Neural Networks of Human Nature and Nurture
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Neural networks of human nature and nurture DANIEL S. LEVINE* University of Texas at Arlington, U. S. A. Abstract Resumen Neural network methods have facilitated the unifi - Los métodos de redes neuronales han facilitado la cation of several unfortunate splits in psychology, unifi cación de varias desafortunadas divisiones en including nature versus nurture. We review the psicología, incluida la de naturaleza versus crianza. contributions of this methodology and then dis- Se revisan aquí las contribuciones de esta metodo- cuss tentative network theories of caring behavior, logía, para luego examinar las propuestas teóricas of uncaring behavior, and of how the frontal lobes basadas en redes acerca de la conducta de cuidado, are involved in the choices between them. The la conducta de descuido y cómo los lóbulos fronta- implications of our theory are optimistic about les están involucrados en las elecciones entre éstas. the prospects of society to encourage the human Las implicaciones de nuestra teoría son optimistas potential for caring. acerca de los prospectos de la sociedad para forta- Key words: neural networks, nature, nurture, lecer el potencial humano para el cuidado. caring behavior, frontal lobe. Palabras clave: redes neuronales, naturaleza, crianza, conducta de cuidado, lóbulo frontal. The interdependent web est: they have infl uenced the popular culture by promoting a fragmented view of human beings. Nature versus nurture? This is one of the many false Ultimately, such fragmenting of our consciousness splits and dichotomies that have retarded research tends to encourage the development of toxic “us in psychology at different times in its history. Tryon versus them” relationships between people (see (1993, 1995) summed up some of these dichoto- also Papini, 2008). mies as follows: But since about the mid-1980s, there has been a surge of interdisciplinary research fi ndings that (1) Mind vs. Body; (2) Biological (Nature) vs. Envi- promise to heal some of these psychological splits. ronmental (Nurture); (3) Language Learning (Innate The development of modern brain imaging tech- vs. Learned); (4) Subjective vs. Objective; (5) Lear- niques has spurred the growth of cognitive neu- ning vs. Development; (6) Holistic vs. Atomistic; (7) roscience and greater productive collaboration Individual (Ideographic) vs. General (Nomothetic); between experimental psychologists and neuros- (8) Experimental vs. Naturalistic; (9) Awareness vs. cientists. The development of modern computa- Conditioning; (10) Freedom vs. Determinism; (11) tional neural network (or connectionist) techniques Human (Cognitive) vs. Animal (Associative) Learning has fostered a greater understanding of dynamical (Tryon, 1995, p. 303). interconnections among elements and subsystems of the mind. The new disciplines that have emer- Why should we care about these academic dis- ged—cognitive neuroscience and neural network tinctions? They are of more than academic inter- theory—have the potential to infl uence popular * Please send correspondence to: Daniel S. Levine Department of Psychology University of Texas at Arlington. 501 S. Nedderman Drive, Arlington, TX 76019-0528, USA. E-mail: [email protected]. http://www.uta.edu/psychology/faculty/levin. 82Avances en Psicología Latinoamericana/Bogotá (Colombia)/Vol. 26(1)/pp. 82-98/2008/ISSN1794-4724 Neural networks of human nature and nurture culture in ways that promote better mutual un- cognitive neuroscience, is one of the “tunnels” derstanding. between them (for a history and overview, see In particular, biologically relevant and plausible Levine, 2000). Neural networks are mathematical neural networks have helped to heal Tryon’s dicho- and computer models that are composed of simu- tomy (2), namely, nature versus nurture. The naive lated brain regions, or in some cases psychological conventional belief is that a theoretical, quantita- constructs, and connections between those regions tive approach to understanding human personality or constructs. At the same time, the networks are would favor genetic over environmental causes. designed with the goal of achieving with computer But the naive belief is wrong because the brain’s simulations some results that can be interpreted as function is to mediate between the internal world of analogous to some set of behavioral data. the body and the external world of the environment. So how is a “neural network” defi ned? The best And in order to mediate successfully, the brain is defi nition so far, though a very imperfect one, is able to change as a result of experience. As the probably the one developed in 1988 by a team of neural network pioneer Stephen Grossberg put it, neural network experts from a study commissio- “brains self-organize on a relatively fast time scale ned by the United States Department of Defense through development and life-long learning, and do (DARPA): so in response to non-stationary, or rapidly chan- ging, statistical properties of their environments” a neural network is a system composed of many… pro- (Grossberg, 2000, p. 244). cessing elements operating in parallel whose function Neural networks, because they are built on the is determined by network structure, connection streng- mathematics of dynamical systems, treat genetic ths, and the processing performed at computing ele- and environmental infl uences as interacting parts ments or nodes. ... Neural network architectures are of a seamless whole, or as some religions call it, an inspired by the architecture of biological nervous “interdependent web.” Genetics provides behavio- systems, which use many simple processing elements ral tendencies, whereas environments infl uence the operating in parallel... expression of these tendencies. The enhancement or inhibition of any genetic capacity is a function of This defi nition is not completely satisfactory the interaction of inherited temperament and perso- because not all mathematically studied neural nality with experiences. These experiences affect networks can be parceled cleanly into nodes and temperament and personality, and thus behavior, connections: some are studied more as continuous, by altering brain chemistry and structure (see Eisler even chaotic, processes (see, e.g., Freeman, 2003). & Levine, 2002). What do the nodes or elements in the DARPA defi nition mean? Typically, though not always, What neural networks are and are not scientists conceptualize the nodes as large groups of neurons (brain cells). Experiments from neurophy- The “Chunnel” across the English Channel between siology laboratories have suggested that the electri- Britain and France was built from both ends, with cal patterns of single neurons and the biochemistry laser technology used to make them meet in the of the synapses between neurons are irregular in middle. By analogy, if one wants to understand the their organization. But if some irregularities at the biological basis of human behavior, one needs to levels of single neurons and synapses are averaged go back and forth between the biology of the brain out across large groups or brain regions, regular and behavioral functions, and try to get them to connection patterns emerge that are important for “meet in the middle.” behavior. If the biology of the brain is analogous to “Bri- Sometimes nodes correspond to brain areas or tain,” and the psychology of behavior, emotion, specifi c cell types in those brain areas. At other and cognition to “France,” then the growing inter- times, when not enough is known about brain disciplinary fi eld of neural networks, along with processes or when modeling at a functional level Avances en Psicología Latinoamericana/Bogotá (Colombia)/Vol. 26(1)/pp. 82-98/2008/ISSN1794-4724 83 Daniel S. Levine is desired, nodes correspond to cognitive entities learning and conditioning data. The late 1980s and such as the memory of a specifi c word, the tendency early 1990s, buoyed by the interdisciplinary cogni- to approach a specifi c object, or the intensity of a tive science revolution, saw early models of high specifi c emotion. level cognition, including language acquisition, Biologists and clinicians frequently talk in- and its breakdown in various mental disorders. All formally of the “neural networks involved in this these areas are still active, and now a few models or that function,” meaning networks in the actual have appeared in social psychology. By now, while brain: that usage is accurate enough. But what is there is little agreement on the “right” model for inaccurate and misleading is the tendency of some any of these phenomena, the available network researchers to restrict the term “neural network” tools available and empirical knowledge are sophis- to a particular type of network structure that has ticated enough that practically any area of psycho- become popular. These structures are multilayer logy, whether cognitive, behavioral, physiological, (usually three-layer) feedforward networks that social, developmental, or clinical, is amenable to learn prescribed responses from a set of training neural network modeling. data (Rumelhart & McClelland, 1986). Such net- works are often called back propagation networ- Variability of human behavior ks because learning takes place by propagation of changes in connection weights back from the By now there is considerable neurophysiological output nodes of the network to other nodes re- evidence that strengths of many synapses between presenting stored input pattern categories. Back pairs of neurons change when the neurons on both propagation networks