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PNB 2XA3 > Week 3 > Flashcards

Week 3 Flashcards

1
Q

Template theory

A

Recognition of an object follows a ‘match’ between the stimulus and an internal construct (template) of that object

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2
Q

Template theory issues

A
  • Assumes an exact match
  • Requires a huge number of templates for all variations of an object
  • We can recognize abstract objects
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3
Q

Feature theory

A

All objects are composed of separable distinct parts - features
- Like a visual alphabet

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4
Q

Feature theory support

A
  • Neuronal responding to specific things (edge detection, movement detection, colour detection, etc.)
  • Cells respond to a very specific simple feature
  • Visual search tasks (multi-feature takes longer)
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5
Q

Cognitive visual searches

A

Press button when you see…

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6
Q

Conjunction search

A

Look for multiple features of an object in a set (ex. Red and horizontal lines)

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7
Q

Search asymmetries

A

Easier to find the “tilt” than “not tilt” indicating the feature we use is “tilt” not “absence of tilt”

  • Consistent with brain physiology
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8
Q

Pop-out effect

A

Something is immediately recognizable as different in a set

  • happens regardless of number of things in a visual field
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9
Q

Structural theory / recognition by components

A
  • Objects are created by combining geons by their edges
  • Like an object alphabet with 36 distinct geons
  • Geons can be recognized from any angle
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10
Q

Issues with RBC theory

A
  • Some things require very specific idiosyncratic geons (specific) - but they’re supposed to be universal not specific
  • Ignores context
  • Doesn’t explain differences within object type (ex. apples vs oranges)
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11
Q

Recognition by multiple views

A
  • Template theory but we have templates for multiple angles
  • Viewpoint dependent
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12
Q

Face processing

A
  • Faces are processed specially
  • Holistically processed, not feature based
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13
Q

Face inversion effect

A
  • Upside down faces hard to recognize than other upside-down objects
  • Faces usually processed holistically, but must be processed by parts when upside-down
  • Monkeys don’t have this issue bc they’re upside-down often
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14
Q

Prosopagnosia

A

Can’t recognize faces
- Either due to brain damage or just a thing from birth

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15
Q

Super-recognizers

A
  • Can recognize faces super well, even if the picture is degraded (from and odd angle or distance), they haven’t seen them for a while, can tell easily if two faces are from the same person or not

Better eyewitnesses, awkward if they know you but you don’t know them

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16
Q

Tachistoscope

A

An apparatus for the brief exposure of visual stimuli that is used in the study of learning, attention, and perception

ie. pre-computers

17
Q

Recognition threshold

A

Time required to recognize a word

18
Q

Recognition tasks

A
  • Used to do with a tachistoscope
  • Stimulus presentation followed by a post-stimulus mask (ex. Random shapes or letters covering it)
  • Recognition depends on familiarity
    Ex. more frequent words recognized more often when masking task done
  • Also depends on priming
19
Q

Chronometric approach

A

Assume mental processes take time, research how long they take

20
Q

5 things that affect word recognition

A
  1. Frequency
  2. Repetition priming
  3. Context effects
  4. Well-formedness
  5. Overregularization errors
21
Q

Frequency

A
  • People are faster to recognize common words, doesn’t mean we don’t know the uncommon ones

ex. Happy vs. hippy or Happy vs. harpy

  • Word frequency makes it faster to recognize NOT familiarity
22
Q

Repetition priming

A
  • Viewing a word once helps you read it next time
  • Priming is like a brain warm-up
23
Q

Context effects

A
  • Helps us recognize objects and can change what we perceive
  • Top down processing
24
Q

Well formedness

A
  • word superiority effect works with well formed words, even if they’re not real words (ex. LAFE)

Why? Guided by our knowledge of spelling patterns + our bigram detectors

25
Q

Word superiority effect

A

It is easier to perceive a letter when it is part of a word than when it is presented on its own. (ex. of a context effect)

  • not just educated guessing
26
Q

Bigram detectors

A

Detect letter pairs
- in word superiority effect

  • Goes with priming, more common pairings expected
27
Q

Overregularization errors

A
  • Making errors
  • Will misread less common words as more common ones
28
Q

Patient DF

Drawings

A
  • Had brain damage in lateral occipital cortex (LOC) that recognizes objects
  • Drew simple disconnected lines instead of the actual items - when she was looking at them
  • Drew more accurate to real life - when drawing from memory

Example of apperceptive agnosia: can see but can’t organize objects to perceive an entire object

29
Q

Patient Dr. P

A

Associative agnosia

  • Can see but can’t link what they are seeing to basic visual knowledge
  • Thought a glove was a pouch or purse
  • Mistook his wife’s head for a hat
30
Q

Bottom-up vs. top-down

A

Top down processing → concept driven

Bottom up processing → data driven

31
Q

Knowledge representations

A

Local representation → storage in a particular location (ex. preferential response in one cell)

Distributed representation → knowledge represented by a pattern of activations distributed across a network
ex. bigram detectors

32
Q

Reading fonts

A
  • Harder to read fonts seen as written by a less intelligent author
  • It’s more difficult to read words in all caps
    Why?
    All lines the same height and none go below/above (like in lowercase j)
33
Q

Feature net

A
  • How to build a system that recognizes words
  • System of feature detectors wired together
  • Neurons light up and are connected to more neurons
  • Parallel processing models in neural networks
  • Have a good idea of what features make up letters and words
  • Each detector has an activation level
34
Q

Activation of feature detectors

A
  • Each detector has a particular activation level = how energized it is
  • Will eventually reach the detector’s response threshold = it will fire

Are NOT neurons

35
Q

Activation level

A
  • How activated something is
  • Detectors that have recently fired (warm-up effect) or that have fired frequently (exercise effect) will have a higher activation level
36
Q

Efficiency vs. accuracy

Effect of inferences

A

Inferences cause a slight decrease in accuracy but a huge benefit in efficiency

37
Q

McClelland and Rumelhart Model

A
  • Pattern recognition
  • Excites connections for specific words that inhibits connections for others
  • Excitement: activation of one node/detector activates another due to their link

Ex. TRIP inhibits TRAP and TAKE

38
Q

Speed reading

A
  • Due to efficient and accurate inferences
  • Read by inference rather than word-by-word
  • Get faster by moving your finger faster along a line of text, then follow that instead of letting the finger follow you
  • Basically just guessing more than before
  • Easiest for non-technical readings

PNB 2XA3 (8 decks)

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