- Email: [email protected]
Boldly going where no brain has gone: Futures of evolutionary cognitive neuroscience
Futures 43 (2011) 771–776
Contents lists available at ScienceDirect
Futures journal homepage: www.elsevier.com/locate/futures
Boldly going where no brain has gone: Futures of evolutionary cognitive neuroscience Steven M. Platek a,*, Amir Hasicic a, Austen L. Krill a,b a b
Psychology, The School of Liberal Arts, Georgia Gwinnett College, United States School of Biological Sciences, The University of Liverpool, United Kingdom
A R T I C L E I N F O
A B S T R A C T
Article history: Available online 24 May 2011
Evolutionary cognitive neuroscience (ECN) is a new discipline that employs methodology from cognitive neuroscience to study, in vivo, the proximate mechanisms of putative evolved psychological/cognitive adaptations. The formalized discipline is less than five years old, but has already generated a plethora of research as well as extended our understanding of the evolved nature of the mind/brain. Here we briefly recapitulate the antecedents to an evolutionarily informed cognitive neuroscience, attempt to fit ECN into a broader evolutionary psychology framework that seeks to account for evolved adaptations to recurrent problems faced by our ancestors, and discuss the futures of this newly formed discipline by expounding on methodological techniques and theoretical accounts that may pervade our future. We believe, as the Nobel laureate Nikko Tinbergen has suggested, that a complete understanding of the evolved nature of behavior and cognition (i.e., evolved cognitive adaptations) can only come from investigations at both the proximate and ultimate levels and, thus here, we attempt to cast ECN as the proximate sister discipline to evolutionary psychology. When taken together these two disciplines have the potential to uncover how and why the mind works. ß 2011 Published by Elsevier Ltd.
1. Introduction Evolutionary cognitive neuroscience (ECN; [1–3]) integrates comparative neuroscience, archaeology, physical anthropology, paleoneurology, cognitive primatology, evolutionary psychology, and cognitive, social and affective neuroscience in an effort to identify and describe the proximate [4] neural mechanisms that have been forged by natural and sexual selection pressures during human evolutionary history. Understanding these proximate neural mechanisms will help us define what it is to be human, the nature of the human mind, identify comparative neural mechanisms implicated in various cognitive processes in other species, and allow for a more thorough brain-based Darwinian psychology. In its simplest form, evolutionary cognitive neuroscience (ECN) is the merging of the fields of evolutionary psychology and cognitive neuroscience using methodology from both disciplines and guidance from evolutionary meta-theory. In this coalescence, ECN becomes the discipline for investigating the proximate neural mechanisms that drive the ultimate behavioral adaptations often described in evolutionary psychology (EP; e.g., [5]). Thus, ECN is charged with the identification of neural substrates of putative evolved psychological/cognitive adaptations. A recent volume [2] presents the first comprehensive overview (cf. [1,3]).
* Corresponding author at: Department of Psychology, Fayetteville State University, Fayetteville, NC 28301, United States. Tel.: +1 404 734 1023. E-mail address: [email protected] (S.M. Platek). 0016-3287/$ – see front matter ß 2011 Published by Elsevier Ltd. doi:10.1016/j.futures.2011.05.020
772
S.M. Platek et al. / Futures 43 (2011) 771–776
2. Antecedents to evolutionary cognitive neuroscience 2.1. Cognitive neuroscience without evolution Like pre-Darwinian psychology and other social sciences, cognitive neuroscience without evolution will have difficulty accurately describing the functional workings of the human mind and make about as much sense as psychological science without it [5,6]. The number of articles appearing in journals such as The Journal of Cognitive Neuroscience, Cognitive Brain Research, Brain, Neuron, Neuroscience, Social Neuroscience, Trends in Cognitive Science, Trends in Neuroscience, PNAS, and the Journal of Neuroscience answering questions about brain–behavior relationships is staggering. What is more astounding, however, is the dearth of articles that present the results of evolutionarily informed research or interpret the results from an evolutionary perspective (cf. [1,7]). 2.2. Psychological mechanisms, domain specificity, and domain generality Unlike early psychologists and behavioral scientists (e.g., Skinner, Watson) who envisioned organisms as ‘‘blank slates’’ capable of making an infinite number of associations, evolutionary psychology researchers are beginning to shed light on this flawed theoretical approach to behavior analysis [6,8,9]. In fact, many of the emerging studies are contending directly with this ‘‘standard social science model’’ of psychology; i.e., that organisms possess one or more general-purpose learning mechanisms and that ‘‘biology’’ plays little, if any, role in the manifestation of behavior. For example, in his landmark study, Garcia et al. [10] discovered that animals learned to avoid novel food products that made them ill in as little as one learning/conditioning trial – something that had not been demonstrated with any other stimulus class previously. Labeled conditioned taste aversion, this effect describes an adaptive problem that has since been demonstrated in almost every species tested (the exception to this rule appears to be Crocodilians, see [11]). This adaptation serves an important function – do not eat food that makes you ill or you might not survive to reproduce; i.e., being ill could result in a number of fitness disadvantages such as death, inability to avoid predation, inability to search and secure mates, loss of mate value, decreased attractiveness to mates, or complete inability to perform sexually (cf. [12], for more on prepared learning). Studies like these refute the key premises of the so-called standard social science model – there is no general-purpose learning mechanism and organisms are not ‘‘blank slates’’. Rather, learning is a consequence of carefully crafted mechanisms dedicated to solving specific evolutionary problems (cf. [8,13]). Our brains have evolved to be efficient problem-solvers and the problems they are designed to solve are those that our ancestors recurrently faced over human evolutionary history (i.e., domain-specificity). Although domain-specificity seems to be the prevailing theoretical model of the brain in evolutionary psychology, it is important to note that there is also support for the existence of domain-general mechanisms in areas of cognition and learning [14]. We do not have the necessary space to recapitulate those arguments here except to note that through increased research evolutionary cognitive neuroscientists are in a position to address the very nature of this problem in the future. In fact, ECN may be the precise field poised to identify and describe a neurological atlas of evolved cognitive/neural/psychological mechanisms. 2.3. Cognitive neuroscience with evolutionary theoretical guidance Why do we need another discipline? Why is the ECN approach important? How will ECN change the future face of science? Without evolutionary meta-theoretical guidance, cognitive neuroscience will fail to describe with anything but superficial accuracy the human (and animal) mind. Cognitive neuroscience will simply describe the neural mechanisms of brain–behavior relationships from theoretical models derived from standard social science models. In doing so, this only solves half the equation. This approach misses the ultimate (i.e., ‘‘why’’) questions of brain–behavior relationships. By adopting the ECN approach and directly addressing ultimate questions about brain–behavior relationships, scientists will be in a position to better describe the underlying neurocognitive processes and neural correlates that they investigate. Likewise, without cognitive neuroscientific methods, evolutionary psychology may not be able to adequately describe and understand the proximate neurophysiological and neurobiological mediators of the many hypothesized evolved psychological/cognitive adaptations, and hence may never be able to completely describe the evolved nature of the human mind and brain. Without ‘‘peering’’ into the brain with techniques such as modern functional neuroimaging, evolutionary psychological investigations can only describe the cognitive processing of human mental characteristics through behavioral methods, which of course is extremely important! Evolutionary psychology can describe function, but is limited in its description of structure, and thus has little ability to relate function to structure, which might be important, especially in comparative investigations of cognitive evolution. The relationship between structure and function is inherently a problem of evolutionary biology; i.e., the genes that give rise to brain structure and its component nuclei and modularity, as well as its ability to process information, were the combined units of selection. The need for an integrated science of the mind that utilizes evolutionary meta-theoretical guidance to cognitive neuroscientific investigations is overdue, but beginning to flourish. In other words if evolutionary psychology answers the question ‘‘Why does the mind work the way it does?’’ evolutionary cognitive neuroscience ought to be in a position, when merged with evolutionary psychology, to answer the questions ‘‘Why and How does the mind work the way it does?’’
S.M. Platek et al. / Futures 43 (2011) 771–776
773
Recently, application of evolutionary meta-theory has been applied directly to investigations of the cognitive neuroscience kind. For example, O’Doherty et al. [15,16] have begun to investigate neural correlates of facial attraction (cf. Little et al., submitted for publication; Reding and Platek, submitted for publication). O’Doherty et al. [15] discovered that the orbitofrontal cortex appears to be activated when a person finds a face attractive, which suggests that facial attractiveness activates a reward or approach system in the brain. These findings have recently been extended [16] to reveal a more distributed network of activation in the anterior cingulate cortex (ACC), superior temporal sulcus, and amygdala in response to evaluations about attractiveness. Additionally, activation in the ACC and amygdala appear to be sex-dependent, showing increased activation in men only. These areas also are activated when males are asked to imagine [17] or observe [18] their mate engaging in infidelity, which suggests that appraisals of attractiveness of females by males is related to their decisions about fidelity and paternal certainty [19,20]. This work is currently being extended to investigate the role of the menstrual cycle in perceptions of attractiveness among female participants. Reding and Platek (submitted for publication) employed functional magnetic resonance imaging (fMRI) to investigate women’s perception of attractiveness as a function of the menstrual cycle while varying male facial symmetry and masculinity. These findings reveal an interaction between perceptions of attractiveness and the menstrual cycle; i.e., women showed increased activation to masculine faces when scanned in the fertile period of their menstrual cycle. Additionally, when women rated self-resembling faces during their fertile phase, brain areas associated with disgust were activated. This suggests that different systems are employed to drive appetitive (to masculine male faces) during ovulation and aversive (possibly incest avoidance) responses to possible kin during the fertile phase. Together these data suggest that (1) there are sex differences in the neural processing of attractiveness that might be related to evaluations about paternity and sexual fidelity and (2) that in women, activation appears, at least in part, dependent on hormonal state. For additional examples of current ECN research see Platek et al. [2] and Platek and Shackelford [3]. 2.4. Foundations for an evolutionary cognitive neuroscience The foundation of ECN is inherently interdisciplinary in nature. Researchers will have to take heed to Tinbergen’s [4] four ‘‘Why’s’’ and proximate/ultimate dichotomy weaved throughout their investigations. This ethological framework is essential to the survival of ECN in that this framework forms the basis for examination of all behaviors from a biological perspective. 2.5. Futures of evolutionary cognitive neuroscience The second half of the twentieth century saw the rise of two scientific revolutions: the computer revolution and the biotechnological revolution. These two scientific revolutions fueled by an unprecedented growth of new technologies have largely shaped the paths of scientific progress in the twenty-first century and will continue to do so into the future. Evolutionary cognitive neuroscience will both greatly contribute to and benefit from these two scientific revolutions. We speculate, with aplomb, about the applications of evolutionary cognitive neuroscience in certain trends and technologies that can be expected to emerge in the next 100 years. 2.6. ECN in 2025 An unprecedented increase in computer technology continues according to Moore’s law, which states that computer power doubles on average every twenty months [21]. As computer technology becomes more cost-effective, more resources are available to shift the computer industry into the next phase. Cognitive neuroscientists will be working together with computer engineers and programmers to build a first biomolecular supercomputer. Unlike silicon-based computers, a biomolecular computer will be an organic computational device with no semi-conductors. In a biomolecular computer, information is stored in DNA molecules encoded in binary code [22]. It is expected to vastly outperform silicon-based computers both in its information processing capabilities and memory capacity. To build a fully organic computational system, scientists will apply principles of the operation of the human brain; that is, the neural substrates of mental processes discovered in cognitive (and other subdisciplines of) neuroscience. Computer engineers will be working with cognitive neuroscientists to create a microprocessor made of living human neurons connected to a silicon chip via nanotechnological advances. The ultimate aim is to build new neural networks and connect them directly to the human nervous system for purposes of measuring neuronal and subneuronal (optical properties of action potentials) activation, and stimulating specific neuronal networks by delivering microvolt pulses. This project will help scientists better understand many neuropsychiatric disorders and help millions of people around the world who suffer from muscular degeneration, spinal cord injuries, blindness, and other disabilities. Two new technologies on the horizon will shape the application of evolutionary cognitive neuroscience in the second half of the century: the quantum computer and nanotechnology. 2.7. ECN in 2050 A series of major conceptual breakthroughs in nanotechnology over the last two decades (i.e., up to 2050) will have allowed scientists to build the smallest machines of all – nanobots [23]. Self-replicating nanobots, no larger than several
774
S.M. Platek et al. / Futures 43 (2011) 771–776
molecules across, can be safely injected into the bloodstream, and engage in numerous biophysiological tasks such as repairing damaged cells and destroying harmful microbes. What will be the latest breakthrough in nanotechnology, however, is of particular importance to cognitive neuroscience. Scientists will be able to build nanobots that can cross the blood–brain barrier and enter the central nervous system. The M2B4 nanobots (molecular machines beyond the blood–brain barrier) will be able to travel across axons and dendrites, as do neurotransmitters. This class of nanobots will be able to enter a specific brain region and manipulate individual neurons, or reframe and improve the neural networks in that region. The possibilities are tantalizing. A score of neural disorders could be eradicated by a careful application of the M2B4 nanobots. A different class of these nanobots called pM2B4 will be able to scan the brain from the inside to create brain images with high spatial and temporal resolution in real-time. This advance will allow evolutionary cognitive neuroscientists to access and modify brain activation in living organisms including humans in realtime; in other words, evolutionary cognitive neuroscientists employing these new methods will be able to develop withinsubjects research designs in which brain activation is both measured and altered in varying ways to approximate the evolved cognitive nature of the underlying neural mechanisms. A new project will begin that, when completed, will have an even greater impact on science and society than the Human Genome Project (http://www.ornl.gov/sci/techresources/Human_Genome/home.shtml) [24] – it will be called the brain code. To determine precisely how the brain is wired has been the ‘‘Holy Grail’’ of cognitive neuroscience since its inception. It is a gargantuan task that will be undertaken by evolutionary cognitive neuroscientists across the globe and one that could not be completed without the awesome power of a new computing technology – the quantum (parallel) computer. The quantum computer can outperform even the biomolecular computer by orders of magnitude, and complete infinitely more complicated operations than a standard silicon-based computer [22]. Such a device will be needed in order to analyze one hundred trillion (or more) interneuronal connections in the human brain. By using the new micro lasers (1/3 mm across), scientists will now be able to cut post-mortem brain tissue into millions of thin slices and analyze each slice individually, neuron by neuron. When the information gathered from the computer analysis of each brain slice is supplemented with the pM2B4 images and genetic expression algorithms, the quantum computer could yield a three dimensional model of the brain that reveals how the brain is wired, neuron by neuron and gene by gene. For the first time cognitive neuroscientists will have a cognitive neurogenetic homunculus of the brain. The ultimate product of the project will be a new, all-encompassing method of studying the human brain, the Holographic Brain Model (HBM). The visible portion of HBM will be a three dimensional holographic representation of the brain. Scientists will be able to make various functional simulations, for example, stimulating each neuron and interneuronal connections in a specific brain region and observe the brain activity, and subsequent behavioral response, in real time. The HBM will allow scientists for the first time to manipulate neural networks at will, first in animals, and then eventually in human brains in vivo. Moreover, they will be able to remove a piece of endogenous neural net from the HBM and replace it with an electronic neural net that can duplicate the precise firing of the original. By using the HBM, computer scientists will be able to replicate the human brain, neuron by neuron. The HBM will be able to simulate every biological neural network, which will allow for a wide range of testing of the traditional cognitive – connectionist – models [25,26]. For example, an interconnected artificial neural network could be created inside the HBM to observe and examine which adaptive properties of the neural network are more suitable for real-life problem solving, living in extreme environments, or for making the best politicians. In actualization, the HBM will likely take form of what Pribram [27] refers to as the ‘‘holonomic brain’’ [27–29]. In this conception of the brain, various specialized brain substrates function holographically, but the entire brain does not work as one large holographic representation of the brain. With this type of modularized [8] functionality, sectors of the artificial holonomic brain need not be centrally localized. Rather, the specialized holonomic modules would benefit from being in close proximity to mechanics for which they serve. The application of the HMB in education and scientific research will lay the groundwork for the next step of human evolution – creating a new form of intelligent life on earth. 2.8. ECN in 2075 To create a true AI system that has common sense, can learn from its mistakes, and is ‘‘aware’’ of its own existence, AI researchers will, once again, need help from cognitive neuroscience [30]. In order to do that, evolutionary cognitive neuroscientists will have to reverse engineer the human brain [30,31]. By studying the HBM, comparatively across the animal kingdom, they will be in a position to pinpoint key genetic changes that allowed for the exorbitant encephalization observed in hominids – and specifically to better understand the neurobiological underpinnings of higher cognitive abilities in humans. The end product will be a supercomputer with an artificial neural network as complex and as powerful as the human brain, a non-biological form of intelligence made bottom-up, taking guidance from biological design in the year 2075. The AI system will aid the scientific community in the quest to create permanent human colonies in space and terraforming of other planets in the solar system. The hostile conditions in outer space (cosmic rays, solar winds, subfreezing temperature, hostile atmospheres, etc.) will present an enormous challenge for the future course of space exploration. A new species of humanity will be created to overcome those challenges, one that can sustain an environment different from the environment found on earth. Additionally, these individuals will be capable of executing programs that are designed to create self-sustaining bases of operation on other planets. This exploration will aid NASA’s ability to generate devices that supplement humans in their eventual deep space exploration.
S.M. Platek et al. / Futures 43 (2011) 771–776
775
This new species will alter the relationship between humans and machines forever. Based on the traditional concept of a man–machine mixture (e.g., cyborg), the new species will be the greatest and most extensive blend of human and mechanical properties ever seen, and will experience the benefits of enhanced survival abilities. The merger of three scientific endeavors will allow for the creation of the new species: biotechnology, computer science, and cognitive neuroscience. The genetic map provided by the Human Genome Project will enable biogeneticists to manipulate the genetic makeup of the new species giving its members the ability to grow, or regenerate, new organs and body parts, therefore, considerably extending their life span. Additionally, design modifications will be possible because of genetic engineering of various animal adaptations onto what looks generally like a hominid body. For example, imagine the capability of enhancing the auditory system to be able to process echolocation, in addition to variability in sound pressure waves. This could serve to enhance this new species’ ability to navigate new and uninhabitable environments. Mechanical components will also greatly enhance their physical abilities and durability, for example, integrating computer-aided robotic muscles with their biological bodies. By 2075, most complex tissues will be replaceable, with the exception of the brain and parts of the nervous system. The task for ECN will be to find a way of enhancing the mental makeup of the new species. It will involve building upon the intelligence that evolution has bestowed in the human species. Evolutionary cognitive neuroscientists will provide a blueprint of biological intelligence and how intelligence emerged through evolution in order to create a vastly more intelligent human species. By the end of the twenty-first century, the task will be completed and the new species of biomechanical creatures will begin their existence: Numans (New Humans). Although physically resembling humans to account for the uncanny valley effect of dissimilarity [32], Numans are vastly superior to humans in many ways. Their brains will be supplemented with the synthetic neural networks that increase their mental processing powers by orders of magnitude. These exogenously implanted networks will allow for increased short-term memory span, the offloading of cognitive processing for the ability of true parallel neuronal processes to emerge, and WiFi-based connections to universal information databases (e.g., Google, http://www.google.com; experiments on this feature are already underway: http:// www.news.wisc.edu/16576), to name but a few advantages. Due to the fact that Numans are synthetically made they can be constructed from durable materials such as titanium. This will make them immune to biodegradation and less at risk of breakdown or injury. Numans will be truly unique, a species committed to put itself to the fullest possible use. Their task will be to explore extra-solar planets and form the first human colonies in space. As such, they not only represent our intergenerational guinea pigs for space exploration, but they also serve to present a realistic human-esque impression to any intergalactic life forms they might encounter. Evolutionary biologists and cognitive neuroscientists will be onboard the first interstellar ships, eager to find out what, if anything, and how evolution has crafted adaptations on other planets. The major drawback of Numans is that their exogenously implanted technology is non-heritable. An alternative means for NASA or other agencies for successful and safe inter-planetary exploration might be to follow the lead of the recent motion picture Avatar (http://www.avatarmovie.com/). That is, by harnessing the vast power of the brain, coupled with computer technology that can both translate and represent those processes (especially cognition associated with self-awareness) space exploration can take the form of virtual reality. Inter-planetary travel may benefit from the advances in evolutionary cognitive neuroscience by capitalizing on not having to send humans in to space at all. Rather, through a complex computer–machine interface [33] space exploration can be conducted via an avatar conduit. There is some evidence that this is already possible. Researchers at Drexel University have begun development of a video game that can be controlled by the brain alone (i.e., no hand-held controllers) (http://www.voxel6.com/). The further development of technology like the LazyBrains video game is already serving as the precursor to an Avatar-like inter-planetary exploration technology. This research endeavor will be helped by what we know about the brains capacity for controlling its own body, as well as the cognitions involved with incorporating past events into new mental schemas and adjusting and thinking about future (possible) events [34]. An additional added benefit of such brain–computer interfaces that generate avatars may be in search and rescue missions of the aftermath of natural disasters without the threat of additional human loss. 3. Conclusion The years up to 2075 and beyond will bring revolutions in science from an inherently interdisciplinary series of research programs, of which evolutionary cognitive neuroscience will have been a founding father. The future of humanity from a neuroscientific perspective brings with it the thoughts, conjectures, and dreams of science fiction, androids and space exploration. However, we are already close. Android development is well underway in several laboratories around the world. The current goal of these labs is to create working models of humanity; that is, working robots that look, act, sound, and work like humans. There are obvious limitations to the current android models, these falling primarily within the depths of the brains emotional processing mechanisms. Current forms of androids do not ‘‘feel’’ and as such will never come close to a true humanity. Android development, taken together with the old and ever-progressing development of medical implants and prosthetics may actually progress to the point of ‘‘feeling’’ androids [35]. Currently, there are millions of humans walking around with machines in their bodies. Take for example the pacemaker, insulin pump, deep brain stimulator, and cardiac valve. Each of these is the beginning of a human–machine interface. As these two areas evolve, concurrently with advancements in computer technology, bio-nano-technology, and evolutionary cognitive neuroscience, we will come closer with each passing day to a more complete understanding of the brain, how it evolved, and how to predict and possibly target evolutionary changes further. Like social cognitions of the brain (self-awareness, theory of mind, etc.) that evolutionary neuroscientists attempt to understand and describe, researchers are now in a position to go beyond describing how and why
776
S.M. Platek et al. / Futures 43 (2011) 771–776
our (and animal) brains do what they do and harness the vast power of our neural architecture to move our brains and species into unchartered worlds. Acknowledgements Special thanks to Gad Saad for review and helpful comments on a previous draft of this manuscript, and for whom without his guidance this manuscript would not have been possible. S.M.P. is supported by a grant from the Pioneer Fund. References [1] A.L. Krill, et al., Where evolutionary psychology meets cognitive neuroscience: a precise to evolutionary cognitive neuroscience, Evol. Psychol. 5 (2007) 232–256. [2] S.M. Platek, J.P. Keenan, T.K. Shackelford (Eds.), Evolutionary Cognitive Neuroscience, MIT Press, Cambridge, MA, 2007. [3] S.M. Platek, T.K. Shackelford, Foundations in Evolutionary Cognitive Neuroscience, Cambridge University Press, Cambridge, UK, 2009. [4] N. Tinbergen, Derived activities; their causation, biological significance, origin, and emancipation during evolution, Q. Rev. Biol. 27 (1) (1952) 1–32. [5] D.M. Buss, The great struggles of life: Darwin and the emergence of evolutionary psychology, Am. Psychol. 64 (2) (2009) 140–148. [6] D.M. Buss (Ed.), The Handbook of Evolutionary Psychology, Wiley, New York, 2005. [7] G.D. Webster, Evolutionary theory in cognitive neuroscience: a 20 year quantitative review of publication trends, Evol. Psychol. 5 (3) (2007) 520–530. [8] J.H. Barkow, L. Cosmides, J. Tooby (Eds.), The Adapated Mind, Oxford University Press, New York, 1992. [9] L. Cosmides, The logic of social exchange: has natural selection shaped how humans reason? Studies with the Wason selection task, Cognition 31 (3) (1989) 187–276. [10] J. Garcia, F.R. Ervin, R.A. Koelling, Learning with prolonged delay of reinforcement, Psychon. Sci. 5 (1966) 121–122. [11] G.G.J. Gallup, S.D. Suarez, Biotic revenge and the death of the dinosaurs, The Scientist (1987) 10. [12] P. DaSilva, S.J. Rachman, M.E.P. Seilgman, Prepared phobias and obsessions: therapeutic outcomes, Behav. Res. Ther. 15 (1977) 210–211. [13] S. Pinker, The Blank Slate, Viking, New York, 2002. [14] D. Chiappe, K. MacDonald, The evolution of domain-general mechanisms in intelligence and leaning, J. Gen. Psychol. 132 (2005) 5–40. [15] J. O’Doherty, et al., Beauty in a smile: the role of the medial orbitofrontal cortex in facial attractiveness, Neuropsychologia 41 (2003) 147–155. [16] J.S. Winston, et al., Brain systems for assessing facial attractiveness, Neuropsychologia 45 (2007) 195–206. [17] H. Takahashi, et al., Men and women show distinct brain activations during imagery of sexual and emotional infidelity, Neuroimage 32 (3) (2006) 1299–1307. [18] J.K. Rilling, J.T. Winslow, C.D. Kilts, The neural correlates of mate competition in dominant male rhesus macaques, Biol. Psychiatry 56 (5) (2004) 364–375. [19] A.T. Goetz, et al., Adding insult to injury: development and initial validation of the Partner-Directed Insults Scale, Violence Vict. 21 (6) (2006) 691–706. [20] T.K. Shackelford, A.T. Goetz, Comparative evolutionary psychology of sperm competition, J. Comp. Psychol. 120 (2) (2006) 139–146. [21] L.E. Martin, Technomics: The Theory of Industrial Evolution, CRC Press, Boca Raton, FL, 2006. [22] R. Kurzweil, The Singularity is Near: When Humans Transcend Biology, Penguin, New York, 2006. [23] E. Drexel, Engines of Creation: The Come Era of Nanotechnology, Anchor, New York, 1987. [24] J.C. Venter, et al., The sequence of the human genome, Science 291 (2001) 1304–1351. [25] J.L. McClelland, D.E. Rumelhart, P.r. Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 2, MIT Press, Cambridge, MA, 1986. [26] D.E. Rumelhart, J.L. McClelland, P.r. Group, Parallel Distributed Processing: Exploration in the Microstructure of Cognition, vol. 1, Cambridge, MA, MIT Press, 1986. [27] K. Pribram, Holonomic brain theory, Scholarpedia 2 (5) (2007) 2735. [28] K.H. Pribram, E.H. Carlton, Holonomic brain theory in imaging and object perception, Acta Psychol. (Amst) 63 (1–3) (1986) 175–210. [29] K.H. Pribram, D. McGuinness, Attention and para-attentional processing. Event-related brain potentials as tests of a model, Ann. N. Y. Acad. Sci. 658 (1992) 65–92. [30] S.J. Blakemore, J.S. Winston, U. Frith, Social cognitive neuroscience: where are we heading? Trends Cogn. Sci. 8 (5) (2004) 216–222. [31] C.D. Frith, U. Frith, Interacting minds – a biological basis, Science 286 (5445) (1999) 1692–1695. [32] K.F. MacDorman, H. Ishiguro, The uncanny advantage of using androids in cognitive and social research, Interact. Stud. 7 (3) (2006) 297–337. [33] J.R. Wolpaw, D.J. McFarland, T.M. Vaughn, Brain–computer interface research at the Wadsworth Center, IEEE Explore 8 (2) (2000) 222–226. [34] K.K. Spuznar, J.M. Watson, K.B. McDermott, Neural substrats of envisioning the future, Proc. Natl. Acad. Sci. U.S.A. 104 (2) (2007) 642–647. [35] A. Schwartz, et al., Brain-controlled interfaces: movement restoration with neural prosthetics, Neuron 52 (1) (2009) 205–220.
Contents lists available at ScienceDirect
Futures journal homepage: www.elsevier.com/locate/futures
Boldly going where no brain has gone: Futures of evolutionary cognitive neuroscience Steven M. Platek a,*, Amir Hasicic a, Austen L. Krill a,b a b
Psychology, The School of Liberal Arts, Georgia Gwinnett College, United States School of Biological Sciences, The University of Liverpool, United Kingdom
A R T I C L E I N F O
A B S T R A C T
Article history: Available online 24 May 2011
Evolutionary cognitive neuroscience (ECN) is a new discipline that employs methodology from cognitive neuroscience to study, in vivo, the proximate mechanisms of putative evolved psychological/cognitive adaptations. The formalized discipline is less than five years old, but has already generated a plethora of research as well as extended our understanding of the evolved nature of the mind/brain. Here we briefly recapitulate the antecedents to an evolutionarily informed cognitive neuroscience, attempt to fit ECN into a broader evolutionary psychology framework that seeks to account for evolved adaptations to recurrent problems faced by our ancestors, and discuss the futures of this newly formed discipline by expounding on methodological techniques and theoretical accounts that may pervade our future. We believe, as the Nobel laureate Nikko Tinbergen has suggested, that a complete understanding of the evolved nature of behavior and cognition (i.e., evolved cognitive adaptations) can only come from investigations at both the proximate and ultimate levels and, thus here, we attempt to cast ECN as the proximate sister discipline to evolutionary psychology. When taken together these two disciplines have the potential to uncover how and why the mind works. ß 2011 Published by Elsevier Ltd.
1. Introduction Evolutionary cognitive neuroscience (ECN; [1–3]) integrates comparative neuroscience, archaeology, physical anthropology, paleoneurology, cognitive primatology, evolutionary psychology, and cognitive, social and affective neuroscience in an effort to identify and describe the proximate [4] neural mechanisms that have been forged by natural and sexual selection pressures during human evolutionary history. Understanding these proximate neural mechanisms will help us define what it is to be human, the nature of the human mind, identify comparative neural mechanisms implicated in various cognitive processes in other species, and allow for a more thorough brain-based Darwinian psychology. In its simplest form, evolutionary cognitive neuroscience (ECN) is the merging of the fields of evolutionary psychology and cognitive neuroscience using methodology from both disciplines and guidance from evolutionary meta-theory. In this coalescence, ECN becomes the discipline for investigating the proximate neural mechanisms that drive the ultimate behavioral adaptations often described in evolutionary psychology (EP; e.g., [5]). Thus, ECN is charged with the identification of neural substrates of putative evolved psychological/cognitive adaptations. A recent volume [2] presents the first comprehensive overview (cf. [1,3]).
* Corresponding author at: Department of Psychology, Fayetteville State University, Fayetteville, NC 28301, United States. Tel.: +1 404 734 1023. E-mail address: [email protected] (S.M. Platek). 0016-3287/$ – see front matter ß 2011 Published by Elsevier Ltd. doi:10.1016/j.futures.2011.05.020
772
S.M. Platek et al. / Futures 43 (2011) 771–776
2. Antecedents to evolutionary cognitive neuroscience 2.1. Cognitive neuroscience without evolution Like pre-Darwinian psychology and other social sciences, cognitive neuroscience without evolution will have difficulty accurately describing the functional workings of the human mind and make about as much sense as psychological science without it [5,6]. The number of articles appearing in journals such as The Journal of Cognitive Neuroscience, Cognitive Brain Research, Brain, Neuron, Neuroscience, Social Neuroscience, Trends in Cognitive Science, Trends in Neuroscience, PNAS, and the Journal of Neuroscience answering questions about brain–behavior relationships is staggering. What is more astounding, however, is the dearth of articles that present the results of evolutionarily informed research or interpret the results from an evolutionary perspective (cf. [1,7]). 2.2. Psychological mechanisms, domain specificity, and domain generality Unlike early psychologists and behavioral scientists (e.g., Skinner, Watson) who envisioned organisms as ‘‘blank slates’’ capable of making an infinite number of associations, evolutionary psychology researchers are beginning to shed light on this flawed theoretical approach to behavior analysis [6,8,9]. In fact, many of the emerging studies are contending directly with this ‘‘standard social science model’’ of psychology; i.e., that organisms possess one or more general-purpose learning mechanisms and that ‘‘biology’’ plays little, if any, role in the manifestation of behavior. For example, in his landmark study, Garcia et al. [10] discovered that animals learned to avoid novel food products that made them ill in as little as one learning/conditioning trial – something that had not been demonstrated with any other stimulus class previously. Labeled conditioned taste aversion, this effect describes an adaptive problem that has since been demonstrated in almost every species tested (the exception to this rule appears to be Crocodilians, see [11]). This adaptation serves an important function – do not eat food that makes you ill or you might not survive to reproduce; i.e., being ill could result in a number of fitness disadvantages such as death, inability to avoid predation, inability to search and secure mates, loss of mate value, decreased attractiveness to mates, or complete inability to perform sexually (cf. [12], for more on prepared learning). Studies like these refute the key premises of the so-called standard social science model – there is no general-purpose learning mechanism and organisms are not ‘‘blank slates’’. Rather, learning is a consequence of carefully crafted mechanisms dedicated to solving specific evolutionary problems (cf. [8,13]). Our brains have evolved to be efficient problem-solvers and the problems they are designed to solve are those that our ancestors recurrently faced over human evolutionary history (i.e., domain-specificity). Although domain-specificity seems to be the prevailing theoretical model of the brain in evolutionary psychology, it is important to note that there is also support for the existence of domain-general mechanisms in areas of cognition and learning [14]. We do not have the necessary space to recapitulate those arguments here except to note that through increased research evolutionary cognitive neuroscientists are in a position to address the very nature of this problem in the future. In fact, ECN may be the precise field poised to identify and describe a neurological atlas of evolved cognitive/neural/psychological mechanisms. 2.3. Cognitive neuroscience with evolutionary theoretical guidance Why do we need another discipline? Why is the ECN approach important? How will ECN change the future face of science? Without evolutionary meta-theoretical guidance, cognitive neuroscience will fail to describe with anything but superficial accuracy the human (and animal) mind. Cognitive neuroscience will simply describe the neural mechanisms of brain–behavior relationships from theoretical models derived from standard social science models. In doing so, this only solves half the equation. This approach misses the ultimate (i.e., ‘‘why’’) questions of brain–behavior relationships. By adopting the ECN approach and directly addressing ultimate questions about brain–behavior relationships, scientists will be in a position to better describe the underlying neurocognitive processes and neural correlates that they investigate. Likewise, without cognitive neuroscientific methods, evolutionary psychology may not be able to adequately describe and understand the proximate neurophysiological and neurobiological mediators of the many hypothesized evolved psychological/cognitive adaptations, and hence may never be able to completely describe the evolved nature of the human mind and brain. Without ‘‘peering’’ into the brain with techniques such as modern functional neuroimaging, evolutionary psychological investigations can only describe the cognitive processing of human mental characteristics through behavioral methods, which of course is extremely important! Evolutionary psychology can describe function, but is limited in its description of structure, and thus has little ability to relate function to structure, which might be important, especially in comparative investigations of cognitive evolution. The relationship between structure and function is inherently a problem of evolutionary biology; i.e., the genes that give rise to brain structure and its component nuclei and modularity, as well as its ability to process information, were the combined units of selection. The need for an integrated science of the mind that utilizes evolutionary meta-theoretical guidance to cognitive neuroscientific investigations is overdue, but beginning to flourish. In other words if evolutionary psychology answers the question ‘‘Why does the mind work the way it does?’’ evolutionary cognitive neuroscience ought to be in a position, when merged with evolutionary psychology, to answer the questions ‘‘Why and How does the mind work the way it does?’’
S.M. Platek et al. / Futures 43 (2011) 771–776
773
Recently, application of evolutionary meta-theory has been applied directly to investigations of the cognitive neuroscience kind. For example, O’Doherty et al. [15,16] have begun to investigate neural correlates of facial attraction (cf. Little et al., submitted for publication; Reding and Platek, submitted for publication). O’Doherty et al. [15] discovered that the orbitofrontal cortex appears to be activated when a person finds a face attractive, which suggests that facial attractiveness activates a reward or approach system in the brain. These findings have recently been extended [16] to reveal a more distributed network of activation in the anterior cingulate cortex (ACC), superior temporal sulcus, and amygdala in response to evaluations about attractiveness. Additionally, activation in the ACC and amygdala appear to be sex-dependent, showing increased activation in men only. These areas also are activated when males are asked to imagine [17] or observe [18] their mate engaging in infidelity, which suggests that appraisals of attractiveness of females by males is related to their decisions about fidelity and paternal certainty [19,20]. This work is currently being extended to investigate the role of the menstrual cycle in perceptions of attractiveness among female participants. Reding and Platek (submitted for publication) employed functional magnetic resonance imaging (fMRI) to investigate women’s perception of attractiveness as a function of the menstrual cycle while varying male facial symmetry and masculinity. These findings reveal an interaction between perceptions of attractiveness and the menstrual cycle; i.e., women showed increased activation to masculine faces when scanned in the fertile period of their menstrual cycle. Additionally, when women rated self-resembling faces during their fertile phase, brain areas associated with disgust were activated. This suggests that different systems are employed to drive appetitive (to masculine male faces) during ovulation and aversive (possibly incest avoidance) responses to possible kin during the fertile phase. Together these data suggest that (1) there are sex differences in the neural processing of attractiveness that might be related to evaluations about paternity and sexual fidelity and (2) that in women, activation appears, at least in part, dependent on hormonal state. For additional examples of current ECN research see Platek et al. [2] and Platek and Shackelford [3]. 2.4. Foundations for an evolutionary cognitive neuroscience The foundation of ECN is inherently interdisciplinary in nature. Researchers will have to take heed to Tinbergen’s [4] four ‘‘Why’s’’ and proximate/ultimate dichotomy weaved throughout their investigations. This ethological framework is essential to the survival of ECN in that this framework forms the basis for examination of all behaviors from a biological perspective. 2.5. Futures of evolutionary cognitive neuroscience The second half of the twentieth century saw the rise of two scientific revolutions: the computer revolution and the biotechnological revolution. These two scientific revolutions fueled by an unprecedented growth of new technologies have largely shaped the paths of scientific progress in the twenty-first century and will continue to do so into the future. Evolutionary cognitive neuroscience will both greatly contribute to and benefit from these two scientific revolutions. We speculate, with aplomb, about the applications of evolutionary cognitive neuroscience in certain trends and technologies that can be expected to emerge in the next 100 years. 2.6. ECN in 2025 An unprecedented increase in computer technology continues according to Moore’s law, which states that computer power doubles on average every twenty months [21]. As computer technology becomes more cost-effective, more resources are available to shift the computer industry into the next phase. Cognitive neuroscientists will be working together with computer engineers and programmers to build a first biomolecular supercomputer. Unlike silicon-based computers, a biomolecular computer will be an organic computational device with no semi-conductors. In a biomolecular computer, information is stored in DNA molecules encoded in binary code [22]. It is expected to vastly outperform silicon-based computers both in its information processing capabilities and memory capacity. To build a fully organic computational system, scientists will apply principles of the operation of the human brain; that is, the neural substrates of mental processes discovered in cognitive (and other subdisciplines of) neuroscience. Computer engineers will be working with cognitive neuroscientists to create a microprocessor made of living human neurons connected to a silicon chip via nanotechnological advances. The ultimate aim is to build new neural networks and connect them directly to the human nervous system for purposes of measuring neuronal and subneuronal (optical properties of action potentials) activation, and stimulating specific neuronal networks by delivering microvolt pulses. This project will help scientists better understand many neuropsychiatric disorders and help millions of people around the world who suffer from muscular degeneration, spinal cord injuries, blindness, and other disabilities. Two new technologies on the horizon will shape the application of evolutionary cognitive neuroscience in the second half of the century: the quantum computer and nanotechnology. 2.7. ECN in 2050 A series of major conceptual breakthroughs in nanotechnology over the last two decades (i.e., up to 2050) will have allowed scientists to build the smallest machines of all – nanobots [23]. Self-replicating nanobots, no larger than several
774
S.M. Platek et al. / Futures 43 (2011) 771–776
molecules across, can be safely injected into the bloodstream, and engage in numerous biophysiological tasks such as repairing damaged cells and destroying harmful microbes. What will be the latest breakthrough in nanotechnology, however, is of particular importance to cognitive neuroscience. Scientists will be able to build nanobots that can cross the blood–brain barrier and enter the central nervous system. The M2B4 nanobots (molecular machines beyond the blood–brain barrier) will be able to travel across axons and dendrites, as do neurotransmitters. This class of nanobots will be able to enter a specific brain region and manipulate individual neurons, or reframe and improve the neural networks in that region. The possibilities are tantalizing. A score of neural disorders could be eradicated by a careful application of the M2B4 nanobots. A different class of these nanobots called pM2B4 will be able to scan the brain from the inside to create brain images with high spatial and temporal resolution in real-time. This advance will allow evolutionary cognitive neuroscientists to access and modify brain activation in living organisms including humans in realtime; in other words, evolutionary cognitive neuroscientists employing these new methods will be able to develop withinsubjects research designs in which brain activation is both measured and altered in varying ways to approximate the evolved cognitive nature of the underlying neural mechanisms. A new project will begin that, when completed, will have an even greater impact on science and society than the Human Genome Project (http://www.ornl.gov/sci/techresources/Human_Genome/home.shtml) [24] – it will be called the brain code. To determine precisely how the brain is wired has been the ‘‘Holy Grail’’ of cognitive neuroscience since its inception. It is a gargantuan task that will be undertaken by evolutionary cognitive neuroscientists across the globe and one that could not be completed without the awesome power of a new computing technology – the quantum (parallel) computer. The quantum computer can outperform even the biomolecular computer by orders of magnitude, and complete infinitely more complicated operations than a standard silicon-based computer [22]. Such a device will be needed in order to analyze one hundred trillion (or more) interneuronal connections in the human brain. By using the new micro lasers (1/3 mm across), scientists will now be able to cut post-mortem brain tissue into millions of thin slices and analyze each slice individually, neuron by neuron. When the information gathered from the computer analysis of each brain slice is supplemented with the pM2B4 images and genetic expression algorithms, the quantum computer could yield a three dimensional model of the brain that reveals how the brain is wired, neuron by neuron and gene by gene. For the first time cognitive neuroscientists will have a cognitive neurogenetic homunculus of the brain. The ultimate product of the project will be a new, all-encompassing method of studying the human brain, the Holographic Brain Model (HBM). The visible portion of HBM will be a three dimensional holographic representation of the brain. Scientists will be able to make various functional simulations, for example, stimulating each neuron and interneuronal connections in a specific brain region and observe the brain activity, and subsequent behavioral response, in real time. The HBM will allow scientists for the first time to manipulate neural networks at will, first in animals, and then eventually in human brains in vivo. Moreover, they will be able to remove a piece of endogenous neural net from the HBM and replace it with an electronic neural net that can duplicate the precise firing of the original. By using the HBM, computer scientists will be able to replicate the human brain, neuron by neuron. The HBM will be able to simulate every biological neural network, which will allow for a wide range of testing of the traditional cognitive – connectionist – models [25,26]. For example, an interconnected artificial neural network could be created inside the HBM to observe and examine which adaptive properties of the neural network are more suitable for real-life problem solving, living in extreme environments, or for making the best politicians. In actualization, the HBM will likely take form of what Pribram [27] refers to as the ‘‘holonomic brain’’ [27–29]. In this conception of the brain, various specialized brain substrates function holographically, but the entire brain does not work as one large holographic representation of the brain. With this type of modularized [8] functionality, sectors of the artificial holonomic brain need not be centrally localized. Rather, the specialized holonomic modules would benefit from being in close proximity to mechanics for which they serve. The application of the HMB in education and scientific research will lay the groundwork for the next step of human evolution – creating a new form of intelligent life on earth. 2.8. ECN in 2075 To create a true AI system that has common sense, can learn from its mistakes, and is ‘‘aware’’ of its own existence, AI researchers will, once again, need help from cognitive neuroscience [30]. In order to do that, evolutionary cognitive neuroscientists will have to reverse engineer the human brain [30,31]. By studying the HBM, comparatively across the animal kingdom, they will be in a position to pinpoint key genetic changes that allowed for the exorbitant encephalization observed in hominids – and specifically to better understand the neurobiological underpinnings of higher cognitive abilities in humans. The end product will be a supercomputer with an artificial neural network as complex and as powerful as the human brain, a non-biological form of intelligence made bottom-up, taking guidance from biological design in the year 2075. The AI system will aid the scientific community in the quest to create permanent human colonies in space and terraforming of other planets in the solar system. The hostile conditions in outer space (cosmic rays, solar winds, subfreezing temperature, hostile atmospheres, etc.) will present an enormous challenge for the future course of space exploration. A new species of humanity will be created to overcome those challenges, one that can sustain an environment different from the environment found on earth. Additionally, these individuals will be capable of executing programs that are designed to create self-sustaining bases of operation on other planets. This exploration will aid NASA’s ability to generate devices that supplement humans in their eventual deep space exploration.
S.M. Platek et al. / Futures 43 (2011) 771–776
775
This new species will alter the relationship between humans and machines forever. Based on the traditional concept of a man–machine mixture (e.g., cyborg), the new species will be the greatest and most extensive blend of human and mechanical properties ever seen, and will experience the benefits of enhanced survival abilities. The merger of three scientific endeavors will allow for the creation of the new species: biotechnology, computer science, and cognitive neuroscience. The genetic map provided by the Human Genome Project will enable biogeneticists to manipulate the genetic makeup of the new species giving its members the ability to grow, or regenerate, new organs and body parts, therefore, considerably extending their life span. Additionally, design modifications will be possible because of genetic engineering of various animal adaptations onto what looks generally like a hominid body. For example, imagine the capability of enhancing the auditory system to be able to process echolocation, in addition to variability in sound pressure waves. This could serve to enhance this new species’ ability to navigate new and uninhabitable environments. Mechanical components will also greatly enhance their physical abilities and durability, for example, integrating computer-aided robotic muscles with their biological bodies. By 2075, most complex tissues will be replaceable, with the exception of the brain and parts of the nervous system. The task for ECN will be to find a way of enhancing the mental makeup of the new species. It will involve building upon the intelligence that evolution has bestowed in the human species. Evolutionary cognitive neuroscientists will provide a blueprint of biological intelligence and how intelligence emerged through evolution in order to create a vastly more intelligent human species. By the end of the twenty-first century, the task will be completed and the new species of biomechanical creatures will begin their existence: Numans (New Humans). Although physically resembling humans to account for the uncanny valley effect of dissimilarity [32], Numans are vastly superior to humans in many ways. Their brains will be supplemented with the synthetic neural networks that increase their mental processing powers by orders of magnitude. These exogenously implanted networks will allow for increased short-term memory span, the offloading of cognitive processing for the ability of true parallel neuronal processes to emerge, and WiFi-based connections to universal information databases (e.g., Google, http://www.google.com; experiments on this feature are already underway: http:// www.news.wisc.edu/16576), to name but a few advantages. Due to the fact that Numans are synthetically made they can be constructed from durable materials such as titanium. This will make them immune to biodegradation and less at risk of breakdown or injury. Numans will be truly unique, a species committed to put itself to the fullest possible use. Their task will be to explore extra-solar planets and form the first human colonies in space. As such, they not only represent our intergenerational guinea pigs for space exploration, but they also serve to present a realistic human-esque impression to any intergalactic life forms they might encounter. Evolutionary biologists and cognitive neuroscientists will be onboard the first interstellar ships, eager to find out what, if anything, and how evolution has crafted adaptations on other planets. The major drawback of Numans is that their exogenously implanted technology is non-heritable. An alternative means for NASA or other agencies for successful and safe inter-planetary exploration might be to follow the lead of the recent motion picture Avatar (http://www.avatarmovie.com/). That is, by harnessing the vast power of the brain, coupled with computer technology that can both translate and represent those processes (especially cognition associated with self-awareness) space exploration can take the form of virtual reality. Inter-planetary travel may benefit from the advances in evolutionary cognitive neuroscience by capitalizing on not having to send humans in to space at all. Rather, through a complex computer–machine interface [33] space exploration can be conducted via an avatar conduit. There is some evidence that this is already possible. Researchers at Drexel University have begun development of a video game that can be controlled by the brain alone (i.e., no hand-held controllers) (http://www.voxel6.com/). The further development of technology like the LazyBrains video game is already serving as the precursor to an Avatar-like inter-planetary exploration technology. This research endeavor will be helped by what we know about the brains capacity for controlling its own body, as well as the cognitions involved with incorporating past events into new mental schemas and adjusting and thinking about future (possible) events [34]. An additional added benefit of such brain–computer interfaces that generate avatars may be in search and rescue missions of the aftermath of natural disasters without the threat of additional human loss. 3. Conclusion The years up to 2075 and beyond will bring revolutions in science from an inherently interdisciplinary series of research programs, of which evolutionary cognitive neuroscience will have been a founding father. The future of humanity from a neuroscientific perspective brings with it the thoughts, conjectures, and dreams of science fiction, androids and space exploration. However, we are already close. Android development is well underway in several laboratories around the world. The current goal of these labs is to create working models of humanity; that is, working robots that look, act, sound, and work like humans. There are obvious limitations to the current android models, these falling primarily within the depths of the brains emotional processing mechanisms. Current forms of androids do not ‘‘feel’’ and as such will never come close to a true humanity. Android development, taken together with the old and ever-progressing development of medical implants and prosthetics may actually progress to the point of ‘‘feeling’’ androids [35]. Currently, there are millions of humans walking around with machines in their bodies. Take for example the pacemaker, insulin pump, deep brain stimulator, and cardiac valve. Each of these is the beginning of a human–machine interface. As these two areas evolve, concurrently with advancements in computer technology, bio-nano-technology, and evolutionary cognitive neuroscience, we will come closer with each passing day to a more complete understanding of the brain, how it evolved, and how to predict and possibly target evolutionary changes further. Like social cognitions of the brain (self-awareness, theory of mind, etc.) that evolutionary neuroscientists attempt to understand and describe, researchers are now in a position to go beyond describing how and why
776
S.M. Platek et al. / Futures 43 (2011) 771–776
our (and animal) brains do what they do and harness the vast power of our neural architecture to move our brains and species into unchartered worlds. Acknowledgements Special thanks to Gad Saad for review and helpful comments on a previous draft of this manuscript, and for whom without his guidance this manuscript would not have been possible. S.M.P. is supported by a grant from the Pioneer Fund. References [1] A.L. Krill, et al., Where evolutionary psychology meets cognitive neuroscience: a precise to evolutionary cognitive neuroscience, Evol. Psychol. 5 (2007) 232–256. [2] S.M. Platek, J.P. Keenan, T.K. Shackelford (Eds.), Evolutionary Cognitive Neuroscience, MIT Press, Cambridge, MA, 2007. [3] S.M. Platek, T.K. Shackelford, Foundations in Evolutionary Cognitive Neuroscience, Cambridge University Press, Cambridge, UK, 2009. [4] N. Tinbergen, Derived activities; their causation, biological significance, origin, and emancipation during evolution, Q. Rev. Biol. 27 (1) (1952) 1–32. [5] D.M. Buss, The great struggles of life: Darwin and the emergence of evolutionary psychology, Am. Psychol. 64 (2) (2009) 140–148. [6] D.M. Buss (Ed.), The Handbook of Evolutionary Psychology, Wiley, New York, 2005. [7] G.D. Webster, Evolutionary theory in cognitive neuroscience: a 20 year quantitative review of publication trends, Evol. Psychol. 5 (3) (2007) 520–530. [8] J.H. Barkow, L. Cosmides, J. Tooby (Eds.), The Adapated Mind, Oxford University Press, New York, 1992. [9] L. Cosmides, The logic of social exchange: has natural selection shaped how humans reason? Studies with the Wason selection task, Cognition 31 (3) (1989) 187–276. [10] J. Garcia, F.R. Ervin, R.A. Koelling, Learning with prolonged delay of reinforcement, Psychon. Sci. 5 (1966) 121–122. [11] G.G.J. Gallup, S.D. Suarez, Biotic revenge and the death of the dinosaurs, The Scientist (1987) 10. [12] P. DaSilva, S.J. Rachman, M.E.P. Seilgman, Prepared phobias and obsessions: therapeutic outcomes, Behav. Res. Ther. 15 (1977) 210–211. [13] S. Pinker, The Blank Slate, Viking, New York, 2002. [14] D. Chiappe, K. MacDonald, The evolution of domain-general mechanisms in intelligence and leaning, J. Gen. Psychol. 132 (2005) 5–40. [15] J. O’Doherty, et al., Beauty in a smile: the role of the medial orbitofrontal cortex in facial attractiveness, Neuropsychologia 41 (2003) 147–155. [16] J.S. Winston, et al., Brain systems for assessing facial attractiveness, Neuropsychologia 45 (2007) 195–206. [17] H. Takahashi, et al., Men and women show distinct brain activations during imagery of sexual and emotional infidelity, Neuroimage 32 (3) (2006) 1299–1307. [18] J.K. Rilling, J.T. Winslow, C.D. Kilts, The neural correlates of mate competition in dominant male rhesus macaques, Biol. Psychiatry 56 (5) (2004) 364–375. [19] A.T. Goetz, et al., Adding insult to injury: development and initial validation of the Partner-Directed Insults Scale, Violence Vict. 21 (6) (2006) 691–706. [20] T.K. Shackelford, A.T. Goetz, Comparative evolutionary psychology of sperm competition, J. Comp. Psychol. 120 (2) (2006) 139–146. [21] L.E. Martin, Technomics: The Theory of Industrial Evolution, CRC Press, Boca Raton, FL, 2006. [22] R. Kurzweil, The Singularity is Near: When Humans Transcend Biology, Penguin, New York, 2006. [23] E. Drexel, Engines of Creation: The Come Era of Nanotechnology, Anchor, New York, 1987. [24] J.C. Venter, et al., The sequence of the human genome, Science 291 (2001) 1304–1351. [25] J.L. McClelland, D.E. Rumelhart, P.r. Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 2, MIT Press, Cambridge, MA, 1986. [26] D.E. Rumelhart, J.L. McClelland, P.r. Group, Parallel Distributed Processing: Exploration in the Microstructure of Cognition, vol. 1, Cambridge, MA, MIT Press, 1986. [27] K. Pribram, Holonomic brain theory, Scholarpedia 2 (5) (2007) 2735. [28] K.H. Pribram, E.H. Carlton, Holonomic brain theory in imaging and object perception, Acta Psychol. (Amst) 63 (1–3) (1986) 175–210. [29] K.H. Pribram, D. McGuinness, Attention and para-attentional processing. Event-related brain potentials as tests of a model, Ann. N. Y. Acad. Sci. 658 (1992) 65–92. [30] S.J. Blakemore, J.S. Winston, U. Frith, Social cognitive neuroscience: where are we heading? Trends Cogn. Sci. 8 (5) (2004) 216–222. [31] C.D. Frith, U. Frith, Interacting minds – a biological basis, Science 286 (5445) (1999) 1692–1695. [32] K.F. MacDorman, H. Ishiguro, The uncanny advantage of using androids in cognitive and social research, Interact. Stud. 7 (3) (2006) 297–337. [33] J.R. Wolpaw, D.J. McFarland, T.M. Vaughn, Brain–computer interface research at the Wadsworth Center, IEEE Explore 8 (2) (2000) 222–226. [34] K.K. Spuznar, J.M. Watson, K.B. McDermott, Neural substrats of envisioning the future, Proc. Natl. Acad. Sci. U.S.A. 104 (2) (2007) 642–647. [35] A. Schwartz, et al., Brain-controlled interfaces: movement restoration with neural prosthetics, Neuron 52 (1) (2009) 205–220.