Architecture-based motivation (ABM)
Reward-based motivation (RBM)
Based on an earlier version published in 2009:
Newsletter on Philosophy and Computers,
American Philosophical Association, 09, 1, pp. 10--13,
Index of issues:
Newsletter on Philosophy and Computers, American Philosophical Association,
Whole newsletter 09, 1, Fall 2009 (Containing the 2009 version of this paper)
Note on the work of Danielle Bassett on "The curious human"
17 May 2021: I have just stumbled across a very relevant online talk by Danielle Bassett on "The Curious Human", here https://www.youtube.com/watch?v=LtbQ8eZF64s&t=8s
Note on Affordances Added 19 Jan 2019
In retrospect I find it strange that early versions of this paper on motivation failed to mention both affordances and James Gibson. Gibson's work showed clearly that a great deal of control (i.e. detection of possible actions and selection between the possibilities) is concerned with detection and use of affordances, though I have found it useful to broaden his concept of "affordance" to cover more phenomena. It is also now clear that there are close connections between the ideas here and this paper:
In short: control mechanisms are required in addition to factual information and reasoning mechanisms if A is to do anything. This paper is about what forms of control are required. I assume that in at least some cases there are motives, and the control arises out of selection of a motive for action. That raises the question where motives come from. My answer is that they can be generated and selected in different ways, but one way is not itself motivated: it merely involves the operation of ancient mechanisms (produced by biological evolution) in the architecture of A that generate motives and select some of them for action. (Reading Poddiakov's paper referenced above in Jan 2019 made me realise that this paper should have mentioned affordance-detection explicitly as a trigger of new motives, though that was already implicit in the discussion below.)
The thesis I oppose is that all of an agent A's motives to perform actions must be selected on the basis of a belief that acting on those motives will serve the interests of A, or be rewarding for A by achieving something already wanted or valued by A.
That view is very widely held and is based on a lack of imagination about possible designs for working system: partial ignorance of the space of possible designs. I summarise it as the assumption that all motivation must be reward-based. In contrast I claim that at least some motivation may be architecture-based, in the sense explained below.
Instead of talking about "passions" I shall use the less emotive terms, "motivation" and "motive". A motive in this context, is a specification of something to be done or achieved (which could include preventing or avoiding some state of affairs, or maintaining a state or process). The words "motivation" and "motivational" can be used to describe the states, processes, and mechanisms concerned with production of motives, their control and management and the effects of motives in initiating and controlling internal and external behaviours. So Hume's claim, as interpreted here is that no collection of beliefs and reasoning capabilities can generate behaviour on its own: prior motivation is also required.
This view of Hume's claim is expressed well in the Stanford Encyclopedia of Philosophy entry on motivation, though without explicit reference to Hume:
If Hume had known about reflexes, he might have treated them as an alternative mode of initiation of behaviour to motivation (or passions). There may be some who regard a knee-jerk reflex as involving a kind of motivation produced by tapping a sensitive part of the knee. That would not be a common usage. I think it is more helpful to regard such physical reflexes as different from motives, and therefore as exceptions to Hume's claim.
I shall try to show that something like "internal reflexes" in an information-processing system can be part of the explanation of creation and adoption of motives. In particular, adopting "the design-based approach to the study of mind" yields a wider variety of possible explanations of how minds work than are typically considered in philosophy or psychology, and paradoxically even in AI/Robotics, where such an approach ought to be more influential.
NOTE: The ideas presented here are a contribution to the study
of "The meta-configured" genome (work done with Jackie Chappell).
The idea is also used by Luc Beaudoin (who helped with development of the
theory), e.g. in his work on meta-effectiveness:
Before that state is achieved the system can be viewed as having a constantly changing belief-like state, which is the discrepancy between its current state and the minimum potential energy state, and a desire-like state which is that minimum state. In such a physical device the discrepancy between the 'belief-like state' and the 'desire-like state' causes acceleration in the direction of the desire-like state. But that acceleration can cause the desire-like state to be over-shot, after which the direction of acceleration changes (before the direction of motion changes!).
If there is no energy dissipation (e.g. no friction, no viscosity, etc.) the oscillation could continue indefinitely (something like a stupid animal chasing its own tail?). However, normally the system will more or less rapidly dissipate its kinetic energy and get ever closer to attaining the desire-like state, then stop.
Such apparently superficial and misleading comparisons between 'dumb' physical processes and the more sophisticated goal-directed processes found in living organisms may be part of the story of evolution of the more sophisticated systems, such as homeostatic control systems used in many biological processes as explained in http://www.bbc.co.uk/education/guides/z4khvcw/revision
Such dissipative oscillatory mechanisms are among the components of
construction kits used by evolution, discussed in more
Evolution (like human designers more recently, including the inventor of the Watt rotary governor) seems to have found many ways in which systems without goals can be used as goal-directed mechanisms in organisms. Such uses could have evolved before the use of reward-based mechanisms for selecting goals.
Contrast this with the common belief that physical properties of matter cannot account for features of life that include goal-direction, e.g. discussed in
In the history of philosophy and psychology there have been many theories of motivation, and distinctions between different sorts of motivation, for example motivations related to biological needs, motivations somehow acquired through cultural influences, motivations related to achieving or maximising some reward (e.g. food, admiration in others, going to heaven), or avoiding or minimising some punishment (often labelled positive and negative reward or reinforcement), motivations that are means to some other end, and motivations that are desired for their own sake, motivations related to intellectual or other achievements, and so on. Many theorists assume that motivation must be linked to rewards or utility. One version of this (a form of hedonism) is the assumption that all actions are done for ultimately selfish reasons.
There is an alternative to reward-based motivation (RBM), namely architecture-based motivation (ABM), which is not included even in this rather broad characterisation of types of motivation on Wikipedia:
Motivation is also a topic of great concern in management theory and management practice, where motivation of workers comes from outside them e.g. in the form of reward mechanisms (providing money, status, recognition, etc.) sometimes in other forms, e.g. inspiration, exhortation, social pressures, ... I shall not discuss any of those ideas.
In psychology and even in AI, all these concerns can arise, though I am here only discussing questions about the mechanisms within an organism or machine that select things to aim for and initiate and control the behaviours that result. This includes: mechanisms that produce goals and desires, mechanisms that identify and resolve conflicts between different goals or desires, and mechanisms that select means to achieving goals or desires.
Achieving a desired goal G could be done in different ways, e.g.
- select and use an available plan for doing things of type G
- use a planning mechanism to create a plan to achieve G and follow it.
- detect and follow a gradient that appears to lead to achieving G
E.g. if the goal is being on high ground to avoid a rising tide, walk uphill while you can.
There is much more to be said about the forms different motives can have, and the various ways in which their status can change, e.g. when a motive has been generated but not yet selected, when it has been selected, but not yet scheduled, or when there is not yet any clear plan or strategy as to how to achieve it, or whether action has or has not been initiated, whether any conflict with other motives, or unexpected obstacle has been detected, etc.
For a characterisation of some of the largely unnoticed complexity of motives see
L.P. Beaudoin, A. Sloman, A study of motive processing and attention,
Prospects for Artificial Intelligence, IOS Press, 1993
(further developed in Luc Beaudoin's PhD thesis).
Extreme versions of this assumption are found in philosophical theories that all agents are ultimately selfish, since they can only be motivated to do things that reward themselves, even if that is a case of feeling good about helping someone else.
More generally, the assumption is that selection of a motive among possible motives must be based on some kind of prediction about the consequences of achieving or preventing whatever state of affairs is specified in that motive.
This document challenges that claim by demonstrating that it is possible for an organism or machine to have, and to act on, motives for which there is no such prediction. Instead certain motive-triggering mechanisms have been found, in human evolutionary history, useful as a means of collecting information that can be stored when found, in case it can be useful at some later time. This does not imply that particular individuals need to have that ulterior motivation, or any sort of theory about the biological benefits of motive triggering mechanisms.
In other words, it is possible for there to be reflex mechanisms whose effect is to produce new motives, and in simple cases to initiate behaviours controlled by such motives. I shall present a very simple architecture illustrating this possibility below, though for any actual organism, or intelligent robot, a more complex architecture will be required, for reasons given later.
Where the reflex mechanisms come from is a separate question: they may be produced by a robot designer or by biological evolution, or by a learning process, or even by some pathology (e.g. mechanisms producing addictions) but what the origin of such a mechanism is, is a separate question from what it does, how it does it, and what the consequences are.
I am not denying that some motives are concerned with producing benefits for the agent. It may even be the case (which I doubt) that most motives generated in humans and other animals are selected because of their benefit for the individual. For now, I am merely claiming that something different can occur and does occur, as follows:
Not all the mechanisms for generating motives in a particular organism O, and not all the motives produced in O have to be related to any reward or positive or negative reinforcement for O.
What makes them motives is how they work: what effects they have, or, in more complex cases, what effects they tend to have even though they are suppressed (e.g. since competing, incompatible, motives can exist in O).
I think that is false: some forms of learning occur simply because the opportunity to learn arises and the information-processing architecture produced by biological evolution simply reacts to many opportunities to learn, or to do things that could produce learning because the mechanisms that achieve that have proved their worth in previous generations, without the animals concerned knowing that they are using those mechanisms nor why they are using them
At regular intervals another mechanism selects one of the beliefs about processes occurring recently and copies its content (perhaps with some minor modification or removal of some detail, such as direction of motion) to form the content of a new motive in a database of "desires". The desires may be removed after a time.
At regular intervals an intention-forming mechanism selects one of the desires to act as a goal for a planning mechanism that works out which actions could make the desire come true, selects a plan, then initiates plan execution.
This system will automatically generate motives to produce actions that repeat or continue changes that it has recently perceived, possibly with slight modifications, and it will adjust its behaviours so as to execute a plan for fulfilling the latest selected motive.
Why is a planning mechanism required instead of a much simpler reflex action mechanism that does not require motives to be formulated and planning to occur?
A reflex mechanism would be fine if evolution had detected all the situations that can arise and if it had produced a mechanism that is able to trigger the fine details of the actions in all such situations. In general that is impossible, so instead of a process automatically triggering behaviour it can trigger the formation of some goal to be achieved, and then a secondary process can work out how to achieve it in the light of the then current situation.
For such a system to work there is NO need for the motives selected or the actions performed to produce any reward. We have goals generated and acted on without any reward being required for the system to work. Moreover, a side effect of such processes might be that the system observes what happens when these actions are performed in varying circumstances, and thereby learns things about how the environment works. That can be a side effect without being an explicit goal.
A designer could put such a mechanism into robot as a way of producing such learning without that being the robot's goal. Likewise biological evolution could have selected changes that lead to such mechanisms existing in some organisms because they produce useful learning, without any of the individual animals knowing that it has such mechanisms nor how they were selected or how they operate.
Lorenz famously demonstrated that newly hatched geese would imprint on him and
the tendency to follow him persisted across extended development, and in a wide
variety of circumstances, as shown in this video
This mechanism makes it unnecessary for the genome to include the detailed information about appearance of the parent, and avoids the (unsolvable?) problem of developing different genetic encodings for mechanisms for recognizing the right adult to follow, although it does require production of a mechanism capable of very rapid extraction and storage of the appropriate visual information details required to identify the parent, viewed in different contexts, and from different distances at different angles: no mean achievement.
There is no evidence that chicks that follow the imprinted parent do so because they expect any particular kind of reward. No such prediction/expectation mechanism is required for the imprinting strategy to be of great biological value. (Though it can go wrong when individuals imprint on something inappropriate.)
Some motives triggered by perceiving a physical process can involve systematic variations on the theme of the process e.g. undoing its effects, reversing the process, preventing the process from terminating, joining in and contributing to an ongoing process, or repeating the process, but with some object or action or instrument replaced. A mechanism that could generate such variations would accelerate learning about how things work in the environment, if the effects of various actions are recorded or generalised or compared with previous records, generalisations and predictions.
The motives generated will certainly need to change with the age and sophistication of the learner.
Some of the motive-generating mechanisms could be less directly triggered by particular perceived episodes and more influenced by the previous history of the individual, taking account not only of physical events but also social phenomena, e.g. discovering what peers seem to approve of, or choose to do. The motives generated by inferring motives of others could vary according to stage of development. E.g. early motives might mainly be copies of inferred motives of others, then as the child develops the ability to distinguish safe from unsafe experiments, the motives triggered by discovering motives of others could include various generalisations or modifications, e.g. generalising some motive to a wider class of situations, or restricting it to a narrower class, or even generating motives to oppose the perceived motives of others (e.g. parents!).
Moreover some of the processes triggered instead of producing external actions could produce internal changes to the architecture or its mechanisms. Those changes could include production of new motive generators, or motive comparators, or motive generator generators, etc.
For more on this idea see Chapter
6 and Chapter
The Computer Revolution in Philosophy (1978).
Such learning would depend on other mechanisms monitoring the results of behaviour generated by architecture-based motivational mechanisms and looking for both new generalisations, new conjectured explanations of those generalisations and new evidence that old theories or old conceptual systems are flawed -- and require debugging.
Such learning processes would require additional complex mechanisms, including mechanisms concerned with construction and use of powerful forms of representation and mechanisms for producing substantive (i.e. non-definitional) ontology extension.
For more on additional mechanisms required see
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#glang Evolution of minds and languages. What evolved first and develops first in children: Languages for communicating, or languages for thinking (Generalised Languages: GLs)The mechanisms constructing architecture-based motivational sub-systems could sometimes go wrong, accounting for some pathologies, e.g. obsessions, addictions, etc. But at present that is merely conjecture.
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#prague09 Ontologies for baby animals and robots From "baby stuff" to the world of adult science: Developmental AI from a Kantian viewpoint.
http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#toddlers A New Approach to Philosophy of Mathematics: Design a young explorer, able to discover "toddler theorems" (Or: "The Naive Mathematics Manifesto").
In 2009 this paper had a note below referring to White's paper as introducing the idea of architecture-based-motivation (A-B-M) using different terminology.
R.W. White, 1959, Motivation reconsidered: The concept of competence, Psychological Review, 66, 5, pp. 297-333I have now re-read the paper more carefully and find that his concept of "effectance" partly overlaps with the A-B-M idea, but is also different in important ways.
"Effectance motivation similarly aims for the feeling of efficacy, not for the vitally important learnings that come as its consequence."There is no such aim for feelings of any kind necessarily associated with architecture-based motivation, although as a side effect of acting on A-B-M the animal may discover that such motives often produce new kinds of competence and that may make them rewarding. Later the individual may learn how to select actions that are more likely to achieve that, though such learning needs non-trivial learning mechanisms to produce a useful level of generality.
Earlier in the paper he writes:
"In infants and young children it seems to me sensible to conceive of effectance motivation as undifferentiated. Later in life it becomes profitable to distinguish various motives such as cognizance, construction, mastery, and achievement. It is my view that all such motives have a root in effectance motivation. They are differentiated from it through life experiences which emphasize one or another aspect of the cycle of transaction with environment. Of course, the motives of later childhood and of adult life are no longer simple and can almost never be referred to a single root."
All of that is fine. But from my point of view he hasn't noticed the need to go back to a more primitive stage in which actions are simply selected as a "cognitive reflex" response to a situation, as described below, NOT because of any expectation of consequences of any kind, not even improved competence.
Of course, biological evolution may have selected the mechanisms producing A-B-M, because having those mechanisms tends, in some situations, to produce more competent adults. But the young animal knows nothing about that and probably in the earliest stages does not even have any concept of competence or feeling of competence or incompetence. It merely acts and information gets absorbed in the process, which at that stage mostly achieves nothing. Later, patterns in the stored information can be used to create useful strategies for achieving explicit goals. That could be triggered during later stages of gene expression, as in the meta-configured genome theory mentioned above.
He also writes:
"Effectance motivation must be conceived to involve satisfaction -- a feeling of efficacy -- in transactions in which behavior has an exploratory, varying, experimental character and produces changes in the stimulus field. Having this character, the behavior leads the organism to find out how the environment can be changed and what consequences flow from these changes."
That seems to express a commitment to the notion of motive-selection having to be based on some anticipated reward. But the sort of playful, exploratory selection called A-B-M below has no such expectation. The motive just gets created by a "cognitive reflex" mechanism, and once created is a candidate for as a sort of cognitive reflex, and once created is a candidate for selection and action. If there's no competing motive it doesn't need to be selected for any reason. If there are other motives and no reason to choose between them, random selection may be used, or recency, or connection with current perceptual contents.
Some motivational mechanisms "reward" the genomes that specify them, not the individuals that have them.
Similarly, some forms of learning may occur because animals that have certain learning mechanisms had ancestors who produced more offspring than rivals that lacked those learning mechanisms. This could be the case without the learning mechanism specifically benefiting the individual. In fact the learning mechanism may lead to parents adopting potentially suicidal behaviours in order to divert predators from their offspring.
If follows that any AI and cognitive science research based on the assumption that learning is produced ONLY by mechanisms that maximise expected utility for the individual organism or robot, is likely to miss out important forms of learning. Perhaps the most important forms.
One reason for this is that typically individuals that have opportunities to learn do not know enough to be able to even begin to asses the long term utility of what they are doing. So they have to rely on what evolution has learnt (or a designer in the case of robots) and, at a later stage, on what the culture has learnt. What evolution or a culture has learnt may, of course, not be appropriate in new circumstances!
This discussion note does not prove that evolution produced organisms that make use of architecture-based motivation in which at least some motives are produced and acted on without any reward mechanism being required. But it illustrates the possibility, thereby challenging the assumption that ALL motivation must arise out of expected rewards. The ABM theory is capable of explaining far more than the RBM theory of evolution of motivation.
Similar arguments about how suitably designed reflex mechanisms may react to perceived processes and states of affairs by modifying internal information stores could show that at least some forms of learning use mechanisms that are not concerned with rewards, with positive or negative reinforcement, or with utility maximisation (or maximisation of expected utility). My conjecture is that the most important forms of learning in advanced intelligent systems (e.g. some aspects of language learning in human children) are architecture-based, not reward based. But that requires further investigation.
The ideas presented here are very relevant to Robotic projects like CogX, which aim to investigate designs for robots that 'self-understand' and 'self-extend', since they demonstrate at least the possibility that some forms of self-extension may not be reward-driven, but architecture-driven.
Various forms of architecture-based motivation seem to be required for the
development of precursors of mathematical competences described here:
Some of what is called 'curiosity-driven' behaviour probably needs to be re-described as 'architecture-based' or 'architecture-driven'.
To be added:
How the contrast discussed here relates to the distinction made by psychologists
between intrinsic and extrinsic rewards, e.g. in this report:
[This document is still under construction. Suggestions for improvement welcome. It is likely to change.]
Marvin Minsky wrote about goals and how they are formed in The Emotion Machine. It seems to me that the above is consistent with what he wrote, though I may have misinterpreted him.
The Computer Revolution in Philosophy (1978)
Something like the ideas presented here were taken for granted when I wrote The Computer Revolution in Philosophy in 1978. However, at that time I underestimated the importance of spelling out assumptions and conjectures in much greater detail.
Two closely related pieces of work came to my notice after the original version of this paper had been published:
R.W. White, 1959, Motivation reconsidered: The concept of competence,NB
Psychological Review, 66, 5, pp. 297-333
S. Singh, R. L. Lewis, and A. G. Barto, 2009, Where Do Rewards Come From?
Proceedings of the 31th Annual Conference of the Cognitive Science Society,
Eds. N.A. Taatgen and H. van Rijn, Cognitive Science Society, pp. 2601-2606,