Recognizing Objects

Question 1

Visual Form Agnosia

Answer 1

• A disorder called integrative agnosia, caused by parietal cortex damage, involves
a preserved ability to detect whether certain features are present in a display but
a disrupted ability to judge how features are bound together in objects.

Question 2

Principle of nonaccidentalness

Answer 2

Hypothesis that many of the mechanisms underlying perceptual
organization are designed to assume that structural regularities are not
coincidences
• Common fate: unlikely that independent regions are simultaneously
moving in the same direction at the same rate. Assume parts of a single
object
• Good continuation: unlikely that two angles align at their vertices

Question 3

template matching

Answer 3

•Account for very simple letter and numeral matching
• Template stored in long-term memory
• Each letter or numeral is normalized to some standard
representation by finding character axes and
transforming into appropriate orientation and size
• Problems:
• Crossmatching A’S for R’S
• Imprecise discrimination, 0’S and Q’S

Question 4

Feature Theories

Answer 4

Feature Nets
• One possibility for how the visual system recognizes words is
through a system called a feature net.
• The initial layer, at the bottom, comprises detectors for
features.
• Subsequent layers detect more complex patterns like letters,
and then words.

• To explain the word-superiority
effect, the finding that words in
general are better recognized
compared to strings of letters, we
must add another layer to the
network that detects bigrams, or
letter pairs. 

The bigram layer also helps the
system recover from confusion
about individual letters.
• Here, only some letter “O”
features were detected, but this
is compensated for by the higher
baseline activity of the “CO”
detector.

Question 5

McClelland & Rumelhart’s Model of word recognition

Answer 5

McClelland and
Rumelhart’s (1981)
model of word
recognition included
two additions:
• Excitatory and inhibitory
connections between
detectors.
• Top-down connections
from words to letters
and letters to features.

What the network “knows” about spelling, or what it “expects” or
“infers” about the patterns it sees is not locally represented in any
single detector, but rather is a property of the network as a whole.
• This is an example of distributed knowledge.

Question 6

Bottom-up Processing

Answer 6

Bottom-up (or data-driven) processing
• Perceptual information triggers a response in feature detectors.
• Feature detectors in turn excite and inhibit complex-pattern detectors.

Question 7

Top-Down Processing

Answer 7

Top-down (or concept-driven) processing
• Broader patterns of knowledge and expectation trigger responses in complex
pattern detectors.
• Complex pattern detectors in turn excite and inhibit feature detectors.

Question 8

Interactive Models

Answer 8

Models that include both bottom-up
and top-down processing are
known as interactive models.

Question 9

Prosopagnosia

Answer 9

an inability to recognize the faces of familiar people, typically as a result of damage to the brain.