Monthly Archives: July 2011

The multitasking mind

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Cross-posted with permission of OUPblog.

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This week’s guest blogger is Dario Salvucci, a professor of computer science and psychology at Drexel University, and author with Niels Taatgen of The Multitasking Mind. Dr. Salvucci has written extensively in the areas of cognitive science, human factors, and human-computer interaction, and has received several honors including a National Science Foundation CAREER Award.

If the mind is a society, as philosopher-scientist Marvin Minsky has argued, then multitasking has become its persona non grata.

In polite company, mere mention of “multitasking” can evoke a disparaging frown and a wagging finger. We shouldn’t multitask, they say – our brains can’t handle multiple tasks, and multitasking drains us of cognitive resources and makes us unable to focus on the critical tasks around us. Multitasking makes us, in a word, stupid.

Unfortunately, this view of multitasking is misguided and undermines a deeper understanding of multitasking’s role in our daily lives and the challenges that it presents.

The latest scientific work suggests that our brains are indeed built to efficiently process multiple tasks. According to our own theory of multitasking called threaded cognition, our brains rapidly interleave small cognitive steps for different tasks – so rapidly (up to 20 times per second) that, for many everyday situations, the resulting task behaviors look simultaneous. (Computers similarly interleave small steps of processing to achieve multitasking between applications, like displaying a new web page while a video plays in the background.) In fact, under certain conditions, people can even exhibit almost perfect time-sharing – doing two tasks concurrently with little to no performance degradation for either task.

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The brain’s ability to multitask is readily apparent when watching a short-order cook, a symphony conductor, or a stay-at-home mom in action. But our brains also multitask in much subtler ways: listening to others while forming our own thoughts, walking around town while avoiding obstacles and window-shopping, thinking about the day while washing dishes, singing while showering, and so on.

Multitasking is not only pervasive in our daily activities, it actually enables activities that would otherwise be impossible with a monotasking brain. For example, a driver must steer the vehicle, keep track of nearby vehicles, make decisions about when to turn or change lanes, and plan the best route given current traffic patterns. Driving is only possible because our brains can efficiently interleave these tasks. (Imagine the futility of only being able to steer, or plan a route.)

So how has multitasking earned such a negative reputation? In large part, this reputation stems from unrealistic expectations. The brain’s multitasking abilities – like all our abilities – come with limitations: when performing one task, the addition of another task generally interferes with the first task. For many everyday tasks, the interference is negligible or unimportant: your singing may affect your showering, or thinking about your day may affect your dish-washing, but likely not so much that you notice or care.

Other tasks, though, require every ounce of attention and can push past the limits of our multitasking abilities. In driving, the essential subtasks are demanding enough; additional subtasks – texting, dialing, even talking on a phone – increase these demands, and when controlling a 3000-pound vehicle at 65 miles per hour, even these minimal additional demands may lead to unacceptable risks.

Still other tasks do not have safety implications per se, yet most would consider them important enough that multitasking in those contexts is undesirable. A student in class is already multitasking in listening to the teacher, processing ideas, and taking notes. If this student is checking Facebook at the same time, this extra subtask drains mental effort away from the more critical subtasks and dilutes the learning experience.

The problem with multitasking thus lies not in our brain’s inability to multitask efficiently, but in our own priorities and decision-making. When we choose to multitask, we are deciding – consciously or not – to accept degraded performance on one or more of tasks involved. And when we still choose to multitask when it is undesirable (as in the classroom) or unacceptable (as in driving), we should hold ourselves accountable for these decisions. So if you walk into a pole or wreck your car while texting, don’t blame your brain; blame yourself.

How does learning to read affect our brains?

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Sophie Scott is the group leader of the Speech Communication Group at the Institute of Cognitive Neuroscience at University College London (UCL) (UK). She was awarded a Ph.D. at UCL in the acoustic basis of rhythm in speech and then spent several years as a postdoctoral researcher at the Medical Research Council Cognition and Brain Sciences Unit in Cambridge (UK). She currently holds a Wellcome Trust Senior Fellowship and has been funded by the Wellcome Trust since 2001.

Her research uses functional imaging to investigate the cortical basis of human speech perception and production, applying models from primate auditory processing to the neural basis of human perception. She is particularly interested in the different kinds of information conveyed when we speak and how the acoustic information in our voices can be processed in different ways in the brain.

We learn to read in a very different way from learning to speak. Speech is embedded in our social interactions from the minute we are born and even before birth we can hear our mother’s voice in utero_. These prelingual twins=JmA2ClUvUY show how you can understand verbal interactions, before you even have words at your disposal.

Learning to read, in contrast, is something that we largely learn to do when we are at school, where we are specifically instructed how to do it. There are different writing systems:

• Logographic (like Chinese) where a written word or a meaningful part of a word is represented by a single written element (though that symbol may contain phonetic and semantic information).

• Syllabaries: (e.g. Cherokee) where a written element conveys a whole syllable.

• Alphabetic: (e.g. English) where a single written element roughly represents a single speech sound.

Each of these systems has their own unique advantages and disadvantages. Notably, alphabetic writing systems can differ widely in how easy they are to acquire. Children learning to read English, which is highly irregular in both spelling and pronunciation, do less well at reading non-words after a year of reading, in comparison to children learning to read Spanish or Finnish (Aro and Wimmer 2003) English-reading children only catch up on their Finnish peers in grade 4.

In addition to the undoubted many values of literacy, we can see the impact of learning to read in a variety of ways. For example, it is harder to name the colour of the

ink in the word green than in the word grown. This Stroop effect is commonly used to demonstrate how meaning can interfere with cognitive processes – if you are naming ink colours as fast as possible, competing colour names will slow you down. Importantly, this can only occur because once you are a skilled reader, you can’t ‘switch off’ your reading when trying to name the ink colours, which is how the competing semantic information can get into the system. As skilled readers, it is nearly impossible for us not to read words.

The skill of learning to read also forces us to engage with sounds in ways that differ from what we have to do to understand spoken language. Some abilities in the manipulation of speech sounds are present before we learn to read (e.g. being able to tell that two words rhyme), while others emerge as a consequence of our learning to read. Thus, segmental skills – being able to break a word down into separate chunks corresponding to individual speech sounds – are something that we acquire when we learn to read. People who have never read find it hard to split ‘cat’ into ‘c’ ‘a’ and ‘t’ (though not completely impossible).

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Maybe because of the skills we acquire when we learn to read, psychologists and cognitive neuroscientists often use these segmental skills as an index of speech perception ability, despite the fact that people who haven’t learnt to read and who therefore find such tasks hard, can understand speech perfectly well. Because we can break spoken words down into smaller chunks, it is often assumed that this must be a central aspect of speech perception.

This bias towards segments in speech may have had other, more central effects on how we construe the problems of speech perception. It has been suggested, for example, that reading with an alphabetic system has biased us into the belief that the smallest units of speech, phonemes, are perceptual realities in terms of how we process speech from sound to meaning (Boucher VJ 1994 Alphabet-Related Biases in Psycholinguistic Inquiries – Considerations for Direct Theories of Speech Production and Perception, Journal of Phonetics, 22.1: 1 -18). The argument goes that because we can segment speech into phonetic elements (a skill we acquire when we learn to read) and because we are immersed in a reading system which represents spoken words as sequences of alphabetic symbols, that we implicitly assume speech to have these characteristics.

This assumption has had scientific consequences. For a long time, theories and models of spoken word comprehension incorporated a phonetic level of representation (e.g. the TRACE model.) The problem with phonemes is that any one speech sound will be greatly altered by where it is in a word and the sounds around it – in English the sound ‘l’ is very different at the start of the word ‘leaf’ than at the end of the word ‘bell’. There are also co-articulation effects, which refer the ways that the same speech sound is affected by its neighbours: the ‘l’ at the start of ‘let’ differs acoustically from the ‘l’ at the start of ‘led’ because of differences between final ‘t’ or ‘d’ phonemes. This covariance is highly useful to the listener and it makes sense that the perceptual system would preserve this detail. If you are building a computer system to understand speech, for example, you don’t build one to identify particular phonemes, you build it to look across sequences of sound, either groups of phonemes or whole words. Indeed, more recent psychological models of human speech perception explicitly do not make the assumption that phonemes need to be identified prior to comprehension (e.g. Shortlist B, Norris and McQueen 2008.)

At a brain level, can we see any sensitivity in speech perception areas to phonemes, as opposed to sequences of speech sounds? We recently investigated the neural activity seen when people silently rehearse pseudo-words. We varied how long the pseudo-words were in syllables (e.g. sapeth vs sapethetis) and how phonetically complex they were (e.g. sapeth vs stapreth.) This enabled us to separately identify brain areas which are more activated when people try to maintain longer or shorter pseudo-words, from those that are more activated when there are phonetically complex sequences in the material we are rehearsing. Silent rehearsal recruits both auditory and motor brain systems, both of these systems were sensitive to the length of the pseudo-words. In contrast, only the motor output systems were sensitive to the phonetic complexity of the pseudo-words, being more active when phonetically more complex sequences were rehearsed. This finding suggests that auditory areas are less sensitive to specific phonetic details, unlike motor systems. In turn, this may mean that if phonemes are ‘real’ phenomena in the language system, they are implemented in the motor systems, not in perception systems. In other words, we may not need to extract phonemes to understated speech, but they may be important elements in speech production.

My son is currently learning to read and write and watching his delight at solving the ‘problem’ of the sounds in words and what rhymes with what, is a joy and a privilege to see. Overhearing his dad explaining why ‘bird’ contains an ‘r’ letter (short answer, he had a heroic go at pronouncing it as ‘biRRRd’ as if he was from the Scottish highlands) showed me both the problems that written English presents to someone learning it, as well as the dominance reading can cast over what sounds we think there are in words.

Why We Need A New Economics

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david.JPGThis week’s guest blogger is David Orrell, an author, founder of Systems Forecasting, and an Honorary Visiting Research Scholar at the Smith School of Enterprise and the Environment in Oxford. The UK Kindle edition of Economyths is available for a limited time at the highly economical price of 99p.

In January 2009, in the immediate aftermath of the credit crunch, the physicist and hedge fund manager J.P. Bouchaud wrote in the pages of Nature that Economics needs a scientific revolution.

Economyths is an attempt to spell out what such a revolution might look like, and document the exciting developments taking place in economics.

It too is written from an outsider perspective – that of an applied mathematician, working mostly in the area of computational biology. Many of the techniques used in that field, such as network theory and agent-based modelling, are beginning to find widespread applications in economics. But the assumptions they are based on are completely different from those of mainstream economics.

Consider for example the idea that the “invisible hand” of the marketplace drives prices to an optimal equilibrium. This idea is usually attributed to Adam Smith, though as the Czech economist Tomas Sedlacek argues it actually goes back much further.

In the 19th century, neoclassical economists such as William Stanley Jevons and Léon Walras attempted to demonstrate this principle mathematically, based on the idea of Homo economicus, or rational economic man. In the 1950s, economists finally managed to prove that markets would indeed reach a Pareto-optimal equilibrium. But to do so, they had to make numerous assumptions – including rational utility-maximising behaviour, coupled with perfect information, and infinite computational capacity.

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In the 1960s, efficient market theory was proposed as an explanation for why the economy was impossible to predict. Again, it assumed that market participants were rational and acted independently of one another to optimise their own utility. In the 1970s, rational expectations theory was all the vogue. Tools in use today – such as the risk models relied on by banks, or the General Equilibrium Models called on by policy makers – continue to make these assumptions, with at best small modifications.

Of course, no one thinks that people are perfectly rational or independent, or that the economy reaches a perfect equilibrium – but it has been generally believed that these assumptions were good enough to capture the overall behaviour. They could be viewed as representing an ideal economy, to which the actual economy can at least aspire. And they provided policy makers with an excuse for dangerous deregulation of the financial sector – what Adair Turner has called “regulatory capture through the intellectual zeitgeist.”

Unfortunately, as illustrated most recently by the credit crunch, this picture of the economy is highly unrealistic. The behaviour of home owners during the US credit crunch – or for that matter large firms like Lehman Brothers – hardly conforms to the model of rational economic man. And if stock markets are really governed by the invisible hand, then it has a bad case of the shakes.

So-called heterodox economists have long questioned the assumptions behind mainstream economics. But following the credit crunch, there has been an even more concerted effort to develop alternative models which can address issues such as economic inequality, environmental sustainability, human wellbeing, and financial instability. Many of the new ideas are coming from areas of applied mathematics such as nonlinear dynamics, complexity, and network theory.

An example is the agent-based models being used by complexity researchers such as Doyne Farmer to simulate the economy. Models have been developed of artificial stockmarkets in which hundreds of simulated traders buy and sell stocks. Each of the trader “agents” has its own strategy, which adapts in response to both market conditions and the influence of other agents. Instead of settling on a stable equilibrium, it is found that prices experience periodic booms or busts as investors flock in and out of the market. Agent-based models are also used to simulate the highly skewed distribution of wealth in many economies, in which a small percentage of the population sequesters most of the wealth.

Another rich source of new ideas is those other life sciences, biology and ecology. The ecologist Robert May recently joined forces with the Bank of England’s Andrew Haldane to analyse the financial network from a systems perspective. They found that risk metrics used for individual institutions such as banks fail to account for systemic risk.

The financial system has become increasingly interconnected in recent decades. This is good for short-term efficiency, but also means there is an increased risk of contagion from one area to another, which does not register with conventional risk models. As ecologists know, robust ecosystems tend to be built up of smaller, weakly connected sub-networks. Maybe financial regulators can learn a trick from nature, by introducing a degree of modularity and redundancy. An even more urgent issue, of course, is how to make the human economy fit in with the global ecosystem which contains it.

One of the lessons of complex systems research is that it requires collaborations between people from a broad mix of backgrounds. Another is that models are only imperfect approximations of a system. Accurate prediction will always remain elusive. However we can at least base our models on realistic assumptions. And even if we cannot predict the exact timing of the next financial crisis any better than we could the last one, at least we can learn how to make the system more robust in the first place.

The Language of Genetics

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denis.bmp Denis Alexander is this week’s guest blogger. He has spent 40 years in the biological research community in various parts of the world, latterly as Head of the Laboratory of Lymphocyte Signalling and Development at The Babraham Institute, Cambridge which he left in 2008. Since then he has been heading up the new Faraday Institute for Science and Religion at St. Edmund’s College, Cambridge, where he is a fellow.

I have always been fascinated with the public understanding of science, including the many and varied ways in which scientific ideas can migrate out of the lab to populate the worlds of politics, sociology, popular culture and religion. Since finally closing down my research group in immunology a few years ago, I have had the privilege of indulging some of these interests more fully in a way that the pressures of an active research life didn’t really allow.

Recently we brought a group of historians and philosophers to Cambridge to sit round a table for a few days and discuss all the varied ways in which biology has been used and abused for non-biological purposes from 1600 to the present day. So many are the examples that our challenge was not to find sufficient topics or authors, but to restrict ourselves to a series that would eventually lead to a book of reasonable length. The outcome was Biology and Ideology – From Descartes to Dawkins which came out last year [Denis Alexander and Ronald Numbers (eds), Chicago University Press, 2010]. In turn this interest is leading on to a grants programme in which competitive funding applications will be received during this coming year for research on contemporary ways in which biological ideas are being used for good or for ill, purposes well beyond their original scientific contexts.

The area of genetics is one that seems particularly prone to being reported in the media or in the public domain more generally in dramatised ways that often distort the actual science involved. I was therefore particularly pleased to be approached by a publisher recently to write an introductory book on genetics that would not only introduce the science for a general readership, but also address some of the wider ethical and other questions that genetics raises concerning human value and identity. The result is The Language of Genetics – an Introduction [Darton, Longman and Todd, 14 June 2011] published just a few days ago [N.B. although Amazon has some good offers the Faraday Shop is selling at £12/copy plus p&p starting soon after 27th June].

I am a great believer in making a clear distinction between science and the wider issues that arise from science, finding that when the language and concepts of different disciplines are co-mingled, confusion inevitably results. The Language of Genetics therefore has 11 chapters of straight explanatory science, whereas the wider questions arising from genetics are contained within the final chapter 12.

One of the topics I tackle there is the pervasive idea of genetic determinism – that there are such things as genes “for” musicality, intelligence or being a political liberal. Although I think biologists, with rare and unfortunate exceptions, are generally rather careful to describe in their scientific writings what genes actually do, by the time their discoveries get reported in the media, the head-line for the story too often ends up implying that some complex human behavioural trait is largely determined by a single gene.

The genome wide association studies (GWAS) that have proliferated over the past few years are instructive in this respect. One study was carried out on the variation in height between humans, a trait known to be around 70-80% inheritable. The study based on 180,000 individuals came up with 180 different variant gene regions that correlate with variation in height, yet taken together they explain only around 10% of the inheritability. There is a huge amount of “missing inheritability”. Where is it? Being a bit taller or shorter is complex, involving many aspects of our physical being.

Imagine now the genetics of some complex human behaviour which has a supposed element of inheritability, together with our brains with their 10¹¹ neurons and 10¹4 synapses (the precise number, rather unsurprisingly, depends on the precise volume of your brain) – such a scenario does not readily lend itself to interpretations that depend on genetic determinism.

None of this is to say that genetic variation is irrelevant to who we are as individuals – far from it. But The Language of Genetics highlights the way in which the fertilised egg, with its newly acquired unique genome, is from its very first day onwards in intimate interaction with its environment in all its myriad aspects. Rather than reifying the ‘genome’ and the ‘environment’ as if they were separate entities, it is biologically more accurate to see both aspects as thoroughly intertwined. The fascinating fields of evo-devo (chapter 3) and of epigenetics (chapter 10) do much to highlight that insight.

The science of genetics is a fantastic gift to humankind if used wisely. But the greatest gifts can be the most abused; the best protection remains continued awareness and vigilance.