PSYC 575 Cognitive Psychology

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  Goodale and Humphrey

Perception

DISCUSSION ASSIGNMENT INSTRUCTIONS

For each discussion, you are required to provide a discussion thread in response to the provided prompt for each discussion. Each discussion thread must be at
least 500 words, demonstrate course-related knowledge, and include at
least 3 scholarly sources,
not including the course text and/or Bible.

In addition to the discussion thread, you are required to reply to 2 other classmates’ discussion threads. Each discussion reply must be at least 200 words.

Discussion Thread: Goodale and Humphrey

After reading the articles, respond to the following prompts.

· What does the Goodale and Humphrey (1998) article mean by “the field’s preoccupation with vision as sight?”

· How did Goodale and Humphrey’s view of the ventral and dorsal visual stream differ from the earlier theory of Ungerleider and Mishkin (1982)?

· Discuss the evidence presented by Goodale and Humphrey to support their view. How has learning about the brain’s two separate visual systems changed the way you think about your own visual experience?

· Finally, Goodale and Humphrey (1998) refer to the two visual systems as having evolved. Compare and contrast the evolutionary approach to function of the brain with the Tripartite Man’s approach.

The cognitive revolution:
a historical perspective
George A. Miller

Department of Psychology, Princeton University, 1-S-5 Green Hall, Princeton, NJ 08544, USA

Cognitive science is a child of the 1950s, the product of

a time when psychology, anthropology and linguistics

were redefining themselves and computer science and

neuroscience as disciplines were coming into existence.

Psychology could not participate in the cognitive

revolution until it had freed itself from behaviorism,

thus restoring cognition to scientific respectability. By

then, it was becoming clear in several disciplines that

the solution to some of their problems depended cru-

cially on solving problems traditionally allocated to

other disciplines. Collaboration was called for: this is a

personal account of how it came about.

Anybody can make history. Only a

great man can write it.

Oscar Wilde’s aphorism is appropriate. At the time, the
suggestion that we were making history would have been
presumptuous. But anybody can make history; writing
history is another matter. I know something of the
scholarship required and nothing approaching it has
gone into the story I will tell here. But I offer this personal
account in the hope that it might interest and help the real
historians of science.

At the time it was happening I did not realize that I was,
in fact, a revolutionary, and two different stories became
intertwined in my life. They unfolded concurrently but I
will tell the psychological story first.

The cognitive revolution in psychology

The cognitive revolution in psychology was a counter-
revolution. The first revolution occurred much earlier
when a group of experimental psychologists, influenced by
Pavlov and other physiologists, proposed to redefine
psychology as the science of behavior. They argued that
mental events are not publicly observable. The only
objective evidence available is, and must be, behavioral.
By changing the subject to the study of behavior,
psychology could become an objective science based on
scientific laws of behavior.

The behavioral revolution transformed experimental
psychology in the US. Perception became discrimination,
memory became learning, language became verbal beha-
vior, intelligence became what intelligence tests test. By

the time I went to graduate school at Harvard in the early
1940s the transformation was complete. I was educated to
study behavior and I learned to translate my ideas into the
new jargon of behaviorism. As I was most interested in
speech and hearing, the translation sometimes became
tricky. But one’s reputation as a scientist could depend on
how well the trick was played.

In 1951, I published Language and Communication [1],
a book that grew out of four years of teaching a course at
Harvard entitled ‘The Psychology of Language’. In the
preface, I wrote: ‘The bias is behavioristic – not fanatically
behavioristic, but certainly tainted by a preference. There
does not seem to be a more scientific kind of bias, or, if there
is, it turns out to be behaviorism after all.’ As I read that
book today it is eclectic, not behavioristic. A few years
later B.F. Skinner published Verbal Behavior [2], a truly
behavioral treatment of language and communication. By
Skinner’s standards, my book had little or nothing to do
with behavior.

In 1951, I apparently still hoped to gain scientific
respectability by swearing allegiance to behaviorism. Five
years later, inspired by such colleagues as Noam Chomsky
and Jerry Bruner, I had stopped pretending to be a
behaviorist. So I date the cognitive revolution in psychol-
ogy to those years in the early 1950s.

Limitations of information theory

During those years I personally became frustrated in my
attempts to apply Claude Shannon’s theory of information
to psychology. After some initial success I was unable to
extend it beyond Shannon’s own analysis of letter
sequences in written texts. The Markov processes on
which Shannon’s analysis of language was based had the
virtue of being compatible with the stimulus–response
analysis favored by behaviorists. But information
measurement is based on probabilities and increasingly
the probabilities seemed more interesting that their
logarithmic values, and neither the probabilities nor
their logarithms shed much light on the psychological
processes that were responsible for them.

I was therefore ready for Chomsky’s alternative to
Markov processes. Once I understood that Shannon’s
Markov processes could not converge on natural language,
I began to accept syntactic theory as a better account of the
cognitive processes responsible for the structural aspects
of human language. The grammatical rules that govern
phrases and sentences are not behavior. They areCorresponding author: George A. Miller ([email protected]).

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mentalistic hypotheses about the cognitive processes
responsible for the verbal behaviors we observe.

The end of behaviorism

Behaviorism was an exciting adventure for experimental
psychology but by the mid-1950s it had become apparent
that it could not succeed. As Chomsky remarked, defining
psychology as the science of behavior was like defining
physics as the science of meter reading. If scientific
psychology were to succeed, mentalistic concepts would
have to integrate and explain the behavioral data. We were
still reluctant to use such terms as ‘mentalism’ to describe
what was needed, so we talked about cognition instead.

Whatever we called it, the cognitive counter-revolution
in psychology brought the mind back into experimental
psychology. I think it is important to remember that the
mind had never disappeared from social or clinical
psychology. It was only experimentalists in the US who
really believed that behaviorism would work. In my
own case, when I became dissatisfied at Harvard between
B.F. Skinner’s strict behaviorism and S.S. Stevens’
psychophysics, I turned to Jerry Bruner’s social psychol-
ogy, and in 1960 that led to the creation at Harvard of the
Center for Cognitive Studies. Bruner’s group at Bow
Street had been calling themselves the ‘Cognition Project’
for some time, so we simply changed it from a project to a
center. Bruner obtained a grant from the Carnegie
Corporation of New York and Dean Bundy gave us space
to house the enterprise. We assembled a group of bright
young graduates and a few senior scholars who shared our
interests. Peter Wason, Nelson Goodman and Noam
Chomsky had the most influence on my thinking at that
time.

Behaviorism flourished primarily in the US and this
cognitive revolution in psychology re-opened communi-
cation with some distinguished psychologists abroad. In
Cambridge, UK, Sir Frederic Bartlett’s work on memory
and thinking had remained unaffected by behaviorism. In
Geneva, Jean Piaget’s insights into the minds of children
had inspired a small army of followers. And in Moscow,
A.R. Luria was one of the first to see the brain and mind as
a whole. None of these three spent time at the Center but
we knew their work well. Whenever we doubted ourselves
we thought of such people and took courage from their
accomplishments.

I’m happy to say the Harvard Center for Cognitive
Studies was a success. The bright young graduates grew
up to become important psychologists unafraid of words
like mind and expectation and perception and memory. So
that was how I experienced the cognitive revolution in
psychology.

The cognitive revolution and cognitive science

While experimental psychologists were rethinking the
definition of psychology, other important developments
were occurring elsewhere. Norbert Wiener’s cybernetics
was gaining popularity, Marvin Minsky and John
McCarthy were inventing artificial intelligence, and
Alan Newell and Herb Simon were using computers to
simulate cognitive processes. Finally, Chomsky was
single-handedly redefining linguistics.

In the Historical Addendum to Newell and Simon’s
Human Problem Solving [3] they say: ‘1956 could be taken
as the critical year for the development of information
processing psychology’ (p. 878). This is not difficult to
justify. 1956 was the year that McCarthy, Minsky,
Shannon and Nat Rochester held a conference on artificial
intelligence at Dartmouth that was attended by nearly
everyone working in the field at that time. In 1956
Shannon and McCarthy edited Automata Studies [4],
and Minsky circulated a technical report that, after many
revisions, and 5 years later, became his influential article,
‘Steps toward artificial intelligence’ [5].

It was also in 1956 that Jerry Bruner, Jackie Goodenough
and George Austin published A Study of Thinking [6],
which took seriously the notion of cognitive strategies. In
1956 signal-detection theory was applied to perception by
Tanner, Swets, Birdsall and others at Michigan. I
published an article entitled ‘The magical number seven,
plus or minus two’ [7] describing some limits on our human
capacity to process information. In 1956 Ward Goodenough
and Floyd Lounsbury published several articles on
componential analysis that became models for cognitive
anthropology, and J.B. Carroll edited a collection of
papers by Benjamin Lee Whorf on the effects of language
on thought.

In short, 1956 was a good year for those interested in
theories of the mind, but it was only slightly better than
the years just preceding and following. Many were riding
the waves that began during World War II: those of servo
theory, information theory, signal-detection theory, com-
puter theory and computers themselves.

Moment of conception

Newell and Simon were right to put a finger on 1956, which
was not only crucial in their own development but for all of
us. Indeed, I can narrow it down even further. I date the
moment of conception of cognitive science as 11 September,
1956, the second day of a symposium organized by the
‘Special Interest Group in Information Theory’ at the
Massachusetts Institute of Technology [8]. At the time, of
course, no one realized that something special had
happened so no one thought that it needed a name; that
came much later.

The chairman of the organizing committee was Peter
Elias, who had only recently arrived at MIT from a Junior
Fellowship at Harvard. The first day, 10 September, was
devoted to coding theory, but it is the second day of the
symposium that I take to be the moment of conception for
cognitive science. The morning began with a paper by
Newell and Simon on their ‘logic machine’. The second
paper was from IBM: Nat Rochester and collaborators had
used the largest computer then available (an IBM 704 with
a 2048-word core memory) to test Donald Hebb’s neuro-
psychological theory of cell assemblies. Victor Yngve then
gave a talk on the statistical analysis of gaps and its
relation to syntax.

Noam Chomsky’s contribution used information theory
as a foil for a public exposition of transformational
generative grammar. Elias commented that other linguists
had told him that language has all the precision of
mathematics but Chomsky was the first linguist to back

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up the claim. His 1956 paper contained the ideas that he
expanded a year later in his monograph, Syntactic
Structures [9], which initiated a cognitive revolution in
theoretical linguistics.

To complete the second day, G.C. Szikali described some
experiments on the speed of perceptual recognition, I
talked about how we avoid the bottleneck created by our
limited short-term memory, and Swets and Birdsall
explained the significance of signal-detection theory for
perceptual recognition. The symposium concluded on the
following day.

I left the symposium with a conviction, more intuitive
than rational, that experimental psychology, theoretical
linguistics, and the computer simulation of cognitive
processes were all pieces from a larger whole and that
the future would see a progressive elaboration and
coordination of their shared concerns.

The birth of cognitive science

By 1960 it was clear that something interdisciplinary was
happening. At Harvard we called it cognitive studies, at
Carnegie-Mellon they called in information-processing
psychology, and at La Jolla they called it cognitive science.
What you called it didn’t really matter until 1976, when
the Alfred P. Sloan Foundation became interested.

The Sloan Foundation had just completed a highly
successful program of support for a new field called
‘neuroscience’ and two vice-presidents of the Foundation,
Steve White and Al Singer, were thinking that the next
step would be to bridge the gap between brain and mind.
They needed some way to refer to this next step and they
selected ‘cognitive science.’ They created a Sloan Special
Program in Cognitive Science in order to explore the
possibilities.

I learned of the Foundation’s interest in 1977 from
Kenneth A. Klivington, who was on the staff at the
Foundation. My recollection is that Ken had talked to
Marvin Minsky and others at MIT and was considering a
recommendation that the Foundation invest in artificial
intelligence. Shamelessly, I argued that in that case the
Foundation’s money would be spent buying computers. I
claimed that AI was merely part of a much larger
movement. At that time the Sloan Foundation was
sensitive to the charge that it had become part of the
MIT endowment, so my lobbying for a broader constitu-
ency was well received.

Interdisciplinary activities

I argued that at least six disciplines were involved:
psychology, linguistics, neuroscience, computer science,
anthropology and philosophy. I saw psychology, linguistics
and computer science as central, the other three as
peripheral. These fields represented, and still represent,
an institutionally convenient but intellectually awkward
division. Each, by historical accident, had inherited a
particular way of looking at cognition and each had
progressed far enough to recognize that the solution to
some of its problems depended crucially on the solution of
problems traditionally allocated to other disciplines.

The Sloan Foundation accepted my argument and a
committee of people from the several fields was assembled

to summarize the state of cognitive science in 1978, and to
write a report recommending appropriate action. The
committee met once, in Kansas City. It quickly became
apparent that everyone knew his own field and had heard
of two or three interesting findings in other fields. After
hours of discussion, experts in discipline X grew unwilling
to make any judgments about discipline Y, and so forth. In
the end, they did what they were competent to do: each
summarized his or her own field and the editors – Samuel
Jay Keyser, Edward Walker and myself – patched together
a report (Keyser, S.J., Miller, G.A., and Walker, E.,
Cognitive Science in 1978. An unpublished report sub-
mitted to the Alfred P. Sloan Foundation, New York).

Our report had one figure, which is reproduced here
(Fig. 1). The six fields are connected in a hexagon. Each
line in the figure represented an area of interdisciplinary
inquiry that was well defined in 1978 and that involved the
tools of the two disciplines it linked together. Thus,
cybernetics used concepts developed by computer science
to model brain functions elucidated in neuroscience.
Similarly, computer science and linguistics were already
linked through computational linguistics. Linguistics and
psychology are linked by psycholinguistics, anthropology
and neuroscience were linked by studies of the evolution of
the brain, and so on. Today, I believe, all fifteen possible
links could be instantiated with respectable research, and
the eleven links we saw as existing in 1978 have been
greatly strengthened.

The report was submitted, reviewed by another
committee of experts, and accepted by the Sloan Foun-
dation. The program that was initiated provided grants to
several universities with the condition that the funds be
used to promote communication between disciplines. One
of the smaller grants went to Michael Gazzaniga, then at
the Cornell Medical School, and enabled him to initiate
what has since become cognitive neuroscience. As a
consequence of the Sloan program, many scholars became
familiar with and tolerant of work in other disciplines. For
several years, interdisciplinary seminars, colloquia and
symposia flourished.

Fig. 1. Cognitive science in 1978. Each line joining two disciplines represents inter-

disciplinary inquiry that already existed in 1978.

TRENDS in Cognitive Sciences

Psychology

Computer
science

Linguistics

Anthropology

Neuroscience

Philosophy

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Cognitive sciences today

Unfortunately, the Alfred P. Sloan Foundation did not
follow up this initiative, but the interactions stimulated in
the early 1980s have left their mark. Some veterans of
those days question whether the program was successful,
and whether there really is something now that we can call
‘cognitive science’. For myself, I prefer to speak of the
cognitive sciences, in the plural. But the original dream of
a unified science that would discover the representational
and computational capacities of the human mind and their
structural and functional realization in the human brain
still has an appeal that I cannot resist.

References

1 Miller, G.A. (1951) Language and Communication, McGraw-Hill
2 Skinner, B.F. (1957) Verbal Behavior, Appleton-Century-Crofts
3 Newell,A.andSimon,H.A. (1972)HumanProblemSolving,Prentice-Hall
4 Shannon, C.E., McCarthy, J. eds (1956) Automata Studies, Annals of

Mathematics Studies (Vol. 34) Princeton University Press
5 Minsky, M. (1961) Steps toward artificial intelligence. Proc. IRE 49,

8–29
6 Bruner, J.S. et al. (1956) A Study of Thinking, John Wiley
7 Miller, G.A. (1956) The magical number seven, plus or minus two.

Psychol. Rev. 63, 81–97
8 Elias, P. et al. (1956) Information theory. IRE Trans. Information

Theory, IT-2(3)
9 Chomsky, N. (1957) Syntactic Structures, Mouton

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Review TRENDS in Cognitive Sciences Vol.7 No.3 March 2003144

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  • The cognitive revolution: a historical perspective
    • The cognitive revolution in psychology
      • Limitations of information theory
      • The end of behaviorism
    • The cognitive revolution and cognitive science
      • Moment of conception
    • The birth of cognitive science
      • Interdisciplinary activities
      • Cognitive sciences today
    • References

The objects of action and perception

Melvyn A. Goodale*, G. Keith Humphrey

Department of Psychology, University of Western Ontario, London, ON N6A 5C2, Canada

Abstract

Two major functions of the visual system are discussed and contrasted. One function of
vision is the creation of an internal model or percept of the external world. Most research in
object perception has concentrated on this aspect of vision. Vision also guides the control of
object-directed action. In the latter case, vision directs our actions with respect to the world by
transforming visual inputs into appropriate motor outputs. We argue that separate, but inter-
active, visual systems have evolved for the perception of objects on the one hand and the
control of actions directed at those objects on the other. This ‘duplex’ approach to high-level
vision suggests that Marrian or ‘reconstructive’ approaches and Gibsonian or ‘purposive-
animate-behaviorist’ approaches need not be seen as mutually exclusive, but rather as com-
plementary in their emphases on different aspects of visual function. 1998 Elsevier Science
B.V. All rights reserved

Keywords:Vision; Action; Perception

1. Introduction

It is a common assertion that the fundamental task of vision is to construct a
representation of the three-dimensional layout of the world and the objects and
events within it. But such an assertion begs at least two fundamental and interrelated
questions. First, what is vision? Second, what is the nature of the representation that
vision delivers? These questions, which are central to the entire research enterprise
in understanding human vision, form the framework for the present paper. In
attempting to answer these questions, we will contrast what we believe are two
major functions of the visual system. One function of vision is the creation of an
internal model or percept of the external world – a model that can be used in the

0010-0277/98/$19.00 1998 Elsevier Science B.V. All rights reserved
PII S0010-0277(98)00017-1

C O G N I T I O N

Cognition 67 (1998) 181–207

* Corresponding author. Tel.: +1 519 6612070; fax: +1 519 6613961; e-mail: [email protected]

recognition of objects and understanding their interrelations. Most research in object
vision has concentrated on this function (witness the current volume). There is
another function of vision, however, which is concerned not with object recognition,
but with object-directed action. In this case, vision guides our actions with respect to
the world by transforming visual inputs into appropriate motor outputs. We will
suggest that separate, but interacting, visual systems have evolved for the perception
of objects on the one hand and the control of actions directed at those objects on the
other. This ‘duplex’ approach to high-level vision suggests that Marrian or ‘recon-
structive’ approaches and Gibsonian or ‘purposive-animate-behaviorist’ approaches
need not be mutually exclusive and may be actually complementary.

2. What is vision?

Vision gives us sight. In other words, vision gives us an experience of the world
beyond our immediate body surface, a world full of objects and events that are
imbued with meaning and significance. Research in human psychophysics and
perception has concentrated almost entirely on the way in which the visual system
delivers this visual experience (for related discussions of this issue see Georgeson,
1997; Watt, 1991, 1992). Although a good deal of this research has concentrated on
‘low-level’ visual computations, even here it has been generally assumed that the
mechanisms supporting such computations are all part of the same general-purpose
system dedicated to the construction of the visual percept. This fascination with
what and how we ‘see’ has meant that many other functions of vision have either
been ignored or been assumed to depend on the same mechanisms supporting sight.
This preoccupation with vision as sight was nicely described 20 years ago by
Weimer (1977):

Since the time of Aristotle the mind has been regarded as intrinsically sensory
in nature, as a passive black box or window that is (somehow) sensibly
impressed with input from the environment. A root metaphor of mind has
evolved from the common-sense, everyday experience of looking at the world.
Vision, conceived as the passive reception of information that both exists and
possesses an intrinsic psychological character independently of the organism,
became the paradigm exemplar of mental processing (p. 268).

For most people then vision is synonymous with sight; there is nothing more to
vision than visual experience. Even Marr, who was perhaps the most influential
visual theorist in recent years, appears to endorse this ‘plain man’s’ conception of
vision (see p. 3 of Marr, 1982). Yet there is plenty of evidence that much of the work
done by the visual system has nothing to do with sight or experiential perception.
The pupillary light reflex, the synchronization of circadian rhythms with the local
light-dark cycle, and the visual control of posture are but three examples of a range
of visually modulated outputs where we have no direct experience of the controlling
stimuli and where the underlying control mechanisms have little to do with our

182 M.A. Goodale, G.K. Humphrey / Cognition 67 (1998) 181–207

perception of the world. Yet most contemporary accounts of vision, while acknowl-
edging the existence of these ‘extraperceptual’ visual phenomena, still assume that
the main function of the visual system is the construction of some sort of internal
model or percept of the external world (for a detailed discussion of this issue, see
Goodale, 1983a, 1988, 1997). In such accounts, phenomena such as the pupillary
light reflex are seen as simple servomechanisms which, while useful, are not part
of the essential machinery for the construction of the visual percept. But, as we shall
see later, the visual control of much more complex behaviours, such as reaching
out and grasping an object, also appear to depend on mechanisms that are function-
ally and neurally separate from those mediating our perception of that object.
Indeed, the origins of vision may be related more to its contribution to the control
of action than to its role in conscious perception, a function which appears to be a
relative newcomer on the evolutionary scene (Goodale, 1983a, 1988; Goodale et al.,
1996).

2.1. Vision for acting on the world

Vision in many animals can be studied without appealing to the idea of vision as
sight. The reason for this, of course, is that vision evolved in animals, not to enable
them to ‘see’ the world, but to guide their movements through it. Indeed, the visual
system of most animals, rather than being a general-purpose network dedicated to
reconstructing the rather limited world in which they live, consists instead of a set of
relatively independent input-output lines, or visuomotor ‘modules’, each of which is
responsible for the visual control of a particular class of motor outputs.

While evidence for separate visuomotor modules can be found in a broad range of
anatomical, electrophysiological, and behavioral studies, some of the most compel-
ling demonstrations have been provided by experiments with so-called ‘rewired’
frogs. Because the amphibian brain is capable of far more regeneration following
damage than the mammalian brain, it is possible to ‘re-wire’ some retinal projec-
tions, such as those going to the optic tectum in the midbrain, while leaving all the
other retinal projections intact. Thus, the retinotectal projections can be induced to
project to the optic tectum on the same side of the frog’s brain instead of to the optic
tectum on the opposite side, as is the case in the normal animal. In one such
experiment, these unfortunate creatures were shown to demonstrate ‘mirror-
image’ feeding – directing their snapping movements to positions in space that
were mirror-symmetrical to the location of prey objects (Ingle, 1973). They also
showed mirror-image predator avoidance and jumped towards rather than away
from the looming visual stimuli. These results suggest that the optic tectum plays
a critical role in the visual control of these patterns of behavior in the frog. Remark-
ably, however, the same ‘rewired’ frogs showed quite normal visually-guided bar-
rier avoidance as they locomoted from one place to another, even when the edge of
the barrier was placed in the visual field where mirror-image feeding and predator
avoidance could be elicited. As it turns out, the reason they showed normal visual
control of barrier avoidance is quite straightforward; the retinal projections to the
pretectum, a structure in the thalamus just in front of the optic tectum, were still

183M.A. Goodale, G.K. Humphrey / Cognition 67 (1998) 181–207

intact and had not been redirected to the opposite side of the brain. A number of
lesion studies have shown that this structure plays a critical role in the visual control
of barrier avoidance (Ingle, 1980, 1982). Thus, it would appear that there are at least
two independent visuomotor systems in the frog: a tectal system, which mediates
visually elicited prey-catching and predator-avoidance, and a pretectal system which
mediates visually guided locomotion around barriers. In fact, more recent work
suggests that there may be upwards of five or more distinct visuomotor networks
in the amphibian brain, each with its own set of retinal inputs and each controlling
different arrays of motor outputs (Ewert, 1987; Ingle, 1991).

The results of such studies, which point to a good deal of modularity in the
organization of the visuomotor circuitry in the frog, do not fit well with the common
view of a visual system dedicated to the construction of a general-purpose repre-
sentation of the external world. Although the outputs from the different visuomotor
systems described above need to be coordinated, it makes no sense to argue that
the different actions controlled by these networks are guided by a single visual
representation of the world residing somewhere in the animal’s brain. Of course,
the idea of separate visuomotor channels is consistent with the views of some visual
theorists who have argued that vision does more than mediate perception and sub-
serves the visual control of many the different actions that organisms carry out in
their daily lives. ‘Purposive vision’, as this approach is sometimes described, has
emphasized the role of vision in the direct control of actions rather than its con-
tribution to constructing percepts of the world in which those actions might unfold
(e.g. Aloimonos, 1990).

While there is certainly plenty of evidence to suggest that visuomotor modularity
of the kind found in the frog also exists in the mammalian brain (e.g. Ellard and
Goodale, 1986, 1988; Goodale, 1983b, 1996; Goodale and Carey, 1990; Goodale
and Milner, 1982), the very complexity of day-to-day living in many mammals,
particularly in higher primates, demands much more flexible organization of the
circuitry. In monkeys (and thus presumably in humans as well), there is evidence
that many of the phylogenetically ancient visuomotor circuits that were present in
more primitive vertebrates are now modulated by more recently evolved control
systems in the cerebral cortex (for review, see Milner and Goodale, 1995). Thus, the
highly adaptive visuomotor behavior of humans and other higher primates is made
possible by the evolution of another layer of control in a series of hierarchically
organized networks. This idea is reminiscent of the views of John Hughlings Jackson
(e.g. Jackson, 1875), an eminent nineteenth-century British neurologist who was
heavily influenced by concepts of evolution. Jackson tried to explain the effects of
damage to human cerebral cortex by suggesting that such damage removed the more
highly evolved aspects of brain function, so that what one saw in the performance of
many patients was the expression of evolutionarily older mechanisms residing else-
where in the brain. The emergence of more flexible visuomotor control has not been
accomplished entirely by cortical modulation of older circuitry however. The basic
subcortical circuitry has itself changed to some extent and new visuomotor control
systems have also emerged in which visual control of an almost limitless range of
motor outputs is possible. Nevertheless, as we shall see later, for the most part, these

184 M.A. Goodale, G.K. Humphrey / Cognition 67 (1998) 181–207

networks have remained functionally and neurally separate from those mediating
our visual perception of the world.

2.2. Vision for perceiving the world

Although the need for more flexible visuomotor control was one of the demands
on the evolving primate brain, another was related to the need to identify the objects,
to understand their significance and causal relations, to plan a course of action, and
to communicate with other members of the species. In short, the emergence of
cognitive systems and complex social behavior created a whole new set of demands
on vision and the organization of the visual system. Direct sensory control of action
was not enough. As interactions with the world become more complicated and
subtle, motor outputs became quite arbitrary with respect to sensory input. In fact,
many animals particularly humans and other primates, behave as though their
actions are driven by some sort of internal model of the world in which they live.
The representational systems that use vision to generate such models or percepts of
the world must carry out very different transformations on visual input than the
transformations carried out by the visuomotor modules described earlier (the nature
of these differences will be explored later). Moreover, these systems, which generate
our perception of the world, are not linked directly to specific motor outputs but are
linked instead to cognitive systems involving memory, semantics, spatial reasoning,
planning, and communication. But even though such higher-order representational
systems permit the formation of goals and the decision to engage in a specific act
without reference to particular motor outputs, the actual execution of an action may
nevertheless be mediated by dedicated visuomotor modules that are not dissimilar in
principle from those found in frogs and toads. In summary, vision in humans and
other primates (and perhaps in other animals as well) has two distinct but interactive
functions: (1) the perception of objects and their relations, which provides a founda-
tion for the organism’s cognitive life, and (2) the control of actions directed at (or
with respect to) those objects, in which specific sets of motor outputs are pro-
grammed and guided ‘on-line’.

3. Action and perception systems in the primate brain: dorsal and ventral
streams

The evolution of separate systems for visual perception and for the visual control
of action is reflected in the organization of the visual pathways in the primate
cerebral cortex. Over fifteen years ago, Ungerleider and Mishkin (1982) identified
two distinct ‘streams of processing’ in the macaque monkey brain: a so-called
ventral stream projecting from primary visual cortex to inferotemporal cortex and
a so-called dorsal stream projecting from primary visual cortex to posterior parietal
cortex (Fig. 1). Although one must always be cautious when drawing homologies
between monkey and human neuroanatomy (Crick and Jones, 1993), it seems likely
that the visual projections from the primary visual cortex to the temporal and parietal

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lobes in the human brain may involve a separation into ventral and dorsal streams
similar to that seen in the macaque brain. Ungerleider and Mishkin (1982) sug-
gested, on the basis of a number of electrophysiological studies in the monkey,
that the ventral stream plays a critical role in object vision, enabling the monkey
to identify an object, while the dorsal stream is involved in spatial vision, enabling
the monkey to localize the object in space. This interpretation, in which a distinction
is made between identification and localization, is similar to an earlier functional
dichotomy proposed by Schneider (1969), who argued that primary visual cortex
plays an essential role in identifying visual stimuli while the more ancient midbrain
structure, the superior colliculus (another name for the optic tectum in mammals), is
responsible for localizing the stimulus. Ungerleider and Mishkin (1982) have taken
this same distinction and moved it into the cerebral cortex. More recently, however,
Goodale and Milner (1992) (and Milner and Goodale, 1995) have offered a re-
interpretation of the apparent differences in the visual processing carried out by
the two streams of processing emanating from primary visual cortex. Rather than
emphasizing differences in the visual information handled by the two streams
(object vision versus spatial vision or ‘what’ versus ‘where’), their account has
instead focused on the difference in the requirements of the output systems that
each stream of processing serves.

According to Goodale and Milner, the ventral stream plays the major role in
constructing the perceptual representation of the world and the objects within it,
while the dorsal stream mediates the visual control of actions directed at those
objects (for a more detailed discussion, see Goodale and Milner, 1992; Milner
and Goodale, 1995). In other words, processing within the ventral stream allows
the monkey to recognize an object, such as a ripe piece of fruit dangling from a tree,
while processing within the dorsal stream provides critical information about the

Fig. 1. Major routes whereby retinal input reaches the dorsal and ventral streams. The diagram of the
macaque brain (right hemisphere) on the right of the figure shows the approximate routes of the cortico-
cortical projections from the primary visual cortex to the posterior parietal and the inferotemporal cortex
respectively. LGNd, lateral geniculate nucleus, pars dorsalis; Pulv, pulvinar; SC, superior colliculus.

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location, size, and shape of that fruit so that the animal can accurately reach out and
grasp it with its hand or mouth. Notice that in this account, information about object
attributes, such as size, shape, orientation, and spatial location, are processed by both
streams but the nature of that processing is very different. The functional distinction
is not between ‘what’ and ‘where’, but between the way in which the visual infor-
mation about a broad range of object parameters are transformed either for percep-
tual purposes or for the control of goal-directed actions. This is not to say that the
distribution of retinogeniculate inputs does not differ between the two streams, but
rather that the main difference lies in the nature of the transformations that each
stream performs on those two sets of inputs.

3.1. Neuropsychological studies of the dorsal stream

In the intact brain, the two streams of processing work together in a seamless
and unified fashion. Nevertheless, by studying individuals who have sustained
brain damage that spares one of these systems but not the other, it is possible
to get a glimpse of how the two streams differ in the way they each deal with
incoming visual information. For example, patients who have sustained damage
to the superior portion of the posterior parietal cortex, the major terminus of
the dorsal stream, are unable to use visual information to reach out and grasp objects
in the hemifield contralateral to the lesion. Clinically, this deficit is called optic
ataxia (Bálint, 1909). Such patients have no difficulty using other sensory informa-
tion, such as proprioception, to control their reaching; nor do they usually have
difficulty recognizing or describing objects that are presented in that part of the
visual field. Thus, their deficit is neither ‘purely’ visual nor ‘purely’ motor; it is a
visuomotor deficit.

Observations in several laboratories have shown that patients with optic ataxia
not only have difficulty reaching in the correct direction, but they also show deficits
in their ability to adjust the orientation of their hand when reaching toward an
object, even though they have no difficulty in verbally describing the orientation
of the object (e.g. Perenin and Vighetto, 1988). Such patients can also have trouble
adjusting their grasp to reflect the size of an object they are asked to pick up –
although again their perceptual estimates of object size remain quite accurate
(Jakobson et al., 1991; Goodale et al., 1993). To pick up an object successfully,
however, it is not enough to orient the hand and scale the grip appropriately;
the fingers and thumb must be placed at appropriate opposition points on the
object’s surface. To do this, the visuomotor system has to compute the outline
shape or boundaries of the object. In a recent experiment (Goodale et al., 1994b),
a patient (RV) with bilateral lesions of the occipitoparietal region, was asked
to pick up a series of small, flat, non-symmetrical smoothly contoured objects
using a precision grip, which required her to place her index finger and thumb
in appropriate positions on either side of each object. If the fingers were incor-
rectly positioned, the computation of the correct opposition points (‘grasp points’)
can be achieved only if the overall shape or form of the object is taken into
account. Despite the fact that the patient could readily distinguish these objects

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from one another, she often failed to place her fingers on the appropriate grasp points
when she attempted to pick up the objects (Fig. 2).

Such studies suggest that it is not only the spatial location of the object that is
apparently inaccessible for controlling movement in patients with dorsal-stream
lesions, but the intrinsic characteristics of the object as well. It would be incorrect
to characterize the deficits in these patients simply in terms of a disturbance of
spatial vision. In fact, in one clear sense their ‘spatial vision’ is quite intact, since
they can often describe the relative location of objects in the visual field contralateral
to their lesion, even though they cannot pick them up (Jeannerod, 1988). This pattern
of deficits is quite consistent with Goodale and Milner’s proposal that the dorsal
stream plays a critical role in the visuomotor transformations required for skilled
actions, such as visually guided prehension – in which the control of an accurate
grasp requires information about an object’s location as well as its orientation, size,
and shape. It should be emphasized, however, that not all patients with damage to the
posterior parietal region have difficulty shaping their hand to correspond to the
structural features and orientation of the target object. Some have difficulty with
hand postures, some with controlling the direction of their grasp, and some with
foveating the target. Indeed, depending upon the size and locus of the lesion, a
patient can demonstrate any combination of these visuomotor deficits (for review,
see Milner and Goodale, 1995). Different sub-regions of the posterior parietal cor-

Fig. 2. The ‘grasp lines’ (joining points where the index finger and the thumb first made contact with the
shape) selected by the optic ataxic patient (RV), the visual form agnosic patient (DF), and the control
subject when picking up three of the twelve shapes. The four different orientations in which each shape
was presented have been rotated so that they are aligned. No distinction is made between the points of
contact for the thumb and finger in these plots.

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tex, it appears, support transformations related to different motor outputs. Such
modularity within the dorsal stream means that a particular skilled action would
invoke certain combinations of these visuomotor networks and other actions would
invoke quite different combinations.

3.2. Neuropsychological studies of the ventral stream

As we have just seen, patients with optic ataxia often have difficulty reaching
towards and/or picking up objects that they have no difficulty identifying. Are there
patients who show the opposite pattern of visual deficits and spared visual abilities?
In other words, are there patients who can grasp objects quite accurately despite their
failure to recognize what it is they are attempting to pick up? One such patient is DF,
a young woman who developed a profound visual form agnosia following near-
asphyxiation by carbon monoxide. Not only is she unable to recognize the faces of
her relatives and friends or the visual shape of common objects, but she is also
unable to discriminate between such simple geometric forms as a triangle and a
circle. DF has no difficulty identifying people from their voices and she has no
problem identifying objects placed in her hands. Her perceptual problems are exclu-
sively visual. Moreover, her deficit, seems largely restricted to the form of objects.
She can use color and other surface features to identify objects and she can even use
shape from shading to some extent (Humphrey et al., 1994, 1996; Servos et al.,
1993). What she seems unable to perceive are the contours of objects – no matter
how the contours are defined (Milner et al., 1991). Thus, she cannot identify, shapes
whose contours are defined by differences in luminance or color, or by differences in
the direction of motion or the plane of depth. Not surprisingly, DF is also unable to
recognize shapes that are defined by the similarity or proximity of individual ele-
ments of the visual array. A selective deficit in form perception with spared color
and other surface information is characteristic of the severe visual agnosia that
sometimes follows an anoxic episode. Although MRI shows a pattern of diffuse
brain damage in DF that is consistent with anoxia, most of the damage was evident
in the ventrolateral region of the occipital lobe sparing primary visual cortex.

The profound deficit in DF’s form perception cannot be explained by disturbances
in ‘low-level’ sensory processing. In perimetry testing, she was able to detect lumi-
nance-defined targets at least as far out as 30 degrees (Milner et al., 1991). Her
spatial contrast sensitivity also appeared to be normal above about 10 cycles/degree
and was only moderately impaired at lower spatial frequencies (of course, even
though she could detect the presence of the gratings used to measure her contrast
sensitivity, she could not report their orientation; see also Humphrey et al., 1991).
But the most compelling reason to doubt that her perceptual deficit is due to some
sort of low level disturbance in processing is the fact that in another domain,
visuomotor control, she remains exquisitely sensitive to the form of objects!
Thus, despite her inability to recognize the shape, size, and orientation of objects,
she shows strikingly accurate guidance of hand and finger movements directed at
those very same objects. Thus, when she was presented with a pair of rectangular
blocks of the same or different dimensions, she was unable to distinguish between

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them. Even when she was asked to indicate the width of a single block by means of
her index finger and thumb, her matches bore no relationship to the dimensions of
the object and showed considerable trial to trial variability. In contrast, when she
was asked simply to reach out and pick up the block, the aperture between her index
finger and thumb changed systematically with the width of the object as the move-
ment unfolded, just as in normal subjects (Goodale et al., 1991). In other words, DF
scaled her grip to the dimensions of the object she was about to pick up, even though
she appeared to be unable to perceive those object dimensions.

A similar dissociation was seen in DF’s responses to the orientation of stimuli.
Thus, when presented with a large slot which could placed in one of a number of
different orientations, she showed great difficulty in indicating the orientation of the
slot either verbally or even manually by rotating a hand-held card (see Fig. 3, left).
Nevertheless, when she was asked simply to reach out and insert the card, she
performed as well as normal subjects, rotating her hand in the appropriate direction
as soon as she began the movement (see Fig. 3, right). Finally, even though DF could
not discriminate between target objects that differed in outline shape, she could
nevertheless pick up such objects successfully, placing her index finger and
thumb on stable grasp points (see Fig. 2).

Findings such as these are difficult to reconcile with the idea of Ungerleider and
Mishkin (1982) that object vision is the preserve of the ventral stream – for here we
have a patient in whom a profound loss of object perception exists alongside a
preserved ability to use object features such as size, outline shape, and orientation
to guide skilled actions. Such a dissociation, of course, is consistent with the idea

Fig. 3. Polar plots of the orientation of the hand-held card when DF and a control subject were each asked
to rotate the card to match the orientation of the slot (left column) or to ‘post’ the card into the slot (right
column). The orientation of the card on the visuomotor task was measured at the instant before the card
was placed in the slot. In both plots, the actual orientations of the slot have been normalized to vertical.

190 M.A. Goodale, G.K. Humphrey / Cognition 67 (1998) 181–207

proposed by Goodale and Milner (1992) that there are separate neural pathways for
transforming incoming visual information for action and perception. Presumably it
is the latter and not the former that is compromised in DF. In other words, the brain
damage that she suffered as a consequence of anoxia appears to have interrupted the
normal flow of shape and contour information into her perceptual system without
affecting the processing of shape and contour information by the visuomotor mod-
ules comprising her action system. If, as Goodale and Milner have suggested, the
perception of objects and events is mediated by the ventral stream of visual projec-
tions to inferotemporal cortex, then DF should show evidence for damage relatively
early in this pathway. Certainly, the pattern of damage revealed by MRI is consistent
with this interpretation; the major focus of cortical damage is in the ventrolateral
region of the occipital cortex, an area that is thought to be part of the human
homologue of the ventral stream. Primary visual cortex, which provides input for
both the dorsal and ventral streams, appears to be largely intact. Thus, although input
from primary visual cortex to the ventral stream may have been compromised in DF,
input from this structure to the dorsal stream appears to be essentially intact. In
addition, the dorsal stream, unlike the ventral stream, also receives input from the
superior colliculus via the pulvinar, a nucleus in the thalamus (see Fig. 1). Input to
the dorsal stream from both the superior colliculus (via the pulvinar) and the lateral
geniculate nucleus (via primary visual cortex) could continue to mediate well-
formed visuomotor responses in DF.

Nevertheless, it must not be forgotten that DF’s problems arose, not from a
discrete lesion, but from anoxia. Therefore, the brain damage in DF, while localized
to some extent, is much more diffuse than it would be in a patient with a stroke or
tumour. For this reason, any attempt to map the striking dissociation between per-
ceptual and visuomotor abilities in DF onto the ventral and dorsal streams of visual
processing must be regarded as tentative. The proposal is strengthened, however, by
observations in the patients described earlier whose pattern of deficits is comple-
mentary to DF’s and whose brain damage can be confidently localized to the dorsal
stream.

4. Electrophysiological and behavioural studies in the monkey

The functional division of labour between the two streams proposed by Goodale
and Milner is also supported by a large number of studies in the macaque monkey.
Thus, monkeys which show profound deficits in object recognition following infer-
otemporal lesions are nevertheless as capable as normal animals at picking up small
food objects (Klüver and Bucy, 1939), at catching flying insects (Pribram, 1967),
and at orienting their fingers in a precision grip to grasp morsels of food embedded in
small slots placed at different orientations (Buchbinder et al., 1980). In short, these
animals behave much the same way as DF: they are unable to discriminate between
objects on the basis of visual features that they can clearly use to direct their
grasping movements. In addition, there is a long history of electrophysiological
work showing that cells in this area are tuned to specific objects or object features.

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Moreover, the responses of these cells are not affected by the animal’s motor
behavior, but are instead sensitive to the reinforcement history and significance of
the visual stimuli that drive them (for review, see Goodale, 1993; Milner and Good-
ale, 1995). Indeed, sensitivity to particular objects can be created in ensembles of
cells in inferotemporal cortex simply by training the animals to discriminate
between different objects (Logothetis et al., 1995). Finally, there is evidence for a
specialization within separate regions of the ventral stream for the coding of certain
categories of objects, such as faces and hands, which are of particular social sig-
nificance to the monkey (for review, see Logothetis and Sheinberg, 1996; Perrett et
al., 1995).

In contrast to cells in the ventral stream, most visually-sensitive cells in the
dorsal stream are modulated by the concurrent motor behavior of the animal
(e.g. Hyvärinen and Poranen, 1974; Mountcastle et al., 1975). In reviewing the
electrophysiological studies that have been carried out on the posterior parietal
cortex, Andersen (1987) concluded that most neurons in these areas ‘exhibit
both sensory-related and movement-related activity’. The activity of some
visually-driven cells in this region have been shown to be linked to saccadic eye
movements; the activity of others to whether or not the animal is fixating a stim-
ulus; and the activity of still other cells to whether or not the animal is engaged
in visual pursuit or is making goal-directed reaching movements (e.g. Snyder
et al., 1997). Some cells in the posterior parietal area that fire when monkeys
reach out to pick up objects are selective not for the spatially directed movement
of the arm, but for the movements of the wrist, hand, and fingers that are made
prior to and during the act of grasping the target (Hyva¨rinen and Poranen,
1974; Mountcastle et al., 1975). In a particularly interesting recent development,
Sakata and his colleagues have shown that many of these so-called ‘manipulation’
cells are visually selective and are tuned for objects of a particular shape (Sakata et
al., 1992; Taira et al., 1990; for review see Sakata and Taira, 1994; Sakata et al.,
1997). These manipulation neurons thus appear to be tied to the properties of the
goal object as well as to the distal movements that are required for grasping that
object. Finally, it should be noted that lesions in the posterior parietal area in the
monkey produce deficits in the visual control of reaching and grasping similar in
many respects to those seen in humans following damage to the homologous region
(e.g. Haaxma and Kuypers, 1975; Ettlinger, 1977). This review of the monkey
literature is clearly far from complete. Interested readers are directed to Milner
and Goodale (1993; 1995).

5. Neuro-imaging studies in humans

Ten years ago little was known about the organization of the cerebral visual
pathways beyond V1 in humans. With the advent of functional neuroimaging, how-
ever, a wealth of data has suddenly become available. The careful work of Tootell et
al. (1996) has revealed an organization of visual areas in the human brain that is
remarkably similar to that seen in the macaque. Although clear differences in the

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topography of these areas emerges as one moves from monkey to human, the func-
tional separation into a ventral occipitotemporal and a dorsal occipitoparietal path-
way appears to be preserved. Thus, areas in the occipitotemporal region appear to be
specialized for the processing of colour, texture, and form differences of objects
(e.g. Puce et al., 1996; Price et al., 1996; Malach et al., 1995; Kanwisher et al.,
1996). In contrast, regions in the posterior parietal cortex have been found that are
activated when subjects engage in visually guided movements such as saccades,
reaching movements, and grasping (Matsumura et al., 1996).

As in the monkey, there is evidence for specialization within the occipitotemporal
and occipitoparietal visual pathways. Thus, activation studies have identified
regions in the occipitotemporal pathway for the processing of faces that are distinct
from those involved in the processing of other objects (Kanwisher et al., 1997;
Gauthier et al., 1997). Similarly, there is evidence that different areas in and around
the intraparietal sulcus are activated when subjects make saccadic eye movements as
opposed to manual pointing movements towards visual targets (e g. Kawashima et
al., 1996).

Thus, the neuroimaging data are consistent with the idea of two visual streams. In
addition, the results of several studies indicate that areas in the posterior parietal
cortex are involved in the visual control of action while areas in the occipitotemporal
region appear to play a role in object recognition.

6. Differences in the visual transformations mediating action and perception

The division of labour within the organization of the cerebral visual pathways in
primates reflects the two important trends in the evolution of vision in higher
vertebrates that were identified earlier. First, the emergence of a dorsal ‘action’
stream reflects the need for more flexible programming and on-line control of
visually guided motor outputs. It is interesting to note that this stream is intimately
connected not only with the primate forebrain but also with those brainstem struc-
tures such as the superior colliculus and various pontine nuclei that play a critical
role in the programming and control of movement in all vertebrates (Milner and
Goodale, 1995). Thus, one way that the dorsal stream may mediate the visual control
of skilled actions is by modulating the activity of these more phylogenetically
ancient visuomotor networks.

Second, the emergence of a ventral ‘perception’ stream which can parse the visual
array into discrete objects and events means that animals like ourselves can use
perceptual representations of those objects and their relations for long-range plan-
ning, communication and other cognitive activities. Although a separate system for
this kind of reconstructive visual activity is evident in the cerebral cortex of many
mammals (Goodale and Carey, 1990), it is particularly well-developed in humans
and other higher primates. Indeed, the ventral stream projections to the inferotem-
poral cortex, which is intimately connected with structures in the medial temporal
lobe and prefrontal cortex involved in long-term memory and other cognitive activ-
ities, is exquisitely poised to serve as interface between vision and cognition. In

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short, while the dorsal stream allows us visual control of our movements through the
world, it is the ventral stream that gives us sight.

The distinction between vision for perception and vision for action is similar in
many respects to a distinction that Neisser (1989, 1994) has drawn between what he
calls ‘recognition systems’ on the one hand and ‘direct perception systems’ on the
other. According to Neisser, recognition systems mediate the identification and
classification of objects through the accumulation of evidence in relation to stored
representations. Recognition is always defined as a relation of present input to the
past, i.e. in relation to stored information about objects. Direct perception for Neis-
ser is something quite different. Following Gibson (1979), he proposes that the
direct perception system provides information about where we are, where objects
are, and what physical actions those objects can afford – information that is pro-
vided as the animal moves through the world and interacts with it. He proposes that
Gibson’s concept of affordance be limited to the notion of a ‘physical affordance’
since such affordances are entirely specified by the physics of light and do not
depend on stored semantic knowledge about the objects – which is the business
of the recognition systems. The possibility of picking up an object such as a rock and
throwing it does not depend on identifying the object but rather the ‘fit’, or physical
relationship, between our effector organs and the object. In short, the task of direct
perception is the programming and on-line control of action.

The type of information stored and used in recognition is quite different from that
used in the control of action. One example that Neisser (1989) uses to illustrate the
difference between recognition and direct perception is the way each system deals
with the orientation of objects. As several papers in this volume attest to, a large
amount of recent research on object orientation has been concerned with the effects
of object orientation on recognition. Our recognition of an object often suffers
greatly if its orientation does not match the orientation that we have experienced
in the past (e.g. Edelman and Bu¨lthoff, 1992; Humphrey and Khan, 1992; Rock and
DiVita, 1987; Tarr, 1995; see also Biederman and Gerhardstein, 1995; Tarr and
Bülthoff, 1995; for review see Jolicoeur and Humphrey, 1998). In sharp contrast, our
ability to direct a well-formed grasp at an object is not dependent on prior familiarity
with a particular orientation; in fact, we do not need to recognize the object to grasp
it efficiently.

While Neisser’s distinction between recognition and direct perception converges
on our own ideas to some extent, there are some critical differences. For us, the
action system (similar to Neisser’s direct perception system) is entirely concerned
with providing visual information for the programming and control of motor out-
puts. This system contains an array of dedicated visuomotor modules which, when
activated in various combinations, transform visual inputs into directed motor acts.
Neisser, however, suggests that our perception of the spatial location of objects and
their relations is dependent on the direct perception system; the recognition system
for Neisser seems to be concerned only with identifying and classifying objects. In
our scheme, the visuomotor modules that make up the action system do not parti-
cipate in the construction of perceptual representations of the layout or disposition of
objects for cognitive purposes. Instead, it is the perception system which does this.

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Like Neisser, we see the perception system (similar to his recognition system) as
being intimately linked with cognitive processes such as long-term memory; but
unlike Neisser, we see the perception system as providing information, not only
about the identity of objects, but also about their spatial and temporal relations. In
our scheme, the perception system delivers our experience of the world and the
objects within it.

Clearly what distinguishes the perception system from the visuomotor modules
making up the action system is the way in which the visual world is represented in
the brain. Of course, the notion of representation is one of the central ideas in
perception and cognition, although the type(s) of representations used in visual
perception and the very notion of representation itself have been the source of
much debate. Nevertheless, the goal of visual perception is often taken to be the
creation of a representation that is in some sense an internal model of the three-
dimensional world. In this sense, a representation is a reconstruction of the world
(for further critical discussion of this approach see Ballard and Brown, 1992;
Churchland et al., 1994; Tarr and Black, 1994 and accompanying commentaries).
This approach to vision is exemplified by Marr (1982) who concentrated on the
representation of information about objects for the purposes of recognition. Accord-
ing to this approach, the major task of recognition is to reconstruct a detailed and
accurate model or replica of the three-dimensional world on the basis of the two-
dimensional data present at the retinas.

Presumably, the proposed representation is not only important for recognition, but
plays a crucial role in other cognitive activities related to spatial reasoning and the
semantics of objects and scenes. It is the construction of this kind of representation
that we see as the major function of the perception system – a kind of ‘general
purpose’ representation that can serve as the substrate upon which a large range of
cognitive operations can be mounted (in fact, the cognitive operations are them-
selves intimately involved in the construction of the representation upon which they
operate). Of course, the nature of representations used for recognition and other
cognitive acts is far from settled. A large proportion of recent research in object
vision has been directed at uncovering the nature of this presentation as other papers
in this volume attest. It is also clear that although Marr’s approach to object recog-
nition has been very influential, recognition need not entail reconstruction in the way
he envisaged.

Our perception of the world certainly appears remarkably rich and detailed.
Nevertheless much of this perceptual representation is ‘virtual’ and is derived
from memory rather than visual input, (e.g. McConkie and Currie, 1996; O’Regan,
1992; Rensink et al., 1997). Much of the metric information about objects and
their relations is inaccurate and even unavailable (for review, see Intraub, 1997).
And in any case, the metrical information is not computed with reference to
the observer as much as it is to other objects in a visual array (Goodale and
Haffenden, 1998). Indeed, if perceptual representations were to attempt to deliver
the real metrics of all objects in the visual array, the computational load would
be astronomical. The solution that perception appears to have adopted is to use
world-based coordinates – in which the real metric of that world need not be

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computed. Only the relative position, orientation, size and motion of objects is of
concern to perception. For example, we can watch the same scene unfold on tele-
vision or on a movie screen without be confused by the enormous change in the
coordinate frame.

As soon as we direct a motor act towards an object, an entirely different set of
constraints applies. We can no longer rely on the perception system’s ‘general
purpose’ representation. We could not, for example, direct actions towards what
we see on television, however compelling and ‘real’ the depicted scene might be. To
be accurate, the actions must be finely tuned to the metrics of the real world. More-
over, different actions will engage different effectors. As a consequence, the com-
putations for the visual control of actions must not only take into account the real
metrics of the world, they must be specific to the particular motor output required.
Directing a saccadic eye movement, for example, will demand different transforma-
tions of visual input to motor output from those required to direct a manual grasping
movement. The former will involve coordinate systems centred on the retina and/or
head, while the latter will involve shoulder and/or wrist centered coordinates. While
it is theoretically possible that a highly sophisticated ‘general-purpose’ representa-
tion could accommodate such transformations, such a possibility seems unlikely and
unnecessary. Indeed, as we saw earlier, the empirical evidence from a broad range of
studies indicates that visuomotor control in humans and other primates is organized
in much the same way as it is in simpler vertebrates, such as the frog (for review see
Milner and Goodale, 1995). Moreover, these different visuomotor modules work in
real time with only limited ‘memory’. In other words, once a movement is made the
visuomotor coordinates used to program and guide that movement are lost. Even if
the movement is not performed, the coordinates cannot be stored much beyond a
second or two (e.g. Gnadt et al., 1991; Goodale et al., 1994a). Perception of course
has a much longer time course and stores information perhaps in some cases for a
lifetime. In summary, the visuomotor modules within the action system transform
sensory information directly into motor output rather than using reconstructions of
visual scenes. Moreover, as will be described below, such transformations are not
available to consciousness in the way that outputs of perceptual processes usually
are.

7. Dissociations between action and perception in normal subjects

Although the visual fields of the two eyes together span about 200°, most of our
perceptual experience is confined to the few degrees subtended by the foveal and
parafoveal region. In short, we see what we are looking at. Yet as we move through
the world, stepping over curbs, negotiating doorways, and grasping door handles, we
often utilize visual information from the far periphery of vision. This differential use
of the fovea and peripheral visual fields by perception and action systems may
explain why in the monkey there is differential representation of these regions in
the ventral and dorsal streams. The receptive fields of cells in the inferotemporal
cortex almost always include the fovea and very little of the far peripheral visual

196 M.A. Goodale, G.K. Humphrey / Cognition 67 (1998) 181–207

fields whereas cells in the posterior parietal cortex have a very large representation
of the peripheral visual fields (Baizer et al., 1991). Indeed, in some areas of the
dorsal stream, such as the parieto-occipital area, the portion of cortex devoted to the
fovea is no larger than would be expected on the basis of the extent of the visual field
it subtends; i.e. there is no ‘cortical magnification’ of central vision (Gattass et al.,
1985).

If a similar retinotopic organization of cortical areas exists in the human brain,
then one might expect that the visual control of motor behavior might be quite
sensitive to differences in visual stimuli presented in the far peripheral visual field
whereas perceptual judgements of the same stimuli might be relatively insensitive.
In a recent experiment, Goodale and Murphy (1997) presented subjects with five
different rectangular objects of the same overall size but different dimensions. These
objects were presented randomly at different retinal eccentricities that varied from 5
to 70° and subjects were required to categorize each object into one of five pre-
viously learned categories or, in another block of trials, to reach out and grasp the
object across its longitudinal axis. As one might expect, the variability of the sub-
jects’ perceptual categorizations increased substantially as the objects were pre-
sented at more and more eccentric locations. In sharp contrast, the relationship
between the aperture of their grasp (before contact) and the width of the object
was as well-tuned at 70° as it was at 5°. There was also another striking difference
between subjects’ perceptual judgements of the width of the objects and the calibra-
tion of their grasp. Although the subjects reported that objects did not look as wide in
the far periphery as the same objects in more parafoveal regions, the aperture of their
grasp was actually larger for objects in the peripheral visual field (even though the
grasp continued to be well-calibrated with respect to the object’s dimensions). These
dissociations between verbal reports and visuomotor control again emphasize the
specialization of different parts of the visual system for perception and action.

Another way to demonstrate the distinction between perception and action sys-
tems in vision is to look at the way each system deals with objects embedded in
pictorial illusions. Pictorial illusions are, of course, favourite ways of illustrating
interpretive and context-sensitive aspects of visual perception. Consider the Ebbin-
ghaus (or Titchener Circles) Illusion for a moment. In this familiar illusion, two
target circles of equal size, each surrounded by a circular array of either smaller or
larger circles, are presented side by side (see Fig. 4a). Subjects typically report that
the target circle surrounded by the array of smaller circles appears larger than the
one surrounded by the array of larger circles, presumably because of the difference
in the contrast in size between the target circles and the surrounding circles. It is also
possible to make the two target circles appear identical in size by increasing the
actual size of the target circle surrounded by the array of larger circles (see Fig. 4b).

While our perceptual judgements of what we see are clearly affected by the
manipulations of the stimulus array, there is good reason to believe that the calibra-
tion of size-dependent motor outputs, such as grip aperture during grasping, would
not be. After all, when we reach out to pick up an object, particularly one we have
not seen before, the visuomotor networks controlling grasping must compute the
size (and distance) of the object accurately if we are to pick it up efficiently. It is not

197M.A. Goodale, G.K. Humphrey / Cognition 67 (1998) 181–207

enough to know that the target object is larger or smaller than surrounding objects;
the visuomotor module controlling hand aperture must compute its real size. For this
reason, one might expect grip scaling to be refractory to size-contrast illusions.

To test this possibility, Aglioti et al. (1995) developed a three-dimensional ver-
sion of the Ebbinghaus Illusion in which two thin ‘poker-chip’ discs were used as the
target circles. The disks were arranged as pairs on a standard Ebbinghaus annular
circle display (see Fig. 5) drawn on a white background and positioned directly in
front of the subject. Trials in which the two disks appeared perceptually identical but
were physically different in size were randomly alternated with trials in which the
disks appeared perceptually different but were physically identical. The left-right
position of the arrays of large and small circles was of course randomly varied
throughout. Subjects (all of whom had normal vision) were given the following
instructions: if the discs appear equal in size, pick up the one on the right; if they
appear different, pick up the one on the left. Subjects used their right hand and grip
aperture was tracked using standard opto-electronic recording.

Although there was considerable individual variation, all the subjects remained
sensitive to the size-contrast illusion throughout testing. In other words, their choice
of disk was affected by the contrast in size between the disks and the surrounding
circles. As a consequence, they treated disks that were actually physically different

Fig. 4. The ‘Ebbinghaus’ illusion. The standard version of the illusion, the target circles in the centre of the
two arrays appear to be different in size even though they are physically identical, as shown in (A). For
most people, the circle in the annulus of smaller circles appears to be larger than the circle in the annulus
of larger circles. (B) Shows a version of the illusion in which the target circle in the array of large circles
has been made physically larger than the other target circle. The two target circles should now appear to be
perceptually equivalent in size.

198 M.A. Goodale, G.K. Humphrey / Cognition 67 (1998) 181–207

in size as perceptually equivalent and they treated disks that were physically iden-
tical as perceptually different. Remarkably, however, the scaling of their grasp was
affected very little by these beliefs. Instead, the maximum grip aperture, which was
achieved approximately 70% of the way through the reach towards the disk, was
almost entirely determined by the true size of that disk. Thus, on trials in which the
two disks were perceived as being the same size, subjects opened their hand wider
for the larger disk than they did for the smaller one. An example of such a case in
illustrated in Fig. 6a. In fact, as shown in Fig. 6b, the difference in grip aperture for
large and small disks was the same for trials in which the subject believed the two
disks were equivalent in size (even though they were different) as it was for trials in
which the subject believed the two disks were different in size (even though they
were identical). In short, the calibration of grip size seemed to be largely impervious
to the effects of the size-contrast illusion. This difference in the susceptibility of
perceptual judgements and the visual control of prehension was replicated in a
recent study in which subjects had no opportunity to compare their hand opening
with the goal object during the execution of the movement (Haffenden and Goodale,
1998).

The dissociation between perceptual judgements and the calibration of grasping is
not limited to the Ebbinghaus Illusion. The vertical-horizontal illusion is one in
which a vertical line that bisects a horizontal line appears longer than the horizontal
line even though both lines are in fact the same length. Vishton and Cutting (1995)
have recently demonstrated that even though subjects show the usual bias in their
judgements of line length, they did not show a bias when they attempted to reach out
and ‘grasp’ the lines. The relative insensitivity of reaching and grasping to pictorial
illusions has also been demonstrated for the Mu¨ller-lyer illusion (Gentilucci et al.,
1996) and the Ponzo illusion (Ian Whishaw, personal communication).

But why should perception be so susceptible to these illusions while the calibra-

Fig. 5. A line drawing of our three-dimensional version of the Ebbinghaus illusion. Note the infra-red light
emitting diodes (IREDs) attached to the finger, thumb and wrist of the subject.

199M.A. Goodale, G.K. Humphrey / Cognition 67 (1998) 181–207

tion of grasp is not. Take the Ebbinghaus illusion for example. It is possible that the
illusion arises from a straightforward relative-size scaling mechanism, whereby an
object that is smaller than its immediate neighbors is assumed to be smaller than a
similar object that is larger than its immediate neighbors (for review, see Coren and
Girgus, 1978). It is also possible that a computation relating image size and distance
is responsible for the illusion. If the array of smaller circles is assumed to be more
distant from the observer than the array of larger circles, then the target circle within
the array of smaller circles will also be perceived as more distant, and therefore
larger, than the target circle of equivalent retinal image size within the array of larger
circles. In other words, the illusion might simply be a consequence of the perception
system’s attempt to make size constancy judgments on the basis of an analysis of the
entire visual array (Gregory, 1963).

Mechanisms such as these, in which the relations between objects in the visual
array play a crucial role in scene interpretation, are clearly central to the operation of
the perception system. As we gaze across the landscape, some of the objects within
our field of view will be perceived, in an obligatory fashion, as larger or closer than
others. Perception is by its very nature relative. In contrast, the execution of a goal-
directed act like manual prehension depends on metrical computations that are
centered on the target itself. Moreover, the visual mechanisms within the action
system that mediate the control of the grasping movements must compute the real
distance of the object (presumably on the basis of reliable cues such as stereopsis
and retinal motion). As a consequence, computation of the retinal image size of the
object coupled with an accurate estimate of distance will deliver the true size of the
object for calibrating the grip – and such computations may be quite insensitive to

Fig. 6. Graphs illustrating grip aperture in different testing conditions. (A) Representative grip aperture
profiles for one subject picking up a large disc (solid line) and a small disc (broken line) on separate trials
in which he judged the two discs to be identical in size (even though, of course, the two discs were
physically quite different). In both cases, the disc was located on the left hand side of the display. (B)The
mean maximum grip aperture for the 14 subjects in different testing conditions. The two solid bars on the
right indicate the maximum aperture on trials in which the two discs were judged to be perceptually the
same even though they were physically different in size. The two open bars on the left indicate the mean
maximum aperture on trials in which the two discs were judged to be perceptually different even though
they were physically the same size (either two large discs or two small discs).

200 M.A. Goodale, G.K. Humphrey / Cognition 67 (1998) 181–207

the kinds of pictorial cues that drive our perception of familiar illusions. Thus, the
very act by means of which subjects indicate their susceptibility to the illusion (i.e.
picking up one of the two target circles) is itself unaffected by the visual information
driving that illusion. This paradox demonstrates that what we think we ‘see’ is not
always what guides our actions. It also provides evidence for the parallel operation
of the two kinds of visual processing that we described earlier, each apparently
designed to serve quite different purposes, and each characterized by quite different
properties.

8. The action/perception distinction in computational vision

We would suggest that the distinction between vision for perception and vision for
action is relevant to some aspects of a current debate in the computational vision
literature. The debate could be characterized as one between ‘behaviorist or purpo-
sive’ approaches and ‘reconstructive’ approaches to vision. Here we will make some
general remarks that capture only some aspects of the various positions in the debate
as there are many theoretical divergences within both ‘behaviorist’ and ‘reconstruc-
tionist’ proposals that we are overlooking. We are obviously oversimplifying in the
belief that such a caricature captures some significant agreement and divergences in
general orientation that can be mapped on to the distinctions in perception and action
systems that we have proposed. The reader should refer to Tarr and Black (1994) and
the accompanying commentaries on that paper for more detail. Other relevant papers
that could be consulted are Ballard and Brown (1992), Churchland et al. (1994),
Jolion (1994), Sloman (1989) and the collection of papers edited by Aloimonos
(1992).

Researchers who espouse behaviorist approaches to vision are often quite sympa-
thetic to the general framework of Gibson (1979) in his emphasis on the active,
exploratory nature of perception. As a consequence they concentrate on visual
behavior such as obstacle avoidance, reaching and grasping, gaze control, and
other aspects of behavior guided by visual input. It is essentially a ‘motor’ view
of perception (Churchland et al., 1994; Watt, 1993). We would suggest that many of
the visuomotor transformations that occupy the attention of these researchers are
part of the dorsal action system and its associated subcortical and cortical networks.
Thus, the preoccupation with visually guided actions that characterizes behaviorist
approaches to vision has meant that most of the visual mechanisms that are being
studied are those found in the dorsal stream.

In contrast, the reconstructive approach (e.g. Marr, 1982) concentrates on the
creation of a replica of the world ‘out there’ on the basis of the sensory input data
present at the retinas. In a sense, the approach of Marr is a ‘passive’ view of
perception in which the representation is central and the external behavior of the
organism is largely ignored (Ballard and Brown, 1992). For Marr, vision is ‘an
information processing task’ and visual science is conceived as an ‘inquiry into
the nature of the internal representations by which we capture this information
and thus make it available as a basis for decisions about our thoughts and actions’

201M.A. Goodale, G.K. Humphrey / Cognition 67 (1998) 181–207

(p. 3). This approach to vision need not be seen as opposing the behaviorist
approach. Indeed, we would suggest that reconstruction of the external world is
exactly the kind of activity which we believe is carried out by the ventral stream.
Of course, as noted above, there is considerable debate about the way in which
visual mechanisms and stored representations interact in visual perception. What-
ever the particular mechanisms might be that underlie recognition and other percep-
tual/cognitive operations, it is the ventral stream, we believe, that carries them out.
Moreover, the ‘awareness’ that typically accompanies much of visual perception
may also depend, in part, on the ventral stream pathways (for some speculative
accounts of routes to visual awareness, see Crick and Koch, 1995; Goodale and
Milner, 1992; Milner and Goodale, 1995).

Thus, it seems to us that at least some aspects of the arguments about the relative
merits of ‘behaviorist’ and ‘reconstructionist’ approaches to vision are misplaced.
The two approaches are concerned with different visual systems that have different
agendas (see also Neisser, 1989, 1994). Of course we realize that the drawing of
parallels between ‘reconstructionist’ and ‘behaviorist’ approaches and the ventral
and dorsal streams of visual processing will in no way settle the many issues that
concern researchers in computational vision. Nevertheless, we hope our suggestion
will supply some useful distinctions for thinking about these issues.

9. Getting it together: interactions between action and perception

Throughout this paper, we have been advancing the idea that the ventral percep-
tion system and the dorsal action system are two independent and decidedly differ-
ent visual systems within the primate brain. We realize that in doing this we have
overstated our position to some extent. This was a deliberate attempt to counter the
tendency in object vision research to focus on issues such as recognition and other
cognitive operations, without taking into account the actions that are performed on
objects and the particular visuomotor transformations necessary for such actions. It
is obvious that systems for action and those for perception must interact and coop-
erate in the control of behavior. Nevertheless, as we have tried to show, the compu-
tations carried out by the two systems complement one another.

One way to think about the interaction between the two streams, an interaction
that would take advantage of the differences in their computational constraints, is in
terms of a ‘teleassistance’ model. In teleassistance, a human operator uses a sym-
bolic code to communicate with a robot that actually performs the required motor act
on the marked goal object (Pook and Ballard, 1996). In terms of this teleassistance
metaphor, the perceptual-cognitive system in the ventral stream, with its rich and
detailed representations of the virtual scene, would be the human operator. Processes
in the ventral stream identify a particular goal and flag the relevant object in the
scene, perhaps by means of an attention-like process. Once a particular goal object
has been flagged, dedicated visuomotor networks in the dorsal stream (in conjunc-
tion with related circuits in premotor cortex, basal ganglia, and brainstem) can then
be activated to perform the desired motor act. Mechanisms in the dorsal stream,

202 M.A. Goodale, G.K. Humphrey / Cognition 67 (1998) 181–207

while not delivering anything like the visual detail provided by perception, do
provide accurate information about the goal object in effector-specific frames of
reference – and provide this information quickly. This means that a flagged object in
the scene will be processed in parallel by both ventral and dorsal stream mechanisms
– each transforming the visual information in the array for different purposes.

Thus, once a goal object has been selected for goal-directed action, the two
systems process incoming visual information simultaneously – even though the
nature of the visual information that is transformed might be rather different.
Such simultaneous activation will, of course, provide us with visual experience
(via the ventral stream) during the performance of a skilled action (mediated by
the dorsal stream). For example, when we reach out to pick up an interesting book,
we not only form our grasp according to the dimensions and location of that book but
at the same time we might also perceive that it is one we have not seen before.
Moreover, certain objects such as tools demand that we grasp the object in a parti-
cular way so that we can use it properly. In such a case both streams would have to
interact fairly intimately in mediating the final motor output. Certainly there is
evidence that, on the neural level, the two systems are interconnected allowing
for communication and cooperation between them (reviewed in Goodale and Mil-
ner, 1992; Milner and Goodale, 1995). Thus, although there is clearly a division of
labour between the perception and action systems, this division reflects the com-
plementary role the two systems play in the production of adaptive behaviour.

Acknowledgements

The preparation of this manuscript was helped in part by grants from the Medical
Research Council of Canada to M.A.G. and the Natural Sciences and Engineering
Research Council to G.K.H.

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