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Showing posts with label Marc Hauser. Show all posts
Showing posts with label Marc Hauser. Show all posts

Monday, February 29, 2016

Hauser reviews "Why only Us"

Though my interest in Darwin's Problem (DP) is deep, my "expertise," such as it is, is restricted to the logic of the argument. The logic is well known: (i) hierarchical recursion is a distinctive hallmark of human I-languages, (ii) there is no evidence that any other animal displays such recursive powers, (iii) what nonlinguistic evidence there is concerning such powers in humans is of rather recent vintage (roughly 100kya), (iv) logically speaking recursion is an all or nothing affair. The conclusion from (i)-(iv) is that something simple occurred roughly 100kya that in combination with the nonlinguistic cognitive and computational powers extant at the tome in our ancestors allowed for this human species specific capacity to emerge. That's the logic. 

It looks a lot like the logic of PoS arguments in that it starts from a specification of the capacity own interest and argues backwards to the causal mechanisms that could produce it. In other words, just as GGers investigate human linguistic cognition by first describing what it is that has been acquired and inferring from this what the system of acquisition must look like, so too we investigate evolutionary possibilities concerning language by first specifying what it is that has evolved. Sadly, this is not the general methods of investigation. Empiricists (both in psychology and evolutionary biology) seem to think that the direction of argument should be reversed: given that we know what learning and evolution is they conclude that a species specific FL or a species specific characteristics cannot grow in human minds nor have evolved there. The arguments they provide are awful, and not for sophisticated reasons. They are awful because they fail to address what we know about human linguistic capacities. They are based on the premiss, in other words, that the facts that GG has discovered over the last 60 years are bogus. As anyone but a flat earther knows this to be wrong,…[1]

Discussion at this level is where my expertise ends. However, DP is more than just logically interesting (it has potential empirical ramifications) and there is more to the logic than I outlined above (Merge may not be the only unique capacity our linguistic facility manifests (see the review below)). Berwick and Chomsky's Why Only Us (WOU) goes into these issues, and Marc Hauser has thought about them hard. So what better way to get into them more deeply than to ask Marc to do a blog post on WOU. He graciously agreed. Here it is. 

*****

Berwick & Chomsky’s Why only us (2016):
Challenges to the what, when, and why?

Marc D. Hauser

Why only us  [WOU] is a wonderful, slim, engaging, and clearly written book by Robert Berwick and Noam Chomsky.  From the authors’ perspective, it is a book about language and evolution. And of course it is.  However, I think it is actually about something much bigger.  It is an argument about the evolution of thought itself, with language being not only one form of thought, but a domain that can impact thought itself, in ways that are truly unique in the animal kingdom.  Seen in this light, WOU provides a framework for thinking about the evolution of thought and a challenge to Darwin’s claim that the human mind is only quantitatively different from other animals. Since this is an idea that I have championed (Hauser, 2009), I am of course a bit partial! Let me unpack all of this by working through Berwick and Chomsky’s arguments, especially those where we don’t quite agree. 

One caveat up front:  as I have written before, including with Berwick and Chomsky (Hauser et al., 2014), I am not convinced that the ideas put forward here or in WOU are testable: animal capacities are far too impoverished to shed any comparative light on the evolution of human language, and the hominid fossil record is either silent or too recent to be of interest. My goal here, therefore, is to focus on the fascinating ideas raised in WOU,  leaving to the side how or whether such ideas might be confronted by significant empirical tests. 

One of the essential moves in WOU is to argue that MERGE —the simplest recursive operation — is the bedrock of our capacity for infinite expression by finite means, one that generates hierarchical structure. Because no other animal has MERGE, and because MERGE  is simple and the essence of language, the evolutionary process may well have occurred rapidly, appearing suddenly in only one species: modern humans or Homo sapiens sapiens (Hss).  To accept this argument, you have to accept at least five premises:

            1- MERGE is the essence of language
            2- No other animal has MERGE
            3- No other hominid has MERGE
  4- Due to the simplicity of MERGE, it could evolve quickly, perhaps
 due to mutation
            5- Because you either have or don’t have MERGE (there is no
                  demi-MERGE), there is no option for proto-language.

I accept 2 because the comparative literature shows nothing remotely like MERGE.  Whether one looks at data from natural communication, artificial language learning experiments, or animal training studies with human language or language-like tokens, there is simply no evidence of anything remotely recursive.  As Berwick and Chomsky note, the closest one gets is the combinatoric gymnastics observed in birdsong, but these are neither recursive nor do they generate hierarchical structures that shape or generate the variety of meaningful expressions observed in all human languages. 

I also accept 3, though here we don’t really have the evidence to say one way or the other, and even if we did, and it turned out that say Neanderthals had MERGE, it wouldn’t really make much of a difference to the argument.  That is, the fossil record for Neanderthal, though richer than we once thought, says nothing about recursive operations, and nor for that matter does the fossil record for Hss. Both records show interesting signs of creative thought — a topic to which I return — but nothing that would indicate recursive thought or expression.  If evidence emerges that Neanderthals had MERGE, that would simply push back the date of origin for Berwick and Chomsky’s evolutionary account, without changing the core details.    

Let’s turn to 1, 4 and 5 then.  What is interesting about the core argument in WOU is that although Berwick and Chomsky place significant emphasis on MERGE, they fully acknowledge that the recursive machinery must interface with the Conceptual-Intensional system on the one hand, and with the Sensory-Motor system on the other.  However, once one acknowledges the non-trivial roles of CI, SM, and the interfaces, while also recognizing the unique properties of each of these systems, it is no longer possible to accept premise 4, and challenges arise for premise 5.  This analysis lays open the door to some fascinating possibilities, many of which might be explored empirically. I consider a few next.

Berwick and Chomsky devote some of the early material of WOU to review work on vocal imitation in songbirds, including comparative genetic and neurobiological data.  In some ways, the songbird system is a lovely example because the work is exquisitely detailed and shows some nice parallels with our own.  In particular, songbirds learn their song in some of the same ways as young children learn language, including evidence of an innate system that constrains both the timing and material acquired.  However, there are elements of the songbird system that are strikingly different from our own, not mentioned in WOU, but when acknowledged, tell an even more interesting tale about the evolution of Hss — one that is at the same time supportive of the uniqueness claims in WOU while also raising questions about the nature of the uniqueness claim.  Specifically, the songbird system is a striking example of extreme modularity.  The capacity of a songbird to imitate or learn its species-specific song is not a capacity that extends to other calls in its vocal repertoire, nor to any visual display. That is, a songbird can imitate the song material it hears, but nothing else.  Not so for our species, where the capacity to imitate is amodal, or at least bimodal, with sounds and actions copied readily, and from birth. This disconnect from sensory modality is a trademark of human thought, and of course, is a critical feature of our language faculty:  at virtually all levels of detail, including syntax, semantics, phonology, acquisition, and pragmatics, there are no differences between signed and spoken languages. No other animal is like this.  Whether we observe songbirds, dolphins, or non-human primates, an individual born deaf does not emerge with a comparably expressive visual system of communication.  The systems of communicative expression are intimately tied to the modality, such that if one modality is damaged, other modalities are incapable of picking up the tab.  The fact that our language, and even more broadly, our thoughts, are detached from modality, suggests a fundamental reorganization in our representations and computations.  This takes us to CI, SM, MERGE and the interfaces.

Given the modularity of the songbird system, and the lack of imitative capacities in non-human primates, we also need an account of how a motor system capable of imitating sounds and actions evolved.  This is an account of how SM evolved, but also, about how and when SM interfaced with CI and MERGE.  There is virtually no evidence on offer, and it is hard to imagine what kind of evidence could emerge. For example, the suggestion that Neanderthals had a hyoid bone like Hss is interesting, but doesn’t tell us what they were doing with it, whether it was capable of being deployed in vocal imitation, and thus, of building up the lexicon.  And of course, we don’t know whether or how it was connected to CI or MERGE.  But whatever we discover about this account, it showcases the importance of understanding the evolution of at least one unique property of SM.

When we turn to CI, and in particular, lexical or conceptual atoms, we know extremely little about them, even in fully linguistics human adults.  Needless to say, this makes comparative and developmental work difficult.  But one observation seems fairly uncontroversial: many of our concepts are completely detached from sensory experiences, and thus can’t be defined by them. If we take this as a starting point, we can ask: do animals have anything remotely like this?  On one reading of Randy Gallistel’s elegant work, the answer is “Yes.”  All of the empirical work on number, time and space in animals suggests that such concepts are either not linked to or defined by a particular modality, or minimally, can be expressed in multiple modalities.  Similarly, there is evidence that animals are capable of representing some sense of identity or sameness that is not tied to a modality.  If this is right, and even if these concepts are not as abstract as ours, they suggest a potential comparative approach that at this point, seems closed off for our recursive capacity.   Having a comparative evolutionary landscape of inquiry not only aids in our analyses, it also raises a challenge to premises 4 and 5, as well as to Richard Lewontin’s comment (supported by Berwick and Chomsky) that we can’t study or understand the evolution of cognition.  Let me take a small detour to describe a gorgeous series of studies on the evolution of cognition to show what can and has been done, and then return to premises 4 and 5.

In most monogamous species, the male and female share the same home range or territory.  In polygynous species, in contrast, there are several females associated with one male, and thus, the male’s home range area encompasses all of the smaller female home ranges.  Based on this observation, Steve Gaulin and his colleagues (Gaulin & Wartell, 1990; Jacobs, Gaulin, Sherry, & Hoffman, 1990; Puts, Gaulin, & Breedlove, 2007) predicted that the spatial abilities of a monogamous vole would show no sex differences, whereas males would show greater abilities than females in a closely related polygynous vole species. Using a maze running task to test for spatial capacity, results provided strong support for the prediction.  Further, the size of the hippocampus — an area of the brain known to play an important role in spatial navigation — was significantly larger in males of the polygynous species when contrasted with females, whereas no sex differences were found for the monogamous species. This, and several other examples, reveal how one can in fact study the evolution of cognition. Lewontin is, I believe, flatly wrong.

Back to premises 4 and 5. If nonhuman animals have abstract, amodal concepts — as some authors suggest —  then we have a significant line of empirical inquiry into the evolution of this system.  If our concepts are unique — as authors such as Berwick and Chomsky believe —  then there may not be that many empirical options. Perhaps Neanderthals have such concepts, perhaps not. Either way, the evolutionary timescale is short, and the evidence thus far, relatively thin.  On either account, however, there is the pressing need to understand the nature of such concepts as they bear on what I believe is the most interesting side effect of this discussion, and the issues raised in WOU.  In brief, if one concedes that what is unique about language, and thus, its evolutionary history, is MERGE, CI, SM and the interfaces, then a different issue emerges:  are these four ingredients unique to language or part of all aspects of human thought?  Said differently, perhaps WOU is really an account of how our uniquely human system of thought evolved, with language being only one domain in terms of its internal and external systems of expression. Berwick and Chomsky often refer to our Language of Thought, as the core of language, and what is our most dominant use of language: internal thought.  On this view, externalization of this system in expressed language is not at the core of the evolutionary account.  On the one hand, I agree. On the other hand, I think the use of the term of Language of Thought or LOT has confused the issue because of the multiple uses of the word “language.” If the essence of the argument in WOU is about the computations and representations of thought, with linguistic thought being one flavor, then I would suggest we call this system the Logic of Thought.  I suggest this substitution of L-words for two reasons.  Language of Thought implies that the system is explicitly linguistic, and I don’t believe it is.  Further, I think Logic of Thought better captures the abstract nature of the ingredients, including both the recursive operations, concepts, motor routines, and interfaces. 

The Logic of Thought, I would argue, is uniquely human, and underpins not only language, but many other domains as well.  It explains, I believe, why actions that appear similar in other animals are actually not similar at all.  It also provides the ultimate challenge to Darwin’s argument that there is continuity in mental thought between humans and other animals, with differences attributable to quantity as opposed to quality.  In contrast, if the ideas discussed here, and ultimately raised by Berwick and Chomsky are right, then it is the Logic of Thought that is unique to humans.  The Logic of Thought includes all four ingredients: MERGE, CI, SM, and the interfaces. How these components are articulated in different domains is fascinating in its own right, and raises several additional puzzles. For example, if MERGE is the simplest recursive operation, is it one neural mechanism that interfaces with different, domain-specific concepts and actions, or were merge like circuits effectively cloned repeatedly, each subserving a different domain?  The first possibility suggests that damage to this singular MERGE circuit would reveal deficits in multiple domains.  The second option suggests that damage to the MERGE circuit in one domain would only reveal deficits in this domain. To my knowledge, there is no evidence of neuropsychological deficits or imaging studies that point to the nature or distribution of such recursive circuitry. 

In sum, WOU is really a terrific book. It is thought provoking and clear.  What more could you want?  My central challenge is that it paints an evolutionary account that can only work if the essence of language is simple, restricted to MERGE.  But language is much more than this.  As such, there has to be more to the evolutionary process.  By raising these issues, I believe Berwick and Chomsky have challenged us to think about another option, one that preserves their title, but focuses on the logic of thought.  Why only us? Much to think about.


Gaulin, S. J., & Wartell, M. S. (1990). Effects of experience and motivation on symmetrical-maze performance in the prairie vole (Microtus ochrogaster). Journal of Comparative Psychology, 104(2), 183–189.
Hauser, M. D. (2009). The possibility of impossible cultures. Nature, 460, 190–196.
Hauser, M. D., Yang, C., Berwick, R. C., Tattersall, I., Ryan, M. J., Watumull, J., et al. (2014). The mystery of language evolution. Frontiers in Psychology, 5(401), 1–12.
Jacobs, L. F., Gaulin, S. J., Sherry, D. F., & Hoffman, G. E. (1990). Evolution of spatial cognition: sex-specific patterns of spatial behavior predict hippocampal size. Proceedings of the National Academy of Sciences, 87(16), 6349–6352.

Puts, D. A., Gaulin, S. J., & Breedlove, S. M. (2007). Sex differences in spatial ability: evolution, hormones and the brain. Evolutionary Cognitive Neuroscience. MIT Press, pp 329-379.





[1] Talking about flat-earthers and limitless ignorance, it is worth comparing Hauser’s review below to one by V. Evans (yes, that V. Evans) here. The best that I can say is that it is consistent with what I have come to expect from Evans’ work, (viz. it is not worse than his other recent output (tant pis)).

Monday, January 6, 2014

Every silver lining has a cloud (or, the dark side of Merge)

Humans differ from other animals in a variety of ways. One of the most impressive is what Marc Hauser (here) calls “fluid thinking,” the “brain’s capacity to repeatedly combine thoughts and emotions from different domains of knowledge” (189-90). I discussed the conjecture by Liz Spelke and others that human linguistic competence underlies this (here), though at the moment it is, I believe, fair to say, that this hunch has remained pretty underdeveloped and has yet to rise to the level of a full fledged hypothesis. Nonetheless, even in its inchoate form it is interesting to consider the possible properties (both ben- and mal- ign) of a combinatorial demodularized brain when hooked up with an apish neuro-chemistry.  Hauser, in a fascinating book with the tasty title Evilicious, does this for the problem of evil. Yes, you read correctly, this is a big theme book. What makes it impressive, is that it not only spins a hellishly good story, but is didactic and, in addition, a pleasure to read (well sort of, the topic can get pretty disgusting, e.g. see p. 151-2 and p. 205 for a partial inventory of torture methods and instruments).

Hauser starts with the common observation that humans are very good at being very bad. In fact, compared to other animals we are in a league of our own when it comes to inflicting gratuitous pain for gain and/or pleasure.  Hauser gives a tour of the underlying psycho-biology of this capacity for cruelty and provides a pretty suggestive story that links our neuro-chemistry (there’s nice reviews of the various effects of testosterone, serotonin, dopamine etc), brain modules (e.g. the anterior cingulate plays an important role), genetics (there’s some action on the X chromosome that is critical) and our emergent mental fluidity (in part a product of our promiscuous and recent evolved combinatorial capacity (think Merge)) to explain why psychopathic behavior is so natural to humans. Nothing does cruelty, like humans do cruelty. And the same goes for sadism, torture, rape, mass murder, auto-da-fes, stoning, genital mutilation, etc. In effect, the same features that support our remarkable creativity have had the unfortunate consequence of giving us the capacity for evil unparalleled in the rest of the biological world. The road from Da Vinci (or even Ghandi) to Ghengis Kahn is shorter than you might like.

Oddly, the book, at least to me, has a 17th-18th centural feel to it. Hauser writes a bit like how Hume might have written were he among us today. How so? Evilicious is a piece of natural philosophy, combining a deep knowledge of what we have learned in the mental and brain sciences (btw, the footnotes are a great resource all by themselves. Damn he’s read and synthesized a lot!) to explain how people are able to insulate themselves from moral considerations so as to be able to act in unboundedly many heinous ways without even hints of remorse.

There is also a touch of Leibniz-Malebranche. Like them, Hauser aims to explain how evil is the natural concomitant of those human virtues we all prize: imagination, a sense of justice and desert, a taste for risk, etc. To steal a line from Jan Koster, what Hauser shows is how evil is a human App; it’s what you get when you get the kinds of flexible combinatorial brains that allow for human creativity. This argument would have delighted Leibniz and Malebranche in their theodicean moments. But how depressing to think that this might really be the best of all possible worlds (capacity wise at least (see Hauser's nice distinction between evolved repertoires vs evolved capacities p. 230)).


So, Hauser has written a terrific little book. It synthesizes and reviews, in a very readable way, a lot of contemporary research in evolutionary biology, neuro-chemistry, neuro-science, social psychology, philosophy, economics and more. And it does this all the while addressing one of those endlessly fascinating questions: whence evil? I highly recommend it. Reading it might even be good for you.

Thursday, January 10, 2013

Bad Data; an addendum

In my last post on Bad Data I mentioned that exactly one published paper by Hauser (the 2002 Cognition paper) was retracted and that this struck me as pretty slim pickings and not worth all the fuss generated. A kind correspondent pointed out to me that the results of this paper were recently replicated and is coming out. The paper - Chasing Sounds-  is by Julie Neiworth and using a slightly different methodology replicates the earlier findings.  By my count this means that for all of the sturm and drang the forward march of science was impeded not one whit. Indeed, given the last replication, one might conclude that withdrawing the paper has been a greater impediment than publishing it was.

Let me end with one personal remark. There are many scientific vices. Among these are virtuous self righteousness and envy.  Few things are as gratifying as bringing down a high flyer for malfeasance. See, s/he got there by fraud! We can ignore what s/he said (thank god as it gores my ox!).  Of the scientific vices these have often done more harm than deliberate fraud, let alone sloppiness. Moreover, they are much harder to police given how easy they are to dress up as virtue. At any rate, it seems that Hauser is three for three and that fuss in that teapot was more like a mild summer shower rather than a tempest.  Personally I hope that Hauser soon comes back from exile and starts psychologizing.

Tuesday, January 8, 2013

Bad Data


Gary Marcus picks up on a currently popular meme about shoddy empirical hygiene in science. He points to two problems. First, there have been busts of prominent scientists (I will return to this), flurries of retractions, and the emergence of a Blog (Retraction Watch) to monitor experimental malfeasance, which, apparently, is rampant, especially in the biomedical world.  Second, it appears that experiments are all too often unreplicable.  Together, Gary seems to believe, these two problems threaten to slow down the march of scientific understanding, despite the long run self correcting nature of the enterprise. As he puts it:

In the long run, science is self-correcting…Even if nothing changed, we would eventually achieve the deep understanding that all scientists strive for.  But there is no doubt that we can get there faster if we clean up our act.

This all sounds pretty dire. Gary sites one study of fifty-three medical studies and found that forty-seven did not replicate.  And this is the non-fraudulent stuff! At the risk of not being sufficiently panicked, I cannot help wondering how big a problem this really is and whether the meme reveals more about an implicit empiricist philosophy of science than it does a serious problem threatening to appreciably slow down research.

Before saying a bit more, let me shout out very loudly that I AM NOT CONDONING MALPRACTICE AND DISHONESTY. Of course, one should not lie or steal or cheat or practice bad statistical hygiene. However, there are times when problems that look serious are not worth worrying about, or even fixing. Think about the recent Republican hyperventilation about voter registration fraud.  Fixing even legitimate concerns can have undesired side effects. I will mention one below currently raising hackles in syntax. So, stipulating that we want everyone to act honestly and experiment carefully, are the problems Gary mentions really something we should be worried about, at least in our small part of the scientific universe?

Let’s take fraud first.  In case anyone hasn’t heard, Marc Hauser was accused of fabricatingdata. His case was reviewed both by Harvard and the NIH. He was forced to resign for scientific misconduct and though neither “admit[ting] nor deny[ing]  scientific misconduct” he did accept responsibility for “all errors made within the lab.” 

This fraud case always struck me as pretty much a tempest in a teapot.  Hauser was accused of mishandling data in three published papers. Of these, one on Cognition (2002) had to be retracted. The two others reconfirmed the earlier stated results when the data analysis was redone.  In addition, it seems that Hauser also misstated results in some papers that were corrected before publication. All in all, it seems that exactly one published paper proved to be seriously defective and it was pulled.

Curiously, in my opinion, the paper that was pulled had (what to a linguist would be) a pretty boring result.  It was based on other work by Gary Marcus (Marcus et. al in Science 1999) that showed that kids could think algebraically and abstract patterns that eluded standard connectionist devices. It also would have served as an interesting counterpoint to later work byMarcus (see Marcus et. al 2009) that provided evidence that a child’s capacity to “extract abstract rules and regularities from sequences” engaged “at least one learning mechanism that is specially tuned to language.” This latter is really cool for it appears to provide evidence for a linguistically dedicated learning component. Note that the 2002 Cognition piece would have provided evidence against this juicy conclusion. It argued that Tamarins (they don’t talk!) could do the same thing. Given that this paper has been retracted, it seems that the interesting result is still viable. From the little I can gather, the retracted paper had little influence on the direction of other research (e.g. it did not stop Gary from pursuing the interesting hypothesis noted above) and I doubt that it did much to impede the march of science, or the attractiveness of the modularity of learning thesis (or lack thereof, psychologists tend to dislike these kinds of dedicated language results).

So much for fraud. More interesting is the idea that most experiments are not replicable. Gary discusses several ways in which experimentalist troll for significant results and urges, reasonably enough, that these bad practices should be avoided. He also notes that there are institutional incentives that abet these unfortunate tendencies, including only publishing experiments that succeed.  At any rate, the points he makes are reasonable, though I suspect are not the real source of the slow pace of advance in many of the sciences.  Let me explain.

From my very restricted vantage point, the main problem in a lot of “scientific” work is the absence of any (even rough) understanding of the causal architecture of the problem domain. In short, the dearth of any reasonably articulated theory.  This theoretical lacuna arises not because of an absence of enough good data, but because we often have no idea what the underlying causal processes might be or how to generalize from the individual data points we collect.  Consequently, there are many beautifully crafted experiments whose point is completely obscure. Indeed, I often get the impression that psychology is the study of methodologically flawless experiments rather than the study of mental capacities. In this context, rigor is the only game in town and generating bad data the ultimate crime.  In areas where there is a modicum of interesting theory, bad data is not nearly so serious for it is easier to detect and weed out. Eddington’s dictum explains why: Never trust an experiment until it has been verified by theory! Theory serves to filter out experimental detritus.  Where such theory is absent bad data can confuse. But then the main problem with such a discipline is not the prevalence of bad data but the absence of even weak theory. 

Is there a bad data problem in Linguistics? Some seem to think there is, and they have recently again begun to chastise generativists for their irresponsible and errant ways. It has been asserted that the lax ways in which linguists (syntacticians are the cynosure here) collect judgment data, i.e. they consult the intuitions of a handful of native speakers, generate bad data, which consequently result in very poor theories. Indeed, many of the all too frequent pronouncements about the collapse of the generative enterprise often go hand in hand with lots of clucking about the shoddy data collection that is claimed to be endemic. Gibson is the most recent avatar of this meme (though there are others) and Jon Sprouse and Diogo Almeida (S&A) the most prominent ghost busters.

In a series of papers (see herehere, here and here), S&A eviscerate these claims. They do this by retesting the “badly collected” data using more refined testing techniques borrowed from our friends in psychology. They find that the informal methods exploited by linguists are more than good enough. Indeed comparing them to what we typically find in psych work, they are unbelievably reliable (95% of the data is reliably replicable, an unheard level of reliability in the mental sciences) and very sensitive (it takes only a few sentences asked of a few judgers to get this very reliable data). So the shoddy methods we know and love are more than good enough for most of what we do, at least if the more careful experimental methods that Gibson urges are the touchstones of adequacy. This does not mean to say that more careful methods may not be appropriate in some circumstances and for investigating different kinds of problems (c.f. Sprouse’s more recent work discusses examples. Not yet written up so try to go to a talk if he is speaking at a venue near you). These more prissy methods may be useful in the right contexts and linguists should not shy away from using them when appropriate.  However, S&A have demonstrated quite conclusively that the informal methods that are quick and easy to use (no small virtues I might add) are perfectly adequate, indeed surprisingly powerful, and that the theory developed using these methods if inadequate are not inadequate because the data the theory addresses is defective.

There is a popular picture of science that owes a lot to empiricist epistemology: scientists carefully collect data, cautiously develop theories to explain this data, extend these theories by yet more refined methods of data collection and build theories on these purer data points.  It is easy to understand the danger posed by bad data given this conception.  It pollutes the process, adds dirt to the gears of science thereby reducing its efficiency and threatening to derail it. However, this picture is false. Data IS important, but mainly for testing theory and even then how data and theory come together is a very complicated matter.  I am partial to the version of the scientific method urged by Percy Bridgeman: “Use your noodle and no holds barred.”  If there is some reasonable theory for your noodle to work with the bad data problem will annoy but not otherwise impede progress.  In my view, the most serious impediments are not hygienic. Rather, most of the time we just don’t have the foggiest idea what’s going on, and, sadly, that has no quick fix.