Recognizing Objects Flashcards

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1
Q

Recognition

A

Typically:
- Fast
- Effortless
- Prone to error

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2
Q

Fast Pop-Out Search

A

When all items except the target share the same feature, target is easy to find, regardless of the number of distracting items (sometimes called the singleton search)

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3
Q

Slow Conjunction Search

A

Target does not possess any unique features, and only can be identified based on a conjunction of two or more features. The more distracting items there are, the slower the search is

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4
Q

Frequency and Recency

A

Familiar (high-frequency) words are likely to be recognized.
Recently primed words are more likely to be recognized.

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5
Q

Word Superiority Effect

A

The data pattern in which research participants are more accurate and more efficient in recognizing letters if the letters appear within a word (or a word-like letter string) than they are in recognizing letters appearing in isolation

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6
Q

Degree of Well-Formedness

A

A measure of the degree to which a string of symbols (usually letters) conforms to the usual patterns (e.g., the nonword “FIKE” is well formed in English, but “IEFK” is not)

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7
Q

What is Bottom-Up Processing?

A

A sequence of events that is governed by the stimulus input itself

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8
Q

What is Top-Down Processing?

A

A sequence of events that is heavily shaped by the knowledge and expectations that the person brings to the situation

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9
Q

What are Feature Nets?

A

Systems for recognizing patterns that involve a network of detectors, with detectors for features serving as the initial layer in each system

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10
Q

Activation Level

A

A measure of the current status for a node or detector. Activation level is increased if the node or detector receives the appropriate input from its associated nodes or detectors; activation level will be high if input has been received frequently or recently

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11
Q

Response Threshold

A

The quantity of information or activation needed to trigger a response in a node or detector, or, in a neuroscience context, a response from a neuron

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12
Q

Distributed Knowledge

A

Relative activation across nodes more important than local activation within one node

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13
Q

McClelland and Rumelhard

A

Connections can be feed-forward or feedback, and excitatory or inhibitory

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14
Q

Excitatory Connections

A

A link from one node, or one detector, to another, such that activation of one node activates the other

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15
Q

Inhibitory Connections

A

A link from one node, or detector, to another, such that activation of one node decreases the activation level of the other

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16
Q

Errors Due to Overgeneralization

A
  • You see what you expect to see
  • Accuracy sacrificed for efficiency
17
Q

Viewpoint Invariance

A

Our ability to recognize an object regardless of the viewpoint (most of the time)

18
Q

Recognition by Components (RBC) Model

A

A model of object recognition. In this model, a crucial role is played by geons, the (hypothesized) basic building blocks out of which all the objects we recognize are constructed

19
Q

What are Geons?

A

Basic shapes proposed as the building blocks of all complex three-dimensional forms. They are the visual alphabet of object recognition

20
Q

Recognition By Multiple Views

A
  • Multiple viewpoints encoded in memory
  • Mental rotation required if current view does not match any stored views
21
Q

What is Prosopagnosia?

A

A syndrome in which individuals lose their ability to recognize faces and to make other fine-grained discriminations within a highly familiar category, even though their other visual abilities seem intact

22
Q

What is the Inversion Effect?

A

a pattern typically observed for faces in which the specific face is much more difficult to recognize if the face is presented upside-down

23
Q

What is Holistic Recognition?

A

A process in which the ability to identify an object depends on the whole, or the entire configuration, rather than on an inventory of the object’s parts

24
Q

Composite Effect

A
  • Hard to recognize half of face when aligned with another face
  • Evidence that faces are processed holistically (face recognition is based on the relationships between features, not perception of individual features)