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COGNITION, BRAIN, AND CONSCIOUSNESS

ELSEVIER

Chapter One

OUTLINE

1.0 Introduction 3 2.0 An invitation to mind-brain science 3 3.0 Some starting points 4 3.1 Distance: seven orders of magnitude 4 3.2 Time: ten orders of magnitude 6 3.3 The need to make inferences — going beyond the raw observations 7 3.4 The importance of convergent measures 10 3.5 Major landmarks of the brain 10 4.0 Some history, and ongoing debates 13 4.1 The mind and the brain 13 4.2 Biology shapes cognition and emotion 14 4.3 Cajal's neuron doctrine: the working assumption of brain science 16 4.4 Pierre-Paul Broca and the localization of speech production 18 4.5 The conscious and unconscious mind 20 5.0 The return of consciousness in the sciences 25 5.1 How conscious and unconscious brain events are studied today 27 5.2 History hasn't stopped 28 6.0 Summary 29 7.0 End of chapter exercises 30 7.1 Study questions 30 7.2 Drawing exercise 31

1.0 INTRODUCTION

This chapter gives an overview of cognitive neuroscience, the combined study of mind and brain. The brain is said to be the most complex structure in the known universe — with tens of billions of neurons, connected by trillions of transmission points. It can be changed by taking in molecules, as in drinking a glass of wine, and by external stimulation, like listening to exciting news. Some neuronal events happen over a thousandth of a second, while others take decades. In spite of this vast range of working conditions, we know many facts about the mind-brain that are basic and fairly simple. This book aims to let those facts stand out.

2.0 AN INVITATION TO MIND-BRAIN SCIENCE

It is hard to talk about the last dozen years in cognitive neuroscience without using words like 'remarkable' and 'revolutionary'. In a sense, a century of behavioral and brain science has received resounding confirmation with the new technology of brain imaging, the ability to observe the living brain in real time. That does not mean, of course, that we have merely confirmed what we thought we knew. Rather, the ability to record from the living brain has proved to be fruitful in bringing out new evidence, raising new ideas and stirring new questions. Many scientists have a sense that a great barrier — between the study of mind and brain — is being bridged. Historically tangled questions continue to yield quite beautiful insights.

Along with this feeling of progress has come a great expansion. Just 10 years ago, behavioral scientists might not have seen a connection between human cognition and the genetic revolution, with brain molecules, or with the mathematics of networks. Today, those topics are all part of a connected island chain of knowledge. Previously avoided topics are now anchored in plausible brain correlates — topics like conscious experience, unconscious processes, mental imagery, voluntary control, intuitions, emotions, and even the self. Some puzzles seem bigger than before — the nature of the permanent memory trace, for example. There seem to be more continuities than ever before between psychological and brain studies of perception, memory, and language.

In some cases, brain evidence helps to resolve puzzles that psychologists have wrestled with for decades. For example, in the study of attention a debate has raged between 'early' and 'late selection' of attended information. People may pay attention to a coffee cup based on low-level visual features like color, texture and location. Alternatively, they might focus on the coffee cup based on higher-level properties like 'usefulness for drinking hot liquids'. Evidence can be found for both. After decades of debate, brain studies have now shown that attentional selection can affect neurons at almost any level of the visual system. The answer therefore seems to be that there can be both early and late selection, as many psychologists have also argued. In many cases like this we find surprising convergence between brain and behavioral evidence.

3.0 SOME STARTING POINTS

3.1 Distance: seven orders of magnitude

To understand the mind-brain, it helps to have an idea of its orders of magnitude, the powers of ten that tell us the basic units of interest. From front to back, a brain is perhaps a seventh of a meter long. If you take one step, the length of your stride is about one meter (a little more than a yard). If you raise that length to the next order of magnitude, 10 meters, you might obtain the rough length of a classroom. One hundred meters is a standard sprinting distance, and a thousand meters or one kilometer is a reasonable length for a city street. By the time we raise the exponent to 10⁷ meters, or 10 000km, we are already at 6000 miles, the distance from coast to coast in North America, or from Paris to the equator in Europe and Africa. That is ten million steps. In order to understand the most important magnitudes of the brain we can simply imagine going the other way, seven orders of magnitude from one meter to 10^-7 (Table 1.1). Considered this way it is an awesome prospect in size and complexity.

Visible behavior takes place anywhere from a centimeter and up. A finger striking a keyboard moves only a few centimeters. When we speak, our tongue moves only a centimeter or two. A single walking step is about a meter long. Most people are a little less than two meters in height, and the longest neurons in the human body may be about 1 meter.

3.1.1 A note about neurochemicals: the smallest level

Neurotransmitters range in size, and diffuse across gaps between neurons — the synapses — which vary between 25 nanometers to 100 micrometers (Iversen, 2004). Most brain-changing chemicals promote or block molecular communication between nerve cells. The list of everyday chemicals that change the brain includes nicotine, alcohol, oxygen (in the air), toxic gases like carbon monoxide, glucose from the liver and sucrose from foods, chocolate, coffee, nerve toxins like lead, and a long list of medications (Figure 1.3). It is hard to overstate the importance of such molecules in everyday life.

Molecular messengers in the brain can be divided into two large groups. The first group, the neuromodulators, are 'sprayed' throughout large parts of the forebrain from small fountain-like clumps of cell bodies at the base of the brain. These are informally called 'spritzers', because they secrete neurochemicals from widely dispersed axons, to modulate large parts of the brain. However, neuromodulators can have local effects if they lock into specific types of local receptors. For example, while dopamine is spread very widely, the D1/D2 dopamine receptors are believed to have local effects in the frontal cortex related to working memory (Gibbs and D'Esposito, 2006). Thus, a very wide-spread neuro-modulator, dopamine, can have more local effects when it locks into receptors in a specific region of the brain.

The second major group of neurotransmitters have much more localized actions. They are mostly peptides, i.e. small subunits of proteins, which are secreted directly into synaptic gaps. More than 40 types of neuropeptides have now been found throughout the brain. The two best-known examples are glutamate, the most widespread excitatory neurotransmitter in the cortex, and GABA, the most common inhibitory neurotransmitter.

Scientific advances often follow our ability to observe at different magnitudes. The wave of discoveries we are seeing today results from our new ability to observe the living brain. The ability to observe over some seven orders of spatial magnitude makes mind-brain science possible.

3.2 Time: ten orders of magnitude

Human beings function over a great range of time scales (Table 1.2). Behaviorally, one-tenth of a second (100 ms) is an important unit to keep in mind. The fastest (simple) reaction time to a stimulus is about 100 milliseconds, and the time it takes for a sensory stimulus to become conscious is typically a few hundred milliseconds. This makes sense in the environment in which human beings evolved. If you took several seconds to react to a hungry predator, you will soon provide it with a tasty protein snack. Biologically, you would not get a chance to reproduce. On the other hand, if you tried to react as fast as 10 milliseconds — one-hundredth of a second — you would be driving your brain faster than it could combine the sights and sounds of a running tiger. It would be hard to tell what direction a predator might be coming from. So the 100 ms range gives a useful middle ground.

Brain events at different time and spatial scales go on at the same time, like the elements of a symphony — notes, phrases, and whole musical movements. When you listen to a song, you are conscious of only a few notes at any time, but the notes you hear fit into a larger cognitive structure which makes it possible to appreciate how the entire song is developing. Movie frames are shown at 24 images per second, or about 40 milliseconds per frame, to show smooth movement. (That's why they call them movies!) Slower rates than 24Hz start to look jerky, like the early silent movies. However, the plot of a movie takes place over minutes and hours. In a mystery film, if you cannot remember the crime at the beginning, the fact that the perpetrator is found at the end will not make sense. Thus, understanding movie plots requires cognitive integration over many minutes. All these time scales must somehow be combined. Each kind has a structure and a typical time range. The brain keeps track of all simultaneously.

At the longer end of the time scale, it can take years to learn a difficult skill, like skiing or playing guitar. Infants learn their first language over several years, while adults tend to keep their basic personality structure over decades. Such long-term processes depend upon the same brain as 100-millisecond events. In the time domain, therefore, we need to understand about ten orders of magnitude, from one-thousandth of a second (a millisecond) for a single neuron to fire, to more than 100 000 seconds per day, and tens of millions of seconds per year.

3.3 The need to make inferences — going beyond the raw observations

Science depends on a constant process of inference, going from raw observations to explanatory concepts. Thousands of years ago, when human beings began to wonder about lights in the sky like the sun, the moon, and the stars, they noticed that some were predictable and some were not. The 'wanderers' in the night sky were called planete by the Greeks, and we call them 'planets' in English. These wandering lights became a source of fascination. It was not until the 17th century that their paths were understood and predicted. The solution to the wandering lights puzzle was to realize that the planets were giant earth-like spheres revolving in orbit around the biggest object of them all, the sun. It took centuries of argument and observation to settle on that solution. Isaac Newton had to invent the infinitesimal calculus to bring the debate down to a simple equation: planetary orbits can be predicted from the simple fact that gravitational force equals the mass of the orbiting planet times its acceleration. Notice that all those words — 'sun', 'planet', 'force', and 'gravity' — are inferred concepts. They are far removed from the first observations of lights in the sky (Figure 1.4), yet they explain those raw observations: they are explanatory inferences.

All science depends upon careful observations and conceptual inferences. The resulting explanatory framework has been called a 'nomological network' — that is, a network of labeled concepts and relationships, which together provide us a sense of understanding. Along the way, successful science allows us to make more accurate predictions, and to apply the resulting knowledge in ways that sometimes transform life. It all begins with exact observations and plausible inferences.

These basic ideas have a direct bearing on cognitive neuroscience. When we talk about cognition — language, learning, or vision — we also use inferred concepts, which must be firmly anchored in reliable observations. For example, the capacity of immediate memory — the kind we can mentally rehearse — is about seven plus or minus two items, as George A. Miller famously noted in a paper called 'The magical number seven plus or minus two' (1956). That number seems to apply to many kinds of randomly selected items: colors, numbers, short words, musical notes, steps on a rating scale, and so on. The recent consensus is that the actual capacity of immediate memory is even less than seven, about four different items (Cowan, 2001). But the most important point is the remarkable consistency in the data. Try to remember ten different foods on your shopping list, for example, and you will find that only about seven are remembered — and if you are busy thinking about other things, that number drops to four. It is an amazingly narrow limit for a giant brain.

There are only a few basic conditions for obtaining the size of working memory. One is that each item must be attended for only a brief time — perhaps several seconds — so that it cannot be memorized well enough to enter permanent memory. A second condition is that the items must be unpredictable from existing knowledge. If we ask people to remember a regular number series like 0, 5, 10, 15, 20, 25 ... they only need to remember the rule, and it will seem that their working memory capacity is endless. Cognitive concepts like 'working memory' are the product of decades of experimental observations which finally become so solid that we can summarize the evidence in one basic concept (Figure 1.5).

Ideas like working memory have turned out to be useful, but it is quite possible that we will find a more attractive way to think about them tomorrow. All inferred concepts are somewhat tentative. Newton's idea of gravity dominated physics for three centuries, then Einstein found another way to look at the evidence. Scientific concepts are not metaphysical certainties. They are always subject to revision.

Cognitive neuroscience is also based on inferences from raw observations. Because brain scans have the appearance of physical objects that we can see and touch, we are tempted to think that we are seeing 'raw reality' in brain scans. But that is a seductive fallacy. Electroencephalography (EEG) is an inferential measurement of brain activity, as is functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and all the other advanced tools we have today (Box 1.1). Even recording from neurons only gives us a tiny sample of single cell firing among tens of billions of cells. Neurons make perhaps ten thousand connections, and there is evidence that even the input branches of a single neuron (the dendrites) may compute information (Alle and Geiger, 2006). Therefore, measuring the electrical activity of single neurons is only a tiny sample of a very complex dance of molecules and electromagnetic fluxes. Recent imaging techniques are extraordinarily useful, but they still involve inferences about the working brain.

(Continues...)

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Excerpted from COGNITION, BRAIN, AND CONSCIOUSNESS by Bernard J. Baars Nicole M. Gage Copyright © 2010 by Elsevier Ltd.. Excerpted by permission of ELSEVIER. All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
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