School of Computer Science THE UNIVERSITY OF BIRMINGHAM CoSy project CogX project

Discussion Note

Architecture-based motivation vs Reward-based motivation
Aaron Sloman
School of Computer Science
The University of Birmingham
Last updated: 2 Aug 2009
Installed: 25 May 2009 (Liable to change.)

CONTENTS (Provisional)

From time to time I shall create a PDF version of this file here. (Created by Firefox's 'print to pdf' option.)
It will become out of date if I forget to update it after editing the html version.


    "Reason is, and ought only to be the slave of the passions, and
    can never pretend to any other office than to serve and obey them."
    David Hume A Treatise of Human Nature (, 1739-1740

Whatever Hume may have meant by this, and whatever various commentators may have
taken him to mean, I claim that there is at least one interpretation in which
this statement is obviously true, namely: no matter what factual information an
animal or machine A contains, and no matter what competences A has regarding
abilities to reason, to plan, to predict or to explain, A will not actually
do anything unless it has, in addition, some sort of control mechanism that
selects among the many alternative processes that A's information and
competences can support.

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 mechanisms in the
architecture of A that generate motives and select some of them for
action. The view I wish to oppose is that all motives must somehow
serve the interests of A, or be rewarding for A. This view is widely
held and is based on a lack of imagination about possible designs
for working system. 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

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:
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:

  "The belief that an antibiotic will cure a specific infection may move an
   individual to take the antibiotic, if she also believes that she has the
   infection, and if she either desires to be cured or judges that she ought to
   treat the infection for her own good. All on its own, however, an empirical
   belief like this one appears to carry with it no particular motivational impact;
   a person can judge that an antibiotic will most effectively cure a specific
   infection without being moved one way or another."

That raises the question: where do motives come from and why are some possible
motives (e.g. going for lunch) selected and others (e.g. going for a walk, or
starting a campaign for election to parliament) not selected?

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.

This proposal opposes a view that all motives are selected on the basis of the
costs and benefits of achieving them, which we can loosely characterise as the
claim that all motivation is "reward-based".

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.

I shall try to explain why there is an alternative kind of motivation,
architecture-based motivation, which is not included even in this rather broad
characterisation of types of motivation on Wikipedia:
    "Motivation is the set of reasons that determines one to engage in a particular
    behavior. The term is generally used for human motivation but, theoretically, it
    can be used to describe the causes for animal behavior as well. This article
    refers to human motivation. According to various theories, motivation may be
    rooted in the basic need to minimize physical pain and maximize pleasure, or it
    may include specific needs such as eating and resting, or a desired object,
    hobby, goal, state of being, ideal, or it may be attributed to less-apparent
    reasons such as altruism, morality, or avoiding mortality." 

Philosophers who write about motivation tend to have rather different concerns
such as whether there is a necessary connection between deciding what one
morally ought to do and being motivated to do it. For more on this see the
afore-mentioned entry in the Stanford Encyclopedia of philosophy.

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 that underlie processes within an
organism or machine that select things to aim for and which 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, 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 G 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).

Where do motives come from?

It is often assumed that motivation, i.e. an organism's or machine's, selection,
maintenance, or pursuance of some state of affairs, the motive's content, must
be related to the organism or machine having information (e.g. a belief, or
expectation) that achievement of the motive will bring some rewards or benefit,
sometimes referred to as "utility". This could be reduction of some disadvantage
or disutility, e.g. a decrease in danger or pain.

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

My claim

My claim is that an organism (human or non-human) or machine may
have something as a motive whose existence is merely a product of
the operation of a motive-generating mechanism -- which itself may
be a product of evolution, or something produced by a designer, or
something that resulted from a learning or developmental process, or
in some cases may be produced by some pathology. Where the mechanism
comes from and what its benefits are are irrelevant to its being a
motivational mechanism: all that matters is that it should generate
motives, and thereby be capable of influencing selection and
generation of behaviours.

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).

Learning and motivation

Many researchers in AI and other disciplines (though not all) assume that
learning must be related to reward in some way, e.g. through positive or
negative reinforcement.

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.

Architecture-based motivation

Consider a very simple design for an organism or machine. It has a perceptual
system that forms descriptions of a process occurring in the environment. Those
descriptions are copied/stored in a data-base of "current beliefs" about what is
happening in the world or has recently happened.

Simple Architecture

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.

More complex variations

There is no need for the motive generating mechanism to be so simple. Some
motives triggered by perceiving a physical process could 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 10 of The Computer Revolution
in Philosophy (1978).

Mechanisms required

In humans it seems that architecture-based motivation plays a role at various
levels of cognitive development, and is manifested in early play and
exploration, and in intellectual curiosity later on, e.g. in connection with
things like mathematics or chess, and various forms of competitiveness.

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
        Evolution of minds and languages. What evolved first and develops first in children:
        Languages for communicating, or languages for thinking (Generalised Languages: GLs)
        Ontologies for baby animals and robots From "baby stuff" to the world of adult science:
        Developmental AI from a Kantian viewpoint.
        A New Approach to Philosophy of Mathematics: Design a young explorer, able to
        discover "toddler theorems" (Or: "The Naive Mathematics Manifesto").

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.


If all this is correct, then humans, like many other organisms, may have many
motives that exist not because having them benefits the individual but because
ancestors with the mechanisms that produce those motives in those situations
happened to produce more descendants than conspecifics without those mechanisms
did. Some social insect species in which workers act as 'slaves' serving the
needs of larvae and the queen appear to be examples. In those cases it may be
the case that

    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 suicidal behaviours in order to divert predators
from their children.

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.

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'.

[This document is still under construction. Suggestions for improvement
welcome. It is likely to change frequently in the first few years
of its life!]


This is one of a series of notes explaining how learning about underlying
mechanisms can alter our views about the 'logical topography' of a range of
phenomena, suggesting that our current conceptual schemes (Gilbert Ryle's
'logical geography') can be revised and improved, at least for the purposes of
science, technology, education, and maybe even for everyday conversation, as
explained in

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.

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 above had been written:

    R.W. White, 1959, Motivation reconsidered: The concept of competence,
    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,



I wish to thank Veronica Arriola Rios and Damien Duff for helpful comments on an earlier, less clear, draft. Luc Beaudoin drew my attention to the paper by White.

Maintained by Aaron Sloman
School of Computer Science
The University of Birmingham