Can robots make good models of biological behaviour?

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Can robots make good models of biological behaviour?

Barbara Webb

Centre for Computational and Cognitive Neuroscience

Department of Psychology

University of Stirling

Stirling FK9 4LA

Scotland, U.K. <../src/compose.php?>


How should biological behaviour be modelled? A relatively new approach is to

investigate problems in neuroethology by building physical robot models of

biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models - the representation of a hypothesis about a target system - are distinguished from several other relationships also termed 'modelling' in discussions of scientific explanation. Seven dimensions on which simulation models can differ are defined and distinctions between them discussed:

1.Relevance: whether the model tests and generates hypotheses applicable to biology.

2.Level: the elemental units of the model in the hierarchy from atoms to societies.

3.Generality: the range of biological systems the model can represent.

4.Abstraction: the complexity, relative to the target, or amount of detail included in the model.

5.Structural accuracy: how well the model represents the actual mechanisms

underlying the behaviour.

6.Performance match: to what extent the model behaviour matches the target behaviour

7.Medium: the physical basis by which the model is implemented

No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems;that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is in terms of the real problems faced by real animals in real environments.


models; simulation; animal behaviour; neuroethology; robotics; realism; levels.

Barbara Webb joined the Psychology Department at Stirling University in January 1999. Previously she lectured at the University of Nottingham (1995-1998) and the University of Edinburgh (1993-1995). She recieved her Ph.D. (in Artificial Intelligence) from the University of Edinburgh in 1993, and her B.Sc. (in Psychology) from the University of Sydney in 1988.

1. Introduction

'Biorobotics' can be defined as the intersection of biology and robotics. The common ground is that robots and animals are both moving, behaving systems; both have sensors and actuators and require an autonomous control system that enables them to successfully carry out various tasks in a complex, dynamic world. In other words "it was realised that the study of autonomous robots was analogous to the study of animal behaviour" p.60 (Dean, 1998), hence robots could be used as models of animals. As summarised by Lambrinos et al. (1997) et al "the goal of this approach is to develop an understanding of natural systems by building a robot that mimics some aspects of their sensory and nervous system and their behaviour" (p.185).

Dean (op. cit.) reviews some of this work, as do Meyer (1997), Beer et al. (1998), Bekey (1996), and Sharkey & Ziemke (1998), although the rapid growth and interdisciplinary nature of the work make it difficult to comprehensively review. Biorobotics will here be considered as new methodology in biological modelling, rather than as a new 'field' per se. It can then be discussed directly in relation to other forms of modelling. Rather than vague justification in terms of intuitive similarities between robots and animals, the tenets of the methodology can be more clearly stated and a basis for comparison to other approaches established. However, a difficulty that immediately arises is that a "wide divergence of opinion … exists concerning the proper role of models" p. 597 (Reeke & Sporns, 1993) in biological research. For example, the level of mechanism that should be represented in the model is often disputed. Cognitivists criticise connectionism for being too low level (Fodor & Pylyshyn, 1988), while neurobiologists complain that connectionism abstracts too far from real neural processes (Crick, 1989). Other debates address the most appropriate means for implementing models. Purely computer-based simulations are criticised by advocates of sub-threshold transistor technology (Mead, 1989) and by supporters of ‘real-world’ robotic implementations (Brooks, 1986). Some worry about oversimplification (Segev, 1992) while others deplore overcomplexity (Maynard Smith, 1974; Koch, 1999). Some set out minimum criteria for ‘good’ models in their area (e.g. Pfeifer, 1996; Selverston, 1993); others suggest there are fundamental trade-offs between desirable model qualities (Levins, 1966). The use of models at all is sometimes disputed, on the grounds that detailed modelsare premature and more basic research is needed. Croon & van de Vijver (1994) argue

that "Developing formalised models for phenomena which are not even understood on an elementary level is a risky venture: what can be gained by casting some quite gratuitous assumptions about particular phenomena in a mathematical form?" p.4-5. Others argue that "the complexity of animal behaviour demands the application of powerful theoretical frameworks" (Barto, 1991, p.94) and "nervous systems are simply to complex to be understood without the quantitative approach that modelling provides" (Bower, 1992, p.411). More generally, the formalization involved in modelling is argued to be an invaluable aid in theorising - "important because biology is full of verbal assertions that some mechanism will generate some result, when in fact it won’t" (Maynard Smith, 1988, p.231). Beyond the methodological debates, there are also ‘meta’-arguments regarding the role and status of models in both pure and applied sciences of behaviour. Are models essential to gaining knowledge or just convenient tools? Can we ever really validate a model (Oreskes, et al, 1994)? Is reification of models mistaken i.e. can a model of a process ever be a replica of that process (Pattee, 1989; Webb, 1991)? Do models really tell us anything we didn’t already know? In what follows a framework for the description and comparison of models will be set out in an attempt to answer some of these points, and the position of biorobotics with regard to this framework will be made clear. Section 2 will explicate the function of models, in particular to clarify some of the current terminological confusion, and define 'biorobotic' modelling. Section 3 will describe different dimensions that can be used to characterise biological models, and discuss the relationships between them. Section 4 will lay out the position of robot models in relation to these dimensions, and discuss how this position reflects a particular perspective on the problems of explaining biological behaviour.

2. The process of modelling

2.1 The "model muddle" (Wartofsky, 1979)

Discussions of the meaning and process of modelling can be found: in the philosophy of science e.g. Hesse (1966), Harre (1970b), Leatherdale (1974), Bunge (1973), Wartofsky (1979), Black (1962) and further references throughout this paper; in cybernetic or systems theory, particularly Zeigler (1976); and in textbooks on methodology - recent examples include Haefner (1996), Giere (1997), and Doucet & Sloep (1992). It also arises as part of some specific debates about approaches in biology and cognition: in ecological modelling e.g. Levins (1966) and Orzack & Sober (1993); in cognitive simulation e.g. Fodor (1968), Colby (1981), Fodor & Pylyshyn (1988), Harnad (1989); in neural networks e.g. Sejnowski et al (1988), Crick (1989); and in Artificial Life e.g. Pattee (1989), Chan & Tidwell (1993). However the situation is accurately summed up by Leatherdale (1974): "the literature on ‘models’ displays a bewildering lack of agreement about what exactly is meant by the word

‘model’ in relati..... There does seem to be general agreement that modelling involves the relationship of representation or correspondence between a (real) target system and something else(1). Thus "A model is a representation of reality" Lamb, 1987, p.91) or "all [models] provide representations of the world" (Hughes, 1997, p. 325). What might be thought uncontroversial examples are: a scale model of a building which corresponds in various respects to an actual building; and the ‘billiard-ball model’ of gases, suggesting a correspondence of behaviour in microscopic particle collisions to macroscopic object collisions. Already, however, we find some authors ready to dispute the use of the term ‘model’ for one or other of these examples. Thus Kaplan (1964) argues that purely ‘sentential’ descriptions like the billiard-ball example

should not be called ‘models’; whereas Kacser (1960) maintains that only sentential descriptions should be called ‘models’ and physical constructions like scale buildings should . A large proportion of the discussion of models in the philosophy of science concerns the problem that reasoning by analogy is not logically valid. If A and A* correspond in factors x1,…,xn, it is not possible to deduce that they will therefore correspond

in factor xn+1. ‘Underdetermination’ is a another aspect of essentially the same

problem – if two systems behave the same, it is not logically valid to conclude the

cause or mechanism of the behaviour is the same; so a model that behaves like its

target is not necessarily an explanation of the target’s behaviour. These problems

are sometimes raised in arguments about the practical application of models, e.g.

Oreskes et al. (1994) use underdetermination to argue that validation of models is

impossible. Weitzenfeld (1984) suggests that a defence against this problem can be

made by arguing that if there is a true isomorphism between A and A*, the deduction

is valid, and the problem is only to demonstrate the isomorphism. Similar


However, this is not helpful when considering most actual examples of models (unless

one allows there "to be as many definitions possible to isomorphism as to model"

Conant & Ashby, 1991, p.516). In the vast majority of cases, models are not

(mathematical) isomorphisms, nor are they intended to be. Klir and Valach (op. cit.)

go on to include as examples of models "photos, sculptures, paintings, films…even

literary works" (p.115). It would be interesting to know how they intend to

demonstrate a strict homomorphism between Anna Karenina and "social, economic,

ethical and other relations" (op. cit.) in 19th century Russia. In fact it is just

as frequently (and often by the same authors) emphasised that a model necessarily

fails to represent everything about a system. For example Black (1962) goes on to

warn of "risks of fallacies of inference from inevitable irrelevancies or

distortions in the model" (p.223) – but if there is a true isomorphism, how can

there be such a risk? A ‘partial ..

2.2 What use are models?

"There are things and models of things, the latter being also things, but used in a

special way" (Chao, 1960, p.564)

Models are intended to help us deal in various ways with a system of interest. How

do they fulfil this role? It is common to discuss how they offer a

convenient/cost-effective/manageable/safe substitute for working on or building the

real thing. But this doesn’t explain why working on the model has any relevance to

the real system, or provide some basis by which relevance can be judged i.e. what

makes a model a useful substitute? It is easier to approach this by casting the role

of modelling as part of the process of explanation and prediction described in the

following diagram:

Figure 1: Models and the process of explanation

This picture can be regarded as an elaboration of standard textbook illustrations of

either the 'hypothetico-deductive' approach or the 'semantic' approach to science

(see below). To make each part of the diagram clear, consider an example. Our target

- selected from the world - might be the human cochlea and the human behaviour of

pitch perception. Our hypothesis might be that particular physical properties of the

basilar membrane enable differently positioned hair cells to respond to different

sound frequencies. One source of this idea may be the Fourier transform, and

associated notion of a bank of frequency filters as a way of processing sound. To

see what is predicted by the physical properties of the basal membrane we might

build a symbolic simulation of the physical properties we think perform the

function, and run it using computer technology, with different simulated sounds to

see if it produces the same output frequencies as the cochlea (in fact Bekesy first

investigated th...

I have purposely not used the term 'model' in the above description because it can

be applied to different parts of this diagram. Generally, in this paper, I take

'modelling' to correspond to the function labelled 'simulation': models are

something added to the 'hypothesis - prediction - observation' cycle merely as

"prostheses for our brains" (Milinski, 1991). That is, modelling aims to make the

process of producing predictions from hypotheses more effective by enlisting the aid

of an analogical mechanism. A mathematical model such as the Hodgkin-Huxley

equations sets up a correspondence between the processes in theorised mechanism –

the ionic conductances involved in neural firing – and processes defined on numbers

– such as integration. We can more easily manipulate the numbers than the chemicals

so the results of a particular configuration can be more easily predicted. However

limitations in the accuracy of the correspondence might compromise the validity of

conclusions drawn.

However, under the 'semantic' approach to scientific explanation (Giere, 1997) the

hypothesis itself is regarded as a ‘model’, i.e. it specifies a hypothetical system

of which the target is supposed to be a type. The process of prediction is then

described as ‘demonstration’ (Hughes, 1997) of how this hypothetical system should

behave like the target. Demonstration of the consequences of the hypothesis may

involve ‘another level’ of representation in which the hypothesis is represented by

some other system, also called a model. This system may be something already 'found'

- an analogical or source model - or something built - a simulation model (Morgan,
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