What place has learning in the analysis of emotion?

In the initial parts of this book the internal and external expressions of emotions were treated as if they are fixed both in form and in terms of adequate releasing stimuli. However, the previous chapter shows that the exact form of adult emotional reactions depends on early experience – and hence in some sense on learning. Learning is also a critical factor in determining adequate stimuli for many organisms. It will be remembered from Chapter 2 that the adequacy of a stimulus for the release of a state-dependent reflex depended on the interpretation of the stimulus by the animal rather than any gross aspect of the physical characteristics of the stimulus complex.

In this context the study of animal learning is important for two different reasons. First, it gives us a way of studying scientifically (and hence in the terms of the present book biologically) the modifiable aspects of the control of emotional behaviour, which are not amenable to comparative analysis because they differ from individual to individual even within the same species. Second, it provides us with a means of assessing the internal states and processes which give rise to emotional behaviour and the nature of the adequate stimuli which give rise to those states. In this sense learning theory can provide us with an assessment of cognitive factors in animals.

Do physiological changes determine emotion?

Introspection suggests that we perceive some fact and evaluate it; that this evaluation results in an emotional state which causes us to act; and that this emotional state is then accompanied or followed by specific feelings. As was discussed in Chapter 1, James (1884) suggested ‘on the contrary … that the bodily changes follow directly the perception of the exciting fact, and that our feelings of the same changes as they occur is the emotion’ and this has been taken to imply that emotional behaviour would not occur in the absence of feeling.

Cannon (1927) proposed a number of objections to James's position which are at the heart of much of the research we will discuss:

1. Total separation of the viscera from the CNS does not alter emotional behaviour.

2. The same visceral changes occur in very different emotional states and in non-emotional states.

3. The viscera are relatively insensitive structures.

4. Visceral changes are too slow to be the source of emotional feeling.

5. Artificial induction of the visceral changes typical of strong emotions does not produce them.

Teleonomy, a redefinition of purpose

In the previous chapter we discussed the release of behaviour patterns in terms of general control mechanisms without considering how and why such mechanisms might have evolved. In his preface to the 1965 edition of Darwin's book Lorenz cites as a key to modern behavioural biology ‘the fact … that behaviour patterns are just as conservatively and reliably characters of species as are the forms … of bodily structures; … (they) unite the members … of taxonomic units … (and) can become “vestigial” or “rudimentary” just as the latter can. Or on losing one function they may develop another.’

The idea of purpose has for many years been problematic in biology. However, it is possible to discuss some classes of purpose in biology from a scientific point of view – by referring to ‘purposes’ which specifically exclude the existence of any purposive entity as their source. One such alternative is ‘function’ as used by Lorenz.

What is an emotion?

Emotion is a striking feature of human experience. In any one day we may experience fear, love, pity, rage and many more. Emotion colours our everyday thoughts and actions and generates most of the behaviour which makes our friends and neighbours interesting. Much of Art and Literature is devoted to exploring its subtleties and, at a more practical level, emotions sway events in politics and commerce to a frightening extent. Psychology, the science of the mind, might therefore be expected to give pride of place to emotion as a topic of concern.

Unfortunately, there has never been any clear agreement as to what the word means. Amongst philosophers, ‘emotion has almost always played an inferior role … often as an antagonist to logic and reason … Along with this general demeaning of emotion in philosophy comes either a wholesale neglect or at least retail distortion in the analysis of emotions’ (Solomon, 1977, cited by Lyons, 1980, p ix). Psychologists have followed this lead.

Why do physiological changes accompany emotion?

The work of Cannon discussed in Chapter 1 centred on pain and fear as ‘great emotions’. It is also now obvious that ‘in the natural state and particularly in subhuman species, the aggression, attachment and sexual patterns are usually accompanied by autonomic discharge …. The same functional stimuli activate the behaviour pattern and the autonomic nervous system arousal’ (Mandler, 1975, p.136). Cannon suggested, essentially, that most such changes can be viewed as physiological preparations for the situation facing the animal. Even with the broader selection of emotions presented by Mandler, this view is unlikely to be contentious in either mechanistic or teleonomic terms.

Let us consider mechanism first. Particularly with examples like the let-down reflex or salivation (Section 2.4) to guide us we can accept that certain stimuli or the interpretation of those stimuli could result in activity in either the autonomic nervous system or glands under nervous control. The release of compounds such as adrenaline or direct action of the autonomic nervous system can then produce extensive changes in the animal's physiology which can involve increased muscular power, decreased bleeding, etc.

Teleonomy, physiological change and feelings

Chapter 5 discussed a variety of physiological changes which can accompany emotions and which, we argued, adjust the organism's bodily systems in preparation for classes of action frequently required when that emotion is present. Chapter 6 concluded that such changes are not merely of physiological utility but can also play a controlling role in the psychology of emotion. As was noted, the presence of some compound such as adrenaline, or the changes in particular organ systems induced by such compounds, could come, through further evolution, to act as controllers of psychological states. Why, teleonomically speaking, they should do so is, however, not at all clear – and is quite likely to involve rather different reasons for different internal changes and different emotions.

In the present chapter I will discuss a particular behavioural phenomenon, the partial reinforcement extinction effect, and its underlying control. I will present a provisional account of the teleonomy of this phenomenon which will, I hope, show that reasonable teleonomic accounts of the psychological role of physiological changes can be constructed.

Dialectical and non-dialectical interactions in emotion

The previous chapters have, as far as possible, treated the various components of emotion in isolation. One reason for doing this has been simplicity. However, a more important reason has been that, given the likely evolution of emotional systems (Chapters 2, 3), there is no guarantee that the individual ‘components of emotion’ do not have entirely separate control systems from each other. Such separation would not require us to give up emotion as a concept, since teleonomy alone could provide a conceptual link between different components. However, the normal use of the word emotion implies some direct connection between the different components. This chapter, therefore, considers interactions between those aspects of emotion which have been separated by the previous chapters. Throughout, it should be borne in mind that the usual co-occurrence of such components is no justification for treating them as linked. As a corollary to this it should also be remembered that mechanistic links between components of one emotion do not imply mechanistic links between the same components of some other emotion.

Introduction

Computer simulation has become a valuable - even indispensable - tool in the search for viable models of the self-organizing and self-replicating systems of the biological world as well as the inanimate systems of conventional physics. In this paper we shall present selected results from a large number of computer experiments on model neural networks of a very simple type. In the spirit of McCulloch & Pitts (1943) and Caianiello (1961), the model involves binary threshold elements (which may crudely represent neurons); these elements operate synchronously in discrete time. The synaptic interactions between neurons are represented by a non-symmetric coupling matrix which determines the strength of the stimulus which an arbitrary neuronal element, in the ‘on’ configuration, can exert on a second neuron to which it sends an input connection line. Within this model, the classes of networks singled out for study are defined by one or another prescription for random connection of the nodal units, implying that the entries in the coupling matrix are chosen randomly subject to certain overall constraints governing the number of inputs per ‘neuron’, the fraction of inhibitory ‘neurons’ and the magnitudes of the non-zero couplings.

We are primarily concerned with the statistics of cycling activity in such model networks, as gleaned from computer runs which follow the autonomous dynamical evolution of sample nets. An aspect of considerable interest is the stability of cyclic modes under disturbance of a single neuron in a single state of the cycle.

Introduction

During the last decade, a conspicuous theme of experimental and theoretical efforts toward understanding the behavior of complex systems has been the identification and analysis of chaotic phenomena in a wide range of physical contexts where the underlying dynamical laws are considered to be deterministic (Schuster, 1984). Such chaotic activity has been examined in great detail in hydrodynamics, chemical reactions, Josephson junctions, semiconductors, and lasers, to mention just a few examples. Chaotic solutions of deterministic evolution equations are characterized by (i) irregular motion of the state variables, and (ii) extreme sensitivity to initial conditions. The latter feature implies that the future time development of the system is effectively unpredictable. An essential prerequisite for deterministic chaos is non-linear response; and although there are famous examples of chaos in relatively simple systems (e.g. Lorenz, 1963; Feigenbaum, 1978), we expect this kind of behavior to arise most naturally in systems of high complexity. Since biological nerve nets are notoriously non-linear and are perhaps the most complex of all known physical systems, it would be most surprising if the phenomena associated with deterministic chaos were irrelevant to neurobiology. Indeed, there has been a growing interest in the detection and verification of deterministic chaos in biological preparations consisting of few or many neurons. At one extreme we may point to the pioneering work of Guevara et al. (1981) on irregular dynamics observed in periodically stimulated cardiac cells; and, at the other, to the recent analysis by Babloyantz et al. (1985) of EEG data from the brains of human subjects during the sleep cycle, aimed at establishing the existence of chaotic attractors for sleep stages two and four.

The brain and the computer: a misleading metaphor in place of brain theory

Contrary to the philosophy of natural sciences, the brain has always been understood in terms of the most complex scientific technology of manmade organisms, for the simple reason of human vanity. Before and after the computer era, the brain was paraded in the clothing of hydraulic systems (in Descartes' times), and in the modern era as radio command centers, telephone switchboards, learn-matrices or feedback control amplifiers. Presently, it is fashionable to borrow terms of holograms, catastrophes or even spin glasses. Comparing brains to computers, however, has been by far the most important and most grossly misleading metaphor of all. Its importance has been twofold. First, the early post-war era was the first and last time in history that such analogy paved the way both to a model of the single neuron, the flip–flop binary element, cf. McCulloch & Pitts, 1943, and to a grand mathematical theory of the function of the entire brain (i.e., information processing and control by networks implementing Boolean algebra, cf. Shannon, 1948; Wiener, 1948). Second, the classical computer, the so-called von Neumann machine, provided neuroscience with not only a metaphor, but at the same time with a powerful working tool. This made computer simulation and modeling flourish in the brain sciences as well (cf. Pellionisz, 1979).

The basic misunderstanding inherent in the metaphor, nevertheless, left brain theory in an eclipse, although the creator of the computers was the first to point out (von Neumann, 1958) that these living- and non-living epitomes of complex organisms appear to operate on diametrically opposite structuro–functional principles.

Passive cable properties of dendrites

The modeling of dendritic trees was carefully presented and discussed in earlier publications; only a few points will be summarized here. In Rail, 1962 it was shown how the partial differential equation for a passive nerve cable can represent an entire dendritic tree, and how this can be generalized from cylindrical to tapered branches and trees; this paper also showed how to incorporate synaptic conductance input into the mathematical model, and presented several computed examples. In Rail, 1964 it was shown how the same results can be obtained with compartmental modeling of dendritic trees; this paper also pointed out that such compartmental models are not restricted to the assumption of uniform membrane properties, or to the family of dendritic trees which transforms to an equivalent cylinder or an equivalent taper and, consequently, that such models can be used to represent any arbitrary amount of nonuniformity in branching pattern, in membrane properties, and in synaptic input that one chooses to specify. Recently, this compartmental approach has been applied to detailed dendritic anatomy represented as thousands of compartments (Bunow et al., 1985; Segev et al., 1985; Redman & Clements, personal communication).

Significant theoretical predictions and insights were obtained by means of computations with a simple ten-compartment model (Rail, 1964). One computation predicted different shapes for the voltage transients expected at the neuron soma when identical brief synaptic inputs are delivered to different dendritic locations; these predictions (and their elaboration, Rail, 1967) have been experimentally confirmed in many laboratories (see Jack et al., 1975; Redman, 1976; Rail, 1977).