Angels, Devils, and Censors in the Brainneural networks capture basic aspects of both intelligence and biological adaptabil-ity, and have achieved impressive success in modeling some

Angels, Devils, and Censors in the Brain Daniel S. Levine

Department of Psychology, University of Texas at Arlington, Arlington, Tex., USA

Daniel S. Levine, Department of Psychology

University of Texas at Arlington, 501 South Nedderman Drive

Arlington, TX 76019-0528 (USA)

Tel. +1 817 272 3598, Fax +1 817 272 2364, E-Mail

[email protected]

© 2005 S. Karger AG, Basel

1424–8492/05/0021–0002

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Complexus 2004–05;2:x–xx

O R I G I N A L R E S E A R C H P A P E R

Key Words Brain � Emotion � Mind-body problem � Neural network � Personality � Prefrontal cortex � Psychotherapy � Values

Abstract How is the human brain organized to enhance or suppress specifi c types of be-havior? Can we provide a biological explanation for differences between indi-viduals (and societies) in the behaviors they enhance or suppress? Insights from social neuroscience, brain imaging, and neural network theory suggest which cortical-subcortical interactions subserve enhancing or suppressing classes of behaviors. The brain networks proposed for these functions connect four previ-ous research streams from different disciplines. The fi rst stream is Eisler and Levine’s construction of three separate brain networks for the confl icting behav-ior patterns of fi ght or fl ight, dissociation, and bonding – with the orbitomedial prefrontal cortex playing a crucial role in selecting among these patterns. The second is Newman, Grace, O’Reilly, and others’ work on motor ‘gates’ (in the basal ganglia) infl uenced by contextual signals (from the hippocampus) and/or affective signals (from the amygdala). The third is Cloninger’s clinical schema for interacting character and temperament dimensions of personality, which in-

Received: July 5, 2004

Accepted after revision: July 28, 2005

Published online: $ $ $

DOI: 10.1159/0000XXXXX

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Simplexus Mathematical models serve more than

one purpose. Sometimes, they help to cast theoretical ideas or hypotheses in precise form. If a model’s behaviour can be under-stood mathematically, either exactly or in suffi cient approximation, then it may pro-vide an invaluable bridge to experimental tests against relevant observations. But just as often, models have another, less techni-cal use as tools for helping us to think clearly, to be more creative and to ask new questions. In this latter case, models aren’t necessarily right or wrong – they’re either instructive and fruitful, or they aren’t.

For fi fty years, neural networks have proven useful in exploring biological mechanisms underlying processes such as diverse as sensorimotor control, mem-ory and pattern recognition. A neural net-work is a dynamical system made of many relatively simple units – each modeled loosely along the lines of real biological neurons – that interact and infl uence one another. Given a period of ‘training’, such models can learn to identify patterns through trial and error – displaying a ru-dimentary form of memory – while re-maining able to learn new patterns that may emerge in the future. In this sense, neural networks capture basic aspects of both intelligence and biological adaptabil-ity, and have achieved impressive success in modeling some basic neural func-tions – the acquisition of short-term mem-ories, for example, and their transforma-tion into longer-term memories.

But neural networks may also be useful in a metaphorical sense, as a conceptual tool for organizing scientists’ thinking in areas that, for the moment, still lie far be-yond the possibility of any detailed math-ematical understanding. In the present pa-per, psychologist Daniel Levine of the Uni-versity of Texas at Arlington argues that the naturally rich dynamics of neural net-works may provide a powerful framework for understanding the roots of human be-haviour and personality more clearly.

2 Complexus 2004–05;2:x–xx Brain Angels and Devils

fl uence tendencies toward behavioral patterns in specifi c contexts. The fourth is Levine’s mathematical theory (anal-ogous to previous work of Atlan, Kirk-patrick, and others) on the role of noise in switches among attractors in per-sonality space. My theory synthesizes all this research into hypotheses about ‘top-down’ and ‘bottom-up’ interac-tions between general rules (‘censors’) and specifi c behavior tendencies (‘an-gels’ and ‘devils’). Implications are dis-cussed for psychotherapy and other interventions in an individual’s life.

Copyright © 2005 S. Karger AG, Basel

I am at the beginning as I am at the end. I am the sacred circle, spinner of the web of space and time. I am the cosmic ‘And’: life and death, order and chaos, eternal and fi nite.

Lyn Hamilton, The Maltese Goddess

Shiva is referred to as ‘the good one’ or the ‘auspi-cious one.’ Shiva-Rudra is considered to be the de-stroyer of evil and sorrow. Shiva-Shankara is the doer of good. http://www.templenet.com/beliefs/allaboutshiva.htm

You can’t have variations on a theme if you don’t have a theme.

Stephen Grossberg (1977)

Introduction How do people and animals make deci-

sions about what actions to perform and what actions to refrain from performing? How does the sum total of decisions, infl u-enced by genes and culture, form into indi-vidual tendencies for engaging in or avoid-ing classes of behavior – in other words, into personality and character? In the words of the 19th Century American min-ister and educator William DeWitt Hyde, ‘What we choose is what we are’.

Questions about behavioral decision making are now amenable to scientifi c an-swers because neuroscience is making in-creasing contact with both cognitive and social psychology [1, 2] . Also, signifi cant

Specifi cally, Levine suggests that a dy-namical systems perspective, centered around neural networks, can be useful for ordering our thinking about how internal psychic and neurodynamic processes lead us to behave in one way rather than anoth-er. In the paper, he proposes that our rela-tively stable personality types (or charac-ter types) may correspond ultimately to distinct attractors in some dynamical sys-tem that arises by virtue of interactions be-tween various parts of the brain. Levine also attempts to put a little more ‘meat’ on this abstract picture by appealing to the fi ndings of contemporary empirical neu-roscience. He discusses a number of spe-cifi c brain regions, their apparent psycho-logical functions and the interactions be-tween them, and on this basis proposes a speculative and provocative schematic di-agram for the brain’s overall decision-making apparatus. The paper offers no specifi c predictions; rather, it should be considered more as an exercise in creative scientifi c thinking – constrained by em-pirical knowledge – that aims to provoke psychiatrists and other brain researchers to think about human behaviour in dy-namical terms.

Levine begins by describing a perspec-tive – infl uential among psychologists – which holds that specifi c behaviours move from the realm of the potential into that of the actual through a multi-level process that is roughly akin to voting. As proposed by Antonio Damasio, the anticipation of a certain possible behaviour – anything from drug taking, to taking a moral stand against a war, or going to bed early – leads immediately to activity in many parts of the body and mind. These responses, which also depend on the situation or context of the individual, may be pleasant or unpleas-ant, and they all somehow infl uence the probability that the behaviour will ulti-mately take place. As with voting, no one individual response determines the out-come; all have to be tallied and integrated together in some way. Damasio suggests

brain-behavioral relationships are within reach of computational models [3, 4] . This article will review and synthesize some of those developments, framing them in the metaphor of angels and devils that Irun Cohen [5] employed to describe self versus non-self in the immune system.

I take the liberty of borrowing from im-munology since over the years immunol-ogy has borrowed from neuroscience such terms as pattern discrimination, memory, and language [5–7] . Analogies between these two fi elds of biology are apt because the immune system needs a ‘language’ to describe what patterns it will accept (as part of the self) or reject (as part of the non-self). This requires a mechanism akin to the brain’s pattern discrimination. It also requires a mechanism akin to memo-ry, so it can ‘remember’ what harmful things attacked it in the past.

The angel/devil metaphor [5] derives from a painting by Escher ( fi g. 1 ) which consists of white angels alternating with black devils, in such a way that either an-gels or devils can be seen as the fi gure (i.e. focus of concentration) with the other be-ing the ground (absence of fi gure). Cohen [5] suggested that in immunological para-digms, analogously, the focus can either be on self or non-self, but not both at once.

So what are the brain’s analogs of angels and devils? They are the brain’s decisions about what classes of behaviors to enhance or suppress.

A starting point for our inquiry is the widely known work of the clinical neuro-scientist Antonio Damasio [8, 9] . Damasio found brain mechanisms whereby evolu-tionary programs for survival and repro-duction, mediated through emotions, exert a primary infl uence on human decision making. He pointed particularly to the role of the orbitofrontal cortex, a part of the pre-frontal cortex , as the integrator between complex information-processing regions of the cerebral cortex and subcortical areas with strong connections to other organs of the body. Damasio said that plans for pos-

3 Complexus 2004–05;2:x–xx Levine

that such integration takes place in the ‘or-bitofrontal’ cortex, an important region of the brain involved in social adjustment

and in controlling the personality of an in-dividual. The orbitofrontal cortex – many psychologists and neuroscientists be-lieve – is where decisions get made and the potential becomes the actual.

Taking this rough framework as a start-ing point, Levine introduces the terms ‘an-gels’ and ‘devils’ to refer to neurodynamic processes that tend to promote or inhibit some particular behaviour. Specifi cally, as he writes, an angel refers to a ‘stored pat-tern of neural activities that markedly in-creases the probability that some specifi c behavior will be performed in some class of contexts.’ You can think of angels and devils as elementary promoters and inhib-itors for any particular behaviour. Each of us has many angels and devils, which we have either inherited on a genetic basis or learned through culture. Most of us have devils against stealing, for example, or eat-ing rotten meat, and angels for keeping promises and drinking water when thirsty. One might roughly think of angels and devils as the ‘atoms’ of decision-making. Importantly, for any specifi c behaviour in a particular context, we may have many an-gels and devils that directly confl ict with one another.

But angels and devils don’t lead to deci-sions directly on their own. Levine argues that these elementary tendencies feed for-ward into higher-level structures or pro-cesses that somehow ‘integrate’ the angels and devils, and thereby lead to a decision. Two people in the same situation, for ex-ample, and having identical angels and devils for some behaviour and context, might still differ in their actual behaviour. This could be because they are operating with different ‘censors,’ which Levine de-fi nes as ‘abstract behavioral fi lters...which encompass and functionally unify large classes of what we call angels and devils.’ For example, the devil of ‘avoiding sex with anyone other than your spouse’ could be

sible behavior excite somatic markers , that is, anticipation of the behavior leads to ac-tivity patterns in other parts of the body that are either pleasant or unpleasant and thereby bias the likelihood of the plans be-ing implemented. But Damasio was not the fi rst neuroscientist to ascribe such a func-tion to the frontal lobes: Walle Nauta [10] proposed this same type of body-based behavior selection and called it interocep-tive censorship of plans (‘interoceptive’ re-ferring to perceiving sensations from the viscera).

Obviously, different individuals, fami-lies, societies, and cultures can censor our plans in different ways, leading to a wide range of possible angel-devil classifi ca-tions. Also, as Cohen [5] notes for the im-

mune system, either angels or devils can be the fi gure or the ground in the nervous sys-tem. That is, the individual, or society, can focus on what types of behavior it wishes to promote or else on what types of behav-ior it wishes to forbid. Toward the end of this article I relate that distinction to some approaches in clinical psychology.

As with most personality traits, both nature and nurture are involved here. Nur-ture – not just within the family, but via institutions ranging from education and media to organized religion – has a strong infl uence on what we classify as angels or devils. Yet the tendency to create such clas-sifi cations and to act, at least partially, on them seems to be universal and hard-wired.

Fig. 1. ‘Circle Limit IV’ by M.C. Escher. A 1960 print in which the spaces be-tween white angels look like black devils. Copyright 2005, M.C. Escher Company-Holland. All rights reserved. www.mcescher.com.


The bulk of this article will be devoted to constructing a network theory for what brain regions are involved in ‘good/bad’ be-havior classifi cations and what roles each of them might play [11] . Some of the con-nections in this network are identifi ed as likely loci for plastic change, and therefore could be substrates for family or cultural infl uences on the individual’s classifi ca-tions.

In this article, the term angel is used to mean a stored pattern of neural activities that markedly increases the probability that some specifi c behavior will be per-formed in some class of contexts. Such ac-tivity patterns might combine electrical impulse patterns in particular brain re-gions or large collections of neurons, and selectively enhanced synapses (often the result of learning) between particular re-gions. In most examples, neuroscientists do not yet know exactly which neuron pop-ulations and synapses are involved, but there are enough neurobehavioral data available for this article to suggest plausi-ble candidates. The pathways representing angels, so defi ned, include both excitation and inhibition at different loci. These path-ways also link brain regions for perception of sensory or social contexts with other re-gions for planning and performing motor actions, under the infl uence of yet other re-gions involved both with affective valua-tion and with high-level cognition.

The term devil is used in this article to mean the opposite of an angel, i.e. a stored pattern of neural activities that markedly decreases the probability that a specifi c be-havior will be performed in some class of contexts. The devils involve many of the same perceptual, motor, cognitive, and af-fective brain regions as do the angels, but with some differences in patterns of selec-tive excitation and inhibition.

After discussion of likely neural path-ways representing angels and devils, in-cluding some partial computational mod-els, this article considers the next level of complexity in decision making. This is the

part of the censor of ‘honoring committed relationships.’ The same devil can also be, perhaps in a different person, part of the censor of ‘obeying God’s commandments.’ Angels and devils, the atoms of decision-making tendencies, can be organized into higher-level structures in many different ways.

In this sense, the angels and devils are analogous to one layer of neurons that feeds upward to another layer, and a censor is roughly akin to the overall pattern of in-terconnections that leads the network to have one dynamics rather than another. That’s the dynamical view. But Levine goes on to suggest that, in psychological terms, these censors are loosely akin to someone having a certain character, which would generally dispose them to act one way or another depending on the context. In other words, character somehow corresponds to a particular way that the lower level angels and devils get integrated to make deci-sions. Levine’s equation (1) isn’t meant to represent any of this explicitly, only to show the dynamical structure generic to neural network models, in which information at one level feeds upward into another. Any network aiming to represent the dynamics of angels and devils realistically would be immeasurably more complex – and, as of yet, of course, impossible to construct on any meaningful basis – but it would in-clude elements such as Eq. (1) as compo-nents.

Levine then goes on to make all of this more specifi c by discussing a number of specifi c brain regions that could play a role in the abstract picture just described. In particular, he describes three major brain pathways – fi ght or fl ight, cooperate or dis-sociate – which represent common dy-namical routes, based on our evolutionary history, through which decisions frequent-ly get made. These terms refer to archetyp-al behavioural responses to specifi c kinds of situations, each of which has a plausible basis in specifi c neural pathways shared by all people. The idea is that these biologi-

level of abstract behavioral fi lters, possibly involving association areas of the cerebral cortex, which encompass and functionally unify large classes of what we call angels and devils. In honor of Nauta’s [10] in-sights, we call these behavioral fi lters cen-sors . Individual differences in the structure of these neural censors are tentatively re-lated to some psychiatric classifi cations of personality and character.

The human brain has an enormous ca-pacity for actively deciding among the va-riety of possible angel-devil classifi cations. So we begin our inquiry with how humans might choose between alternative angels and alternative devils.

Choices of Angels and Devils One way to approach classifi cations of

good and bad actions is through religion. Religious practice typically includes a pro-scriptive element, a code that designates what forms of behavior are or are not ac-cepted or encouraged. Yet people of faith disagree considerably about what are con-sidered the worst offenses or the most praiseworthy actions.

Many religious scholars agree that dif-ferences between identifi ed faiths (Christi-anity, Islam, Judaism, Hinduism, Bud-dhism, Confucianism, Taoism, Paganism, et cetera) are less profound than differenc-es between the liberal and conservative wings of each faith [12] . Liberal and con-servative religious outlooks particularly differ in their devils. To conservatives, some devils are prohibitions against vio-lating established hierarchical relation-ships, both within human society and be-tween humans and the deity. To liberals, on the other hand, most devils are prohibi-tions against mistreating or disrespecting other humans, animals, or the environ-ment.

Ultimately, both the conservative and liberal impulses are rooted in our evolu-tionary programming. The conservative impulse, we will see, is closely tied to the characteristic fi ght-or-fl ight responses


cally relevant pathways probably have something to do with the action of ‘cen-sors.’

Levine also explores the roles of the or-bitomedial cortex, hippocampus, nucleus accumbens, and other brain regions in de-cision making, as well as psychiatrist Rob-ert Cloninger’s work on personality, which proposes that the common possibilities for individual character can usefully be viewed in terms of three components. As listed by Levine, these are ‘ self-directedness (relating to acceptance of the individual self); coop-erativeness (relating to acceptance of other people); and self-transcendence (relating to acceptance of nature in general).’ Levine suggests that these three character traits, considered as essential by Cloninger, cor-respond closely to the three main path-ways – fi ght or fl ight, cooperate or dissoci-ate – as know empirically from neurosci-ence and cognitive psychology.

The three pathways aren’t meant to be censors taken on their own; rather, in refer-ence to Cloninger’s cube of character (see Levine’s fi g. 7 ); eight possible attractors correspond to the relative weights given to the three pathways – fi ght or fl ight, cooper-ate or dissociate. (Obviously, the eight at-tractors is a simplifi cation, only corre-sponding to the extremes of the various traits. More realistically, every point within the cube should correspond to a different possible character). Each one of these cor-ners corresponds to a particular way of be-having and, so, to a particular censor. Along the way, Levine also reviews evidence that the orbitomedial prefrontal cortex, the hip-pocampus, amygdala and nucleus accum-bens all have some particular role in the biology that underlies such censors.

But for Levine, there is still one further step to take. Angels, devils and censors, and the dynamical attractors to which they might hypothetically be associated, do not quite capture the richness of real human behaviour. While censors may fi x a person’s character, Levine suggests that they are not the fi nal level of decision-making, for an

that humans, like other animals, have de-veloped to cope with danger or stress [13] . Conservatism includes protection of the family as well as the individual. Yet the family itself arises from the part of evolu-tion that makes humans and other animals bond with others of their species and tend their offspring [14, 15] , and this part of evolution generates the cooperation and love that drive the liberal impulse [16] . This includes not only evolutionary pro-gramming for survival of the self and spe-cies, but motivations that go beyond sur-vival and reproduction to creative expres-sion of our human complexity [16–19] .

Eisler and Levine [11] describe possible brain mechanisms for implementing be-haviors that tend to fi t each of these two types of evolutionary programming. Spe-cifi cally, Eisler and Levine discuss neural pathways both for fi ght-or-fl ight responses and for what Taylor et al. [14] termed tend-and-befriend responses. We also discuss pathways for a third set of responses, called dissociative , involving withdrawal from stressful situations [13] . After an introduc-tion about neural networks, the next three sections of this article review neural mech-anisms for the three behavioral classes of fi ght or fl ight, tend and befriend, and dis-sociation.

Eisler and Levine adduced evidence that the orbitofrontal cortex, prominent in Damasio’s work, is the pivotal area for choice between these three gross modes of behavior. Since the orbitofrontal cortex forms links between sensory events and positive or negative affective valuation, such choice among behaviors can be bi-ased by an individual’s experience in his or her family of origin, or in society as a whole. Table 1 summarizes two competing styles of social organization (domination versus partnership) based on this type of programming [20, 21] .

Yet the Eisler-Levine article left open several research questions about brain-be-havior interactions. How does context trig-ger gross modes of behavior? For example,

how can the same person exhibit aggres-sive (fi ght-or-fl ight) behavior, when en-countering hostility, and caring (tend-and-befriend) behavior, when encountering friendliness? How do individuals make personal transitions, say from a tendency toward fi ght-or-fl ight behavior to a greater tendency toward tend-and-befriend be-havior, without decreasing their odds for survival? How are executive functions, such as self-monitoring, involved in choic-es between behavior patterns? My goal in this article is to address such questions in a framework of neural dynamical systems. The framework combines the Eisler-Levine mechanisms, for realization of three broad classes of behavior, with mechanisms for affect- and context-based stimulus selec-tion [22] and for multidimensional per-sonality integration [23] . The network theory builds on previous neural network models of categorization [24] ; self-actual-ization [25] ; balance between selfi shness and empathy [26] , and personality dimen-sions [27] .

To prepare the way, I briefl y review how computational neural network models have been utilized over the last 40 years to bridge the biological and psychological levels of inquiry.

Brain Networks and Model Neural Networks The ‘Chunnel’ across the English Chan-

nel between Britain and France was built from both ends, with laser technology uti-lized to make them meet in the middle. Similarly, those who wish to understand the biological basis of human behavior need to go back and forth between neuro-biology and behavioral functions, and try to get them to ‘meet in the middle’.

The growing interdisciplinary fi eld of neural networks, along with experimental cognitive neuroscience, is one of the ‘tun-nels’ between neuroscience and psycholo-gy. The types of neural networks we dis-cuss are mathematical and computer mod-els composed of simulated brain regions


individual with fi xed censors may some-times exhibit different behaviours. As he suggests, ‘... the most creative individuals possess strong character-based controls (censors) but can, in unusual circumstanc-es, override their censors.’ As an example, he points to a man who normally refrains from stealing but would steal drugs if needed to save his wife’s life. Somehow, higher-level ‘executive’ controls must come into play in a way that enables an individ-ual to overcome censors that would other-wise fi x his or her character and behav-iour.

Taking the metaphorical connection with neural networks further – and mak-ing an intriguing connection with phys-ics – Levine goes on to suggest that ‘discon-tent’ could play a role in this higher-level ‘executive’ control, which monitors the overall needs of a person. Specifi cally, he suggests that discontent could be dynami-cally similar to noise, and thereby permit a person to move from one attractor to an-other – knocking the dynamical system from one character attractor to another. His discussion on pages ��–�� on ‘simulated annealing’ use the mathematics again to provide a useful metaphor and picture for thinking about character and its changes. There may be other brain ‘modules’, he suggests, that monitor the needs of the organism and their level of satisfaction, and institute noise like per-turbations to help the system seek better states. Finally, in his fi gure 11 , he offers an overall schematic diagram meant to depict a very rough ‘best guess’ of how the overall apparatus of the human brain works to-gether to make decisions.

The remainder of the paper then goes on to make some suggestions about how the dynamical and complex systems per-spective might help therapists in general. Again, Levine’s point isn’t to make any spe-cifi c predictions, or to explain any specifi c data. Rather, it is to explore how a dynami-cal metaphor – derived from the study of lower level neural processes and embodied

and connections between them. The net-works are designed with the goal of achiev-ing with computer simulations some re-sults that can be interpreted as analogous to some set of behavioral or neural or psy-chological data.

Construction of these models some-times goes ‘top down’ from observed hu-man or animal behavior. At other times it goes ‘bottom up’ from the physiology of neurons comprising the brain. Like tunnel building it starts at either end (with psy-chological data or with neurobiological data) and then refi ned to make it fi t better with the other end. The level of under-standing that is reached is often suffi cient to suggest experimental predictions at any of several levels (e.g. behavior – normal or pathological; single-neuron responses; EEG patterns; neurochemistry, or brain imaging).

The term neural network is also used for some nonbiological networks with indus-trial applications, and does not yet have a universally accepted defi nition. Perhaps the closest is the following [28] :

a neural network is a system composed of many sim-ple processing elements operating in parallel whose function is determined by network structure, con-nection strengths, and the processing performed at computing elements or nodes. … Neural network architectures are inspired by the architecture of bio-logical nervous systems, which use … processing elements operating in parallel.

What is meant by the nodes (or ele-ments ) in this defi nition? Nodes are most often identifi ed with large groups of neu-rons or with brain regions, whose bound-aries may not yet be precise. However, even though nodes seldom represent individual neurons, there are many models wherein some node activity patterns are similar to single-neuron electrical activity patterns in some brain regions relevant for the be-havior being modeled.

In the late 1960s, several modelers be-gan to develop principles for fi tting biolog-ically relevant neural network architec-tures to specifi c cognitive and behavioral functions. This led to models requiring partial verifi cation on both the physiologi-cal and the behavioral levels, and a ‘toolkit’ of modeling techniques and modules still in wide use.

Table 1. Two competing models of social organization, which Eisler [20, 21] calls the dominator and partnership models

Component Dominator model Partnership model

Social structure authoritarian structure of rigid rankings and hierarchies of domi-nation

egalitarian social structure of linking and hierarchies of actu-alization

Violence andfear

high degree of fear and socially accepted violence and abuse – from wife and child beating, rape, and warfare, to emotional abuse by ‘superiors’ in families, work-places, and society at large

mutual trust and low degree of fear and social violence, since these are not required to main-tain rigid rankings of domina-tion

Belief system relations of control/domination presented as normal, desirable, moral

relations of partnership/respect presented as normal, desirable, moral

Adapted from Eisler [21, p. 212] with the permission of the New World Library.


formally in the mathematical models of neural networks – might be helpful in un-derstanding human behaviour as deter-mined by the entire brain. Psychologists, psychiatrists and neuroscientists will ulti-mately judge how useful this suggestive perspective really is.

Mark Buchanan

In particular, Stephen Grossberg [29] developed differential equations using node activity variables and connection strengths between nodes, which were in-spired by the psychologist Clark Hull’s [30] notions of stimulus trace and associative strength . For each stimulus A , the stimulus trace x A ( t ) measures how active the mem-ory for A is at any given time t . For each pair of stimuli A and B , the associational strength w AB ( t ) measures how strong the sequential association AB is in the net-work’s memory at time t . Stimulus traces are analogous to short-term memory (STM) and associative strengths are analo-gous to long-term memory (LTM). The de-cay rate for LTM traces is set much smaller than the decay rates for STM traces.

Since STM storage capacity is bounded, stimulus traces often compete for storage by means of inhibitory connections be-tween the nodes representing those traces, whereby an increase in the trace x A ( t ) tends to decrease the value of the trace x B ( t ) and vice versa. Such an interaction is called lateral inhibition .

Associative learning and lateral inhibi-tion are two major organizing principles in the toolkit for making models of more complex cognitive phenomena. These principles and a few others, along with their functional signifi cance, are shown in table 2 . An example of a network that com-bines several principles is the adaptive res-onance theory (ART) network for categori-zation, which was introduced by Carpenter and Grossberg [24] , with many later varia-tions. Adaptive resonance networks have two layers of nodes that code individual features and categories, with learnable connections between layers in both direc-tions, and lateral inhibition between com-peting categories.

Principles such as those shown in table 2 refl ect general neural operations that are likely to occur, with variations, in different parts of the brain (for example, there can be lateral inhibition between representa-tions of different percepts, different cate-

gories, different emotions, different action plans, or different movements). Since the mid-1990s, network architectures have combined general toolkit principles with more direct physiological knowledge about specifi c brain areas and specifi c modula-tory transmitter systems.

The nodes in Grossberg’s differential equations typically interact by means of shunting excitation , proportional to the dif-ference of activity from a maximum value, and shunting inhibition , proportional to the difference of activity from a minimum value (such as 0). A typical example of these shunting interactions is the set of equations for arbitrarily many nodes con-nected via lateral inhibition . These were developed by Grossberg and Levine [31] to model transformation and storage of sen-sory STM patterns, and form the basis for more complex networks involved in the multidimensional personality models we discuss later [25, 26] , so are listed below:

� � � � � �1

,n

ii i i i i k

k

dx Ax B x f x x f xdt ��

1,…, n i �

(1)

where x i is the activity of the i th node, rep-resenting some sensory attribute or fea-ture; A is a decay constant; B i is the maxi-mum activity of the i th node, and the rela-tive sizes of B i represent attentional biases toward the attribute that the node repre-sents, and f is a monotone increasing func-tion (often a sigmoid function).

With this theoretical framework in mind, we examine brain mechanisms for

each of the gross modes of behavior la-beled fi ght-or-fl ight, dissociation, and tend-and-befriend. Then we return to an-gels and devils, interpreted as complex, context-sensitive schemata for selection and integration of behavioral modes.

Brain Mechanisms for Fight-or-Flight The early 20th Century physiologist

Walter Cannon [32] coined the term fi ght-or-fl ight to describe the body’s appropriate responses to stressful stimulation. His work described brain mechanisms for fi ght-or-fl ight involving activity of path-ways connecting the hypothalamus, the brain region most closely connected with visceral systems, and two endocrine glands, the pituitary and adrenal glands. These pathways, known as the hypotha-lamic-pituitary-adrenocortical (HPA) axis ,

Table 2. Summary of some important principles in neural network organization

Associative learning, to enable strengthening or weakening of connections by contiguity or probable causalityLateral inhibition, to enable choices between competing percepts, drives, categorizations, plans, or behaviorsOpponent processing, to enable selective enhancement of events that change over timeInterlevel resonant feedback, to enable reality testing of tentative classifi cations

Adapted from Hestenes [92] with the permission of Lawrence Erlbaum Associates.


are involved in the production of the hor-mone cortisol , which is typically released during stress. These systems are active in normal individuals during acute stress sit-uations, and changes in receptor proper-ties make them chronically active in indi-viduals who have been abused or other-wise undergone trauma [13] .

Another substance typically released during fi ght-or-fl ight responses is norepi-nephrine (NE), the neurotransmitter asso-ciated with arousal. In addition to the hy-pothalamic-pituitary-adrenocortical axis, the stress system, common to all mam-mals, includes the prime NE-producing nucleus in the brainstem, called the locus ceruleus . The stress system also includes parts of the amygdala and other loci in the limbic system, which process the degree of fearfulness associated with stimuli, and re-gions of the hypothalamus, especially a re-gion called the paraventricular nucleus (PVN) which is important for controlling endocrine secretion.

Figure 2 shows a very simplifi ed picture of these interactions. The biochemical pre-cursor to cortisol, known as corticotro-phin-releasing factor , in addition to being produced by the adrenal cortex, is utilized as a neural transmitter in parts of the lim-bic system and hypothalamus [33] . There is pharmacological evidence that cortisol signals reach the NE-producing locus ce-ruleus [34] , and that this nucleus in turn sends NE signals to the amygdala and hy-pothalamus. All those areas in turn gener-ate behavioral responses to stress (fi ghting or fl eeing) as well as responses of both the hypothalamic-pituitary-adrenocortical axis (endocrine) and the sympathetic au-tonomic nervous system (which affects the viscera). Thus a positive feedback loop tends to enhance and perpetuate the stress response once it gets going, unless the ex-ternal environment becomes substantially less stressful. In the case of chronic stress – such as childhood abuse – the system shown in fi gure 2 becomes more excitable

so that even mildly unpleasant events can generate activity in this loop.

Brain Pathways for Dissociation Perry et al. [13] found that chronic

childhood abuse does not always lead to a prevalence of fi ght-or-fl ight behaviors. In a subclass of abused children, predominant-ly females, it leads instead to dissociative behaviors which are opposite to fi ght-or-fl ight: they involve emotional withdrawal, disengagement, and trying to feel good.

The brain interactions involved in dis-sociative responses are less well worked out than those in fi ght-or-fl ight responses. Based on reviews of the data [13, 35] we conjecture that some of the same brain ar-eas are involved as in fi ght-or-fl ight re-sponses ( fi g. 2 ), but with differences in bio-chemical activation patterns. For example, dissociated individuals manifest less NE activity than those in a fi ght-or-fl ight mode. Yet in both dissociative and fi ght-or-fl ight patterns, cortisol levels are generally high. Also, for both patterns there are low levels of oxytocin , an important hormone for tend-and-befriend behavior.

Dissociative responses involve dysfunc-tions of the reward system, whose most im-portant neurotransmitter is dopamine . Drug addiction is an example that may of-fer clues to other dissociative responses. Koob and LeMoal list common themes in the study of all drugs of abuse. They note

Fig. 2. Part of the interactive feedback system between cortico-trophin-releasing factor (the precursor to cortisol) and NE stress-related systems in the brain. The basal and lateral nuclei of the amygdala receive inputs from the cortex and particularly respond to fear-inducing stimuli. These areas project to the central nucleus of the amygdala which projects to the hypothalamus and au-tonomic regions of the brainstem, including the locus ceruleus. Other parts of the limbic system which may be involved in these interactions are not shown here for simplicity. Arrows denote ex-citatory connections (reprinted from Eisler and Levine [11] with the permission of Kluwer Academic Publishers).


that these drugs tend to interact with the brain’s reward system in such a way that once a drug has become associated with reward, progressively more of the drug is needed to achieve the same state of reward. Figure 3 describes the reward system on which addiction operates.

Koob and LeMoal [36] review evidence that excess cortisol disrupts the proper functioning of the brain’s reward system by generating compulsive activity in a circuit that includes the prefrontal cortex and parts of the thalamus and basal ganglia. As a consequence of this neural activity pat-tern, behavior that once led to pleasure is repeated even after it leads to much less pleasure.

Brain Pathways for Tend-and-Befriend Caring and bonding responses involve

an intricate system of interacting brain ar-eas, hormones, and neurotransmitters. A peptide hormone called oxytocin is spe-cifi cally important for positive emotions relating to social and family connections.

Thomas Insel [37] and Insel et al. [38] have studied two species of North Ameri-can rodents that are closely related but have radically different social organiza-tion. These are the prairie vole , which is monogamous with strong male-female pair bonding and both parents involved in care of young, and the montane vole , which is promiscuous with fathers uninvolved

with young. They found that oxytocin at-taches to receptor molecules in reward-re-lated areas of the brain in the pair-bonding prairie vole but not in the promiscuous or non-bonding montane vole. Also, in female prairie voles, pair bonding – with the fi rst male they smell after reaching puberty – can be induced by direct injections of oxy-tocin, and abolished by drugs that reduce the amount of oxytocin [38] . This pattern of oxytocin binding to reward sites in the brain seems to carry over to other bonding mammals, including humans and apes [39, 40] .

Oxytocin administration in both male and female rats causes decreases in blood pressure and in the amount of the stress hormone cortisol [41, 42] . More generally, oxytocin reduces activity in the sympathet-ic nervous system , the set of brain connec-tions to the heart and endocrine glands ac-tivated during fi ght-or-fl ight.

The physiological antistress effects of oxytocin are known to occur in association with both lactation and sexual intercourse. Uvnäs-Moberg [41] reviewed evidence suggesting that oxytocin is also released by other forms of pleasurable social contact, such as mutual grooming in animals and supportive friendship in humans.

Eisler and Levine [11] proposed a neu-ral network theory of human tend-and-be-friend responses based on the simpler brains of voles and their involvement in male-female pair bonding. The theory builds on results showing that oxytocin and vasopressin , the two peptide hormones most important for pair bonding in these animals, both have different binding pat-terns in the brain of the pair-bonding prai-rie vole than in the brain of the non-bond-ing montane vole [38] . Also, the theory builds on results about gender differences [43] . Oxytocin is more associated with ma-

Fig. 3. Circuit diagram of the reward system. Key regions include parts of the nucleus accumbens, amygdala, and prefrontal cortex. Opioid peptides and dopamine modulate interactions between these regions. Drug addictions (and, we conjecture, other dis-sociative behaviors) involve dysregulation of the reward circuit, which is expressed as compulsive behavior of the loop between the cortex, nucleus accumbens, pallidum, and thalamus. Arrows denote excitatory connections, fi lled circles inhibitory connections (adapted from Koob and LeMoal [36], Copyright 2001, with per-mission from Elsevier Science).


ternal behavior and vasopressin with pa-ternal behavior, yet pair bonding can be abolished in either male or female prairie voles by drugs that block brain receptors for either of the two peptides. This suggests that both peptides are required for pair bonding in both sexes, and more recent re-sults clarify the complementary behavior-al roles of these two substances [44, 45] .

Our theory of bonding ( fi g. 4 ) is based on the assumption that brain regions that oxytocin and vasopressin bind more to in the prairie vole than in the montane vole are regions that (in both voles and hu-mans) play a role in bonding behavior. The key area for oxytocin binding is the nucleus accumbens , a part of the basal ganglia in-

volved in the dopamine-modulated stimu-lus-response system [38] . The key area for vasopressin bonding is an area called the diagonal band that produces the neu-rotransmitter acetylcholine , believed to be involved in selective attention [46] . The acetylcholine signal connects, among oth-er areas, to the hippocampus, the key area for consolidating STMs. These data sug-gest that oxytocin is related to the part of the bonding process that drives behavior via (social/sexual) reward, and vasopres-sin is related to the part of the bonding pro-cess that focuses attention on relevant stimuli (e.g. on the opposite-sex conspe-cifi c with whom the animal is forming a pair bond). The other parts of the network

of fi gure 4 (regions of hypothalamus, mid-brain, and basal ganglia) are inspired by a previous neural network model of how be-haviors can become conditioned due to unexpected rewards [47] .

The Orbitomedial Prefrontal Cortex and Choice Having separately outlined neural path-

ways for fi ght-or-fl ight, dissociative, and tend-and-befriend responses ( fi g. 2–4 ), we next address how decisions are made about what pathways to activate in what contexts. Evidence from various sources suggests that the orbital region of the prefrontal cor-tex, and the medial prefrontal region that adjoins it, subserve this decision process,

Fig. 4. Proposed network related to subcortical bonding effects of oxytocin and vasopressin. PPTN is the pedunculopontine tegmental nucleus, a part of the midbrain. Ventral pallidum is a part of the basal ganglia. Both of these areas, along with the lateral part of the hypothalamus and the nucleus accumbens, are known to be part of the neural circuit for processing rewards. Arrows between boxes represent excitatory (glu-tamatergic?) connections; fi lled circles represent inhibitory (GABAergic?) connections; semicircles represent modifi able connections (reprinted from Eisler and Levine [11] with the permission of Kluwer Academic Publishers).


along with pathways connecting those re-gions with the basal ganglia and thalamus. Other evidence suggests these decisions are infl uenced by context through links to the basal ganglia from the hippocampus. We start with detailing the role of the or-bitomedial prefrontal cortex.

The orbital and medial prefrontal cor-tex has long been regarded as the part of the human brain that most strongly medi-ates complex emotional responses. This has been suspected since the famous 19th Century patient Phineas Gage lost the abil-ity to make plans and respond appropri-ately to social situations after being injured in the orbitofrontal cortex by a railroad ac-cident in which an iron rod went through his cheek and out the top of his head. This region is unique in the extent of its connec-tions both to high-order sensory and as-sociation areas of the cortex and to emo-tion-related areas below the cortex [10] .

From Gage’s case and other patient studies [8] , as well as animal lesion studies, neuroscientists have reached a consensus that the orbitomedial prefrontal cortex forms and sustains mental linkages be-tween specifi c sensory events in the envi-ronment – for example, particular people or family and social structures – and posi-tive or negative affective states. This part of the prefrontal cortex creates such linkages via connections between neural activity patterns in the sensory cortex that some-how refl ect the infl uence of past sensory events, and other neural activity patterns in subcortical regions that refl ect innate or learned expressions of emotional states.

It seems plausible that the brain region mediating emotional valences attached to objects and classes of objects [48, 49] also mediates activation of large classes of re-sponses such as fi ght-or-fl ight, dissocia-tion, and tend-and-befriend. How might this occur?

The orbitomedial prefrontal cortex con-nects reciprocally with several subcortical brain areas that play roles in emotional regulation. One of them is the part of hy-pothalamus called PVN ( fi g. 2 ). Different parts of the PVN contain various hor-mones including oxytocin, vasopressin, and corticotrophin-releasing factor, the precursor of the stress hormone cortisol. The prefrontal cortex does not synapse di-rectly onto the PVN, but synapses onto an area called the dorsomedial hypothalamus that sends inhibitory neurons to the PVN [50] , as shown in fi gure 5 . These connec-tions from the dorsomedial hypothalamus to the PVN are mediated by GABA ( � -ami-no butyric acid ), the brain’s commonest in-hibitory transmitter, and infl uence selec-tive activation of one or another PVN hor-mone-producing subregion.

But a mechanism is still needed to translate positive and negative emotional linkages into action tendencies or avoid-ances – that is, into what this article calls angels and devils. This fi ts with the popu-lar idea of a gating system: a brain network that selects sensory stimuli for potential processing, and motor actions for potential performance. Frank et al. [51] (see also Newman and Grace [22] and O’Donnel and Grace [52] ) place the gating system in

pathways between the prefrontal cortex, basal ganglia, and thalamus ( fi g. 6 a). The link from the basal ganglia to the thalamus plays the role of disinhibition, i.e. allowing (based on contextual signals) performance of actions whose representations are usu-ally suppressed.

The most important gating area within the basal ganglia is the nucleus accum-bens. Many neuroscientists identify the nucleus accumbens as a link between mo-tivational and motor systems, and there-fore a site of action of rewards – whether these rewards come from naturally rein-forcing stimuli such as food or sex, learned reinforcers such as money, or addictive drugs [53] .

Clearly, then, infl uences on the nucleus accumbens from other brain areas are key to choices about which stimulus or action representations are allowed through the gate. Newman and Grace [22] identify two

Fig. 5. Part of the stress-regulating interactions between the prefrontal cortex and the hypothalamus [50]. PVN p /PVN m = Parvocellular and magnocellular part of the PVN. Different parts of the PVN are known to have neural connections with one another, but it is not known whether there is direct inhibition between these two parts or whether the inhibition between oxytocin and stress hormone systems operates through effects of these hormones on other areas such as the limbic system (reprinted from Eisler and Levine [11] with the permission of Klu-wer Academic Publishers).


major infl uences on that area: one dealing with context, the other with emotion.

The Hippocampus and Context Figure 6 b shows some infl uences on the

gating system of the nucleus accumbens. The infl uences from the hippocampus are particularly strong: O’Donnell and Grace [52] showed that active hippocampal con-nections can change single accumbens neurons from an inactive to an active state. Since the hippocampus encodes contextu-al associations for working memory, this

can be a vehicle biasing the gates in favor of contextually relevant stimuli.

As Newman and Grace [22] note, there is also a competing bias in favor of emo-tionally salient stimuli, regardless of the context. This is mediated by connections to the accumbens from the amygdala ( fi g. 6 b), which has long been considered an im-portant region for encoding affective va-lences of stimuli. The hippocampal inputs, associated with ‘cold’ cognition, operate on a slow time scale and promote selective sensitivity to a long-standing task. The amygdalar inputs, associated with ‘hot’

cognition, promote sensitivity to strong and short-duration emotional demands.

Both these inputs are relevant to angel and devil selection. The hippocampal in-puts, however, are more relevant to such meaningful choices as those based on eth-ical values or social adaptation.

Yet the neural processes these authors describe are embedded in a complex neu-ral milieu of predilections, values, and so-ciocultural infl uences that vary enormous-ly between individuals. Knowing that a hippocampal context can release a pre-frontal-accumbal behavioral pattern, we

Fig. 6. a Schematic representation of loops between several parts of the prefrontal cortex, basal ganglia (nucleus accumbens and ventral pal-lidum), and the thalamus. The nucleus accumbens serves as a ‘gate,’ selectively inhibiting the ventral pallidum and thereby disinhibiting tha-lamic excitation of contextually or emotionally relevant stimulus representations at the prefrontal cortex. b Some major pathways involving the nucleus accumbens. VTA = Ventral tegmental area, one of the two midbrain nuclei that produce dopamine (adapted from Newman and Grace [22] with permission from Elsevier Science).


still must ask about the long-term develop-ment of relationships between specifi c contexts and positive or negative valences for specifi c actions, i.e. of context-sensitive angel and devil classifi cations. And how might that process be infl uenced by what has been called the dance between genes and culture [54] ?

We now look at some personality clas-sifi cations manifesting the evolutionary development of angel and devil selection.

Levels of Devils and Changes of Angels The research psychiatrist Robert Clon-

inger has developed a remarkably system-atic, empirical, and developmentally based classifi cation of human personality ten-dencies and their role in mental disorders [23, 55, 56] . The cornerstones of Clon-inger’s formulation are the dimensions of personal temperament (which he sees as largely inherited) and of character (largely formed during development).

The temperament dimensions Clon-inger identifi es are ‘ harm avoidance (anxi-ety proneness vs. risk taking), novelty seek-ing (impulsiveness vs. rigidity), reward de-pendence (approval seeking vs. aloofness), and persistence (determination vs. fi ckle-ness)’ ( [23 , p. 177] emphasis mine). Build-ing on varying amounts of these traits while embedded in a family and a culture, individuals develop varying degrees of the three main components of character: self-directedness (relating to acceptance of the individual self); cooperativeness (relating to acceptance of other people), and self-transcendence (relating to acceptance of nature in general).

Cloninger and his coworkers relate the healthiest mental functioning to simulta-

neous high levels of all three character di-mensions (self-directedness, cooperative-ness, and self-transcendence). They iden-tify different types of personality disorders as defi ciencies in one or another of these dimensions. For example, moodiness is considered as a defi cit of self-directedness, whereas paranoia (or fanaticism) is a defi -cit of cooperativeness, and schizotypic (or disorganized) behavior is a defi cit of both those dimensions. A simplifi ed version of their schema is shown in the ‘character cube’ of fi gure 7 .

How do Cloninger’s character dimen-sions relate to the concepts of angels and devils as developed herein? His variables are defi ned in a way that is suggested not by brain measurements but by clinical pa-tient self-reports. Yet he established some robust correlations and factor analyses among these variables, which suggests that they might point the way to development of more precise, yet qualitatively analo-gous, dynamical system variables.

First of all, Cloninger’s three character dimensions seem closely tied to the three behavior classes of fi ght-or-fl ight, tend-and-befriend, and dissociation. High self-directedness combined with low coopera-tiveness and self-transcendence leads to an authoritarian personality (see fi g. 7 ), which exists in a perpetual fi ght-or-fl ight mode. High cooperativeness combined with low values of the other two dimen-sions leads to a dependent personality, a compulsive tending and befriending even when contexts call instead for defending one’s interests or for spending time alone. High self-transcendence with low values of the other two dimensions leads to a disor-ganized state, which some psychiatrists correlate with dissociative pathology [57] .

On the other hand, the healthy person-ality that Cloninger seeks is able to move between fi ght-or-fl ight, tend-and-be-friend, and dissociative responses when the context calls for each one of them. An individual with such a personality can

Fig. 7. The character cube: character subtypes emerging from interactions of dimensions of character (reproduced with permis-sion from the Center for Psychobiology of Personality, Washing-ton University, St Louis, Mo., USA).


fi ght or fl ee when his or her interests are in danger, can bond and network with others to create needed social supports, and can meditate or withdraw from the world when required to gather strength.

How do we describe personality profi les within a complex dynamical system?

The cube of fi gure 7 suggests treating each of eight personality profi les on the corners as attractors for the system. While Cloninger describes the attractors as points with 0 and 1 values for his three character dimensions [creativity is (1, 1, 1), moodi-ness is (0, 1, 1), melancholia is (0, 0, 0), et cetera], it is probably more accurate to think of each of them as multidimension-al variables representing connection strengths at many brain loci. Each attrac-tor embodies a different set of meta-rules for contextually sensitive angel and devil classifi cation – in other words, a different censor .

Such meta-rules are instantiated in the brain through the interaction of hippo-campal context, amygdalar affective va-lence, and prefrontal-accumbal action schemata (cf. fi g. 7 ). Later we discuss a dy-namical theory of Cloninger’s character

and temperament dimension by Sam Lev-en [27] , involving these and many other brain regions.

Yet independent of brain region assign-ments, one can apply to this personality domain some previously developed, more abstract dynamical theory of switching between attractors [25] . This theory in-cludes a quantitative criterion for deciding which of two attractors is more affectively desirable.

The most widely used mathematical technique for moving a dynamical system out of a suboptimal attractor and toward a more desirable attractor is called simulated annealing , as developed by Kirkpatrick et al. [58] . This consists of ‘heating’ the sys-tem with random noise when it is far away from its declared optimal state, then ‘cool-ing’ it by reducing or eliminating that noise when it is close to the optimal state. A role for noise in promoting self-organization and complexity had been proposed earlier by Atlan [59] .

Levine [25] suggested a neural network theory of self-actualization [19] based on a variant of simulated annealing ( fi g. 8 ). This theory involves attractor shifts in a

competitive ( on-center off-surround ) mod-ule; that is, each node sends excitatory sig-nals to itself and inhibitory signals to the other nodes. This type of module is de-scribed by a competitive dynamical system of equations, e.g. the previously mentioned equation 1 of this article. Cohen and Gross-berg [60] generalized these competitive equations to

� � � � � �1

, 1,…, nn

ii i i i ik k k

k

dx a x b x c d x idt �

� ��

�

(2)

where a i and d k are arbitrary nonnegative functions and c ik are positive constants such that c ik = c ki . Cohen and Grossberg proved that equation 2 always, as time goes to infi nity, asymptotically approach some steady state. This is because the system de-fi ned by equation 2 possesses a system en-ergy function ( Lyapunov function ) that is always nonincreasing along trajectories of equation 2 as time increases. The Lyapu-nov function is

� � � � � �1 0

ixn

i i i i ii

V x b d d� � ��

��

(3)

� � � �, 1

1

2

n

jk j j k kj k

c d x d x�

� �

Hence, the steady state approached is ei-ther a local minimum or the global mini-mum for the function V defi ned by equa-tion 3.

Levine [25] interpreted a competitive network such as the one defi ned by equa-tion 2 as encoding fundamental needs of the organism, which compete via lateral inhibition as in fi gure 8 . The function V is

Fig. 8. If the current state of the Cohen-Grossberg-based needs module has a larger energy function than some alternative state detected by the world model module, the net signal (excitatory minus inhibitory) to the creative discontent node is positive. The discontent node, when activated, in turn sends noise which per-turbs the needs module so it can move toward a different attract-ing state (adapted from Levine [25] with the permission of Law-rence Erlbaum Associates).


interpreted as an overall ‘distress level’ from unmet needs of the network or or-ganism. States with smaller values of V are affectively more desirable; hence, the glob-al minimum of that function is interpreted as an optimal state of the system.

This competitive module is supervised by a ‘world modeler’ module, possibly analogous to working memory areas in the dorsolateral prefrontal cortex [61] . The world modeler imagines and makes ‘cop-ies’ of various possible states of the need subsystem and calculates the Lyapunov function for each, in search of a state with a lower V (i.e. a state that is closer to opti-mal). If V of the current state, say x, is larg-er than V of some other projected state, say x ’, the combination of excitation from the needs module and inhibition from the world modeler produces a signal of mag-nitude V ( x ) – V ( x’ ) to a ‘creative discontent’ i module. When thus activated, the discon-tent module in turn sends random noise ii back to the needs module, which can move that module out of a suboptimal local min-imum.

Wandering between different minima can be instantiated in fi gure 8 by regula-tion of: (1) activities of the competitive needs module, in which tonic signals can move the module’s behavior toward either ‘winner-take-all’ (nonzero activity in only one node) or ‘stable coexistence’ (nonzero activity in many nodes), and (2) gain of signals from creative discontent activity to the noise signal generator.

Where might any of fi gure 8 be located in the brain? A plausible locus for the needs module is the set of mutually inhibiting hormonal generators in the PVN of the hy-pothalamus, shown in fi gure 5 . If there is a

clear ‘winner’ of the competition among PVN nodes, that winner could send a sig-nal activating one of the neural circuits for gross behavioral modes; for example, the stress-related PVN p could selectively acti-vate either the fi ght-or-fl ight circuit ( fi g. 2 ) or the dissociative circuit ( fi g. 3 ), whereas the bonding-related PVN m could activate the tend-and-befriend circuit ( fi g. 4 ). The discontent signal may be identifi able with some part of the amygdala. The effects of fronto-amygdalar connections, in addition to those arising from the world modeler module of fi gure 8 , could include control of the gain of the noise signal from the dis-content module. This suggestion comes from the clinical observation that frontally damaged individuals can express frustra-tion when their actions are ineffective, but this frustration does not lead them to change their actions [62] .

The amygdala is also heavily innervated by NE projections from the locus ceruleus ( fi g. 2 ). People severely defi cient in NE tend toward learned helplessness, that is, re-duced confi dence in their ability to control events [63] . Perhaps an intermediate NE level could generate a milder form of learned helplessness whereby a person feels confi dent only about satisfying a lim-ited set of needs. In the network of fi gure 8 , NE signals could directly affect the com-petitive needs module, making its dynam-ics more winner-take-all (i.e. only one or a few nodes have nonzero asymptotic activ-ity) with a low NE level, or more coexistent (i.e. most nodes have nearly equal asymp-totic activities) with a high NE level. That is, a larger NE level moves the module to-ward attracting states that satisfy a greater number of needs. This idea is based on Theorem 8 of Grossberg [64] showing that nonspecifi c excitatory signals to all nodes in a competitive on-center off-surround network can lead to such coexistent dynamics. iii

How can equation 2 for the needs mod-ule be modifi ed to incorporate the princi-ples of simulated annealing? To the right

hand side of each equation should be add-ed the effect of stochastic noise which cor-responds to ‘exploration’ of n -dimensional space, and can therefore be either positive or negative. The noise should have mean 0 and average amplitude that increases with a ‘temperature’ parameter [58, 65] that de-termines the likelihood of moving out of a local minimum. The temperature should be proportional to the difference V ( x ) – V ( x 0 ), where x = ( x 1 , x 2 , …, x n ) is the cur-rent state and x 0 = ( x 01 , x 02 , …, x 0n ) is the optimal state the world modeler detects. It should also be proportional to a variable gain N ( t ), which may be roughly identifi ed with initiative and can be infl uenced by the NE level, executive control, or external stimuli.

In simulated annealing applied to dis-crete-time neural systems [65] , each noise-induced change is ‘tested’ to see if it in-creases or decreases the Lyapunov func-tion V ( x ). If the change in x decreases V it occurs automatically, but if the change in-creases V the probability that the change is ‘accepted’ decreases exponentially with the change in V according to a Boltzmann dis-tribution from statistical mechanics. In our continuous-time system, we mimic this effect by letting the infl uence of noise decrease as dV ( x )/ dt increases, if that de-rivative is positive. The rate of exponential decay of the Boltzmann distribution should be slower for higher values of the tempera-ture T .

In equation 2, denote the right-hand side,

� � � � � �1

,n

i i i i ik k kk

a x b x c d x�

� ��

�

by F i ( t ). Then by the chain rule, because dx i / dt = F i ( t ) for each i , the derivative of V with respect to t is

� �1

.n

kkk

V F tx�

��

i In the study by Levine [25] this node was labeled ‘negative af-fect,’ but since then it was renamed because its long-term effects on mood may be positive. ii The same signal could be made chaotic instead of random with similar effects. iii What is the effect of NE levels that are too high? From psychiatric observation, too much NE can lead to hyperarousal and anxiety ([Allen Cahill, pers. commun., May, 2004] cf. fi g. 2 ).


This expression combined with the tem-perature and the Boltzmann distribution yields modifi ed equations such as

dtdxi

(4)

��

�

�

��

�

��

��

�

�

T

FxV

ttF

n

kk

kii

1

k )0),((max

exp)()(

��

where � i ( t ), i = 1, …, n , are the n indepen-dent components of a white noise process (each normally distributed with mean 0 and standard deviation 1);

� � � � � � � �1

;n

i i i i i ik k kk

F t a x b x c d x�

� ��

� �

T = ( V ( x ) – V ( x 0 )) N ( t ).

The right hand side of equation 4 denotes that if the Lyapunov function of the equa-

tions with noise added is increasing, the ef-fect of the noise decreases exponentially with the amount of the positive time de-rivative of V at a rate dependent on tem-perature T . In further research I will inves-tigate how different patterns of time chang-es in the gain parameter N ( t ) affect whether the system defi ned by equation 4 converges to the optimal attractor x 0.

In some cases [25] , the suboptimal local minimum is a global minimum for anoth-er Lyapunov function over some, but not all, nodes of the needs module. In person-ality terms, this means a mental state that satisfi es some but not all needs. Levine and Jani [26] investigated the effects of multi-ple needs, i.e. multiple biasing criteria in a lateral inhibitory neural network with equations (given in the Appendix) similar to equation 1 of this article. In equation 1, the maximum activity B i of each node rep-resents an attentional bias toward attri-butes the node represents. Levine and Jani [26] made the biases time varying and had

two nodes, one node representing selfi sh-ness and the other empathy.

If the attentional biases B i are fi xed over time, equation 1 constitutes a special case of the Cohen-Grossberg equations (equa-tion 2) and therefore always converges to a steady state as time increases. Yet Levine and Jani [26] found that if biases are made to shift over time between favoring selfi sh-ness (as in fi g. 9 a) and favoring empathy (as in fi g. 9 b), periodic or chaotic asymp-totic behavior can occur ( fi g. 10 a) The change from one regime to another occurs when either variable (selfi shness or empa-thy node activity) gets below a fi xed thresh-old. Periodic oscillations are obtained be-tween the two variables, with selfi shness and empathy node activities each becom-ing larger at regular intervals.

Levine and Jani [26] achieved a steady state balanced between selfi shness and empathy by adding to the network a third node representing an idealized frontal lobe executive ( fi g. 9 c). This frontal node exerts ‘mediation’ which ensures that neither the self-interest nor the empathic claims are neglected for too long. The infl uence of the frontal mediation parameter causes biases in favor of empathy or selfi shness to shift when either variable is too low, but to shift in a gradual rather than a sudden manner ( fi g. 10 b).

Leven [27] developed a comprehensive biologically based theory of Cloninger’s four temperament dimensions and three

Fig. 9. Network for tug of war between selfi shness and empathy as previously reported [26]. Competition occurs in this network between selfi shness and empathy, because each node excites it-self and inhibits the other. Attentional biases are represented in the fi gure by a difference in boldness between the two circular self-excitatory arrows. a Network with a bias toward selfi shness. b Network with a bias toward empathy. c Network with the ad-dition of a frontal lobe executive. This executive receives signals from both the selfi shness and empathy nodes. Based on the size of those signals, the frontal node either adds to or subtracts from the amount of bias toward selfi shness or empathy (as indicated by arrows or fi lled circles going to the darker curved arrows).


character dimensions. His objective was to develop a neurobiological theory of the creative process. While Leven’s goals were somewhat different from ours, we can adapt some of his formulation of the char-acter and temperament dimensions in combination with Levine’s theory of at-tractor shifts [25] . The combined neurody-namic theory suggests how humans can achieve higher levels of devils and con-structive changes of angels.

Table 3 shows the brain areas and trans-mitters that Leven [27] associated with the different dimensions. These were based on a wide range of biobehavioral data and analogies with other existing psychologi-cal theories of creativity. Leven included tentative suggestions for neural network structures that might represent each of the seven dimensions shown in table 3 .

Fig. 10. a Oscillatory interaction between selfi shness (dashed line) and empathy (solid line) node activities. Biases in the net-work shift whenever either node activity gets too small. b Graph of ‘selfi shness’ and ‘empathy’ activities that achieve steady states with the addition of a frontal executive node.

Table 3. Theory of brain areas and transmitters relating to Cloninger’s temperament and character dimensions

Dimension Brain system Relevant stimuli Behavioral response

TemperamentNovelty seeking dopaminergic novelty pursuitReward dependent noradrenergic reward workHarm avoidant serotonergic punishment avoidancePersistent GABA/prefrontal hippocampus ambiguity tolerance frustration resisting

CharacterSelf directed orbitofrontal/D1 dopamine system goal focus internal locusCooperative dorsolateral prefrontal/oxytocin other’s affect empathySelf transcendent medial frontal/endorphin global good selfl essness

Adapted from Leven [27] with the permission of Lawrence Erlbaum Associates.


Cloninger also laid out a theory of char-acter development over the course of life. In the healthiest people, the development is toward high levels of each of the three character dimensions. In others, different pathologies may emerge at different stages, with the ‘highest’ pathology being a judg-mental dualism stemming from insuffi -cient self-transcendence. Cloninger’s theo-ry partly parallels other investigators’ the-ories of moral development [16, 66, 67] .

As table 3 indicates, developing higher levels of character (and thereby more com-plex angels and devils) is likely to depend on several parts of the prefrontal cortex. Prefrontal, particularly its dorsolateral re-gion, is the last part of the cortex to develop fully in adolescence [11, 16, 68] . Yet if we return to the brain network interactions depicted in fi gure 6 , it seems clear that all angels and devils, both the primitive ones of childhood and the more abstract ones of adulthood, have a fi nal common pathway. All the ‘angel behaviors’ go through, and all the ‘devil behaviors’ are actively barred from, the gates at the nucleus accumbens.

The hippocampus activates a represen-tation of the current context, which in turn activates the angel and devil representa-tions relevant to that context. But the hip-pocampal-accumbens links are not the likely site of the learned affective associa-tions. The hippocampus is not an area for LTM, but for short- and intermediate-term working memory [69, 70] . During those memory stages, the hippocampus is acti-vated at specifi c locations by the context and then reinstates neural activity pat-terns at cortical locations relevant to that context. Longer-term storage occurs at cortical sites: in the case of stored affective valences, this is likely to be at connections from the orbital prefrontal cortex to the amygdala (see ref. 71] for a computational model). The changes that actually affect motor behaviors (‘do’ and ‘don’t’ instruc-tions, approach toward or avoidance of an object) are likely to occur at connections from the amygdala back to the medial pre-

frontal cortex and also from the orbitome-dial prefrontal cortex to the nucleus ac-cumbens.

How, in human development as Clon-inger describes, does the nature of the neu-ral representations associated with posi-tive or negative valence become gradually more complex? These representations are likely to be located at all areas of the pre-frontal cortex. Dehaene and Changeux [72] proposed that the semantic and working memory areas of the dorsolateral prefron-tal cortex serve as a generator of diversity , that is, a creator of different possible rules for action. The affective circuits in the or-bital prefrontal cortex and amygdala then ‘censor’ these possible rules based on the rewards or punishments the system has re-ceived from following these rules. A lay-ered neural network has been utilized to simulate data whereby monkeys learn a complex rule after a simpler rule is tried and fails to predict rewards [73] .

But developmental changes toward more complex angels and devils are not al-ways total or permanent. They may be re-versed under stress, or they may depend on a specifi c mood or context for their mani-festation. The next section suggests neural dynamical mechanisms for such ‘negative’ as well as ‘positive’ growth in an individu-al’s personality. Then the concluding sec-tion suggests roles for psychotherapy in such growth within a framework of com-plex systems.

The Whole (Escher) Picture Now let us revisit the concept of neural

censors: large, abstract collections of inter-related angels and devils. To that end we ask the following meta-questions:

(1) Each corner on the cube of fi gure 7 (which also represents an attractor for a mental state network such as that of fi g. 8 ) can be interpreted as a censor. It represents a tendency to engage in certain specifi c be-haviors, in particular contexts, and avoid certain other behaviors. In complex system terms, what is the relationship between the

neural representations of these censors and the neural representations of the spe-cifi c angels and devils the censors com-prise?

(2) What are the neural mechanisms by which stress leads to reversal of the process fi gure 8 depicts? That is, how does stress move the system away from the creative corner of Cloninger’s cube ( fi g. 7 ) and to-ward less adaptive attractors on other cor-ners? Can our complex dynamical system perspective tell us what interventions in an individual’s life (by the individual her/himself, or by others such as psychothera-pists) can most effectively counteract such mental reversals?

With regard to question 1, connections between angels, devils, and censors, fi rst note that the censorship discussed herein includes the interoceptive or visceral cen-sorship Nauta [10] proposed, but also in-cludes cognitive informational elements. The states represented by each attractor of fi gure 8 generate rules for action or nonac-tion that combine visceral and cognitive processes.

Leven [27] ascribed the functions of self-direction, cooperation, and self-tran-scendence primarily to the orbital, dorso-lateral, and medial (in particular, anterior cingulate) regions of the prefrontal cortex, respectively ( table 3 ). These parts of the cortex are not isolated ‘centers’ for these functions (in the phrenology sense), but extensively interconnected [74, 75] . Lev-en’s theory suggests that each of Clon-inger’s eight personality profi les (creative, fanatical, disorganized, moody, dependent, organized, autocratic, and melancholic; see fi g. 7 ) represents a different attracting state of the dynamical system involving all three prefrontal areas and the connections between them.

From fi gure 6 , the major brain networks for encoding the content of specifi c angels and devils are the loops between the or-bital prefrontal cortex, the nucleus accum-bens, and the mediodorsal thalamus. To make loop activity patterns into actual an-


gels and devils, one needs also to include contextual gating via hippocampus and af-fective gating via amygdala.

Once again, the brain region where the ‘censor’ loops (orbitofrontal, dorsolateral, and cingulate) and the ‘angel/devil’ loops (orbitofrontal, mediodorsal thalamus, and nucleus accumbens) intersect is the orbi-tofrontal cortex. This is why individuals damaged in that region, such as Damasio’s patients [8] and their 19th Century precur-sor Phineas Gage, cannot develop behav-ioral controls appropriate for their charac-ter. Such controls do develop in most people who are not brain damaged – both the most creative individuals and those whose per-sonalities land at other corners of fi gure 7 .

In fact, the most creative individuals possess strong character-based controls (censors) but can in unusual circumstanc-es, override their censors. This can happen in contexts of strong cooperative needs (conjectured to be expressed via connec-tions to orbitofrontal from dorsolateral prefrontal). Kohlberg [66] reported a man who normally refrains from stealing but would steal drugs if needed to save his wife’s life. It can also happen in contexts of strong self-transcendence (conjectured to be via connections to orbitofrontal from anterior cingulate) – as when a mystical experience overrides censors against out-ward emotional expression.

Hence the orbitofrontal cortex links specifi c angels and devils to character cen-sors, as well as to contexts. Levine et al. [71] developed a neural network model, using shunting nonlinear differential equations, that simulates affectively infl uenced choic-es on a gambling task. In their decision net-work, a superfi cial layer of the orbitofrontal cortex interacts with the thalamus and nu-cleus accumbens, whereas a deeper orbito-frontal layer interacts with dorsolateral prefrontal and anterior cingulate. Both lay-ers of the orbitofrontal cortex have learn-able connections with the amygdala: these connections are sites where affective va-lences of choices (e.g. between two gam-

bles) can be learned from the positive or negative consequences of making those choices. The nucleus accumbens nodes are connected with each other by mutual inhi-bition; hence, this layer serves as a locus for choices between alternative actions. If this model is accurate, the connection of angels and devils with censors requires not only an intact orbitofrontal cortex but also in-tact connections between layers of that part of cortex.

Some of the same connections of the prefrontal cortex, amygdala, nucleus ac-cumbens, and thalamus are involved in our network for linking angels and devils with censors ( fi g. 11 ). This provides a mecha-nism whereby activating a specifi c censor (e.g. the censor against challenging hierar-chies or the censor against harming peo-ple; cf. table 1 ) strengthens the angels and devils it is associated with. Conversely, ac-tivating a specifi c angel or devil (e.g. the devil of avoiding non-marital sex or the angel of helping a homeless person) strengthens the censors it is associated with. (Note: the same angel or devil can be associated simultaneously with two con-fl icting censors! That case has implications for psychotherapy and is treated in the next section.)

Since censors are more abstract than angels and devils, I conjecture that the neural structures representing those types of constructs are connected by some neu-ral architecture with interconnected ‘high-er’ and ‘lower’ layers. One such network ar-chitecture is Carpenter and Grossberg’s ART [24, 76] . The ART network was origi-nally designed for sensory pattern catego-rization, but the theory it employs is appli-cable to many parts of the brain and many cognitive systems, specifi cally to connec-tions between any two subsystems at dif-ferent levels of abstraction. The higher-lev-el subsystem classifi es vector patterns of activities of the lower level subsystem. The different category nodes in ART compete with one another via lateral inhibitory con-nections similar to those described in

equation1. ‘Adaptive’ refers to the continual updating of these classifi cations through experience-dependent connection chang-es at both ‘top-down’ and ‘bottom-up’ con-nections between the two levels. Thus ART combines the two key network principles of lateral inhibition and associative learn-ing ( table 2 ).

Figure 11 includes a possible schema for ART-like connections between angel/devil and censor subnetworks, showing the rel-evant brain areas. The relative strengths of different interactions within the network of fi gure 11 in turn infl uence the probabil-ities of the different gross modes of behav-ior (fi ght-or-fl ight, dissociation, and tend-and-befriend) shown in fi gures 2–4 , as discussed below.

With regard to question 2, stress effects and reversals, there is evidence from hu-man and animal studies that many forms of stress (e.g. physical restraint, exposure to a predator, or social abuse) have short- or long-term effects on various brain structures [77, 78] . Chronic or severe stress tends to reduce neural plasticity in the hip-pocampus, the site of consolidating mem-ories for objective information. At the same time, stress increases neural plasticity and enhances neural activity in the amygdala, a site of more primitive emotional process-ing.

What might be the implications of this hippocampus/amygdala complementarity for decision making, and thereby for angels and devils?

Recall from fi gure 6 b that the hippo-campus and amygdala both send inputs to the ‘gates’ at the nucleus accumbens, the likely fi nal common pathway for angels and devils. The hippocampal inputs bias accumbens gating toward stimuli or ac-tions that are appropriate for the current context. The amygdalar inputs, on the oth-er hand, bias gating toward stimuli or ac-tions that evoke strong emotions – wheth-er such emotions arise from rational cog-nitive appraisal or from more primary, bodily based desires or fears. Hence, pro-


Fig. 11. Possible (simplifi ed) neural circuit connecting angels, devils, and censors. (i) The ART module on the left connects attributes and cat-egories of possible actions, with the categories coding vector patterns of attribute values which are modifi able by bottom-up and top-down connections. Two of the attributes are emotional salience and task relevance. Relative weight of attributes can change with context: amygdalar activation (increased by severe or persistent stress) selectively enhances the weight of salience, whereas hippocampal activation (decreased by stress) enhances the weight of relevance. Blue denotes connections that are weakened by stress; red denotes connections that are enhanced by stress. (ii) The categorization ART module ‘lifts’ to another ART module connecting angels and devils (i.e. simple behavior rules) with censors (categories of behavior rules). Part of the amygdala, through conjunction of attribute signals from the cortex and affective valences (+ or –) from the hypothalamus, encodes attachment of positive or negative valence to attributes of behaviors. Part of the orbitomedial pre-frontal cortex likewise encodes attachment of positive or negative valence to categories of behaviors. (iii) The striatum, infl uenced both by emotional salience and task relevance, selectively activates (via the direct pathway to thalamus) or inhibits (via the indirect pathway to thala-mus) motor implementations of specifi c actions at the anterior cingulate. (iv) The anterior cingulate and deeper layers of the orbital prefrontal cortex exert top-down executive control on both ART modules. This executive system responds to context and thus tends to enhance activities of task relevance nodes. Note that the amygdala plays two separate roles in the network, as explained in the text.


longed or intense stress might shift the bal-ance of the gating away from hippocam-pus-mediated actions based on context ap-propriateness toward amygdala-mediated actions based on short-term emotional sa-lience. This would bias the organism away from the tend-and-befriend behaviors de-scribed in fi gure 4 , and toward either the fi ght-or-fl ight behaviors of fi gure 2 or the dissociative behaviors of fi gure 3 .

Thus far, most data about stress effects (in rodents, monkeys, or humans) have dealt with memory consolidation, not de-cision making. The neural mechanisms for stress effects on memory involve long-term plastic changes in synapses primar-ily at two hippocampal regions called CA3 and CA1 , whereas the output of the hippo-campus to the nucleus accumbens action gate ( fi g. 6 b) is from a different hippocam-pal region called the subiculum . But how information is represented in memory strongly affects decisions based on that in-formation [79] . A plausible neural sub-strate for such infl uence of memory on de-cision making involves well-known neural projections from CA3 to CA1 and thence to subiculum [70] . Stress-related alterations of neuronal structure in CA3 and CA1 could be transmitted to the subiculum, and thereby reduce overall hippocampal infl u-ence on selective gating at the nucleus ac-cumbens.

This is a mechanism by which stress can lead to either temporary or permanent re-versal of the path toward creativity in Clon-inger’s schema for character development ( fi g. 7 ). As fi gure 11 indicates, there is feed-back between specifi c angels and devils (i.e. decisions about what to do and not to do) and censors (i.e. angel/devil classifi ca-tions which help to form character). This means that more ‘amygdala-based’ instead of ‘hippocampal-based’ angels and devils will tend to lead to less creative censors.

Recall that the interaction of angels/devils and censors in fi gure 11 is based on the interaction of attributes and categories in ART [24] . Shifts between ‘hippocampal’

emphasis under low stress and ‘amygdalar’ emphasis under high stress might be mod-eled by a variant of ART that includes at-tribute-selective attentional biases. Such an attentionally biased ART network was utilized by Leven and Levine [80] in their model of consumer preference shifts (spe-cifi cally, New Coke’s unpopularity in the market after it was favored over Old Coke in taste tests); in their network relative weighting of different attributes (sweet-ness versus familiarity) changes with con-text (taste test vs. market).

The shift in relevant attributes enables stress to shift character away from the ba-sin of attraction of the creative corner in Cloninger’s cube and toward one of the other corners. For example, a sensitive, self-transcendent artist who has chronic diffi culty with focus may remain in or near the creative state during a good period but then be driven by a major stress into the moody state of lowered self-direction. Or a practical person who has struggled to learn to slow down and ‘smell the roses’ may be pushed back by such a stress into the organized state of lowered self-transcendence. Finally, a self-directed person who has struggled to get along with his or her neighbors may be pushed back into the fanatical state of lowered cooperativeness. All these attractor basin shifts could be ir-reversible if the stress is chronic (as with abused children [13] ) but reversible if the stress is quickly relieved.

In fi gure 11 the amygdala plays an am-biguous role. It is selectively activated in stressful situations where current strong emotions ‘fl ood’ the system and executive control is weakened. Yet the amygdala is also shown as a key area for valuation of stimuli or actions in general, regardless of the stress level. This ambiguity refl ects a controversy among neuroscientists as to whether this brain region is primarily spe-cialized for negative emotion or is involved in emotion, affect, and valuation in general [81] . A more sophisticated neural network theory, in progress, may resolve this con-

troversy. Such a theory would consider complementary functions of different amygdalar subregions and of right versus left brain hemispheres. It would refl ect the hypothesis that stress leads to explosive activity in the amygdala, but not optimal function of that region. Prefrontal execu-tive signals could have a net inhibitory ef-fect on amygdalar activation but at the same time sharpen the selectivity of amyg-dalar responses. This supports an outlook closer to Spinoza’s than to Descartes’: ra-tional executive control modulates emo-tions rather than suppressing them [82] .

How to Repaint the Picture Can the last section’s complex neural

system analysis yield insights about effects of different types of interventions in the individual’s life (or even the life of society as a whole)? Which interventions increase the likelihood of moving the target toward the creative attractor – the censor whose angels most enhance life and whose devils least destroy it?

First let us return to our starting point, the angel and devil metaphors from immu-nology [5] . The immune system shares some of the brain’s plasticity: at least it can be classically conditioned under infl uenc-es from the brain [see ref. 83 for a review]. Yet the analogy between the nervous and immune systems is far from perfect. The main function of the immune system is protection of the self. The brain includes protection (i.e. survival) of its major func-tions, but is also involved in reproduction. The reproductive (sexual and parenting) instincts, in turn, generate a complex sche-ma of social functions that promote coop-eration and, sometimes, love [16, 84] . Moreover, human beings have motivations not just for survival but for well-being: self-actualization, pleasurable stimulation, mastery, and meaning all can be powerful motivators [11, 18, 19] . Neither reproduc-tion nor self-actualization has a clear ana-log in the immune system.


Now what about the ‘fi gure-ground’ problem Cohen introduced in his Escher painting analogy? Do humans and societ-ies differ in their emphasis on behavioral angels (collectively) versus devils (collec-tively)?

Let us revisit our earlier discussion of liberal and conservative religions. It is tempting to call the liberal outlook ‘angel oriented’ and the conservative outlook ‘devil oriented.’ Yet close examination does not bear this out. Cloninger’s temperament dimensions ( table 3 ) include reward de-pendence, which is somewhat analogous to angel orientation, and harm avoidance, analogous to devil orientation. Yet the sta-tistical data analysis Cloninger and his col-leagues performed found those two tem-perament dimensions to be independent, not opposites. Moreover, their character di-mensions ( fi g. 7, table 3 ) emerged from the same analysis and were not positively or negatively correlated with any of the tem-perament dimensions.

Hence the creativity corner of fi gure 7 is not more or less devil or angel oriented than any of the other corners of that fi gure. It simply has different devils and different angels. This supports Cohen’s contention that:

The devils, like the angels, demand due attention. Harmony in and beyond the picture is proper atten-tion to the right subject at the right time, combined with a suitable response [5 , p. 50]

The need to be cognizant of both angels and devils – and, just as important, to con-tinually create ever more adaptive angels and devils throughout life – poses chal-lenges for anyone seeking to intervene in his or her own life or another’s [85, 86] . I next discuss some of these challenges, framing them (for defi niteness) in the con-text of psychotherapy.

The fi rst challenge for therapists is to strike the right balance (which is different for different clients) between angel and devil orientation. The early stages of Freud-

ian psychoanalysis were sometimes re-garded as devil oriented, focusing on the need for a strong censor (superego) to sup-press dangerous sexual or aggressive im-pulses [87] . Humanistic psychologists such as Maslow [19] saw their own ap-proach, by contrast, as angel oriented: en-couraging the individual’s impulses to be creative and to develop her or his potential. Yet there are also angel-oriented strains within the Freudian tradition. For exam-ple, an alternative to suppression is subli-mation – redirecting energy associated with socially censored impulses to other creative activities that are socially accept-able.

This suggests that a practitioner work-ing in almost any psychotherapy tradition can emphasize either angels or devils or both. But Cohen’s comments about the Escher picture [5] suggest that neglecting either angels or devils for too long is un-healthy for the client. For example, in the United States in the late 20th Century, many well-meaning therapists tried to lib-erate clients from overly restrictive family and religious ties. In the process those therapists oriented completely toward the client’s angels and neglected devils. This often had the unintended effect of enhanc-ing the client’s focus on self-interest to the neglect of community obligations or reci-procity – thereby, indirectly, weakening so-cial bonds necessary for the client’s wel-fare.

This is the danger of interventions that are not intellectually founded in the com-plexity of human dynamics. The theoreti-cal biologist and philosopher Henri Atlan [88] shows that the current science of com-plexity can impact on ethics and human relations in a more constructive manner than the ‘nothing but’ reductionism in-spired by earlier science. His philosophy of complexity is infl uenced by Spinoza’s mind-body monism [9] , just as reduction-ist approaches had been infl uenced by Descartes’ mind-body dualism. As the sys-tem-oriented psychoanalyst Ana Maria

Aleksandrowicz [89 , p. 59] notes, Atlan’s approach to complexity enables ‘overcom-ing dichotomies without slipping into sim-plifying generalizations: a keen perception of the meaningful weight to be assigned to several levels of the description of reality’.

Specifi cally, this suggests that the thera-pist needs to mediate between two sets of rules, one based on self-interest, the other based on reciprocity. Such a mediating role is analogous to the role of the prefrontal node in Levine and Jani’s [26] neural net-work model that synthesizes two indepen-dent action criteria such as self-direction and cooperativeness (see the Appendix).

The second challenge for therapists is to employ different interventions for defi cits in different character dimensions. Many therapists, instead, tend to treat everyone according to the principles of one school (Freudian, Gestalt, Jungian, cognitive, et cetera). Whereas any school of thought can be used creatively and sensitively, too often reliance on a single school becomes a ‘one size fi ts all’ application – analogous to the work of automobile mechanics called ‘bat-tery specialists’ or ‘transmission special-ists’ because they treat a wide range of car problems by repairing a single part.

Cloninger [55] lists types of therapy that tend to concentrate on each of the di-mensions (for self-directedness, psycho-analysis and cognitive-behavioral tech-niques; for cooperativeness, logotherapy and Rogers’ branch of humanistic psychol-ogy; for self-transcendence, Jungian psy-chology). Even if this author’s mapping of therapy schools to character dimensions is oversimplifi ed, it suggests the need for a more comprehensive theory than any of the current schools provide. Skillful thera-pists make those kinds of changes intui-tively, but would be aided by a hospitable, biologically sound theoretical formula-tion.

The third challenge is that different cen-sors can share some of the same angels and devils. As fi gure 11 indicates, there is feed-back between angels/devils and censors, so


that any behavioral pattern of approach or avoidance within a specifi c censor tends to reinforce that censor in general – thereby, indirectly, reinforcing other angels and devils that are part of that censor. Hence, if a therapist works on switching a client from a moody or organized or fanatical censor ( fi g. 7 ) to a creative censor, some be-haviors common to both the old and new censors may adventitiously strengthen the old censor. Levine and Leven describe an example:

... suppose one became, as a single person, overly cautious in entering sexual relationships, which led to blocks against creativity in other areas of life. Then suppose one gets into a good marriage, and AIDS is epidemic. Caution in sex outside of marriage is now appropriate, for marital stability and health of the two partners. But on some brain level, the cur-rent appropriate behavior reinforces the earlier ti-midity which suppressed creativity [90 , p. 209].

Again the therapist encountering such patterns can benefi t from a complex sys-tem understanding. A constructive re-sponse combines awareness that the client has not yet fully moved toward the entire set of behaviors associated with the more creative pattern and respect for how much he or she has accomplished so far in mov-ing toward it.

Using an academic metaphor, this can be called ‘giving the client partial credit’. iv The sensitive therapist (or anyone else) also needs to give partial credit if someone has reverted under stress to a behavior pat-tern he or she had previously outgrown in a less stressful situation. Whether people should be given credit for skills they have learned incompletely is a controversial is-sue in the psychology of human decision making [see ref. 79, 91 for summaries]. The pioneering decision psychologist Amos Tversky argued that a skill cannot be con-

sidered learned until it can be performed without undue effort in any context and with minor task variations. By contrast, the evolutionary psychologist Gerd Gigerenzer argued that all prevailing behavioral re-sponses are adaptive because they were se-lected for in evolution. My own position, which the authors of these summaries share, falls between these two opposites. It says that not all behavior is adaptive, but decision-making skills that have once been learned and then lost under stress can be reacquired if stress is reduced or new cop-ing mechanisms learned. Moreover, the re-acquisition is more rapid if the individual attends to her or his previous learning.

What does this mean for the neurody-namics of wandering in character space between attractors on Cloninger’s cube? It suggests a mechanism whereby explicit memory of a previous period of time in the attraction basin of any of the attrac-tors (e.g. the creative one) biases later searches toward that basin. Such directed searching utilizes a system of self-moni-toring, centered in the complex-working memory regions of the prefrontal cortex (particularly its dorsolateral region). This system is a further elaboration of the one that generates imagined representations of alternative states in the ‘self-actualiza-tion’ network of fi gure 8 [see ref. 86 for more theory].

Memory for past states suggests the mathematical concept of hysteresis . Hys-teresis means that the current location in the n -dimensional space of character/cen-sorship does not uniquely determine fu-ture locations. This too has implications for therapy. Current stresses may inhibit the client’s memory of a past creative pe-riod. In those cases, the therapist can sometimes relieve that stress and thereby disinhibit the memory.

Our complex system analysis suggests many ways that therapy, and every other human interaction, profoundly affects the nature of our angels, devils, and censors. But Irun Cohen is right that both angels

and devils will always be present and need attention. Both are aspects of every god or goddess humans have ever imagined. Lib-eral and conservative, male and female, Western and Eastern, may differ in our genes but not in our aspirations, and the brain system that embodies such aspira-tions is our common heritage.

Appendix: Equations of Levine and Jani for Selfi shness and Empathy

Let x 1 be the activity of the selfi shness node and x 2 be the activity of the empathy node. For the simu-lations shown in fi gure 5 , these two variables satis-fi ed the differential equations

(5a)� � � �1

1 1 1 1 20.5 0.2 dx x B x sig x xdt��

� � � �22 2 2 2 10.5 0.2

dx x B x sig x xdt��

(5b)

where B 1 and B 2 denote bias weights (for selfi shness and empathy, respectively), and sig is a sigmoid function. Specifi cally, sig (x) is fi rst set to equal

� �1

1 exp 20 10x� � ��

then truncated to 1 if that function value is greater than 1, and to 0 if that function value is less than 0. Initial values of x 1 and x 2 are set to random amounts between 0 and 1. B 1 = 10 and B 2 = 1 until the value of x 2 dips below 1.2, at which time the biases reverse. After that time B 1 = 1 and B 2 = 10 until x 1 dips below 1.2, at which time the bias reverse again, and the cy-cle goes on. The three terms constituting the right-hand side of each differential equation 4a and 4b represent, for i = 1, 2, exponential decay of activity (–0.5 x i ); self-excitation strengthened by high atten-tional bias ( b i – x i ); and inhibition from the opposite node (–0.2 x j , where j 0 i ).

The simulations of fi gure 7 were also based on equations 5a and 5b, except that the biases them-selves changed gradually over time according to an-other set of differential equations:

(6)i ii

i i

Frontal for xdBFrontal for xdt

��

� ��

1, 2.i �

Frontal is a parameter measuring the strength of prefrontal executive node activity, and in these sim-

iv A more colorful metaphor for giving others a break is due to the neural network pioneer Warren McCulloch. When criticized for not having developed completely airtight theories, McCulloch was apt to respond: ‘Don’t bite my fi nger, l ook where I am pointing.’


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ulations is fi xed at a value of 10. � i is a threshold that changes over time according to the equation

(7)0.2 0.10 0.1

i ii

i i

for xdfor xdt

��

� ��

The values of the � i increase while the system is far from its equilibrium value and then stay constant when it is close to equilibrium, where both x i values are suffi ciently high, indicating coexistence.

Acknowledgment

I wish to acknowledge the assistance of several friends who helped nourish my angels and rein in my devils – notably Ana Maria Aleksandrowicz, Ri-ane Eisler, Nilendu Jani, Sam Leven, Lorraine Levine, and David Loye.


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