape. [back]
5. A. Dix (2005). the brain and the web – a quick backup in case of accidents. Interfaces, 65, pp. 6-7. Winter 2005.
   http://www.hcibook.com/alan/papers/brain-and-web-2005/ [back]

In this case the issue is the distributed nature of cognition within the brain and the inadequacy of central executive models. In support of this, Clark (p.39) cites Mitchel Resnick at length and I’ll reproduce the quote:

“people tend to look for the cause, the reason, the driving force, the deciding factor. When people observe patterns and structures in the world (for example, the flocking patterns of birds or foraging patterns of ants), they often assume centralized causes where none exist. And when people try to create patterns or structure in the world (for example, new organizations or new machines), they often impose centralized control where none is needed.” (Resnick 1994, p.124)¹

The take home message is that we tend to think in terms of centralised causes, but the world is not like that. Therefore:

(i) the way we normally think is wrong

(ii) in particular we should expect non-centralised understanding of cognition

However, if our normal ways of thinking are so bad, why is it that we have survived as a species so long? The very fact that we have this tendency to think and design in terms of centralised causes, even when it is a poor model of the world, suggests some advantage to this way of thinking.

Of course, this may simply be an accident of our neural architecture … in which case it would be important for (ii), or may be adapted for a hunter gatherer life, but not 21st Century living – but again would be interesting for (ii). However, the fact that we are still here means it is certainly not too unsuccessful.

Whatever the reason, the fact that we think in these terms is itself an empirical data point for understanding human cognition. We have brains that tend to seek centralised solutions – what are the neural and cognitive mechanisms that drive this and what are the environmental reasons that make it work.²

There are two factors at work here, one is about the way we see the world and the other about the way we plan and act on it.

At the level of perception, one of the Gestalt laws is that things that move together belong together. Even if bushes hide most of a predator from view, the several disconnected tiny moving fragments still form one large animal you need to avoid.

Yes this is a gross simplification of reality. Look at a rock – it is an ‘it’, a single thing – but in fact it is not, it is simply the decentralised activities of millions of millions of millions of millions of atoms interacting, largely locally, with one another. It is not so far unlike Resnick’s flock of birds. However, their general coherence of motion and substance makes it sensible to regard it as one thing. As a scientist understanding the decentralised emergent phenomenon is interesting, but as a gardener wanting to move the rock it is an intellectual luxury and the (incorrect) centralised view of a the rock makes sense.

In real world problems, sometimes decentralised solutions work, other times they don’t.

I was once driving in Rome on a Saturday night (happily with a native Roman to guide me). It was after 11pm so they turned off all the traffic lights (as Italians ignore them anyway) and we came to a massive crossroads with completely full three lane roads in all directions. The space was filled with a criss-cross of apparently grid-locked cars and I thought we would be stuck there until a policeman came, but my navigator told me to simply drive. Every time the slightest gap opened, be it only a few inches, I would edge forward. Eventually, but after a relatively short time, we found ourselves at the other side. Thinking afterwards I realised that always some car was able to get out and I fact the ‘greedy’, decentralised algorithm worked perfectly.

Driving is different in north-west Scotland where there are long stretches of narrow single-track roads with passing places. When you spot a vehicle coming you watch out for a passing place and whichever of you gets to the one first waits there. If you notice too late and meet, then the person closest to a passing place may need to reverse. This is another local, slightly more polite, but semi-greedy algorithm, with each person making independent choices and trying to proceed, but taking into account immediately close road users. However, when single track roads get too full, this can fail. In situations, like the passing goods-train puzzles, where lines of vehicles have to pass with only single passing place, then often long lines of vehicles have to backup, go forward again, reverse again, in apparently disorganised ways – and ways in which each single driver cannot understand from local conditions alone. People have to get out their cars and start to coordinate their efforts.

Note that the decentralised strategies work remarkably well, and when they do require less effort than coordination. However, the reason that we do more than that, and think in ways that have an (at least behavioural) appearance of centralised control is because for certain problems this is needed – not least in complex social and technological situations.

With a HCI hat on, when we come to designing for people, we get the best solutions not when we ignore one aspect or another, but when we recognise the relative strengths of the two and how they can work together.

I recall when I was a child (yes over 30 years ago), seeing a television report about Benetton’s new CAD systems. The problem was cutting out rolls of cloth to make pieces for clothing. Traditionally an experienced cutter would arrange the pieces for a single garment as tightly as possible (to avoid waste), whilst ensuring proper orientations. These were then cut using a special form of guillotine. The new system of cutting from the roll allowed them to take the pieces for several garments and organise them over a long run of cloth for cutting. Doing several garments at once offered savings in terms of less wastage, but was a more challenging arrangement tasks … hence computer aid. The computer would take pieces initially arranged on (virtual) fabric and ‘jiggle’ them until they fitted closer with les waste. However, an experienced cutter would oversee this process and make large scale changes, “what if we tried this large piece over here?” The computer’s activity would have been serial, but could have been parallelised as it involved effectively lots of small local decisions. However, the human made strategic decisions, that themselves made use of the human’s internal associative pattern recognition, but from the point of view of the large system were effectively more centralised. Here a combination of centralised and decentralised thinking/computation together addressed a problem neither could solve on their own.

Finally, it is interesting to reflect on the ability demonstrated in both Clark and Resnick’s writing. They look in at our modes of thinking, see that they are often over-simplistic in terms of assuming central control when there is none, and then consider how to address this. This highly reasoned and reflective process does not arise naturally from decentralised thought that would simply go on using the same old ways of thinking, but is the product of exactly the more ‘rational’ linear, centralised thinking that they seek to expose as outmoded.

1. Mitchel Resnik (1994). Turtles Termites and Traffic Jams: Explorations in Massively Parallel Microworlds. MIT Press. [back]
2. This is similar to the argument in my previous post on the power of sequential thinking, where I pondered the complexity of establishing sequence within an underlying parallel and distributed neural superstructure … but also discussed the advantages it brings. Sequentiality and central control are of course closely linked. [back]

This reminded me of results of brain scans (sadly, I can’t recall the source), which showed that the areas in the brain where you store concepts like ‘chair’ are different from those where you store the sound of the word – and also I’m sure the spelling of it also.

This makes sense of the “tip of the tongue” phenomenon, you know that there is a word for something, but can’t find the exact word. Even more remarkable is that of you know words in different languages you can know this separately for each language.

So, musing on this, there seem to be very good reasons why, even within our own mind, we hold multiple representations for the “same” thing, such as chair, which are connected, but loosely coupled.

In an artificial “brain” like COG, the computational units are physically separate. In our brains things are much less discrete, but we know do have well defined locality for certain functions (e.g. Broca’s area for speech). Also, while there is debate about the extent to which we know what we are doing (or perhaps more important know what we are about to do) still it is clear that at least low-level functions operate semi-autonomously, but for instant reaction (pain withdrawal) and also for controlled actions (play a guitar chord).

In particular, there are particular brain lesions that mean that the patient cannot choose what to say, yet still vaguely grammatical but entirely meaningless utterances are constantly made – rather like James Joyce In between are cases where “nearly right” words come out, perhaps table, or cushion instead of chair. So for speech it seems a “higher level” part of the brain decides what we want to say and makes gentle suggestion for what this should be, but a different part does the final stringing together of words and it is this part that ‘knows’ the rules of grammar, the way words connect into each other and what the words sound like (although the grammar and aural elements may themselves be generated separately). This is rather like the intention to walk and the fine movement of muscles needed to move each leg.

Obviously the representations needed for saying ‘chair’ in a sentence are about the way it fits into grammatical structure, agreement with verbs, the sound of the word, and eventually (maybe at another level again) the way the lips need to be formed and air expelled. In contrast for choosing what to say, it is the semantics of chairs, the fact that you sit on them, they have legs, etc. that are important. The “planning to speak” bit needs to know there is a suitable word, what that word means and whether it will fit with other words, but does not need to know the details of how to say it. Similarly the “planning to move” bit needs to know roughly that legs can move in the desired way, but not the details of movement. Planning needs a model of action (speech or movement) and the model needs to be close enough to reality for it to work most of the time, but without all the details.

So the representations at a higher level need to share or independently represent enough of the lower level functions to be able to make appropriate suggestions for lower level action, but each will also represent different things. In addition, there needs to be some linkage between the two representations. Suppose you form the intention to say something like “The chair has four legs”. (Note “something like” because as you form the intention to say the thing the exact words will probably not be there.) In order for this intention to lead to the words “The chair has four legs”, something has to link the planning ‘chair’, with the saying ‘chair’.

This is not unlike human communication; we need both shared vocabulary and a level of shared meaning: so that when I say to a garage mechanic “the clutch is not working” it is the same thing we are referring to. However, we can also each have additional meanings, annotations etc.: the mechanic will know how the clutch works better than I do.

However, whereas human verbal communication has to be pushed through a discrete medium of signs, it seems more likely that there is a level of direct (but diffuse) connection between the ‘concepts’ used in our brains at different levels of activity.

1. Andy Clark. Being There. MIT Press. 1997. ISBN 0-262-53156-9. book@MIT [back]

“that’s impossible” he said.

“why” I asked, “I’m not saying they are similar, just that there is the same computational potential”

“Computers are sequential” he said, “brains are associative”.

Further attempts to reason, likening it to other forms of simulation or emulation, simply met with the same flat response, a complete unwillingness to entertain the concept.

Partly this is to do with the feeling that this somehow diminishes us as people, what for me was a form of play with numbers, for him was perhaps an assault on his integrity as a human. I guess as a Christian I’m used to the idea that the importance of a person is not that we are clever or anything else, but that we are loved and chosen. So, I guess, for me this is less of an insult to my idea of being who I am.

This aside it is interesting that the reason given was about the mode of computation: “computers are sequential” vs. the massively parallel associativity of the human brain.

Of course if the computational substrate is all the PCs connected to the Intenet then this is hardly purely sequential and in fact one of the reasons that you could not ‘run’ a brain simulation on the Internet is that communication is too slow. Distributed computation over 100s of millions of PCs on the internet could not synchronise in the way that long-range synapses do within our brains.

Amongst other things it is suggested that our sense of consciousness is connected with the single track of synchronised activity enabled by the tight interconnections and rapid feedback loops within our brains¹. In contrast, individual computers connected to the onternet compute far faster than they can communicate, there could be not single thread of attention switching at the rate that our minds can.

If the internet were to think it would be schizophrenic.

Sequence is also imprtant in other ways. As the man said, our brians are associative. When considering spreading activation mechanisms for intelligent internet interfaces, one of the problems is that associative stuff gets ‘mixed up’. If London has a high level of activation, why is that? In a designed computational framework it is possible to consider mutiple ‘flavours’ of activations spreading through a network of concepts, but our brains do not do this, so how do they mange to separate things.

Now to some extent they don’t – we get an overall feel for things, not seeing the world as little pieces. However, it is also important to be able to more or less accurately ascribe feelings and associations to things. Consider one of those FBI training ranges were bank terrorists and hostages pop out from behind windows or doors. Your aim is to shoot the terrorists and save the hostages. But, if you see a robber holding a hostage how do you manage to separate the ‘bad and kill’ feelings and properly ascribe them only to the terrorist and not the hostage.

The answer may well be due exactly to the switching of attention. Even with both terrorist and hostage are next to each other, as mental attention shifts momentarily to one and then the other, the mental associations also shift. Rodney Cotterill in Enchanted Looms describes two levels of attention switich². One near conscious and taking around 500ms and one connected with more low-level visual attention (sometimes called a visual searchlight) at 20-50ms. It is probably the slower timescales that allow fuller webs of association to build and decay, but maybe there are other intermediate timescales of attention switching as well.

If this is right then the rapid sequential shifts of attention could be essential for maintaining the individual identity of percepts and concepts.

If we look at concepts on their own, another story of sequence unfolds.

There is a bit of a joke among neuroscientists about grandmother cells. This is the idea that there is a single neuron that in someway encodes or represents your grandmother³

Looking at this purely from a computing science perspective, even if there were not neurological reasons for looking for more distrubuted representations, there are computational ones. If concepts were stored in small local assemblies of neurons (not single ones to allow some redundancy and robsutness) and even a reasonably large part of our brains were dedicated to concept memory, then there just seems too few ‘concept-slots’.

If we used 100 neurons per concept and 10% of the brain for concept memory, we would only have space for around 10 million concepts. A quick scan through the dictionary suggests I have a reconition vocabuary of arounf 35,000 words, so that means I’d have less than 300 other concepts per dictinary word one. Taking into account memories of various kinds, it justs seems a little small. If we take into account the interconnections then we have plenty of potential long-term storage capacity (1/2 petabyte or so), but not if we try to use indiviudal groups of neorons to represent things. Gradmother cells are simpy an inefficient use of neurons!

Now there is also plenty of neurological evidence for more distributed storage. Walter Freeman describes how he and his team lovingly chopped the tops off rabbits’ skulls, embeded electrodes into their olfactory bulbs and then gently nursed them back to health⁴. The rabbits were then presented with different smells and each smell produced a distinctive pattern of neuron firings, but these patterns exteded across the bulb, not localised to a few neurons.

If neurons had ‘continuous’ levels of activation it would be possible to represent things like “1/2 think it is a dog 1/2 think it is a fox”, simply as an overlay of the activation of each. However, if this were the case, and one could have in mind any blend of concepts, then an assembly of N neurons would still only be able to encode up to N concepts as the concepts patterns would form a set of basis vectors for the N-dimensional vector space of possible activation levels (a bit of standard linear algebra).

In fact, neurons tend to behave non-linearly and in many areas there are patterns of inhibition as well as mutual excitement and disinhibition, leading to winner-takes-all effects. If this is true of the places where we represent concepts for short term memory, conscious attention, etc., then this means instead of representations that ‘add up’, we have each pattern potentially completely different, similar to the way binary numers are encoded in computer memory: 1010 is not a combination of 1000 and 0010 but completely different.

In principle this kind of representation allows 2^N (two to the power of N) rather than N different concepts using the same N neurons … In reality, almost certainly representations are less ‘precise’ allowing some levels of similarity in representations etc., so the real story will be more complex, but the basic principle holds that combinations of thresholding and winner-takes-all allow more distinct concepts than would be possible if combinations of concepts can occur more freely.

However, notice again that higher capacity to deal with more concepts is potentially bought at the cost of being able to think of less things ‘at once’ – and the side effect is that we have to serialise.

Returning back to the “computers are sequential, brains are associative” argument, whilst not denying the incredible parallel associativity of human memory, actually there seems as much to wonder about in the mechanisms that the brain ‘uses’ for sequentiality and the gains it gets because of this.

1. see Gerald Edelman, Wider then the Sky, Yale University Press, 2004, ISBN 0-300-10229-1 [back]
2. Rodney Cotterill, Enchanted Looms: Conscious Networks in Brains and Computers, Cambridge University Press, 1998, ISBN 0-521-62435-5. See p. 244 for 500ms switching and pp. 261 and 265 for 20-50ms spotlight/searchlight of attention [back]
3. Although the grandmother cell this is generally derided as oversimplisitic, there is evidence that there is more neuron specialisation then previously thought [[see Mind Hacks: evidence for 'Grandmother Cells']]. Also it is easier to encode relationships if there are single patches than configuratiin sof neurons, so perhaps we have both mechanisms at work. [back]
4. Walter J. Freeman, How Brains Make Up Their Minds, Phoenix, 1999, ISBN 0-75381-068-9. See p. 95 onwards for rabbit olfactory bulb experiments. [back]

The book is largely about one message – that a scientific study of consiousness can only take into account third party accessible knowledge about first part experience. In other words I can only base a scientific study on what I can tell of other people’s consciousness from their actions, words and any available brain scanning etc.

Dennett has a meticulous rhetoric, but I found two broad facets of his argument weak, one more about rheteric and one substance.

First somewhat disingenuously he does not say that a scientific study of consciousness would yield a complete account of consciouness, but effectively the implication is there. That is he does not say that consciouness is no more than its phenomenial effects … but implies it.

Second, being a philosopher he focuses on incontrovertible evidence, whereas as scientists and humans often reasonable evidence is sufficient.

The first point is obvious and yet easily underestimated. A ‘scientific’ study of literature could formulate many known literary rules (aliteration, rhyme, etc.) and may even find new ones, and indeed poets in particular are happy to perform such analyses. However, we do not expect such rules to be a complete account of literture.

The second point is more substantive, but does interact with the first.
Dennett takes issue with philosophers who posit some form of non-sentient zombie (often called ‘Mary’) who/which nonetheless behaves otherwise exactly like a human including things that might appear to be conscious. They then say “but of course Mary is not conscious”. Dennett objects to the ‘of course’, which is really a statement about prior beliefs/assumptions (although Dennett, of course, frequently does the same with his beliefs!).

Dennett posits a Robo-Mary which is entirely mechanical/electronic and yet emulates perfectly the brain circuitry of a person and so can work out how the person would react and then reacts similarly. From the outside and by all her (emulated) subjective reactions she appears to be conscious. She would pass any ‘Turing Test’ for consciousness and yet many, perhaps most, would say she is not. The implication (from the first weakness) is that we are no more conscius than she (it?).

Actually I don’t object to the idea that such a creature may indeed be conscious, but I’d need more evidence than I would for a human, not because Robo-Mary is a machine, but becasue she is designed to appear conscious.

Robo-Mary is in fact a Robo-Mata-Hari, a spy, a robot in human clothing.

A good enough actor may convince you he is feeling happy, sad, or in love, and you may not be able to tell the differece between the act and the real thing, but that does not mean happiness, saddness and love are no more than their appearance.

As a philosopher, you cannot have incontrovertible evidence that a person’s emotions are real, not just a facade. However, as a human it would be unreasonable to therefore dismiss all expressions of emotion.

Some (well many) years ago, I worked with people at York who creating one of the first ADA compilers. There was a validation suite of programs that had to compile and run correctly for the compiler to get an official stamp from the ADA standards agency. I used to wonder about writing a program that recognised each of the tests cases and simply spat out the right code for each one. Any other program given to the program would simply print an error message and stop. The program would pass the test suite and could get the stamp as being a validated compiler, and yet would be completely useless. It would be a cheat ADA compiler.

Imagine if I sold such a cheat compiler. Any judge would regard it as fraud – whilst it passed the test, it is clearly not an ADA compiler. The test is there to validate things that are designed to be ADA compilers, not things designed to pass the test. So, the cheat ADA compiler is not adequately validated by the test, just becase it is designed to pass it.

Robo-Mary is designed to pass the consciousness test … indeed any consciousness test. We perhaps could never incontrovertibly tell whether Robo-Mary was conscious or simply acting conscious. However, when faced with another human being, an ordinary Mary, who is not designed specifically to appear conscious, it is reasonable to assume that she experiences similar things to me when she describes her experience in similar terms. I can never incontrovertibly tell that Mary is conscious, but it is reasonable to believe so. And it is equally reasonable to base a scientific study on such defeasible observations.

Turning back to Robo-Mary; convincing machine cosciousness would not come from machines designed to appear conscious, but more ‘by accident’. Perhaps one day my intelligent automated vacuum cleaner will say to me “Alan, have you ever watched those dust motes in the sunlight”.