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References
Goldsmith, J. (1990). Autosegmental and Metrical Phonology.
Oxford: Blackwell.
Goldsmith, J. (1994). Tone languages. Encyclopedia of Language
and Linguistics. Pergamon Press.
Goldsmith, J., Ed. (1995). The Handbook of Phonological Theory.
Oxford: Blackwell.
Hyman, L. (1978). Tone and/or accent. In D. J. Napoli, Ed., Ele-
ments of Tone, Stress, and Intonation. Washington, DC: Geor-
getown University Press, pp. 1–20.
McCawley, J. (1978). What is a tone language? In V. A. Fromkin,
Ed., Tone: A Linguistic Survey. New York: Academic Press, pp.
113–131.
Odden, D. (1995). Tone: African languages. In J. Goldsmith, Ed.,
The Handbook of Phonological Theory. Oxford: Blackwell, pp.
444–475.
Pike, K. (1948). Tone Languages: A Technique for Determining the
Number and Type of Pitch Contrasts in a Language, with Stud-
ies in Tonemic Substitution and Fusion. University of Michigan
Publications in Linguistics, no. 4. Ann Arbor: University of
Michigan Press.
Yip, M. (1995). Tone in East Asian languages. In J. Goldsmith,
Ed., The Handbook of Phonological Theory. Oxford: Black-
well, pp. 476–494.
Further Readings
Beckman, M., and J. Pierrehumbert. (1986). Intonation structure in
Japanese and English. Phonology Yearbook 3: 255–309.
Bolinger, D. (1985). Two views of accent. Journal of Linguistics
21: 79–123.
Duanmu, S. (1996). Tone: An overview. Glot International 2: 3–10.
Fromkin, V., Ed. (1978). Tone: A Linguistic Survey. New York:
Academic Press.
Hyman, L., and R. Schuh. (1974). Universals of tone rules: Evi-
dence from West Africa. Linguistic Inquiry 5: 81–115.
Pulleyblank, D. (1986). Tone in Lexical Phonology. Dordrecht:
Reidel.
van der Hulst, H., and N. Smith, Eds. (1988). Autosegmental Stud-
ies on Pitch Accent. Dordrecht: Foris.
Top-Down Processing in Vision
Perception represents the immediate present, what is hap-
pening around us as conveyed by the pattern of light falling
on our RETINA. And yet the current pattern of light alone
cannot explain the stable, rich experience we have of our
surroundings. The problem is that each retinal image could
have arisen from any of a vast number of possible 3-D
scenes. That we rapidly perceive only one interpretation
tells us that we see far more than the immediate information
falling on our retina. The highly accurate guesses and infer-
ences that we make rapidly and unconsciously are based on
a wealth of knowledge of the world and our expectations for
the particular scene we are seeing. The influences of these
sources beyond the images on the retina are collectively
known as top-down influences.
Both top-down analyses and the complementary bottom-
up processes use local cues to assign depth to the regions of
an image. They differ in the manner in which they resolve the
ambiguity of the local cues. A bottom-up analysis, part of
MID-LEVEL VISION and SURFACE PERCEPTION, makes direct
links between local geometrical features and depth. For
example, whenever one object partially covers another, the
visible contours of the more distant object terminate at the
outer boundary of the nearer one, forming what are called T-
junctions. When a T-junction is encountered in an image, this
logic can be reversed: the stem of the T is designated a con-
tour of a more distant, partially hidden object and the top of
the T is assigned to the outer boundary of a nearer object.
A top-down process, on the other hand, depends on the
content of the image and its analysis by processes of HIGH-
LEVEL VISION. Cues operate by suggesting objects—a nose
contour might suggest a face, for example—and then stored
information about that object’s structure can be applied to
the assignment of depth in the image. Other features in the
image are then examined to verify or reject the postulated
object. The cues used for the initial selection of potential
objects are not limited to the current images but include pre-
ceding images as well as nonvisual sources which affect our
expectations for the scene. The sources of object knowledge
which are called upon may be built up over both evolution-
ary or individual time scales.
Our guesses for appropriate internal models are best
when we know what to expect in a scene. Upon opening a
door to a classroom, for example, we expect to see desks
and a black or white board. If these elements are present in
the scene, they are rapidly interpreted. Incongruent elements
are seen less reliably as Biederman (1981) showed when he
reported increased errors in identifying fire hydrants pre-
sented in kitchens or sofas floating over city streets than
when they were presented in their usual contexts. As Bied-
erman’s example demonstrates, top-down analyses work
because there is a great deal of semantic redundancy in the
content of a scene—noses are expected to be seen along
with mouths, cars with roads, classrooms with desks, and
sofas with coffee tables; moreover, noses, cars, and sofas
have typical shapes so that once a few distinctive features
have implied the presence of say, a car, the other expected
features of a car can be verified or even just assumed to be
present.
Textbook examples of top-down processing typically
make use of images with two or more equally likely inter-
pretations which are sometimes referred to as ILLUSIONS. A
hint as to which interpretation to see may then trigger one or
the other, as in the examples shown here. (a) Two faces, or
one vase, or one face behind a vase (Costall 1980); (b) a man
playing a saxophone seen in silhouette, or a woman’s face in
sharp shadow (Shepard 1990); and (c) a sphere in a four-
point setting or a white angel (Tse 1998). In these instances,
Figure 1.
AuQ:
Location?
