What happens when our networks are damaged? How do they cope with the impact of soft brain tissue against the hard skull in traffic accidents, or the strokes or neural degeneration that may occur in our later years?

A great deal of research has been done on the effect of strokes on our various mental abilities, and computer models have been designed to mimic this sort of damage, so I will have the most to say about how our networks cope with strokes. Traffic accidents tend to cause a very different sort of damage, to an area which has been difficult for connectionist theory to explore, and I will try to explain the problems involved. I will have only a few words to say about the connectionist explanation of what happens in Alzheimer's disease, as very little is known about it.

What do strokes do to our networks?

So far we have seen how our knowledge networks are built up slowly and gradually over the years to provide us with ways of acting in the world, methods of distinguishing one thing from another, memories of what happened a long time ago and information about what has just occurred. What happens when these networks are damaged? When someone has a stroke – when an artery in the brain is blocked by a blood clot and the surrounding area no longer receives blood to nourish its neurons, so that many of them die all at once? Or when someone has a brain hemorrhage – when an artery in the brain bursts and floods the surrounding area with blood, again killing many neurons at once?

I first heard of connectionism in 1982, when I began studying cognitive psychology. I had read Edward deBono's pioneering work, The Mechanism of Mind, twenty years earlier, and I had found it fascinating, but at that time the term “connectionism” had not yet been invented. When I learned about semantic networks, in which concepts were represented as points connected by links of various sorts, it seemed to me that concepts were much too rich to be described as mere points. Instead, I imagined them as long tangled threads meandering around in several dimensions, and I imagined the links between the concepts as the points where these threads met.

When I described this image to my cognitive psychology professor, Benny Shanon, he said, “That's the new theory everyone is talking about – it's called connectionism.” He had just ordered the brand-new book on the topic, Hinton and Anderson's collection of papers, Parallel Models of Associative Memory, and was waiting for it to arrive. When the book came we spent a lot of time arguing over who should get to read it first. Each of us would take it home for a week or two and try to read a few pages, then give it to the other for the next week or two. On the one hand, the new ideas were fascinating, but on the other, they were very difficult to grasp.

Over the years since then I have read a great many papers on connectionism, but none of them was easy enough to recommend to a beginner.

Now that we have some idea of how the neurons in the brain form networks and how these networks operate, we are in a position to take a good look at the question of how the mind works. Remember that we are assuming that the functions of the mind – how we learn new things, how we remember the things we have learned, how we combine the things we have learned to create new entities – are embodied to a great extent in the workings of the brain. The question we are now asking is how these functions of the mind are shaped by the paths taken by currents of electricity and wandering molecules within the brain.

The latest theories about how the functions of the mind are grounded in the operations of the brain are known as “connectionist” theories or models. The difference between the theories and the models that is important for our purposes here is that the theories are attempts to formulate general rules about how the mind performs its various functions, whereas the models are attempts to simulate these functions on computers. The theories are like all new scientific theories – they are systems of generalizations that are based on a new way of looking at accumulated observations which no longer seem to fit the old theories very well. The new generalizations are then examined to see how they could be tested, and experiments are carried out to see if the predictions made by the new theories are fulfilled.

Is there any way that all this new knowledge can help us in our daily life? In addition, is it possible that it might be of some use to psychologists in helping their clients?

Since we have spent most of the book starting with questions and looking for answers, let us try it the other way around now: Let us start with the answers and see what questions they can answer, as on the old television quiz show Jeopardy, which has recently been revived. There is a good reason for this, aside from the obvious one that I can avoid those questions I have no answers for. The deeper reason is that there are some questions that most of us don't think of asking at all, and starting with the answers may lead to some of these less obvious questions.

How to study more efficiently

The first answer is that the knowledge structures in our inner networks change very gradually when we provide them with new information. One obvious question to which this may provide an answer is “What is the best way to study new material?” What we have learned in this book is that our mental networks cannot handle large amounts of new information at once because they can make only small changes in the way our knowledge is organized. Thus the best way to learn new information is to study small amounts at a time and keep trying to think of ways to integrate it with what we already know.

Until now we have been discussing how we learn and organize our general knowledge – the names and properties of things, such as dogs and cats, or mothers and fathers, and the relations between them. All these may be called permanent memories, as we generally retain this sort of knowledge throughout life, and it changes only occasionally, such as when we hear about a new type of mother known as a “surrogate mother.” But when we think of all this knowledge as a type of memory, we immediately begin to think of the other kinds of things we need to remember – namely, the specific things that happen to us or that we do in our daily life, and the things we are intending to do. These may be called temporary memories, as they involve specific occasions and do not need to be remembered for a long time. In this chapter I discuss some of the essential differences between the two types of memory and describe what is known about the way they are embodied in different types of neural networks in the brain.

Differences between the two types of memory

Permanent and temporary memories differ in many ways. For example, the fact that a kitchen is a place to eat is part of my permanent memory, while the fact that I opened up a new box of cornflakes this morning is part of my temporary memory. Permanent memory is time- and person-independent: The truth of the statement “A kitchen is a place to eat” does not change if I say it today or tomorrow, and it does not depend on whether I say it or you say it.

Debates between Harry Jerison and Ralph Holloway enlightened and expanded our understandings of the evolution of the human brain and human mind for a period of several decades. Jerison argued that quantitative increases in neural tissue and in neural information processing capacity were the most important determinants of human intelligence (Jerison, 1973). Holloway took a seemingly contradictory stance, that the human brain had been reorganized in relationship to the brains of other primates and that this reorganization was the primary determinant of human mental capacities (Holloway, 1966). These debates have waned in recent years with the realizations that quantitative change and neural reorganization are not mutually exclusive phenomena. Indeed, in mammals, increased brain size correlates with or predicts various forms of brain reorganization including decreased neuronal density, increased ratios of connections to neurons, increased numbers of gyri and fissures, increased neuronal specialization, and increased size of the neocortex, cerebellum, hippocampus, corpus striatum, and diencephalon and other neural structures (Finlay & Darlington, 1995; Gibson & Jessee, 1999; Jerison, 1973, 1982, 1985). Increased numbers of neural processing units or ‘modules’ would also be expected to result in increased amounts of neural tissue (Preuss, Chapter 7, this volume).

These considerations suggest that among closely related mammalian species those with the larger brains may well have the greatest mental abilities. Not only will they have greater overall information processing capacities, but also, in many cases, they will have increased numbers of neural modules, increased size of the neocortex and of other higher neural processing areas, and increased neural connectivity.

In hominid evolution, the shape of the inner frontal bone in the median sagittal plane has, in contrast to the outer vault, not changed since at least the Plio-Pleistocene (Bookstein et al., 1999). Nonetheless, inner vault size increased significantly (by ∼ 11%) while the size of the outer frontal profile did not. Thus, two of the more interesting questions to pose are: ‘At which other positions of the skull have major shape and size changes taken place?’ and ‘Could it be that the exocranium is involved in shape changes while the endocranium is involved in size changes?’. We approach these questions by analyzing general shape and size of both the exo- and endocranium in the median sagittal plane. Importantly, because the inner surface of the braincase provides information concerning brain evolution (Jerison, 1973) and because of the synevolution of cerebellar and frontal lobes (Seidler et al., 1997), our investigation also includes the occipital bone.

Geometric relations in the median-sagittal plane

Our sample includes 21 crania of modern humans of both sexes (10 females, 11 males): 15 from Central Europe, two San and two Bantu, and two Papuans. To these we added the stereolithographs of three mid- Pleistocene fossil hominid crania (Seidler et al., 1997): Kabwe (Broken Hill 1; Woodward, 1921), Petralona (Kokkoros & Kanellis, 1960), both of uncertain age – but probably in excess of 200 000 years old; and Atapuerca SH5 cranium (Arsuaga et al., 1993), about 300 000 years old.

A progressive enlargement of the hominid brain started about 2 million years ago, probably from a bipedal, australopithecine form with a brain size comparable to that of a modern chimpanzee. Since then, a threefold increase in endocranial volume has taken place, leading to one of the most complex and efficient structures in the animated universe, the human brain. In view of the central importance placed on brain evolution in explaining the success of our species, one may wonder whether there are physical limits that constrain its processing power and evolutionary potential.

In this paper I will explore some of the design principles and operational modes that underlie the information processing capacity of the cerebral cortex in primates, and I will argue that with the evolution of the human brain we have nearly reached the limits of biological intelligence.

Biological limits to brain size

The human brain contains about 100 billion neurons, more than 100,000 km of interconnections, and has an estimated storage capacity of 1.25 × 1012 bytes (Cherniak, 1990; Hofman, 2000). These impressive numbers have led to the idea that our cognitive capabilities are virtually without limit. The human brain, however, has evolved from a set of underlying structures that constrain its size, and the amount of information it can store and process. In fact, there are a number of related factors that interact to limit brain size, factors that can be divided into two categories: (1) energetic constraints, and (2) neural processing constraints (Fig. 6.1).

Original comparative data on the brains of apes are scarce. The study of the evolution of the human brain and the human mind depends largely on the availability of such evidence. Are there certain features or aspects of the organization of various components of the brain that can be identified as uniquely human? What kind of reorganization took place in the neural circuitry of the hominid brain after the split from other hominoids? How can species-specific adaptations in behavior and cognition be recognized in the underlying neural substrates?

Recent advances in non-invasive neuroimaging techniques used for the analysis of brain structures in vivo in humans can now also be applied to the comparative study of the extant hominoids. Magnetic resonance imaging (MRI) and 3-D reconstruction allow for the identification and quantification of many neural structures across species. Use of living subjects avoids issues of shrinkage involved in postmortem tissue processing, facilitates the study of larger samples and also permits the study of species chronically underrepresented (e.g. bonobos, gorillas, orangutans). These new techniques allow for the quantification of selected lobes and smaller sectors of the brain as well as for a more accurate analysis of sulcal and gyral patterns.

The anatomy of the human brain has been traditionally studied either on gross postmortem specimens or in processed histological sections under the microscope. Attempts to image the living brain used, until recently, conventional radiography, a technique that relied on the differential absorption of X-rays by different components of the brain and its covers.

Humans and chimpanzees share a common prehominid ancestor and, together with gorillas, constitute a group of closely related primates (Falk, 1987a; Ruvolo, 1997; Deinard & Kidd, 1999). Although they have similar body sizes, humans and chimpanzees differ completely in terms of encephalization. The human brain is indeed about three times larger than the chimpanzee's. This dramatic increase in brain size started more than 2 million years ago and characterizes the human lineage. In contrast to the generally acknowledged consensus that brain size relative to body size is a better measure of increased cognitive performance than is absolute brain size, it has recently been suggested that higher cognitive capacity is more closely related to absolute brain size and that absolute brain size more closely reflects the cognitive differences between humans, great apes, and monkeys than encephalization indices (Rumbaugh et al., 1996; Gibson et al., 1998; Gibson et al., Chapter 5, this volume).

Increased brain size is nevertheless also accompanied by decreased interhemispheric transfer speed and thus decreased cognitive processing speed. In order to maintain processing power, the number of elements clustered in one hemisphere is therefore expected to increase. This principle may be at the origin of hemispheric specialization (Ringo et al., 1994). More recently, Anderson (1999) showed by analytical means that if interneural conduction time increases proportionally with interneuronal distance, spatial clustering of interneuronal connections is the only way to increase the number of synaptic events occuring in a given period of time.

I am pleased to accept the title that Dean Falk and Kathleen Gibson assigned me for this concluding essay. And of course I thank them for arranging the meeting of the American Association of Physical Anthropologists in my honor. Most of all I must thank the contributors at that meeting and the others who have taken time to prepare the chapters in this book, which commemorates that meeting. In my judgment it would be inappropriate for me to comment on those excellent chapters, to argue with some of them or to agree with others. The chapters speak well for themselves, I will leave commentary to the journals, such as Current Anthropology or Brain and Behavior Sciences, that specialize in it. It has been a great pleasure to be involved with these activities.

I will depart from my assignment in three ways. First I must write about where I would go from here rather than prescribe for others. The chapters in this book present better prescriptions than I am competent to offer for the route our field as a whole can take. Second, I would like to write about where we have been, because my particular route is so much one involving the fossil evidence that I think it takes some explaining. Finally, I have to write about more than only primates, because my emphasis has been and continues to be on the evolution of the vertebrate brain, including the primates among the mammals.

Despite the emphasis on brain size in the classic paleoneurological literature, it has long been recognized that species-specific adaptations have neurological substrates that depend on more than just overall brain size. This concept is embodied in Harry Jerison's principle of proper mass (1973, p. 8): ‘The mass of neural tissue controlling a particular function is appropriate to the amount of information processing involved in performing the function.’ Thus brains do not merely enlarge globally as they evolve, their cortical and internal organization also changes in a process known as reorganization. The chapters in Part II explore the neurological underpinnings of some of the senses, adaptations, and cognitive abilities that are important for primates.

It is fitting that this section opens with a chapter on cerebral diversity by Todd Preuss that provides a clear synopsis of mammalian cortical anatomy, thus laying the groundwork for the rest of the volume. Preuss details the tension between classical studies that emphasize the microanatomical similarities between brains of different mammals and therefore highlight brain size as a main focus of natural selection, and more recent investigations that reveal a remarkable diversity in mammalian cortical organization and therefore emphasize neurological reorganization as a driving force during brain evolution (Preuss, 1993, 1995). There is no doubt where Preuss comes down in this debate, as is clear from his observation that ‘to focus exclusively on the ancestral features of cortical organization that mammals share, however, is to ignore those features of cortical organization that distinguish one group of mammals from another and provide the basis for their particular behavioral and cognitive abilities.’

Beginning with the 1973 publication of his classic monograph, Evolution of the Brain and Intelligence, Harry Jerison's ongoing research has had a profound impact on the questions, methods, and theoretical framework that continue to shape the field of brain evolution. On April 2,1998, researchers from Europe, Africa, and the United States gathered in Salt Lake City at the sixty-seventh annual meeting of the American Association of Physical Anthropologists to take part in a symposium that recognized and celebrated Harry Jerison's intellectual influence on the development of our discipline. The session, entitled ‘Current findings on mammalian, primate, and human brain evolution: A symposium in honor of Harry J. Jerison’, was the impetus for the present volume. In addition to contributions from participants at that symposium, several prominent investigators who could not be present have also contributed chapters. Although the fourteen chapters that comprise the bulk of this volume were intended to be ‘state-of-the-art’ reviews of the different sub-areas of brain evolution, many authors have gone much further by contributing new results of original research that would normally appear in peer-reviewed journals, and by providing glimpses of where they think the field is headed as we enter the twenty-first century. That they have chosen to do so is a reflection of their admiration, respect, and fondness for Harry Jerison.

Standard proverbs often give us no guidance because they come in contradictory pairs, as in the relevant contrast for this preface: ‘Fools rush in where angels fear to tread’ vs. ‘Nothing ventured, nothing gained’ or ‘In for a penny, in for a pound.’ The best scientists try to balance these extremes by not wasting precious time on the truly undoable and unanswerable, while remaining open (indeed eager) to try the ‘crazy’ experiment that just might work. Science would become stodgy and stymied if practitioners did not often risk the second part of this pairing. (In one of the saddest ‘science stories’ I have ever heard, developmental biologist Eddy de Robertis told me that, when he proposed his utterly nutty, and brilliantly successful, experiment to search for homologs of Drosophila homeobox genes in vertebrates, only two members of his lab refused to participate for fear of being branded as fools – both graduate students. I do understand that pressures for conformity may fall more strongly upon beginners than upon established seniors. But if people won't think big and take risks at the outset of their careers, how will they ever develop this most essential of all habits among truly accomplished scientists?)

I met Harry Jerison in the early 1960s, when I was an undergraduate at Antioch College, and he a scientist at a local research lab, and a professor.

Recently it was demonstrated that brains of chimpanzees (Pan troglodytes) show a humanlike differentially enlarged left planum temporale, an area that in humans is part of the receptive ‘language’ region of the cerebral cortex (Gannon et al., 1998b). This new finding addressed a highly controversial issue within the disciplines of neurology, neurobiology, evolutionary biology, anthropology, linguistics and psychology, to name but a few. Prior to this finding it had been widely accepted that pronounced hemispheric asymmetry of this brain language region was unique to humans and, as such, could readily have been included as a component within the pervasive ‘human language organ’ (linguistics-based) concept formulated by Noam Chomsky (Chomsky, 1972). Not surprisingly, this prospective paradigm shift gave rise to many new questions and criticisms. For example, it is not clear whether this left hemisphere-lateralized language region is as remarkable in other closely related primates, particularly other great apes, but also lesser apes and even Old World monkeys. Based upon a century of human studies, it would appear reasonable to hypothesize that the markedly asymmetric planum temporale is involved with ape ‘language’ or other species specific, interindividual communication modalities. However, although this region is markedly anatomically lateralized to the left hemisphere in apes, it may not be functionally analogous. Further, as recent studies (Binder et al., 1996) have suggested, the planum temporale may not be involved with high-level receptive language processing in humans but may simply represent an early stage relay station.