Naturalized philosophy

I went to graduate school in philosophy a long time ago. At that time, there was a premium put on “naturalized” research, the idea being that good philosophy needed grounding in a “real” (non-philosophical) discipline. It was a time when Newton and Einstein and Boyle and Godel and Poincare joined the usual dead white European males that we all know and love in the pantheon of philosophical greats. In this setting, it is no surprise that Chomsky and his work made frequent appearances in the pages of the most prestigious philo journals and was a must read for a philosopher of language. It actually took some effort for the discipline to relegate Chomsky to the domain of “philosophical naïf” (I think this was Putnam’s phrase) and it coincided with endless debates about the implications of the referentialist worldview for narrow content and semantic meaning. IMO, this work did not deliver much in the way of insight, though it did manage to make quite a few careers. At any rate, Chomsky’s exit from the main stage coincided with a waning of the naturalizing project and a return to the metaphysical (and metalinguistic) abstruseness that philosophy is, it appears, endemically attracted to. If nothing else, de-naturalizing philosophy establishes academic protective boundaries providing philosophy with a proprietary subject matter that can protect deep thinkers from the naturalizers and their empirical pretentions.[1] Why do I mention this? Because I am a big fan of the kind of naturalized philosophy that the above mentioned luminaries practiced and so I am usually on the lookout for great examples thereof.

What are the distinctive marks of this kind of work? It generally rests on a few pretty “obvious” empirical premises and demonstrates their fertile implications. Chomsky’s work offers an excellent illustration.

What is Chomsky’s most significant contribution to philosophy (and indeed linguistics)? He identified three problems in need of solution: what does a native speaker know when s/he knows her/his native language? What meta-capacity underlies a native speaker’s capacity to acquire her/his native language? And how did this meta-capacity arise in the species?  These are the big three questions he put on the table. And the subsequent questions they naturally lead to: How do native speakers use their knowledge to produce and understand language, how do LADs use their meta-capacity to acquire their native capacity? How are one’s knowledge of language embodied in wetware?  The last three rely on glimmers of answers to the first three. Chomsky has taught us how to understand the first three. 

Here’s the argument. It is based on few really really really obvious facts. First, that nothing does language like humans do. Birds fly, fish swim, humans do language. It is a species specific capacity unlike anything we find anywhere else. Second, a native speaker displays linguistic creativity. This means that a native speaker can use and understand an unbounded number of linguistic objects never before encountered and does this relatively effortlessly. Third, any kid can reflexively acquire any language when placed in the right linguistic environment (linguistic promiscuity), an environment which, when one looks even moderately closely, vastly underdetermines the knowledge attained (poverty of the linguistic stimulus). These three facts make it morally certain that part of linguistic competence implies internalization of a G, that the human meta-capacity of interest involves a higher order capacity to acquire certain kinds of Gs and not others and that this meta-capacity rests on some distinctive species specific capacities of humans. These three conclusions rest solidly on these obvious facts and together they bring forth a research program: what properties to human Gs have and what is the fine structure of the meta-capacity. That Gs exist and that FL/UG exists is trivially true. What their properties are is anything but.[2]

As FoLers know, Chomsky has recently added a third question to the agenda: how FL/UG could possibly have arisen. He argues that the relative rapidity of the emergence of FL and its subsequent stability argues for an intriguing conclusion: that the change that took place was necessarily pretty small and that whatever is proprietary to language must be quite minor. I tend to think that Chomsky is right about this, and that it motivates a research program that (i) aims to limit what is linguistically special while (ii) demonstrating how this special secret sauce allows for an FL like ours in the context of other more cognitively and computationally general mental capacities it is reasonable to believe that our pre-linguistic ancestors enjoyed. Imo, this line of thinking is less solidly based on “obvious” facts, but the line of inquiry is sufficiently provocative to be very inviting. Again, the details are up for grabs, as they should be.

So what are the marks of naturalized philosophy? Identifying questions motivated by (relatively) straightforward facts that support a framework for asking more detailed questions using conventional modes of empirical inquiry. Chomsky is a master of this kind of thinking. But he is not alone. All of the above is actually in service of advertising another such effort by Randy Gallistel. The paper of interest, which is a marvelous piece of naturalized philosophy appeared here in TiCS. I want to say a word or two about it.

Gallistel’s paper is on the coding question. The claim is that this question has been effectively ignored in the cog-neuro world with baleful effects. The aim is to put it front and center on the research agenda and figure out what kind of neural system is compatible with a reasonable answer to that question. The argument in the paper is roughly as follows.

First, there is overwhelming behavioral evidence that animals (including humans) keeps track of numerical quantities (see box 1, (3)). Hence the brain must have a way to code for number. It must be able to store these numbers in some way and must be able to transmit this stored information in signals in some way. So it must be able to write this information to memory and read this information from memory.

Second, if the brain does code for number it must do so in some code. There are various kinds, but the two the paper discusses are hash/rate/tally codes vs combinatorial codes (4-5). The former are “unary” codes. What this means is that “to convey a particular number one must use as many code elements are the numerosity to which the number refers.” Thus, if the number is 20 then there are 20 hash marks/strokes/dotes whatever representing the number.

The paper distinguishes such codes from “combinatorial” codes. These are the ones we are familiar with. So for example, ‘20’ conveys the number 20 and does so by using 10 digits in order sensitive configurations (i.e. 21 differs from 12). Note, combinatorial code patterns are not isomorphic to the things they represent.[3] 

The paper explores the virtues of combinatorial codes as against hash/rate/tally codes. The latter are “vastly more efficient” by orders of magnitude. Rate codes “convey 1 bit per spike” (5) while it is known that spike trains convey between 3-7 bits per spike. Rate codes are very energy expensive, combinatorial codes can be “exponentially smaller” (6). Last of all, there is evidence that spike trains use combinatorial codes because “reordering the intervals changes the message” (recall ‘21’ vs ‘12’), as expected if they spike trains are expressing a combinatorial code. 

The conclusion: the brain uses a combinatorial code, and this is interesting because this seems to require that the code be “symbolic” in the sense that its abstract (syntactic) structure matters for the information being conveyed.  And this strongly suggests that this info is not stored in synapses as supposed in a neural net system.

This last conclusion should not be controversial. When first put on the market of ideas, neural nets were confidently sold as being non-representational. Rumelhart and McClelland focused on this as one of their more salient properties and Fodor and Pylyshyn criticized such models for precisely this reason. The Gallistel paper is making the additional point that being asymbolic is, in addition to being cognitively problematic, is also neurophysiologically a problem as the kind of codes we are pretty sure we need are the kinds that neural nets are designed not to support. And this means that these are the wrong neuro models for the brain: “In neural net models, plastic synapses are molded by experience” and were intended to model “associative bonds” which “were never conceived of as symbols, and neither are their neurobiological proxies” (8).

Note, we can conclude that neural nets are the wrong model even if we have no idea what the correct model is. We can know what kind of code it is and what this means for the right neurophysiology without knowing what the right neurophysiology is. And if the codes are combinatorial/symbolic then there is no way that the right physiology for memory can be neural nets. This takes the Fodor-Pylyshyn critique on major step further.

So, if not in nets, what kind of architecture. Well, you all know by now. The paper notes that we can get everything we want from a chemical computer. We can physically model classical von Neuman/Turing machines in chemistry, with addresses, reading from, writing to, etc. Moreover, chemical computation has some nice biological features. Complex chemicals can be very stable for long periods of time (what we want from a long term memory store), and writing to such molecules is very energy efficient (8). In addition, chemical computing can be fast (some “can be altered on a nanosecond time scale”) and we know of instances of this kind of chemical computing that are behaviorally relevant. Last chemical computations are very energy efficient. Both storing and computing can be done cheaply if done chemically.

All of this leads to the conclusion that the locus of neurobiological computing is chemical and where are the relevant chemicals? Inside the cell. So, in place of neural nets we have the “cell intrinsic memory hypothesis” (1). Happily, there is now evidence that some computing gets done intra-celluarly (1-2). But if some gets done there…

This paper is great naturalized philosophy: we argue from pretty simple behavioral evidence that a certain kind of coding format is required and then that these kinds of formats prefer certain kinds of physical systems to support such codes and end with conclusions about the locus of the relevant computations. Thus we move from numbers are required, to combinatorial codes are the right kind, to neural nets won’t cut it to chemical computing within the cell. The big open meaty empirical question is what particular combinatorial code is exploited. It’s the cog-neuro analogue of how DNA stores genetic information and uses it. Right now, we do not know. At all.

This last analogy to DNA is important, and, IMO, is the strongest reason for thinking that this line of thinking is correct. Conventional computers provide an excellent model of how computation can be physically implemented. We know how to chemically “build” a conventional computer. We know that biology already uses chemistry to store and use information in hereditary and development. Is it really plausible that this in place machinery is not used for cognitive computation? Or as the paper puts it in the last sentence: “Why should the conveyance of acquired information proceed by principles fundamentally different from those that govern the conveyance of heritable information?” Why indeed! Isn’t the contrary assumption (the machinery is there for the using but it is never used) biologically scandalous? Wouldn’t Darwin be turning in his grave if he considered this? Isn’t assuming it to be false a kind of cognitive creationism? Yup, connetionists and neural net types are the Jerry Falwells of biology! Who would have thunk it: the road from Associationsim to Creationism is paved with Empiricist intentions. Only Rationalism and Naturalized Philosophy can save you.

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[1] Let me quickly add that I consider philosophy training a very useful aid to right thinking. Nothing allows you to acquire the feel for good argumentation than a stressful philosophical workout. And by “good” I mean understanding how premises related to conclusions, how challenging premises can allow one to understand how to evaluate conclusions, understanding that it is always reasonable to ask what would happen to a conclusion should such and such a premise be removed etc.  In other words, philosophy prizes deductive structure and this is a useful talent to nurture regardless of what conclusions you are interested in netting and premises you are interested in frying.

[2] Chomsky, as you all know, not only posed the questions but showed how to go about empirically investigating them. This is what puts him in with the Gods: he discovered interesting questions and figured out technology relevant to answering them.

[3] Thus, the numeral’s patterning represents the number in the former but not the latter. The difference between the two kinds of codes is similar to the one made (here) between patterns that track the patterning and those that do not.

Brain mechanisms and minimalism

I just read a very interesting shortish paper by Dehaene and associates (Dehaene, Meyniel, Wacongne, Wang and Pallier (DMWWP) that appeared in Neuron. I did not find an open source link, but you can use this one if you are university affiliated. I recommend it highly, not the least reason being that Neuron is a very fancy journal and GG gets very good press there. There is a rumor running around that Cog Neuro types have dismissed the findings of GG as of little interest or consequence to brain research. DMWWP puts paid to this and notes, quite rightly, that the problem lies less with GG than with the current state of brain science. This is a decidedly Gallistel inspired theme (i.e. the cog part of cog-neuro is in many domains (e.g. language) healthier and more compelling than the neuro part and it is time for the neuro types to pay attention and try to find mechanisms adequate for dealing with the well grounded cog stuff that has been discovered rather than think it msut be false because the inadequate and primitive neuro models (i.e. neural net/connectionist) don’t have ways of dealing with it) and the more places it gets said the greater the likelihood that CN types will pay attention. So, this is a very good piece for the likes of us (or at least me).

The goal of the paper is to get Cog-Neuro Science (CNS) people to start taking the integration of behavioral, computational and neural as CNS’s main central concern. Here is the abstract:

A sequence of images, sounds, or words can be stored at several levels of detail, from specific items and their timing to abstract structure. We propose a taxonomy of five distinct cerebral mechanisms for sequence coding: transitions and timing knowledge, chunking, ordinal knowledge, algebraic patterns, and nested tree structures. In each case, we review the available experimental paradigms and list the behavioral and neural signatures of the systems involved. Tree structures require a specific recursive neural code, as yet unidentified by electrophysiology, possibly unique to humans, and which may explain the singularity of human language and cognition.

I found the paper interesting in at least three ways.

First, it focuses on mechanisms, not phenomena. So, the paper identifies five kinds of basic operations that reasonably underlies a variety of mental phenomena and takes the aim of CNS to (i) find where in the brain these operations are executed, (ii) provide descriptions of circuits/computational operations that could execute such operations and (iii) investigate how these circuits might be/are neutrally realized.

Second, it shows how phenomena can be and have been used to probe the structure of these mechanisms. This is very well done for the first three kinds of mechanisms: (i) approximate timing of one item relative to the proceeding one, (ii) chunking items into larger units, and (iii) the ordinal ranking of items. Things get more speculative (in a good way, I might add) for the more “abstract” operations: the coding of “algebraic” patterns and nested generated structures.

Third, it gives you a good sense of the kinds of things that CNS types want from linguistics and why minimalism is such a good fit for these desires.

Let me say a word about each.

The review of the literature on coding time relations is a useful pedagogical case. DMWWP reviews the kind of evidence used to show that organisms “maintain internal representations of elapsed time” (3). It then look for “a characteristic signature” of this representation and the “killer” data that supports the representational claim. It then reviews the various brain locations that respond to these signature properties and review the kind of circuit that could code this kind of representation, arguing that “predictive coding” (i.e. ones that “form an internal model of input sequences”) is the right one in that it alone accommodates the basic behavioral facts (4) (basically minsmatched negativity effects without an overt mismatch). Next, it discusses a specific “spiking neuron model” of predictive coding (4) that “requires a neurophysiological mechanism of “time stamp” neurons that are tuned to specific temporal intervals,”  which have, in fact, been found in various parts of the brain. So, in this case we get the full Monte: a task that implicates signature properties of the mechanism, that demands certain kinds of computational circuits, realized by specific neuronal models, realized in neurons of a particular kind, found in different parts of the brain. It is not quite the Barn Owl (see here), but it is very very good.

DMWWP do this more or less again for chunking, though in this case “the precise neural mechanisms of chunk formulation remain unknown” (6). And then again for ordinal representations. Here there are models for how this kind of information might be neutrally coded in terms of “conjunctive cells jointly sensitive to ordinal information and stimulus identity” (8). These kinds of conjunctive neurons seem to be all over the place, with potential application, DMWWP suggests, as neuronal mechanisms for thematic saturation.

The last two kinds of mechanisms, those that would be required to represent algebraic patterns and hierarchical tree-like structures are behaviorally very well-established but currently pose very serious challenges on the neuro side. DMWWP observes that humans, even very young ones, demonstrate amazing facility in tracking such patterns. Monkeys also appear able to exploit similar abstract structures, though DMWWP suggests that their algebraic representations are not quite like ours (9). DMWWP further correctly notes that these sorts of patterns and the neural mechanisms underlying them are of “great interest” as “language, music and mathematics” are replete with such. So, it is clear that humans can deploy algebraic patters which “abstract away from the specific identity and timing of the sequence patterns and to grasp their underlying pattern,” and maybe other animals can too. However, to date there is “no accepted neural network mechanism to accomplish this and it looks like “all current neural network models seem too limited to account for abstract rule-extraction abilities” (9). So, the problem for CNS is that it is absolutely clear that human (and maybe monkey) brains have algebraic competence though it is completely unclear how to model this in wet ware. Now, that is the right way to put matters!

This last reiterates conclusions that Gallistel and Marcus have made in great detail elsewhere. Algebraic knowledge requires the capacity to distinguish variables from values of variables. This is easy to do in standard computer architectures but is not at all trivial in connectionist/neural net frameworks (as Gallistel has argued at length (e.g. see here)). Indeed, one of Gallistel’s main arguments with such neural architectures is their inability to distinguish variables from their values, and to store them separately and call them as needed. Neural nets don’t do this well (e.g. they cannot store a value and later retrieve it), and that is the problem because we do and we do it a lot and easily. DMWWP basically endorses this position.

The last mechanism required is one sufficient to code the dependencies in a nested tree.[1] One of the nice things about DMWWP is that it recognizes that linguistics has demonstrated that the brain codes for these kinds of data structures. This is obvious to us, but the position is not common in the CNS community and the fact that DMWWP is making this case in Neuron is a big deal. As in the case of algebraic patterns, there is no good models of how these kinds of (unbounded) hierarchical dependencies might be neurally coded. The DMWWP conclusion? The CNS community should start working on the problem. To repeat, this is very different from the standard CNS reaction to these facts, which is to dismiss the linguistic data because there are no known mechanisms for dealing with it.

Before ending I want to make a couple of observations.

First, this kind of approach, looking for basic computational mechanisms that are implicated in a variety of behaviors, fits well with the aims of the minimalist program (MP). How so? Well, IMO, MP has two immediate theoretical goals: to show that the standard kinds of dependencies characteristic of linguistic competence are all different manifestations of the same underlying mechanism (e.g. are all instances of Merge). Were it possible to unify the various modules (binding, movement, control, selection, case, theta, etc) as different faces of the same Merge relation and were we able to find the neural “merge” circuit then we would have found the neural basis for linguistic competence. So if all grammatical relations are really just ones built out of merges, then CNSers of language could look for these and thereby discover the neural basis for syntax. In this sense, MP is the kind of theory that CNSers of language should hope is correct. Find one circuit and you’ve solved the basic problem. DMWWP clearly has bought into this hope.

Second, it suggests what GGers with cognitive ambitions should be looking for theoretically. We should be trying to extract basic operations from our grammatical analyses as these will be what CNSers will be interested in trying to find. In other words, the interesting result from a CNS perspective is not a specification of how a complicated set of interactions work, but isolating the core mechanisms that are doing the interacting. And this implies, I believe, trying to unify the various kinds of operations and modules and entities we find (e.g. in a theory like GB) to a very small number of core operations (in the best case just one). DMWWP’s program aims at this level of grain, as does MP and that is why they look like a good fit.

Third, as any MPer knows, FL is not just Merge. There are other operations. It is useful to consider how we might analyze linguistic phenomena that are Merge recalcitrant in these terms. Feature checking and algebraic structures seem made for each other. Maybe memory limitations could undergird something like phases (see DMWWP discussion of a Marcus suggestion on p. 11 that something like phases chunk large trees into “overlapping but incompletely bound subtrees”). At any rate, getting comfortable with the kinds of mental mechanisms extant in other parts of cognition and perception might help linguists focus on the central MP question: what basic operations are linguistically proprietary? One answer is: those operations required in addition to those that other animals have (e.g. time interval determination, ordinal sequencing, chunking, etc.).

This is a good paper, especially so because of where it appears (a very leading brain journal) and because it treats linguistic work as obviously relevant to the CNS of language. The project is basically Marr’s, and unlike so much CNS work, it does not try to shoehorn cognition (including language) into some predetermined conception of neural mechanism which effectively pretends that what we have discovered over the last 60 years does not exist.

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[1] DMWWP notes that the real problem is dependencies in an unbounded nested tree. It is not merely the hierarchy, but the unboundedness (i.e. recursion) as well.

Here are three short things to read

Here are three short things to read.

First, a piece by Randy Gallistel (sent to me by Kleanthes, thx) where he discusses our current state of knowledge in Cog Neuro. He takes it for granted that we are all Marrians now (something that indicates what a terrific optimist he is). He also assumes that all of us accept the “computational theory of mind” and that we are comfortable with assuming a roughly Rationalist conception of cognition and the brain, one, that Kant, for example, would have been very comfortable with. Here is Randy:

Second, we have learned from behavioral experiments that foundational abstractions such as space, time, number, and probability play fundamental roles not only in our own mentation but also in the cognition and behavior of animals that we thought had no minds at all — rodents and insects, for example. We have learned from neuroscience experiments that signals based on these abstractions — spatial- and temporal-location signals, for example — are seen in individual neurons in very small brains. The number of neurons in the brain of a typical insect is about the same as the number in one voxel of a human functional magnetic resonance image (fMRI). Thus, both behavioral data and neurobiological data have taught us that it does not take a human-size brain to compute locations in space and time, to count, or to estimate uncertainty. Nor does it take extensive experience; many insects live only a few days to a few weeks, and rodents already display behavior based on these abstractions when they are at most a few months old.

So, quick learning without “extensive experience” and lots of innate structure concerning the primitives of space, time, number, probability a.o. No blank slate here.

Randy believes that we have learned that this Rationalist picture of the mind/brain is correct, though problems of detail abound. Where then are the mysteries? Well, you can guess given that it is Randy. In his own words:

What we haven’t yet learned are the answers to the computational questions that we have learned to ask. We do not yet know how the brain implements the basic elements of computation (the basic operations of arithmetic and logic). We do not yet know the mind’s computational primitives. We do not yet know in what abstract form (e.g., analog or digital) the mind stores the basic numerical quantities that give substance to the foundational abstractions, the information acquired from experience that specifies learned distances, directions, circadian phases, durations, and probabilities. Much less do we know the physical medium in nervous tissue that is modified in order to preserve these empirical quantities for use in later computations.

In other words, we know that quite a bit about the mental computations and the implications this has for brains, but we really know very little about how brains embody these computations. We don’t know how the physical bases of the required computations (e.g. how do brains store numbers? How do they add and subtract them? What’s the brain analogue of a register? Or writing to memory? Or …) In fact, to put this in Chomsky terms, Randy catalogues the problem of “the physical basis of memory in the brain” as a mystery, not a problem. This, I am quite sure, would come as a surprise to most CNSers. Why? Because as Randy has amply demonstrated elsewhere, most CNSers are still hyper-Empiricists (see here). They deny that we know what Randy is sure that we do. This is too bad. For if the critical Randy is right, then one of the reasons the physical basis of cognition is a mystery (and will remain one for quite a while) is that the bulk of the CNS community is asking the wrong questions and hence looking for the wrong mechanisms in the wrong places.

Here is a second piece. It is on sexism in science, in this case a real one, viz. physics. It is depressing reading. Many of the problems cited, though very serious (e.g. being propositioned and gropped by advisors who drop you as an advisee if rebuffed), are obviously disgusting and, I believe, uncontroversially horrible. I agree that we are slow to call out egregious offenders and I agree that this almost certainly serves to dampen scientific curiosity of those on the receiving end. But at least these things are now acknowledged to be disgusting. People are now fired for such offenses and ridiculed for their views and behaviors (e.g. presidents of prestigious instituions have become ex-presidents for saying otherwise). This is a good thing and I believe (hope) that over time this overt bad behavior will be weeded out and become as unacceptable as overt racism and homophobia are now.

What worries me more are the subtle forms of sexism. Here are two observations from the post:

As one male physicist has reputedly put it, ‘only blunt bright bastards make it in the field’. Though that has never been wholly true (think of the gentle genius Michael Faraday), it sums up sentiments that run deep through the physical sciences community, creating psychological and sociological barriers not only for women but also for many men. (10)

…most science forums were invented by men, are headed by men, and maintained by men to sustain the interests of overwhelmingly male audiences. It’s not just women who should be asked to change. (13)

Changing these attitudes will be harder. We do have a conception of “what being smart” looks like. Brash, pushy, talkative, argumentative, to name four traits. We (or at least I) value argument and disputation as the route to knowledge. I also recognize that this might not be everyone’s preferred method of thinking, though it is enshrined in standard scientific practice (e.g. journals, conferences, colloquia). The piece made me wonder about how to change this and whether we should (i.e. what we might loose were we to do so).

Let me be upfront: I find that one of the things that has disimproved since my early days is the readiness to critically evaluate competing proposals. There is a cost to letting a thousand flowers bloom, especially if some of the flowers are dangerous weeds dressed up in attractive petals. So, I believe that criticism is called for and bluntness in the evaluation of ideas is not vice. However, I can also see that there might be a down side to this. So it seems plausible to me that one of the endowments of privilege is a thicker skin (though in my experience, nobody’s skin is actually all that thick) and hence greater tolerance when it comes to having ones favorite ideas savaged. Less privilege, more susceptibility to the ravages of criticism. This makes sense to me.

What I am less clear about is how to modulate this without eliminating strong criticism, which I believe is a necessary part of making scientific progress. In the best of all possible worlds, we would attack ideas not the people that hold them and so nobody would take criticism personally. However, academics (in fact most people) often identify themselves with their ideas. And they do so for good reason. Theories/proposals are like works of art, personal creations. Thus, being told that these ideas are not worth the time of day (if not worse) is not something that one generally takes impersonally. So criticism hurts the scientist not only the scientific proposal. And if one is not that confident to begin with, well, there will be a downside.

However, there is also a downside to not engaging in very vigorous criticism. A good part of science involves focusing the community on the right questions and approaches. There is rhetoric to scientific persuasion and part of that can involve harsh criticism. Bad ideas need to be weeded out and the process of doing this is seldom petty. So Lake Wobegon science where every idea is pretty and above average is, IMO, a recipe for stagnation.

At any rate, let me know what you think. Especially as it applies to our little pleasant part of the scientific universe. How is this in linguistics? Cogneuro? Psycho? What can we do about it? Should we do anything about it? Can we have our cake and eat it too. DO we need a new ethics of discourse and if so what should it look like?

Last point: there are also many obviously less sybtle barriers to the advancement of women and others in science. These too are important. Please flag them and discuss. Btw, FoL has relatively few female commenters. Your opinions would be valuable here. In fact, let me make an offer: anyone wishing to post on this topic rather than merely comment, send me your stuff and I will seriously try to post it (subject to the usual caveats, of course).

Last paper: I found this paper on bees fascinating. It has nothing to do with anything. Put it in my fun category with singing mice.