Hominin Endocast Topography: an Analysis Using Geographic Information Systems

HOMININ ENDOCAST TOPOGRAPHY: AN ANALYSIS USING GEOGRAPHIC INFORMATION SYSTEMS

Melissa Boas

A Thesis Submitted to the Faculty of

The College of Arts and Letters

in Partial Fulfillment of the Requirements for the Degree of

Master of Arts

Florida Atlantic University

Boca Raton, Florida

December 2012

ACKNOWLEDGEMENTS

I would like to thank everyone that assisted me in the writing of my Masters thesis. That includes everyone that contributed to the selection of my research topic, the research process, the writing, the data analysis, and the revision. I would like to first thank my advisor, Dr. Douglas Broadfield for directing me to such an interesting choice of methodology. I would also like to thank Dr. Ralph Holloway for letting me use his collection and for providing his insight on the topic. My other thesis committee members,

Dr. Clifford Brown and Dr. Kate Detwiler, deserve thanks for their helpful critique of both my methodology and my writing. Dr. Charles Roberts and Professor James

Gammack-Clark were excellent resources for my GIS questions. Dr. Delson graciously allowed me to use his 3D laser scanner and Claudia Astorino and the other NYCEP students taught me how to use it; I cannot thank them enough. Last, but not least, my friends and family provided me with the support needed to complete this research. Thank you Kendra Philmon and Brittany Burdelsky for being my writing partners.

ABSTRACT

Author: Melissa Boas

Title: Hominin Endocast Topography: An Analysis Using Geographic Information Systems

Institution: Florida Atlantic University

Thesis Advisor: Dr. Douglas Broadfield

Degree: Master of Arts

Year: 2012

This study examined the topography of prefrontal molds of human endocasts using three-dimensional laser scanning and geographic information systems (GIS) in order to carry out intra-species comparisons. Overall brain topography can indicate when major reorganizational shifts in brain structure happened in our evolutionary history and these shifts may indicate major shifts in cognition and behavior. Endocasts are one of the sole sources of information about extinct hominin brains; they reproduce details of the brain’s external morphology. Analysis of endocast morphology has never been done using GIS methodology. The use of GIS helps to overcome previous obstacles in regards to endocast analysis. Since this methodology is new, this research focuses on only one species, Homo sapiens and the area of focus is narrowed to the frontal lobe, specifically

Broca’s cap. This area is associated with speech in humans and is therefore of evolutionary significance. The variability in lateralization of this feature was quantified.

HOMININ ENDOCAST TOPOGRAPHY: AN ANALYSIS USING GEOGRAPHIC INFORMATION SYSTEMS

TABLES ...... vii FIGURES...... viii I. BRAIN EVOLUTION...... 1 II. PREVIOUS METHODS...... 12 III. GEOGRAPHIC INFORMATION SYSTEMS (GIS)...... 18 IV. FRONTAL LOBE ...... 22 V. METHODS ...... 25 VI. RESULTS...... 29 VII. DISCUSSION AND CONCLUSIONS...... 41 APPENDIX A...... 43 APPENDIX B...... 46 LITERATURE CITED ...... 57

TABLES

Table 1. Area and slope measurements ...... 30 Table 2. Left and right hemispheric area for Broca's cap ...... 31 Table 3. Left and right minimum slope values for Broca's cap ...... 32 Table 4. Left and right maximum slope values for Broca's cap ...... 33 Table 5. Left and right slope range values for Broca's cap...... 34 Table 6. Left and right mean slope values for Broca's cap...... 34 Table 7. Left and right slope standard deviations for Broca's cap...... 35

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FIGURES

Figure 1. Paired t-test for left and right hemispheric area of Broca's cap...... 36 Figure 2. Paired t-test for left and right minimum slope values for Broca's cap ...... 37 Figure 3. Paired t-test for left and right maximum slope values for Broca's cap...... 38 Figure 4. Paired t-test for left and right slope range values for Broca's cap...... 39 Figure 5. Paired t-test for left and right mean slope values for Broca's cap ...... 40 Figure 6. Scan Studio scan screen with settings ...... 43 Figure 7. Scan Studio screen showing the trim, align, and fuse selections ...... 44 Figure 8. Excel sheet with xyz data points ...... 46 Figure 9. ArcMap screen showing the add data button ...... 47 Figure 10. ArcMap screen showing the display xy data option...... 47 Figure 11. Display xy data menu showing appropriate settings ...... 48 Figure 12. Export data dialogue box with appropriate settings displayed...... 49 Figure 13. IDW dialogue box with correct specifications ...... 50 Figure 14. Screen showing how to deselect original data points and copy a layer ...... 51 Figure 15. ArcMap screen displaying hillshading ...... 52 Figure 16. Slope dialogue box with correct specifications...... 53 Figure 17. New Shapefile dialogue box with correct specifications ...... 54 Figure 18. ArcMap screen displaying Broca's cap layer ...... 55 Figure 19. Zonal Statistics as Table dialogue box with correct specifications...... 56

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I. BRAIN EVOLUTION

Advanced cognitive abilities are a defining characteristic of our species, Homo sapiens. However, discrete and tangible traits are often used to define human beings in order to set us apart from the rest of the animal kingdom and cognition is something that is too broad and vague. Some traits that are commonly discussed are bipedality, tool- making, and burial of the dead. Where as we are unique, at least in the primate order, in the form of locomotion we employ, this trait does not seem to address what really sets us apart. The hominin ability to create and use complex tools was long used as a metaphor for our advanced intelligence (Henshilwood et al., 2001; Klein, 2009; McNabb et al.,

2004). This metaphor, however, lost some of its power when it came to light that other species are capable of tool production. Burial of the dead is also used as a marker of our advanced cognition (Belfer-Cohen & Hovers, 1992; Gargett et al., 1989; Hayden, 1993;

Hovers et al., 2000; Noble, 1993; Rak et al., 1994). This human trait is used as a metaphor to show our advanced emotional state and capacity for symbolic knowledge.

However, it has been shown that elephants also have death rituals, so this cannot truly be seen as a defining characteristic of our species and lineage (Bradshaw, 2004; Moss, 1992;

Poole, 1996).

These three traits have something else in common. They are all relatively easily recognized in the fossil and archeological record. The positioning of the foreman magnum and other morphological characteristics are indicative of bipedalism. Stone tools

found in association with fossil hominins indicate an early ability to create tools.

Intentional burial sites, sometimes with grave goods, indicate burial practices. However, since brains do not fossilize, it is hard to track the evolution of the brain and cognition, so we are forced to extrapolate from evidence in the fossil and archaeological record.

Certain findings like stone tools and intentional burials have behavioral implications; however, there are other ways to study the evolution of human cognition and behavior.

Both paleoneurology and comparative studies can shed light on the topic.

Brain evolutionary studies can be loosely broken down into two methodological types. The first involves comparative neuroanatomical studies of extant species and the second involves paleoneurology, the study of brain evolution through the use of the fossil record. The former is considered an indirect method whereas the latter is considered a direct method. Comparative work can be done at the cytoarchitectural level and at the gross anatomical level, or it can be done through functional brain imaging (Bienvenu et al., 2011). Paleoneurologists look at endocasts, impressions of the cranial vault. These endocasts can be natural, fossilized impressions, or they can be man-made, created secondarily from fossilized calvaria. In a way, the study of endocasts is also indirect; this is because endocasts are not fossilized brains themselves, but rather impressions from the inside of the skull. Endocasts reproduce the morphology of the brain case, which in turn reproduces some features of the external morphology of the brain. Only some features are visible due to the fact that the brain’s surface is separated from by calvaria by three meningeal layers, cerebrospinal fluid, and blood vessels (Bienvenu et al., 2011). (For a comprehensive review of paleoneurology see Bruner, 2003 and Holloway et al., 2004).

The extent to which endocasts can reliably predict the morphology of the calvaria and brain has been studied. Falk, Hildebolt, and Vannier found that in chimpanzee endocasts, “The relationship between the squamosal suture and asterion differs markedly between the outsides of skulls and endocasts” (1994). Schoenemann et al. created virtual endocasts from plaster endocasts and from the corresponding calvaria and compared them (2007). The differences between the endocasts were small with only two exceptions

(Schoenemann et al., 2007). The inferior areas around the cranial base and the temporal poles were both slightly larger in the plaster endocasts and Broca’s area was slightly smaller in the plaster endocast for humans (Schoenemann et al., 2007). More studies must also be performed in order to test the reliability of how well endocasts can predict external brain morphology.

Despite the variations noted in these studies, overall, it seems that endocasts provide reliable replications of certain cranial features, but alone they are not a sufficient tools for studying cognitive evolution because hominin fossils are rare and often fragmentary. These fragmentary and distorted finds are often not adequate for endocasting. Both paleoneurological and comparative methodologies should be used in order to compliment and support each other. Since these two methods give researchers the only means by which to study the brain, they must be utilized to their fullest potential.

It is important to get the full picture because brain evolutionary studies indicate important milestones in the evolution of our species’ cognition and behavior, arguably the features that most differentiate us from the rest of the animal kingdom. However, due to the scope of this paper and the nature of my research, I will only talk about comparative data that directly compliments paleoneurological findings.

Endocast analyses are first and foremost used in order to estimate cranial capacity

(Conroy et al., 1998, 2000). This is the most basic measurement that can be obtained from fossil evidence. This, however, is not sufficient in itself as an indicator of cognitive abilities and complexities. According to Holloway, cranial capacity is given too much attention; it is used to determine taxonomy, behavioral complexity, and intelligence, but it is a poor indicator (Holloway, 1966). For one, the difference in cranial capacity between Homo sapiens and the australopithecines is about 1,000 cc (Holloway, 1966).

This is not far from the amount of variation that is seen within our species, and within our species variation is not correlated with differences in behavior or intelligence (Holloway,

1966). Therefore, there must be some other variable at work.

A perfect example of why body size must be considered when examining brain size and morphology comes from the species Homo floresiensis (Sereno & Tootell,

2005). Despite the small size of its brain, this species is associated with stone tools

(Morwood et al., 2004). Some researchers did not believe that this could be possible; they believed that increased cranial capacity was a prerequisite for complex cognition and therefore claimed that H. floresiensis did not deserve its own species designation. They believed it was instead a pygmy or microcephalic. A three-dimensional computed tomographic (3DCT) generated virtual endocast for this species was created and analyzed; Falk et al. compared LB1, H. floresiensis, to Australopithecus, Homo erectus,

Homo sapiens, a human pygmy, and a human mircrocephalic specimen and came to the conclusion that the H. floresiensis does indeed deserve its own species designation; it is not an microcephalic or a pygmy (Falk et al., 2005). The encephalization quotient likens the specimen to an australopithecine, but its shape likens it to H. erectus (Falk et al.,

2005). The frontal and temporal lobes are more derived and the lunate sulcus is in a more derived position (Falk et al., 2005). A later analysis identified seven derived features, “a caudally positioned occipital lobe, lack of a rostrally-located lunate sulcus, a caudally- expanded temporal lobe, advanced morphology of the lateral prefrontal cortex, shape of the rostral prefrontal cortex, enlarged gyri in the frontpolar region, and an expanded orbitofrontal cortex” (Falk et al., 2009). This is consistent with the species’ ability to produce tools; they had higher cognitive abilities despite an overall small brain size (Falk et al., 2005). Their brains were reorganized without expansion (Falk et al., 2009).

The presence of overall brain reorganization in the cerebral cortex, not just brain expansion is therefore one indicator of change in behavior and cognition in our early ancestors. If the brain reorganized before it expanded, one would expect to observe non- allometric growth; not all areas of the brain would increase at the same rates. Therefore, changes in the position of brain landmarks across species can serve as an indicator of cognitive evolution. One such landmark is the lunate sulcus. The lunate sulcus is the boundary of the primary visual cortex in chimpanzees.

The evolutionary changes in the positioning of this landmark have been used to substantiate the claim that human-like behavior evolved early as opposed to late in our evolutionary past. This is because, in general, the cerebral cortex is associated with complex cognitive behavior, so changes in its organization indicate changes in behavioral complexity (Holloway, 1966). More specifically, a decrease in the size of the primary visual cortex has broad implications. It is significantly smaller than expected in humans than what is expected for a primate species with our brain size (Stephan et al., 1981).

This, in and of itself, was most likely not selected for; it was instead the result of an

overall increase in the posterior parietal association cortex (Holloway et al., 2003). This area of the brain is associated with a wide variety of complex cognitive tasks (Platt &

Glimcher, 1999; Culham & Kanwisher, 2001). Specifically, it is linked with increased visuospatial abilities and decision-making and these abilities may have been beneficial in the changing environment that was inhabited by Australopithecines (Platt & Glimcher,

1999; Yuan et al., 2002; Holloway et al., 2003).

The idea that the brain reorganized early in our evolutionary history has been speculated for quite some time. Raymond Dart published a paper on the discovery of the

Taung child, a juvenile Australopithecus africanus (1925). He claimed that the lunate sulcus is distinguishable on the natural endocast and that it is present in a more human- like position, indicating an expansion in the parietal and temporal lobes (Dart, 1925).

This, according to Dart, along with other morphological data, supports his claim that A. africanus is “an extinct race of apes intermediate between living anthropoids and man”

(1925). Dart’s findings support the hypothesis that brain reorganization preceded overall brain enlargement.

This was a contentious stance to take at the time and it is still debated now. At the time of Dart’s findings, he had little to no support from his colleagues. Keith, Clark, and

Smith were the most outspoken critics of his work. Keith instead identified a new depression, one located in a more anterior position, as the lunate sulcus (1925, 1931).

Clark claimed that Dart misidentified the lunate sulcus and said the depression was actually the location of the lambdoid suture (Clark et al., 1936; Clark, 1947). Smith’s opposition to Dart’s conclusions about the importance of the Taung find was most surprising because it was Smith himself that first identified the lunate sulcus by claiming

that the structure in apes has a homologous structure in humans (Smith, 1904).

Interestingly, his criticism never involved a criticism of Dart’s placement of the lunate sulcus, only a criticism of the conclusions Dart came to (Smith, 1925). It wasn’t until the

1940s that Schepers revisited Dart’s work and stood behind it (1946). Now Falk is Dart’s biggest critic while Holloway and others continue to find supporting evidence for early reorganization involving the lunate sulcus.

Falk argues that the lunate sulcus is in a more ape-like position in

Australopithecines; she says if reorganization happened early in our evolutionary history, it was at a finer level, not at an overall external morphological level (Falk, 1980). She backs this position with her own analyses of the fossil record. Her examination of the

Taung endocast indicates that the medial end of the lunate sulcus is in a chimpanzee-like position (Falk, 1983). She identifies the medial end of the lunate sulcus, but does not mark the entire lunate sulcus because she claims it is not visible (Falk, 1989). Her ratio describing the position of the lunate sulcus for Taung falls only 1.5 standard deviations from the mean ratio for chimpanzees (Falk, 1989).

Others have used comparative data to back up this interpretation of the fossil record (Armstrong et al., 1991). Armstrong et al. used the gyrification index to compare the degree of cortical folding in chimpanzees to humans and found similarity in the degree of cortical folding in the caudal cortex, but a significantly higher degree of cortical folding in all other areas of the human brain (1991). Since there is conservation in the degree of cortical folding, differences in folding must have arose around the same time as changes in brain size; this supports Falk’s position that the lunate sulcus of the

Taung child is in a pongid-like position (Armstrong et al., 1991).

Holloway disagrees with this interpretation and provides his own. He believes that reorganization happened earlier in our evolutionary history and that there is evidence of this morphological change in the fossil record. In his early publications, he used cord measurements and stereoplotting in order to back his claim that the Taung and Hadar endocasts display a more human-like morphology (Holloway, 1981a, 1981b, 1983,

1984). Broadfield and Holloway took a digitized figure of the endocast and overlaid

Dart’s original drawing in order to come to the conclusion that their interpretation matches that of Dart’s; the lunate sulcus is in a posterior, human-like position (2005).

Yuan, Broadfield, and Holloway also use comparative data to back their hypothesis (Yuan et al., 2002; Holloway et al., 2003). They show that the neurogenetic variation upon which natural selection acts was most likely present in Australopithecines by showing that variation in the volume of the primary visual cortex is not only present in humans (Gilissen et al., 1995), but is also present in chimpanzees (Yuan et al., 2002;

Holloway et al., 2003). They present evidence that two chimpanzees have a significantly more posterior placement of their lunate sulci (Yuan et al., 2002; Holloway et al., 2003).

If such a degree of variation is seen in chimpanzees, then an increase in brain size is not a prerequisite of reorganization.

Holloway also points to evidence for other types of cerebral reorganization that are seen in chimpanzees and other great apes (Holloway et al., 2003). Certain features of the cerebral cortex of humans may have had precursors that can be seen in chimpanzees; therefore, they may have originated at least around the time that we shared a common ancestor (Holloway et al., 2003). This means that brain reorganization began early in our evolutionary history. For one, it has been shown through the study of cadaver brains and

through magnetic resonance imaging (MRI) that the planum temporale, a part of

Wernicke’s area, of great apes shows a leftward asymmetry; this is also seen in humans

(Gannon et al., 1998; Hopkins et al., 1998). Also, asymmetries in the inferior frontal gyrus, Broca’s area, have been described in chimpanzees, bonobos, and gorillas

(Cantalupo & Hopkins, 2001). There are also asymmetries in Sylvian fissure length

(Heilbroner & Holloway, 1988; Yeni-Komshian & Benson, 1976).

These areas of the brain are associated with language in humans and may also play a role in gestural communication in other great apes. Due to their presence in other species, it seems that language centers began to evolve somewhere around 15 million years ago and may have evolved as a result of selection for greater cortical folding which led to greater gyrification (Hopkins et al., 1998). It has also been shown that only African apes have large, spindle-shaped cells in the anterior cingulated cortex (Nimchinsky et al.,

1999). They may have arisen as an adaptation over the last 15-20 million years; this area of the brain is associated with higher cognitive functions (Nimchinsky et al., 1999). Other homologues have been revealed in the organization of the primary visual cortex (Preuss et al., 1999). When the cortical architecture of the primary visual cortex of humans, chimpanzees, orangutans, Old World monkeys, and New World monkeys is compared, similarities between chimpanzees and humans can be drawn (Preuss et al., 1999).

Chimpanzees seem to have an intermediate morphology; they are more derived than other primates in the study, but humans have evolved a unique cortical morphology that is even more derived than that of chimpanzees (Preuss et al., 1999).

Holloway not only provides his own analysis of the evidence, he also criticizes the work of Falk. He says her stereoplotter methodology is flawed and that the

conclusions she draws from them does not make sense when the gross morphology of the

Taung cast is examined as a whole; Taung does not display an ape-like morphology

(Holloway, 1981a). He again tested Falk’s hypothesis in 1984; he used tape-arc measurements on six chimpanzee brain casts and compared them to measurements taken from Taung (Holloway, 1984). He came to the conclusion that his own stereoplotting results from 1981 are supported (Holloway, 1984). He also criticizes the work of

Armstrong et al. and the conclusion that they came to using the gyrification method

(Holloway, 1992). Holloway points out that they ignore the differences in volume of the primary visual cortex between species, so the areas of overlap that Armstrong et al. speak of are not homologous (1992).

Falk counters in multiple publications with a critique of Holloway’s critiques. She says that Holloway used tape-arc measurements in order to get measurements from chimpanzee endocasts, but he does not follow the same methodology when examining the Taung endocast; when this is done, she claims that her measurements for Taung fall within Holloway’s range for chimpanzees (Falk, 1985a).

Observations about overall differences in the external morphology and sulcal patterns of hominin endocasts have also been made. Falk addresses the natural endocasts of australopithecines and notes the differences in the central sulcus and the frontal lobe

(Falk, 1979). Falk also compares the sulcal patterns of australopithecine natural endocasts to that of humans and other great apes; she comes to the conclusion that these patterns most closely resemble the patterns of chimpanzees (Falk, 1980). She prepared an endocast from the partial calvaria of AL 162-28 and upon examination, came to the conclusion that the specimen has an ape-like cortical morphology (Falk, 1985b).

Holloway later criticized this interpretation stating that Falk made an error in the orientation of the endocast; Falk dismissed this criticism (Holloway & Kimbel, 1986;

Falk, 1986).

II. PREVIOUS METHODS

The current methodology has not resolved these debates over the interpretation of the fossil record. The methods used to study the topic of brain evolution via endocast analysis mentioned above have relied primarily on landmark-based data. There are some limitations to this methodology. For one, the data has led to very different interpretations, as reviewed by Falk (2009). It also relies on assumptions about structural and functional homologies. This can be called into question because changes in body size can lead to unaccounted for changes in morphology; also, areas of the brain can duplicate, leading to unexpected changes in structure and function. An overall topographic analysis would supplement these other methods. However, Falk’s previous analyses of overall differences in the external morphology have been fraught with controversy as well.

Dart and Schepers’ early methodology relied on direct inspection of the Taung endocast (Falk, 2009). Their work, as well as the work of Falk and Holloway, also involved the measurement of lengths and relationships of sulci (Falk, 2009). This methodology mostly revolves around chord measurements, stereoplotting, and caliper measurements.

These same types of measurements can also be taken on virtual endocasts. Falk et al. used cranial capacities in combination with caliper measurements and measurements on virtual endocasts in order to provide an overall assessment of endocasts from all available hominin species (2000). This analysis led her to the conclusion that

Australopithecines are ancestral to the Homo lineage (Falk et al., 2000). She also found that Paranthropus has a smaller cranial capacity than originally thought; according to her analysis of these measures, the species also had less derived temporal and frontal lobes that were not a result of morphological constraints (Falk et al., 2000). Falk and Clarke also used these methods in order to analyze a new reconstruction of the Taung endocast

(2007). They created a virtual endocast with the use of 3DCT because they claim it is easier to take and compare measurements in the same plane; this is difficult to achieve on an actual endocast (Falk & Clarke, 2007). These electronic measurements are also more repeatable (Falk & Clarke, 2007). The measurements taken on this virtual endocast were linear measurements and measurements of distance, length, and width much like caliper and cord measurements (Falk & Clarke, 2007). Falk and Clarke use these measurements, cranial capacity, and overall morphological observations in order to carry out their assessment (2007).

3DCT reconstructions can prove useful superior to other methods such as conventional CT scans, but this methodology can also have its drawbacks (Vannier et al.,

1985). Holloway and Broadfield do not agree with Falk’s reconstruction methods (2011).

They emphasize the importance of finding the midsagital plane; they claim Falk does not do so accurately (Holloway & Broadfield, 2011). They also emphasize the need to assume symmetry, which she also does not do (Holloway & Broadfield, 2011). All in all, their argument is that Falk’s reconstruction is too far removed from the original specimen

(Holloway & Broadfield, 2011). A more fundamental problem is that CT scans prove useful for rendering an endocast from a braincase if a natural endocast is not available or if an endocast has not been created, but there are other methods that have higher

resolution capacity. Three dimensional laser scanners are better at producing high resolution, high quality virtual representations of endocasts.

Measurements taken on actual endocasts as well as virtual endocasts rely on landmark data. The use of landmark measurements can prove problematic when analyzing brain reorganization. The presence of landmarks can be variable both within and across species. The lunate sulcus is reliably present in chimpanzees, but it is not always present in humans. It is also difficult to determine if such structures are homologous in all apes, including hominins. It has been argued that the lunate sulcus does not even correspond to a functional area in humans and is not, in fact, a homologue across great ape species. Allen et al. studied this region of the brain using high-resolution

MRI in humans and came to the conclusion that in most cases, what was deemed the lunate sulcus from surface examination was in fact a collection of much smaller sulci; this is not the case in other great apes (2006). They argue that histological and functional imaging data supports their claim that these structures are not homologous (Allen et al.

2006).

As mentioned above, Grafton Elliot Smith was the first to claim that these sulci are homologous (1904). This claim was based largely on his findings that showed a functional association between the lateral anterior boundary of the primary visual cortex and what he deemed the lunate sulcus in humans (Allen et al., 2006). Allen et al. say that this homologous relationship is an assumption on which all subsequent research on the topic has been based; they argue that the lunate sulcus in chimpanzees is not homologous to the lunate sulcus in humans (2006). It is not always present in humans and only very rarely does it have a relationship to the primary visual cortex (Allen et al., 2006). In

chimpanzees, however, no matter the position of the lunate sulcus, it is always associated with the boundary of the primary visual cortex (Allen et al., 2006). They use this as evidence for their claim that previous estimates of brain reorganization have been too conservative; they hypothesize that changes in this region of the brain have not been passively altered by expansion of the parietal region (Allen et al., 2006). The evidence they cite for this claim of extensive reorganization is the loss of a lunate sulcus in humans, or if the lunate sulcus is present, the loss of the structural relationship with the primary visual cortex (Allen et al., 2006). They also point to the overall reduction in size of the primary visual cortex (Allen et al., 2006; Stephan et al., 1981).

In general, it is difficult to determine homologies for many reasons. Intermediary species are not well represented and at times, endocasts cannot be studied with the current methods because there are no visible landmarks. Also, species differ in body size, and “brain areas can duplicate, fuse, or reorganize between and within lineages” (Sereno

& Tootell, 2005: 135). All species are a result of an independent evolutionary tract; when comparing two closely related species it is assumed that areas of the brain in similar locations are homologous because they did not have the time to diverge greatly. This, however, is a big assumption and the only way to mitigate it is to increase sample sizes and examine as many species as possible using as many methods as possible.

Body size is something that can greatly affect differences in cortical folding between species and can be another reason why landmarks can prove unreliable. Non- primary cortex has increased in apes and studies have been done to examine why (Bush

& Allman, 2004a). However, “as primate body size (and, correspondingly, brain size) increases, the non-primary cortex beyond V1 systematically grows faster than V1 in

every primate” (Sereno & Tootell, 2005: 136). In other words, the reason why apes, especially humans have more non-primary cortex could simply be an effect of larger body size. The frontal cortex of primates has also been shown to hyperscale with increased body size (Bush & Allman, 2004b). “Frontal cortex hyperscaling is actually greater in lemurs and lorises (strepsirrhines) than that in the primate line leading to humans (haplorhines)” (Sereno & Tootell, 2005: 136). This increase in cortex is accommodated by the addition of sulci; “sulci multiply rather than just expand” (Sereno

& Tootell, 2005: 137). This is also why stereoplotting can prove problematic (Sereno &

Tootell, 2005).

Brain areas do not just change in size and reorganize, they can also become duplicated and this makes it “impossible to construct a topological 1:1 map between such brains” (Sereno & Tootell, 2005: 138). Allman and Kaas explain that areas of the brain can duplicate during evolution much like genes and body parts and these duplicated areas develop new functions (1974).

Not only is landmark based methodology limited in the above ways, it also cannot give an overall topographic analysis. A topographic analysis can also be limited by many of the above-mentioned factors, but in conjunction with landmark data, a more comprehensive analysis can be performed, yet studies of overall shape and topographic differences have largely not been performed (Bienvenu et al., 2011). Holloway wrote about overall endocranial shape variation in the great apes, but he did not look at specific differences among the species (1981b). Bruner, Bienvenu, and others have studied endocranial shape using geometric morphometrics (Bruner, 2004; Bienvenu et al., 2011).

This type of methodology requires the mapping of landmarks, so all of the same

limitations of the other methodologies mentioned above apply. Also, fragmentary remains are not very useful with this methodology because all landmarks are needed for an analysis. A different type of topographic approach has been taken with actual brains; the gyrification index has been used to measure the overall level of cortical folding in the brain (Armstrong et al., 1991). This, unfortunately, is not applicable to the study of endocasts because this level of cortical folding is not represented on a cast of the braincase. If one wants to compare the level of lateralization of a particular feature or the difference in size or slope of a feature between species, a topographic analysis is valuable. Geographic information systems can be used to do just this.

III. GEOGRAPHIC INFORMATION SYSTEMS (GIS)

GIS is used to collect, store, and analyze spatio-temporal coordinates. A comprehensive definition of GIS explains that it is, “An organized collection of computer hardware, software, geographic data, and personnel designed to efficiently capture, store, update, manipulate, analyze, and display all forms of geographically referenced information” (Environmental Systems Research Institute, 1995). This technology is traditionally applied to the analysis of geography and its early application was related to land registration, environmental applications, and topographic mapping in such fields as anthropology, geology, biology, paleontology, and environmental science

(Environmental Systems Research Institute, 1995; Anemone et al., 2011). The first modern application was in the 1960s; Tomlinson used it to create and maintain an inventory of land resources in Canada (Anemone et al., 2011; Tomlinson, 1998). It is now applied to many fields including, “planning and zoning, property assessment and land records, parcel mapping, public safety, and environmental planning” (Environmental

Systems Research Institute, 1995). In the field of anthropology, it is widely used in order to aid in the creation of maps of archaeological sites.

Not all GIS models are created in the same way; there are three different types of data models (Anemone et al., 2011). They are vector, raster, and triangulated irregular networks (TINs) (Anemone et al., 2011). Vector models use (x,y) or (x,y,z) coordinates to describe the location of small objects, vector lines to describe objects or features with a

length but no width, and linear boundaries to describe area features (Anemone et al.,

2011). Raster data models, on the other hand are useful for representing continuous phenomena; these are variables that “vary continuously over an area and do not necessarily have sharp boundaries” (Anemone et al., 2011: 20-21). The TIN data model is useful because it takes the advantages of both vector and raster models and combines them (Anemone et al., 2011). “TINs are a series of linked triangles, where the heights of the corners of the triangles correspond to the z value of the surface. The triangles form

‘facets’ that show the overall form of the surface” (Anemone et al., 2011: 21).

This data can be used to create a digital elevation model (DEM), “a lattice of spot elevation measurements that can be used to derive a number of indices that characterize topographic surfaces” (Anemone et al., 2011: 22; Moore et al., 1991). Methods for gathering data points rely on the collection of points or a point cloud of (x,y,z) coordinates in order to create a DEM (Anemone et al,, 2011; Ungar & Williamson, 2000;

Zuccotti et al., 1998; Jernvall & Selanne, 1999). GIS software can then be used to calculate things such as area, volume, length, slope, and angles of different features

(Anemone et al., 2011; Ungar & M’Kirera, 2003).

GIS has been used recently in the field of biological anthropology to successfully create three-dimensional models of dentition in order to look at occlusal topography, wear during mastication, and the relationship between morphology and diet (M’Kirera &

Ungar, 2003; Ungar, 2004; Merceron et al., 2006). The cusps and grooves of teeth are similar to geographic features, but on a smaller scale and can therefore be analyzed in a similar fashion. The link between morphology and diet in extant species can be applied to the fossil record through the use of GIS. Previous methods quantified shearing crests;

however, this can only be done for unworn teeth (Anemone et al., 2011; Kay, 1975). GIS, on the other hand, can be used with worn teeth. It was first used along with reflex microscopy in order to look at occlusal morphology of primate teeth (Reed, 1997). GIS was then used in conjunction with scanning electron microscopy (SEM) and laser confocal microscopy but these methods for gathering data points are long and tedious, prone to inter-observer error, not replicable, and are subject to many interpretations

(Anemone et al., 2011; Teaford, 1994; Jernvall & Selanne, 1999; Ungar et al., 2003).

Ungar, the leading researcher in the field, used three-dimensional laser scanning in order to gather data points which allows for the study of worn teeth; this facilitates the study of fossilized teeth (Ungar & Williamson, 2000; Ungar & M’Kirera, 2003; Ungar, 2004).

GIS can be applied in biological anthropology in other novel ways. Since GIS is traditionally used in order to assess the topography of spherical objects, such as the surface of the planet, it should also be possible to apply this methodology to spherical objects on a smaller scale. Endocasts serve as a perfect example. Z coordinates can be collected in order to analyze slope, relief, and overall topography of cranial vault casts.

This is important because it will allow for endocast comparisons that do not rely solely on set landmarks and measures between them. As stated above, this is an issue because landmarks can erode and they can be difficult to identify. Landmarks themselves can also be controversial because their homology is not always agreed upon. It is an appropriate methodology for this specific project because it can assess topography of a specific area of the endocast and it can be used for comparing structures in the left and right hemisphere. Namely, slope and area can be analyzed for Broca’s cap, the inferior portion

of the inferior frontal gyrus. I used it in order to look at the topography of the prefrontal cortex in order to compare the slope of Broca’s cap in the left and right hemispheres.

IV. FRONTAL LOBE

For the purpose of this study, the area under consideration has been narrowed to the frontal lobe. The frontal lobe is the lobe of the brain anterior to the central sulcus. It can be broken down into three functional areas, the primary motor area, the premotor area, and the prefrontal cortex (Schoenemann, 2006). The primary motor area controls muscle movement; the premotor area plans complex muscle movement; and the prefrontal cortex is involved with higher cortical functions (Schoenemann, 2006).

The frontal lobe has been studied in the past in order to draw conclusions about the level of behavioral complexity in early hominins and according to Holloway these studies are fraught with misinterpretation of data and unsound correlations (1966).

Holloway’s main concern is that the assertion has been made that the size of the frontal lobe is an indicator of behavioral complexity when in fact, according to Holloway, “In almost all cases where actual primate brains have been measured rather than endocasts, the quantitative data are clearly against the interpretation of a relatively large frontal lobe in Man” (1966: 105-106). Semendeferi and others have shown that the human frontal lobe is no larger than expected for an ape brain of our size (Semendeferi & Van Hoesen,

1997; Semendeferi & Damasio, 2000).

It cannot be ignored, however, that the frontal lobe, specifically the prefrontal cortex, is the location where many cognitive abilities of interest to anthropologists are centered. It is the sight of many social cognitive functions including those related to

planning, language, and social interactions (Schoenemann, 2006). It is also involved in creative thinking, decision-making, emotional behavior, and working memory

(Semendeferi & Van Hoesen, 1997). Past studies have shown that prefrontal white matter volume is larger than expected in humans whereas gray matter volume is at an expected level (Schoenemann et al., 2005). Schenker et al. showed that humans have higher than predicted volumes of white matter in areas near the cortical surface (2005). This may be due to differences in subcortical areas or structures as well as increased interconnectivity or it may be due to more functional divisions within the frontal lobe (Schoenemann,

2006; Semendeferi et al., 2002). It may, however, just be due to allometric growth

(Schoenemann, 2006).

An evolutionary or functional explanation is more likely because prefrontal cortex is the specific area of the frontal lobe that is larger than expected; increase is not evenly distributed throughout the frontal lobe. The primary motor area and the premotor area are actually both smaller than expected for a primate brain of our size (Blinkov & Glezer,

1968). As Schoenemann stated, “If the entire frontal lobe is approximately as large as expected given overall human brain size, yet the two portions of the frontal lobe reviewed above (the primary motor and premotor areas) are significantly smaller, then the rest of the frontal lobe (i.e., the prefrontal) must necessarily be larger than expected”

(Schoenemann, 2006: 386). Also interspecies variation in neural connectivity in the frontal lobe of primate species is present in species with similar absolute brain size

(Schenker et al., 2005). According to Schenker et al., “Given that these regions are part of neural systems with distinct functional attributes, we suggest that the observed

differences may reflect different evolutionary pressures on regulatory mechanisms of complex cognitive functions, including social organization” (2005: 447).

Also, the prefrontal area is wider and Broca’s cap has become increasingly lateralized in humans; there is a strong leftward asymmetry in regards to its overall size that is evident upon gross morphological inspection (Bienvenu et al., 2011; Falk et al.,

2009; Sherwood et al., 2003). Brain lateralization is a sign of hemispheric specialization.

Specifically, asymmetries in Broca’s area are associated with language (Sherwood et al.,

2003). The left hemisphere is dominant in regards to language in 95% of humans; this has been shown through functional imaging as well as cortical stimulation (Branche et al.,

1964; Ojemann, 1991; Petersen et al., 1988; Sherwood et al., 2003). As mentioned above, asymmetries in the language areas of the brain are also seen in the great apes, indicating that Broca’s area was under selection early in our evolutionary past and may have been selected for because it conferred an advantage by allowing for more nuanced and symbolic communicative abilities (Gannon et al., 1998; Cantalupo & Hopkins, 2001). An analysis of the topography of this area could provide insight into the evolution of this region of the brain. This is important because Broca’s cap is observable in endocasts and plays a crucial role in human language, a cognitive ability that defines our species and seems to have been selected for early in our evolutionary history.

V. METHODS

Methodological standards for dental topographic research have already been set

(M’Kirera & Ungar, 2003; Ungar, 2004; Merceron et al., 2006). This research follows these standards, replacing the samples of dentition with fourteen human prefrontal endocast molds. The endocast molds are from Dr. Ralph Holloway’s collection at

Columbia University. His collection was used because it is extensive and available for access. The specimen numbers that are associated with the molds have been lost, so no sex, age, or other identifying information is known.

A three-dimensional laser scanner was used in order to create models of the molds. A laser scanner was used because it is capable of quickly collecting large amounts of high-resolution data. Laser scanners are used in a wide-range of anthropological contexts; they have been used in association with GIS, in forensic facial reconstruction, and in the examination of early hominin footprints (Shahrom et al., 1996; Santamaria et al., 2007; Bennett et al., 2009). Ungar used a Surveryor 500 by Laser Design Inc., but that model is best for small objects like teeth. I used the NextEngine scanner because it is better for the size objects I scanned and because it is highly accurate and it produces very clean data.

The NextEngine Model 2020i Desktop Laser Scanner was used in conjunction with Scan Studio 1.3.0. Bracket style scans were run at the macro range setting. Bracket scans create a model from three scans, a center scan, a left of center scan, and a right of

center scan. This setting was chosen over the 360-degree scan because the reverse side of the mold is not needed. The molds were placed at a distance from the scanner that made them appear clear in the computer view; a set distance was not established. The molds varied slightly in size and therefore had to be placed at varying distances from the scanner in order to obtain the clearest models. Two to three scans were performed for each specimen, depending on how many were needed in order to capture the entire surface. At least one scan from the inferior view and at least one scan from the anterior view were run. The align tool was used in order to put these scans together. In order to align the scans, colored pins were placed at identical points on the different scans. Align worked best when these points were spaced far apart and when they were not all in a line.

If possible, the points were selected so as to create as close to an equilateral triangle as possible. The points where the pins were placed were not based on set landmarks, but instead on identifying areas that were clear on the specific molds. Distinct and clearly identifiable sulci, gyri, or imperfections on the mold were chosen. The trim tool was used to remove anomalous points created by the scanning of the platform and the putty used to hold the molds in place. Once the scans were aligned and trimmed, the fuse tool was used and the data was saved as .xyz files. For further detail see Appendix A.

These xyz files were converted into Excel tables and then imported and analyzed using ArcGIS 10 (ESRI, Inc., Redlands, CA), the leading GIS software. The Spatial

Analyst and 3D Analyst extensions were used. The data was added and then exported as a shape file. This layer was then converted to a raster data set. The inverse distance weighted (IDW) tool was used to interpolate a raster surface by calculating new data points using a weighted average of the available points; the new points are created within

the discrete set of known coordinates using multiple variables. The weights are assigned based on the inverse of the distance to each available point. This is done in order to provide a smooth surface for better viewing of the data and to provide a raster data set so further calculations can be determined. This method is deterministic, meaning the same output is always produced for a given input. Originally my sample size was larger, but molds that had a large anterior portion did not convert to a raster format properly because of the deterministic nature of the methodology. The anterior portion, if too large, began to slightly curve posteriorly, creating overlap with the points on the inferior surface. When these samples were removed from my data set, I was left with 14 specimens.

An elevation color ramp was chosen for the layer for better visualization. A hillshading layer was also added in order to better show the form of the endocast mold and to add to the effectiveness of the visual model. Hillshading creates a shaded relief from a surface raster. It takes into consideration the illumination source angle. A slope analysis was run for the entire surface of the endocast. The rate of change in z-values from each cell of the raster surface was calculated and displayed visually as a new layer.

A new shape file layer was added and features were created that defined Broca’s cap for each hemisphere. The freestyle polygon tool was used and Broca’s cap was defined by the boundaries of the inferior frontal gyrus from the inferior view. The elevation visualization aided in allowing me to choose the lowest point of the sulcal grooves around Broca’s cap. Zonal statistics were run in order to compare slope between the left and the right hemispheres in each individual. Zonal statistics was used to calculate area, minimum slope, maximum slope, slope range, mean slope, and standard deviation in slope. For detailed instructions see Appendix B.

The hypotheses tested are all concerning the area and slope of Broca’s cap. The prediction is that the surface area as well as the maximum and minimum slope, and therefore also the mean slope and range of slope values, of the left and right hemispheres will be statistically different for each individual.

VI. RESULTS

The results show that there are statistically significant differences in slope between the left and the right hemispheres in Broca’s cap as shown on prefrontal endocast molds. Tables 1 through 7 show the raw data. In Table 1, area and slope measurements for Broca’s cap are given for the left and right hemispheres of the 14 prefrontal endocast molds. Overall area, minimum slope, maximum slope, slope range, mean slope, and standard deviation in slope are provided.

Table 1. Area and slope measurements

Upon inspection it becomes clear that most specimens exhibit either an approximately equal Broca’s cap area between hemispheres or a larger Broca’s cap in the left hemisphere. There is, however, quite a bit of variation as well as some marked exceptions. Specimens AMNH 3 and AMNH 8262, for example, have noticeably larger right Broca’s caps. (Table 2)

Table 2. Left and right hemispheric area for Broca's cap

The minimum slope values for both hemispheres for most specimens are, as expected, around or below 1. Again, however, there is variation as well as a few noticeable exceptions. Specimens 997748, AMNH 99 122, and AMNH 8262 have higher than expected minimum slope values for the left hemisphere, while specimen AMNH 99

122 has a higher than expected minimum slope value for the right hemisphere. (Table 3)

Table 3. Left and right minimum slope values for Broca's cap

Most specimens exhibit either an approximately equal Broca’s cap maximum slope value between hemispheres or a greater maximum slope for Broca’s cap in the right hemisphere. There is only one specimen that varies from this trend, AMNH 8262.

AMNH 8262 is also the only specimen with aberrant range and standard deviation values that are smaller than expected for the left hemisphere. This is because of both its unusually high minimum slope value and its unusually low maximum slope value.

Likewise, most specimens exhibit either an approximately equal mean slope between hemispheres or a slightly higher mean value for the right hemisphere. Specimen AMNH

4666 is the only exception. (Tables 4, 5, 6, and 7)

Table 4. Left and right maximum slope values for Broca's cap

Table 5. Left and right slope range values for Broca's cap

Table 6. Left and right mean slope values for Broca's cap

Table 7. Left and right slope standard deviations for Broca's cap

Paired t-tests and Wilcoxon signed rank tests were performed for all area and slope data in order to test if the differences between hemispheres for each individual are significant. F-tests were performed to test for equal variance between hemispheres. There was no statistical difference found in the difference between the area of the left and right

Broca’s caps. The paired t-test yielded a p value of 0.219 (Figure 1). The Wilcoxon signed rank tests yielded a p value of 0.149. The standard deviations for the area values were high for both the left and right hemispheres, illustrating the wide range of variability

(Table 2). The variance is assumed equal; the F-test yielded a p value of 0.266.

Figure 1. Paired t-test for left and right hemispheric area of Broca's cap

There was no statistical difference found in the difference between the minimum slope of the left and right Broca’s caps. The paired t-test yielded a p value of 0.308

(Figure 2). The Wilcoxon signed rank tests yielded a p value of 0.397.

Figure 2. Paired t-test for left and right minimum slope values for Broca's cap

The paired t-test and Wilcoxon signed rank test both show that there is a statistically significant difference between the maximum slope of the left and right inferior frontal gyri. The p value for the paired t-test is 0.001 (Figure 3). The p value for the Wilcoxon signed rank test is 0.002. Some specimens showed very little difference in this value, whereas others showed a markedly high rightward asymmetry (Table 4). The variance between hemispheres is assumed equal; the F-test yielded a p value of 0.299.

Figure 3. Paired t-test for left and right maximum slope values for Broca's cap

The paired t-test and Wilcoxon signed rank test both show that there is a statistically significant difference between the range of slope values for the left and right inferior frontal gyri. The p value for the paired t-test is 0.004 (Figure 4). The p value for the Wilcoxon signed rank test is 0.002. Again, some specimens showed very little difference in this value, whereas others showed a markedly high rightward asymmetry

(Table 5). And again, the variance between hemispheres is assumed equal; the F-test yielded a p value of 0.092.

Figure 4. Paired t-test for left and right slope range values for Broca's cap

The paired t-test and Wilcoxon signed rank test both show that there is a statistically significant difference between the mean slope values for the left and right inferior frontal gyri. The p value for the paired t-test is 0.005 (Figure 5). The p value for the Wilcoxon signed rank test is 0.010. Again, some specimens showed very little difference in this value, whereas others showed a markedly high rightward asymmetry

(Table 6). And again, the variance between hemispheres is assumed equal; the F-test yielded a p value of 0.830.

Figure 5. Paired t-test for left and right mean slope values for Broca's cap

VII. DISCUSSION AND CONCLUSIONS

Through visual inspection of the human brain, it is clear that Broca’s cap exhibits leftward lateralization. However, these findings do not indicate an asymmetry in area and instead indicate an opposite, rightward asymmetry for slope. Whereas this is surprising at first, it still shows asymmetries exist between the hemispheres. The large degree in variation in area data combined with the small sample size could be the reason why the difference in area between hemispheres was not significant. Further studies with larger sample sizes are needed. Whereas it is accepted that the size of Broca’s cap shows a leftward asymmetry and the left side of the brain has more variation in morphology of the inferior frontal gyrus, it seems that the right gyrus displays a steeper slope.

There is a possible hypothesis for why the data indicates a rightward asymmetry in slope for Broca’s cap while previous studies show leftward asymmetry in both structure and function. Perhaps since the area encompassed by the left inferior frontal gyrus is larger, it is also more flattened out comparably. On the other hand, the right gyrus has to fit in a smaller area. The volume may be smaller in the right hemisphere, but maybe not on the same magnitude as the surface area is smaller. In order to accommodate more volume per surface area, comparatively speaking, the slope may be increased. Only future studies that test the relationship between surface area and slope, volume and slope, and surface area and volume will shed light on this interpretation.

This methodology proved useful for the analysis of brain topography and once a standard is set with the use of complete Homo sapiens samples, the same methods can be used in order to assess endocasts from fossil hominins. These comparisons can highlight areas of the brain that are variable both within species and between species, one such area being Broca’s cap. This can shed light on when in evolutionary history major topographic shifts occurred, hinting at brain reorganizational events and changes in cognition and behavior, including language.

APPENDIX A

Endocast topographic analysis instructions: procedures for gathering data using the NextEngine Model 2020i Desktop Laser Scanner and Scan Studio 1.3.0

I. Prepare to scan the endocast mold (Figure 6).

A. Click on the green scan arrow.

B. Choose bracket positioning with 10 divisions.

C. Select the highest level of standard definition, which equates to ten thousand points per inches squared definition quality in order to get high-resolution results without compromising time.

D. Choose neutral for the target selection because the molds are neither dark nor light in color.

E. Choose the macro range since the molds must be placed between five and nine inches from the scanner in order to get a clear view on the preview screen.

F. Place the mold on the turntable and place the turntable five to nine inches from the scanner. The distance is dependent upon the size of the mold. Place it wherever it provides the clearest view on the preview screen in Scan Studio.

Figure 6. Scan Studio scan screen with settings 43

II. Scan the mold.

A. First stand the mold upright so the inferior portion is facing the scanner and the anterior portion is facing up. Click the scan button.

B. Click the scan button and set the specifications to the same as for the first scan.

C. Next, place the mold upside down, so the inferior portion of the mold is facing up and the anterior end of the mold is facing the scanner. Click the scan button again.

III. Trim, align, and fuse the scans (Figure 7).

A. Click the trim button and choose a selector tool. While the plus sign is highlighted, click on all of the scanned points that are not part of the mold, including the turntable surface and anything that was picked up in the background. Click the trim button.

B. If any of the surface of the mold is selected accidentally, click the minus button and select the area.

C. Click the align button. Place at least three colored markers on the same point on both of the scans. Pins can be placed on gyri or sulci or they can be placed on mold abnormalities such as dimples. The alignment works best if the markers are placed in a rough triangular formation as opposed to a straight line.

D. Once the alignment is performed, click the fuse button and then click the fuse button again.

Figure 7. Scan Studio screen showing the trim, align, and fuse selections 44

IV. Save the complete scan.

A. Save as both a scan studio file as well as a .xyz file.

APPENDIX B

Endocast topographic analysis instructions: procedures for analyzing data using ArcGIS 10 (ESRI, Inc., Redlands, CA) with Spatial Analyst and 3D Analyst Extensions

I. Update the .xyz file (Figure 8).

A. Right click on the .xyz file, and open it in Notepad, Microsoft Word, or another word processor.

B. Select all and copy the points.

C. Paste them as text in Microsoft Excel or another spreadsheet program.

D. Use the Text Import Wizard in Microsoft Excel to make a separate column for the x, y, and z data points. a. Choose the Delimited file type and click next. b. Choose space as the delimiter and click next. c. Choose general as the column data format and click finish.

E. Insert a new top row and label the first three columns x, y, and z respectively.

F. Save the file.

Figure 8. Excel sheet with xyz data points 46

II. Open the file in ArcGIS.

A. Open ArcMap 10 and open a blank document.

B. Click the add data button, choose the file, and choose the spreadsheet sheet you want (Figure 9).

Figure 9. ArcMap screen showing the add data button

C. Right click on the sheet and choose display xy data (Figure 10).

Figure 10. ArcMap screen showing the display xy data option 47

D. Make sure the X, Y, and Z fields are correct and click ok (Figure 11).

Figure 11. Display xy data menu showing appropriate settings

E. You will get an error that says the table does not have object-ID Field. Click ok.

F. Right Click on the sheet event and choose data and then export data.

G. Export should say all features. Use the same coordinate system as this layer’s source data, and choose an output location. Choose a file name and location and save it as a shapefile. Click ok. (Figure 12)

Figure 12. Export data dialogue box with appropriate settings displayed

H. It will ask you if you would like to add the exported data to the map as a layer. Click Yes.

I. Remove the original layer by right clicking on it and selecting remove.

III. Turn on extensions.

A. Click on the Customize drop-down menu from the top toolbar and click extensions.

B. Make sure 3D Analyst and Spatial Analyst are selected. Click close.

IV. Use the IDW Tool.

A. Open the ArcToolbox window.

B. Expand the 3D Analyst Tools and then Raster Interpolation.

C. Select IDW.

D. Choose from the dropdown menu the Shape file that you created as the input point features.

E. Make sure the Z value field says Z

F. Choose an output raster name and location.

G. Leave all other areas as the default and click OK (Figure 13).

Figure 13. IDW dialogue box with correct specifications

H. Once the IDW command is completed, deselect the original data points, right click on the new raster layer, and click copy (Figure 14).

Figure 14. Screen showing how to deselect original data points and copy a layer

I. Change the Table of Contents view to list by drawing order, right click on layers, and paste the copied layer.

J. Right click on the newly pasted layer and select properties.

K. Under the display tab change the transparency to 40% and under the symbology tab choose stretched for the show selection.

L. Right click on the color ramp and deselect graphic view.

M. Choose elevation # 1 for the new color ramp and click OK.

V. Add hillshading (Figure 15).

A. In the ArcToolbox window, expand Raster Surface under the already expanded 3D Analyst Tools. Select Hillshade.

B. Choose the raster from the input raster dropdown menu.

C. Choose a file name and location for the hillshade raster.

D. Leave all other areas as the default and select OK.

E. Open the properties menu for the layer and change the transparency to 40%.

Figure 15. ArcMap screen displaying hillshading

VI. Run a slope analysis on the raster data set (Figure 16).

A. In the ArcToolbox window, under the already expanded Raster Surface, select Slope.

B. Choose the raster from the input raster dropdown menu.

C. Choose a file name and location for the slope raster.

D. Select Degree for Output Measurement.

E. Leave the Z Factor as 1 and select OK.

F. Once the layer is complete, you can unselect it in the Table of Contents, as it does not assist in visualization. Instead, the calculated values are what are useful.

Figure 16. Slope dialogue box with correct specifications

VII. Create a new shapefile layer (Figure 17).

A. In the Catalog menu, right click on the main folder that contains layer files.

B. Select New and then select Shapefile.

C. Create a name for the layer and select the Polygon Feature Type.

D. Select OK.

Figure 17. New Shapefile dialogue box with correct specifications

VIII. Create polygon features in your new layer that encompass Broca’s area for the left and right hemispheres (Figure 18).

A. Right click on the new Shapefile layer and select Edit Features and then Start Editing.

B. Select the Freehand Construction Tool and draw a closed shape around each of Broca’s caps.

C. Use the Edit Vertices and Reshape Feature Tool to edit as you go.

D. When finished, select Editor, Save Edits, and then Stop Editing.

Figure 18. ArcMap screen displaying Broca's cap layer

IX. Run zonal statistics on the slope data and display the results as a table (Figure 19).

A. Right click on the new layer and select Open Attribute Table.

B. In the table menu, select Add Field. Add a field called Zone.

C. While the attribute table is still open, right click on the layer again and select to edit the layer.

D. Edit the values in the attribute table so that one of Broca’s caps is named zone 1 and the other is named zone 2.

E. In the ArcToolbox menu, select Spatial Analyst Tools, Zonal, and Zonal Statistics as Table.

F. Under Input raster or feature zone data, select the layer that contains the Broca’s cap features.

G. In Zone field, select Zone.

H. For Input value raster, select the slope layer.

I. For Output table, select a save location.

J. Leave the rest of the settings at default.

K. Select OK.

Figure 19. Zonal Statistics as Table dialogue box with correct specifications 56

WORKS CITED

Allen JS, Bruss J, Damasio H. 2006. Looking for the lunate sulcus: a magnetic resonance

imaging study in modern humans. The Anatomical Record 288A:867-876.

Allman JM, Kaas JH. 1974. A crescent-shaped cortical visual area surrounding the

middle temporal area (MT) in the owl monkey (Aotus trivirgatus). Brain Research

81:199-213.

Anemone RL, Conroy GC, Emerson CW. 2011. GIS and paleoanthropology:

Incorporating new approaches from the geospatial sciences in the analysis of

primate and human evolution. Yearbook of Physical Anthropology 54:19-46.

Armstrong E, Zilles K, Curtis M, Scheicher A. 1991. Cortical Folding, the lunate sulcus

and the evolution of the human brain. Journal of Human Evolution 20:341-348.

Belfer-Cohen A, Hovers E. 1992. In the eye of the beholder: Mousterian and Natufarian

burials in the Levant. Current Anthropology 33:463-471.

Bennett MR, Harris KWK, Richmond BG, Braun DR, Mbua E, Kiura P, Olago D,

Kibunjia M, Omuombo C, Behrensmeyer AK, Huddart D, Gonzalez S. 2009.

Early hominin morphology based on 1.5-million-year-old footprints from Ileret,

Kenya. Science 323:1197-1201.

Bienvenu T, Guy F, Coudyzer W, Gilissen E, Roualdes G, Vignaud P, Brunet M. 2011.

Assessing endocranial variations in great apes and humans using 3D data from

virtual endocasts. American Journal of Physical Anthropology 145:231-246.

Blinkov SM, Glezer II. 1968. The Human Brain in Figures and Tables. New York:

Plenum. 482 pp.

Bradshaw IGA. 2004. Not by bread alone: symbolic loss, trauma, and recovery in

elephant communities. Society & Animals 12:143-158.

Branche C, Milner B, Rasmussen T. 1964. Intracarotid sodium amytal for the

lateralization of cerebral speech dominance: observations in 123 patients. Journal

of Neurosurgery 21:399-405.

Broadfield D, Holloway RL. 2005. The lunate sulcus in Taung: where is it? American

Journal of Physical Anthropology S40:78.

Bruner E. 2003. Fossil traces of the human thought: paleoneurology and the evolution of

the genus Homo. Journal of Anthropological Sciences 81:29-56.

Bruner E. 2004. Geometric morphometrics and paleoneurology: brain shape evolution in

the genus Homo. Journal of Human Evolution 47:279-303.

Bush EC, Allman JM. 2004a. Three-dimensional structure and evolution of primate

visual cortex. Anatomical Record 281A:1088-1094.

Bush EC, Allman JM. 2004b. The scaling of frontal cortex in primates and carnivores.

Proceedings of the National Academy of Sciences 101:3962-3966.

Cantalupo C, Hopkins WD. 2001. Asymmetric Broca’s area in great apes. Nature

414:505.

Clark WE Le Gros. 1947. Observations on the anatomy of the fossil Australopithecinae.

Journal of Anatomy 81:300-333.

Clark WE Le Gros, Cooper DM, Zuckerman S. 1936. The endocranial cast of the

chimpanzee. The Journal of the Royal Anthropological Institute of Great Britain

and Ireland 66:249–268.

Conroy GC, Falk D, Guyer J, Weber GW, Seidler H, Recheis W. 2000. Endocranial

capacity in Sts 71 (Australopithecus africanus) by three-dimensional computed

tomography. The Anatomical Record 258:391-396.

Conroy GC, Weber GW, Seidler H, Tobias PV, Kane A, Brunsden B. 1998. Endocranial

capacity in an early hominid cranium from Sterkfontein, South Africa. Science

280:1730-1731.

Culham JC, Kanwisher NG. 2001. Neuroimaging of cognitive functions in human

parietal cortex. Current Opinion in Neurobiology 11:157-163.

Dart R. 1925. A. Australopithecus africanus: The man-ape of South Africa. Nature

115:195-199.

Environmental Systems Research Institute. 1995. Understanding GIS: The ARC/INFO

Method: Self-Study Workbook: Version 7 for UNIX and OpenVMS. Cambridge,

U.K.: GeoInformation International.

Falk D. 1979. On a new australopithecine partial endocast. American Journal of Physical

Anthropology 50:611-614.

Falk D. 1980. A reanalysis of the South African australopithecine natural endocasts.

American Journal of Physical Anthropology 53:525-539.

Falk D. 1983. The Taung endocast: a reply of Holloway. American Journal of Physical

Anthropology 60:479-489.

Falk D. 1985a. Apples, oranges, and the lunate sulcus. American Journal of Physical

Anthropology 67:313-315.

Falk D. 1985b. Hadar AL 162-28 endocast as evidence that brain enlargement preceded

cortical reorganization on hominid evolution. Nature 313:45-47.

Falk D. 1986. Falk replies to Holloway and Kimbel (Endocast morphology of Hadar

hominid AL 162-28). Nature 321:536-537.

Falk D. 1989. Ape-like endocast of ‘ape-man’ Taung. American Journal of Physical

Anthropology 80:335-339.

Falk D. 2009. The natural endocast of Taung (Australopithecus africanus): insights from

the unpublished papers of Raymond Arthur Dart. Yearbook of Physical

Anthropology 52:49-65.

Falk D, Clarke R. 2007. Brief communication: new reconstruction of the Taung endocast.

American Journal of Physical Anthropology 134:529-534.

Falk D, Hildebolt C, Smith K, Morwood MJ, Sutikna T, Brown P, Jatmiko, Saptomo

EW, Brunsden B, Prior F. 2005. The brain of LB1, Homo floresiensis. Science

308:242-245.

Falk D, Hildebolt C, Smith K, Morwood MJ, Sutikna T, Jatmiko, Saptomo EW, Prior F.

2009. LB1’s virtual endocast, microcephaly, and hominin brain evolution. Journal

of Human Evolution 57:597-607.

Falk D, Hildebolt C, Vannier M. 1994. Relationship of squamosal suture to asterion on

external skull surfaces versis endocasts of pongids: implications for Hadar early

hominid AL 162-28. American Journal of Physical Anthropology 93:435-439.

Falk D, Redmond Jr. JC, Guyer J, Conroy GC, Recheis W, Weber GW, Seidler H. 2000.

Early hominid brain evolution: A new look at old endocasts. Journal of Human

Evolution 38:695-717.

Gannon PJ, Holloway RL, Broadfield DC, Braun AR. 1998. Asymmetry of chimpanzee

planum temporale: humanlike pattern of Wernicke’s brain language area

homolog. Science 279:220-222.

Gargett RH, Bricker HM, Clark G, Lindly J, Farizy C, Masset C, Frayer DW, Monet-

White A, Gamble C, Gilman A, Leroi-Gourhan A, Martinez Navarrete MI, Ossa

P, Trinkaus E, Weber AW. 1989. Grave shortcomings: the evidence for

Neandertal burial. Current Anthropology 30:157-190.

Gilissen E, Iba-Zizen MT, Stievenart JL, Lopez A, Trad M, Cabanis EA, Zilles K. 1995.

Is the length of the calcarine sulcus associated with the size of the human visual

cortex? A morphological study with magnetic resonance tomography. Journal of

Brain Research 36:451-459.

Hayden B. 1993. The cultural capacities of Neandertals: a review and re-evaluation.

Journal of Human Evolution 24:113-146.

Heilbroner PL, Holloway RL. 1988. Anatomical brain asymmetries in New World and

Old World monkeys: stages of temporal lobe development in primate evolution.

American Journal of Physical Anthropology 76:39-48.

Henshilwood CS, d’Errico F, Marean CW, Milo RG, Yates R. 2001. An early bone tool

industry from the Middle Stone Age at Blombos Cave, South Africa: implications

for the origins of modern human behaviour, symbolism and language. Journal of

Human Evolution 41:631-678.

Holloway RL. 1966. Cranial capacity, neural reorganization, and hominid evolution: A

search for more suitable parameters. American Anthropologist 68:103-121.

Holloway RL. 1981a. Revisiting the South African Taung australopithecine endocast: the

position of the lunate sulcus as determined by the stereoplotting technique.

American Journal of Physical Anthropology 56:43-58.

Holloway RL. 1981b. Exploring the dorsal surface of hominoid brain endocasts by

stereoplotter and discriminant analysis. Philosophical Transactions of the Royal

Society of London. Series B, Biological Sciences 292:155-166.

Holloway RL. 1983. Cerebral brain endocast pattern of Australopithecus afarensis

hominid. Nature 303:420-422.

Holloway RL. 1984. The Taung endocast and the lunate sulcus: a rejection of the

hypothesis of its anterior position. American Journal of Physical Anthropology

64:285-287.

Holloway RL. 1992. The failure of the gyrification index (GI) to account for volumetric

reorganization in the evolution of the human brain. Journal of Human Evolution

22:163-170.

Holloway RL, Broadfield DC. 2011. Technical note: the midline and endocranial volume

of the Taung endocast. American Journal of Physical Anthropology 146:319-322.

Holloway RL, Broadfield DC, Yuan MS. 2003. Morphology and histology of

chimpanzee primary visual cortex indicate that brain reorganization predated

brain expansion in early hominid evolution. The Anatomical Record 273A:594-

602.

Holloway RL, Broadfield DC, Yuan MS. 2004. The human fossil record, vol. 3, brain

endocasts: the paleoneurological evidence. Hoboken, NJ: John Wiley and Sons.

Holloway RL, Kimbel WH. 1986. Endocast morphology of Hadar hominid AL 162-28.

Nature 321:536.

Hopkins WD, Marino L, Rilling JK, MacGregor LA. 1998. Planum temporale

asymmetries in great apes as revealed by magnetic resonance imaging (MRI).

NeuroReport 9:2913-2918.

Hovers E, Kimbel WH, Rak Y. 2000. The Amud 7 skeleton – still a burial. response to

Gargett. Journal of Human Evolution 39:253-260.

Jernvall J, Selanne L. 1999. Laser confocal microscopy and geographic information

systems in the study of dental morphology. Palaeontologia Electronica, 2:1-18.

Kay RF. 1975. The functional adaptations of primate molar teeth. American Journal of

Physical Anthropology 43:195-215.

Keith A. 1925. The fossil anthropoid ape from Taungs. Nature 115:234-236.

Keith A. 1931. New discoveries relating to the antiquity of man. London: W. W. Norton.

Klein RG. 2009. The human career: human biological and cultural origins. Chicago: The

University of Chicago Press.

McNabb J, Binyon F, Hazelwood L. 2004. The large cutting tools from the South African

Acheulean and the question of social traditions. Current Anthropology 45:653-

677.

Merceron G, Taylor S, Scott R, Chaimanee Y, Jaeger J-J. 2006. Dietary characterization

of the hominoid Khoratpithecus (Miocene of Thailand): evidence from dental

topographic and microwear texture analysis. Naturwissenschaften 93:329-333.

M’Kirera F, Ungar P. 2003. Occlusal relief changes with molar wear in Pan troglodytes

and Gorilla gorilla gorilla. American Journal of Primatology 60:31-41.

Moore ID, Grayson RB, Ladson AR. 1991. Digital terrain modeling: a review of

hydrological geomorphological and biological applications. Hydrological

Processes 5:3-30.

Morwood MJ, Soejono RP, Roberts RG, Sutikna T, Turney CSM, Westaway KE, Rink

WJ, Zhao J-x, van den Bergh GD, Rokus Awe Due, Hobbs DR, Moore MW, Bird

MI, Fifield LK. 2004. Archaeology and age of a new hominin from Flores in

eastern Indonesia. Nature 431:1087-1091.

Moss C. 1992. Echo of the elephants: the story of an elephant family. New York: Willian

Morrow, Inc.

Nimchinsky EA, Gilissen E, Allman JM, Perl DP, Erwin JM, Hof PR. 1999. A neuronal

morphologic type unique to humans and great apes. Proceedings of the National

Academy of Sciences 96:5268-5273.

Noble W, Davidson I. 1993. Tracing the emergence of modern human behavior:

methodological pitfalls and a theoretical path. Journal of Anthropological

Archaeology 12:121-149.

Ojemann GA. 1991. Cortical organization of language. Journal of Neuroscience 11:2281-

2287.

Petersen SE, Fox PT, Posner MI, Minton M, Raichle ME. 1988. Positron emission

tomographic studies of the cortical anatomy of single-word processing. Nature

331:585-589.

Platt ML, Glimcher PW. 1999. Neural correlates of decision variables in parietal cortex.

Nature 400:234-238.

Poole J. 1996. Coming of age with elephants: a memoir. New York: Hyperion Press.

Preuss TM, Qi H, Kaas JH. 1999. Distinctive compartmental organization of human

primary visual cortex. Proceedings of the National Academy of Sciences

96:11601-11606.

Rak Y, Kimbel WH, Hovers E. 1994. A Neandertal infant from Amud Cave, Israel.

Journal of Human Evolution 26:313-324.

Reed DNO. 1997. Contour mapping as a new method for interpreting diet from tooth

morphology. American Journal of Physical Anthropology 104(S24):194.

Santamaria J, Cordon O, Damas S, Aleman I, Botella M. 2007. A scatter search-based

technique for pair-wise 3D range image registration in forensic anthropology. Soft

Computing 11:819-828.

Schenker NM, Desgouttes AM, Semendeferi K. 2005. Neural connectivity and cortical

substrates of cognition in hominoids. Journal of Human Evolution 49:547-569.

Schepers GWH. 1946. The endocranial casts of the South African ape-men. In: Broom R,

Schepers GWH, editors. The South African fossil ape-man; the

Australopithecinae. Pretoria: Transvaal Museum. 155–272 pp.

Schoenemann PT. 2006. Evolution of the Size and Functional Areas of the Human Brain.

Annual Review of Anthropology 35:379-406.

Schoenemann PT, Gee J, Avants B, Holloway RL, Monge J, Lewis J. 2007. Validation of

plaster endocast morphology through 3D CT image analysis. American Journal of

Physical Anthropology 132:183-192.

Schoenemann PT, Sheehan MJ, Glotzer DL. 2005. Prefrontal white matter volume is

disproportionately larger in humans than in other primates. Nature Neuroscience

8:242-252.

Semendeferi K, Damasio H. 2000. The brain and its mail anatomical subdivisions in

living hominoids using magnetic resonance imaging. Journal of Human Evolution

38:317-332.

Semendeferi K, Lu A, Schenker N, Damasio H. 2002. Humans and great apes share a

large frontal cortex. Nature Neuroscience 5:272-276.

Semendeferi K, Van Hoesen GW. 1997. The evolution of the frontal lobes: a volumetric

analysis based on three-dimensional reconstructions of magnetic resonance scans

of human and ape brains. Journal of Human Evolution 32:375-388.

Sereno MI, Tootell RBH. 2005. From monkeys to humans: what do we now know about

brain homologies? Current Opinion in Neurobiology 15:135-144.

Shahrom AW, Vanezis P, Chapman RC, Gonzales A, Blenkinsop C, Rossi ML. 1996.

Techniques in facial identification: computer-aided facial reconstruction using a

laser scanner and video superimposition. International Journal of Legal Medicine

108:194-200.

Sherwood CC, Broadfield DC, Holloway RL, Gannon PJ, Hof PR. 2003. Variability of

Broca’s area homologue in African great apes: implications for language

evolution. The Anatomical Record Part A 271A:276-285.

Smith GE. 1904. The morphology of the retrocalcarine region of the cerebral cortex.

Proceedings of the Royal Society of London 73:59–65.

Smith GE. 1925. The fossil anthropoid ape from Taungs. Nature 115:235.

Stephan H, Frahm H, Baron G. 1981. New and revised data on volumes of brain

structures on insectivores and primates. Folia primatologica 35:1-29.

Teaford MF. 1994. Dental microwear and dental function. Evolutionary Anthropology 3:

17-30.

Tomlinson R. 1998. The Canada geographic information system. In: Foresman T, editor.

The history of geographic information systems. Upper Saddle River, NJ: Prentice

Hall. 21–32 pp.

Ungar PS. 2004. Dental topography and diets of Australopithecus afarensis and early

Homo. Journal of Human Evolution 46:605-622.

Ungar PS, Brown CA, Bergstrom TS, Walker A. 2003. Quantification of dental

microwear by tandem scanning confocal microscopy and scale-sensitive fractal

analyses. Scanning 25:185-193.

Ungar PS, M’Kirera F. 2003. A solution to the worn tooth conundrum in primate

functional anatomy. Proceedings of the National Academy of Sciences 100:3874-

3877.

Ungar PS, Williamson M. 2000. Exploring the effects of toothwear on functional

morphology: a preliminary study using dental topographic analysis.

Palaeontologia Electronica 3:1-18.

Vannier MW, Conroy GC, Marsh JL, Knapp RH. 1985. Three-dimensional cranial

surface reconstructions using high-resolution computed tomography. American

Journal of Physical Anthropology 67:299-311.

Yeni-Komshian GH, Benson DA. 1976. Anatomical study of cerebral asynmmetry in the

temporal lobe of humans, chimpanzees, and rhesus monkeys. Science 192:387-

389.

Yuan MS, Broadfield DC, Holloway RL. 2002. Brain reorganization in hominid

evolution: histological confirmation in chimpanzee. American Journal of Physical

Anthropology S34:169-170.

Zuccotti LF, Willianson MD, Limp WF, Ungar PS. 1998. Technical note: modeling

primate occlusal topography using geographic information systems technology.

American Journal of Physical Anthropology 107:137-142.