biologically primary and secondary knowledge?

David Geary is an evolutionary psychologist who developed the concept of biologically primary and biologically secondary knowledge, popular with some teachers. I’ve previously critiqued Geary’s ideas as he set them out in a chapter entitled Educating the Evolved Mind. One teacher responded by suggesting I read Geary’s The Origin of Mind because it explained his ideas in more detail. So I did.

Geary’s theory

If I’ve understood correctly, Geary’s argument goes like this:

The human body and brain have evolved over time in response to environmental pressures ranging from climate and diet through to social interaction. For Geary, social interaction is a key driver of evolved brain structures because social interactions can increase the resources available to individuals.

Environmental pressures have resulted in the evolution of brain ‘modules’ specialising in processing certain types of information, such as language or facial features. Information is processed by the modules rapidly, automatically and implicitly, resulting in heuristics (rules of thumb) characteristic of the ‘folk’ psychology, biology and physics that form the default patterns for the way we think. But we are also capable of flexible thought that overrides those default patterns. The flexibility is due to the highly plastic frontal areas of our brain responsible for intelligence. Geary refers to the thinking using the evolved modules as biologically primary, and that involving the plastic frontal areas as biologically secondary.

Chapters 2 & 3 of The Origin of Mind offer a clear, coherent account of Darwinian and hominid evolution respectively. They’d make a great resource for teachers. But when Geary moves on to cognition his model begins to get a little shaky – because it rests on several assumptions.

Theories about evolution of the brain are inevitably speculative because brain tissue decomposes and the fossil record is incomplete. Theories about brain function also involve speculation because our knowledge about how brains work is incomplete. There’s broad agreement on the general principles, but some hypotheses have generated what Geary calls ‘hot debate’. Despite acknowledging the debates, Geary’s model is built on assumptions about which side of the debate is correct. The assumptions involve the modularity of the brain, folk systems, intelligence, and motivation-to-control.

modularity

The general principle of modularity – that there are specific areas of the brain dedicated to processing specific types of information – is not in question. What is less clear is how specialised the modules are. For example, the fusiform face area (FFA) specialises in processing information about faces. But not just faces. It has also been shown to process information about cars, birds, butterflies, chess pieces, Digimon, and novel items called greebles. This raises the question of whether the FFA evolved to process information about faces as such (the Face Specific Hypothesis), or to process information about objects requiring fine-grained discrimination (the Expertise Hypothesis). Geary comes down on the Faces side of the debate on the grounds that the FFA does not “generally respond to other types of objects … that do not have facelike features, except in individuals with inherent sociocognitive deficits, such as autism” (p.141). Geary is entitled to his view, but that’s not the only hotly debated interpretation of the evidence.

folk systems

The general principle of ‘folk’ systems – evolved forms of thought that result from information being processed rapidly, automatically and implicitly – is also not in question. Geary admits it’s unclear whether the research is “best understood in terms of inherent modular constraints, or as the result of general learning mechanisms” but comes down on the side of children’s thinking being the result of “inherent modular systems”.  I couldn’t find a reference to Eleanor Rosch’s prototype theory developed in the 1970s, which explains folk categories in terms of general learning mechanisms. And it’s regrettable that Rakison & Oakes’ 2008 review of research into how children form categories (that also lends weight to the general learning mechanisms hypothesis) wasn’t published until three years after The Origin of Mind. I don’t know whether either would have prompted Geary to amend his theory.

intelligence

In 1904 Charles Spearman published a review of attempts to measure intellectual ability. He concluded that the correlations between various specific abilities indicated “that there really exists a something that we may provisionally term “General Sensory Discrimination” and similarly a “General Intelligence”” (Spearman p.272).

It’s worth looking at what the specific abilities included. Spearman ranks (p. 276) in order of their correlation with ‘General Intelligence’, performance in: Classics, Common Sense, Pitch Discrimination, French, Cleverness, English, Mathematics, Pitch Discrimination among the uncultured, Music, Light Discrimination and Weight Discrimination.

So, measures of school performance turned out to be good predictors of school performance. The measures of school performance correlated strongly with ‘General Intelligence’ – a construct derived from… the measures of school performance. This tautology wasn’t lost on other psychologists and Spearman’s conclusions received considerable criticism. As Edwin Boring pointed out in 1923, ‘intelligence’ is defined by the content of ‘intelligence’ tests. The correlations between specific abilities and the predictive power of intelligence tests are well-established. What’s contentious is whether they indicate the existence of an underlying ‘general mental ability’.

Geary says the idea that children’s intellectual functioning can be improved is ‘hotly debated’ (p.295). But he appears to look right past the even hotter debate that’s raged since Spearman’s work was published, about whether the construct general intellectual ability (g) actually represents ‘a something’ that ‘really exists’. Geary assumes it does, and also accepts Cattell’s later constructs crystallised and fluid intelligence without question.

Clearly some people are more ‘intelligent’ than others, so the idea of g initially appears valid. But ‘intelligence’ is, ironically, a ‘folk’ construct. It’s a label we apply to a set of loosely defined characteristics – a useful shorthand descriptive term. It doesn’t follow that ‘intelligence’ is a biologically determined ‘something’ that ‘really exists’.

motivation-to-control

The motivation to control relationships, events and resources is a key part of Geary’s theory. He argues that motivation-to-control is an evolved disposition (inherent in the way people think) that manifests itself most clearly in the behaviour of despots – who seek to maximise their control of resources. Curiously, in referring to despots, Geary cites a paper by Herb Simon (Simon, 1990) on altruism (a notoriously knotty problem for evolution researchers). Geary describes an equally successful alternative strategy to despotism, not as altruism but as “adherence to [social] laws and mores”, even though the evidence suggests altruism is an evolved disposition, not merely a behaviour.

Altruism calls into question the control part of the motivation-to-control hypothesis. Many people have a tendency to behave in ways that increase their control of resources, but many tend to collaborate and co-operate instead, strategies that increase individual access to resources, despite reducing individual control over them. The altruism debate is another that’s been going on for decades, but you wouldn’t know that to read Geary.

Then there’s the motivation part. Like ‘intelligence’, ‘motivation’ is a label for a loosely defined bunch of factors that provide incentives for behaviour. ‘Motivation’ is a useful label. But again it doesn’t follow that ‘motivation’ is ‘a something’ that ‘really exists’. The biological mechanisms involved in the motivation to eat or drink are unlikely to be the same as those involved in wanting to marry the boss’s daughter or improve on our personal best for the half-marathon. The first two examples are likely to increase our access to resources; whether they increase our control over them will depend on the circumstances. Geary doesn’t explain the biological mechanism involved.

biologically primary and secondary knowledge

In The Origin of Mind, Geary touches on the idea of biologically primary and secondary competencies and abilities but doesn’t go into detail about their implications for education. Instead, he illustrates the principle by referring to the controlled problem solving used by Charles Darwin and Alfred Wallace in tackling the problem of how different species had arisen.

Geary says that such problem solving requires the inhibition of ‘heuristic-based folk systems’ (p.197), and repeatedly proposes (pp.188, 311, 331, 332) that the prior knowledge of scientific pioneers such as Linnaeus, Darwin and Wallace “arose from evolved folk biological systems…as elaborated by associated academic learning” (p.188). He cites as evidence the assumptions resulting from religious belief made by anatomist and palaeontologist Richard Owen (p.187), and Wallace’s reference to an ‘Overruling Intelligence’ being behind natural selection (p.83). But this proposal is problematic, for three reasons:

The first problem is that some ‘evolved’ folk knowledge is explicit, not implicit. Belief in a deity is undoubtedly folk knowledge; societies all over the world have come up with variations on the concept. But the folk knowledge about religious beliefs is usually culturally transmitted to children, rather than generated by them spontaneously.

Another difficulty is that thinkers such as Linnaeus, Darwin and Wallace had a tendency to be born into scholarly families, so their starting point, even as young children, would not have been merely ‘folk biological systems’. And each of the above had the advantage of previous researchers having already reduced the problem space.

A third challenge is that heuristics aren’t exclusively biologically primary; they can be learned, as Geary points out, via biologically secondary knowledge (p.185).

So if biologically primary knowledge sometimes involves explicit instruction, and biologically secondary knowledge can result in the development of fast, automatic, implicit heuristics, how can we tell which type of knowledge is which?

use of evidence

Geary accepts contentious constructs such as motivation, intelligence and personality (p.319) without question. And he appears to have a rather unique take on concepts such as bounded rationality (p.172), satisficing (p.173) and schemata (p.186).

In addition, Geary’s evidence is not always contentious; sometimes it’s his conclusions that are tenuous. For example, he predicts that if social competition were a driving force during evolution, “a burning desire to master algebra or Newtonian physics will not be universal or even common. Surveys of the attitudes and preferences of American schoolchildren support this prediction and indicate that they value achievement in sports … much more than achievement in any academic area” (pp.334-5), citing a 1993 paper by Eccles et al. The ‘surveys’ were two studies, the ‘American schoolchildren’ 865 elementary school students, the ‘attitudes and preferences’ competence beliefs and task values, and the ‘academic areas’ math, reading and music. Responses show some statistically significant differences. Geary appears to overegg the evidential pudding somewhat, and to completely look past the possibility that there might be culturally transmitted factors involved.

conclusion

I find Geary’s model perplexing. Most of the key links in it – brain evolution, brain modularity, the heuristics and biases that result in ‘folk’ thinking, motivation and intelligence – involve highly contentious hypotheses.  Geary mentions the ‘hot debates’ but doesn’t go into detail. He simply comes down on one side of the debate and builds his model on the assumption that that side is correct.

He appears to have developed an overarching model of cognition and learning and squeezed the evidence into it, rather than building the model according to the evidence. The problem with the second approach of course, is that if the evidence is inconclusive, you can’t develop an overarching model of cognition and learning without it being highly speculative.

What also perplexes me about Geary’s model is its purpose. Teachers have been aware of the difference between implicit and explicit learning (even if they didn’t call it that) for centuries. It’s useful for them to know about brain evolution and modularity and the heuristics and biases that result in ‘folk’ thinking etc. But teachers can usually spot whether children are learning something apparently effortlessly (implicitly) or whether they need step-by-step (explicit) instruction. That’s essentially why teachers exist. Why do they need yet another speculative educational model?

references

Eccles, J., Wigfield, A., Harold, R.D.,  & Blumenfeld, P. (1993). Age and gender differences in children’s self‐and task perceptions during elementary school, Child Development, 64, 830-847.

Gauthier, I., Tarr, M.J., Anderson, A.W., Skudlarski, P. & Gore, J.C.  (1999). Activation of the middle fusiform ‘face area’ increases with expertise in recognizing novel objects, Nature Neuroscience, 2, 568-573.

Rakison, D.H.  & Oakes L.M. (eds) (2008). Early Category and Concept Development.  Oxford University Press.

Simon, H.A. (1990). A mechanism for social selection and successful altruism. Science, 250, 1665-1668.

Spearman, C.  (1904).  ‘General Intelligence’ objectively determined and measured.  The American Journal of Psychology, 15, 201-292.

 

 

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cognitive science: the wrong end of the stick

A few years ago, some teachers began advocating the application of findings from cognitive science to education. There seemed to be something not quite right about what they were advocating but I couldn’t put my finger on exactly what it was. Their focus was on the limitations of working memory and differences between experts and novices. Nothing wrong with that per se, but working memory and expertise aren’t isolated matters.

Cognitive science is now a vast field; encompassing sensory processing, perception, cognition, memory, learning, and aspects of neuroscience. A decent textbook would provide an overview, but decent textbooks didn’t appear to have been consulted much. Key researchers (e.g. Baddeley & Hitch, Alloway, Gathercole), fields of research (e.g. limitations of long-term memory, neurology), and long-standing contentious issues (e.g. nature vs nurture) rarely got a mention even when highly relevant.

At first I assumed the significant absences were due to the size of the field to be explored, but as time went by that seemed less and less likely.  There was an increasing occurrence of teacher-B’s-understanding-of-teacher-A’s-understanding-of-Daniel-Willingham’s-simplified-model-of-working-memory, with some teachers getting hold of the wrong end of some of the sticks. I couldn’t understand why, given the emphasis on expertise, teachers didn’t seem to be looking further.

The penny dropped last week when I read an interview with John Sweller, the originator of Cognitive Load Theory (CLT), by Ollie Lovell, a maths teacher in Melbourne. Ollie has helpfully divided the interview into topics in a transcript on his website. The interview clarifies several aspects of cognitive load theory. In this post, I comment on some points that came up in the interview, and explain the dropped penny.

1.  worked examples

The interview begins with the 1982 experiment that led to Sweller’s discovery of the worked example effect. Ollie refers to the ‘political environment of education at the time’ being ‘heavily in favour of problem solving’. John thinks that however he’d presented the worked example effect, he’d be pessimistic about the response because ‘the entire research environment in those days was absolutely committed to problem solving’.

The implication that the education system had rejected worked examples was puzzling. During my education (1960s and 70s) you couldn’t move for worked examples. They permeated training courses I attended in the 80s, my children’s education in the 90s and noughties, and still pop up frequently in reviews and reports. True, they’re not always described as a ‘worked example’ but instead might be a ‘for instance’ or ‘here’s an example’ or ‘imagine…’. So where weren’t they? I’d be grateful for any pointers.

2 & 3. goal-free effect

Essentially students told to ‘find out as much as you can’ about a problem, performed better than those given specific instructions about what to find out. But only in relation to problems with a small number of possible solutions – in this case physics problems. The effect wasn’t found for problems with a large number of possible solutions.   But you wouldn’t know that if you’d only read teachers criticising ‘discovery learning’.

4. biologically primary and secondary skills

What’s determined by biology or by the environment has been a hugely contentious issue in cognitive science for decades. Basically, we don’t yet know the extent to which learning is biologically or environmentally determined.  But the contentiousness isn’t mentioned in the interview, is marginalised by David Geary the originator of the biologically primary and secondary concept, and John appears to simply assume Geary’s theory is correct, presumably because it’s plausible.

John says it’s ‘absurd’ to provide someone with explicit instruction about what to do with their tongue, lips or breath when learning English. Ollie points out that’s exactly what he had to do when he learned Chinese. John claims that language acquisition by immersion is biologically primary for children but not for adults. This flies in the face of everything we know about language acquisition.

Adults can and do become very fluent in languages acquired via immersion, just as children do. Explicit instruction can speed up the process and help with problematic speech sounds, but can’t make adults speak like a native. That’s because the adults have to override very robust neural pathways laid down in childhood in response to the sounds the children hear day-in, day-out (e.g. Patricia Kuhl’s ‘Cracking the speech code‘). The evidence suggests that differences between adult and child language acquisition are a frequency of exposure issue, not a type-of-skill issue. As Ollie says: “It’s funny isn’t it?  How it can switch category. It’s just amazing.”  Quite.

5. motivation

The discussion was summed up in John’s comment: “I don’t think you can turn Cognitive Load Theory into a theory of motivation which in no way suggests that you can’t use a theory of motivation and use it in conjunction with cognitive load theory.

 6. expertise reversal effect

John says: “As expertise goes up, the advantage of worked examples go down, and as expertise continues to go up, eventually the relative effectiveness of worked examples and problems reverses and the problems are more helpful than worked examples”.

7. measures of cognitive load

John: “I routinely use self-report and I use self-report because it’s sensitive”. Other measures – secondary tasks, physiological markers – are problematic.

8. collective working memory effect

John: “In problem solving, you may need information and the only place you can get it from is somebody else.” He doesn’t think you can teach somebody to act collaboratively because he thinks social interaction is biologically primary knowledge. See 4 above.

9. The final section of the interview highlighted, for me, two features that emerge from much of the discourse about applying cognitive science to education:

  • The importance of the biological mechanisms and the weaknesses of analogy.
  • The frame of reference used in the discourse.

biological mechanisms

In the final part of the interview John asks an important question: Is the capacity of working memory fixed? He says: “If you’ve been using your working memory, especially in a particular area, heavily for a while, after a while, and you would have experienced this yourself, your working memory keeps getting narrower and narrower and narrower and after a while it just about disappears.”

An explanation for the apparent ‘narrowing’ of working memory is habituation, where the response of neurons to a particular stimulus diminishes if the stimulus is repeated. The best account I’ve read of the biological mechanisms in working memory is in a 2004 paper by Wagner, Bunge & Badre.  If I’ve understood their findings correctly, signals representing sensory information coming into the prefrontal area of the brain are maintained for a few seconds until they degrade or are overridden by further incoming information. This is exactly what was predicted by Baddeley & Hitch’s phonological loop and visual-spatial sketchpad. (Wagner, Bunge and Badre’s findings also indicate there might be more components to working memory than Baddley & Hitch’s model suggests.)

John was using a figure of speech, but I fear it will only be a matter of time before teachers start referring to the ‘narrowing’ of working memory. This illustrates why it’s important to be aware of the biological mechanisms that underpin cognitive functions. Working memory is determined by the behaviour of neurons, not by the behaviour of analogous computer components.

frame of reference

John and Ollie were talking about cognitive load theory in education, so that’s what the interview focussed on, obviously.  But every focus has a context, and John and Ollie’s frame of reference seemed rather narrow. Ollie opens by talking about ‘the political environment of education at the time [1982]’ being ‘heavily in favour of problem solving’. I don’t think he actually means the ‘political environment of education at the time’ as such. Similarly John comments ‘the entire research environment in those days was absolutely committed to problem solving’. I don’t think he means ‘the entire research environment’ as such either.

John also observes: “It’s only been very recently that people started taking notice of Cognitive Load Theory. For decades I put papers out there and it was like putting them into outer-space, you know, they disappeared into the ether!” I first heard about Cognitive Load Theory in the late 80s, soon after Sweller first proposed it, via a colleague working in artificial intelligence. I had no idea, until recently, that Sweller was an educational psychologist. People have been taking notice of CLT, but maybe not in education.

Then there’s the biologically primary/secondary model. It’s ironic how little it refers to biology. We know a fair amount about the biological mechanisms involved in learning, and I’ve not yet seen any evidence suggesting two distinct mechanisms. The model appears to be based on the surface features of how people appear to learn, not on the deep structure of how learning happens.

Lastly, the example of language acquisition. The differences between adults and children learning languages can be explained by frequency of exposure and how neurons work; there’s no need to introduce a speculative evolutionary model.

Not only is cognitive load theory the focus of the interview, it also appears to be its frame of reference; political issues and knowledge domains other than education don’t get much of a look in.

the penny that dropped

Ever since I first heard about teachers applying cognitive science to education, I’ve been puzzled by their focus on the limitations of working memory and the characteristics of experts and novices. It suddenly dawned on me, reading Ollie’s interview with John, that what the teachers are actually applying isn’t so much cognitive science, as cognitive load theory. CLT, the limitations of working memory and the characteristics of experts and novices are important, but constitute only a small area of cognitive science. But you wouldn’t know that from this interview or most of the teachers advocating the application of cognitive science.  There’s a real risk, if CLT isn’t set in context, of teachers getting hold of the wrong stick entirely.

references

Geary, D. (2007).  Educating the evolved mind: Conceptual foundations for an evolutionary educational psychology, in Educating the evolved mind: Conceptual foundations for an evolutionary educational psychology, JS Carlson & JR Levin (Eds). Information Age Publishing.

Kuhl, P. (2004). Early language acquisition: Cracking the speech code. Nature Reviews Neuroscience 5, 831-843.

Wagner, A.D., Bunge, S.A. & Badre, D. (2004). Cognitive control, semantic memory          and priming: Contributions from prefrontal cortex. In M. S. Gazzaniga (Ed.) The Cognitive Neurosciences (3rd edn.). Cambridge, MA: MIT Press.