Week 3

Question 1

Template theory

Answer 1

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

Question 2

Template theory issues

Answer 2

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

Question 3

Feature theory

Answer 3

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

Question 4

Feature theory support

Answer 4

• 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)

Question 5

Cognitive visual searches

Answer 5

Press button when you see…

Question 6

Conjunction search

Answer 6

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

Question 7

Search asymmetries

Answer 7

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

• Consistent with brain physiology

Question 8

Pop-out effect

Answer 8

Something is immediately recognizable as different in a set

• happens regardless of number of things in a visual field

Question 9

Structural theory / recognition by components

Answer 9

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

Question 10

Issues with RBC theory

Answer 10

• 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)

Question 11

Recognition by multiple views

Answer 11

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

Question 12

Face processing

Answer 12

• Faces are processed specially
• Holistically processed, not feature based

Question 13

Face inversion effect

Answer 13

• 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

Question 14

Prosopagnosia

Answer 14

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

Question 15

Super-recognizers

Answer 15

• 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

Question 16

Tachistoscope

Answer 16

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

ie. pre-computers

Question 17

Recognition threshold

Answer 17

Time required to recognize a word

Question 18

Recognition tasks

Answer 18

• 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

Question 19

Chronometric approach

Answer 19

Assume mental processes take time, research how long they take

Question 20

5 things that affect word recognition

Answer 20

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

Question 21

Frequency

Answer 21

• 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

Question 22

Repetition priming

Answer 22

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

Question 23

Context effects

Answer 23

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

Question 24

Well formedness

Answer 24

• 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

Question 25

Word superiority effect

Answer 25

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

Question 26

Bigram detectors

Answer 26

Detect letter pairs - in word superiority effect - Goes with priming, more common pairings expected

Question 27

Overregularization errors

Answer 27

- Making errors - Will misread less common words as more common ones

Question 28

Patient DF | Drawings

Answer 28

- 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

Question 29

Patient Dr. P

Answer 29

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

Question 30

Bottom-up vs. top-down

Answer 30

Top down processing → concept driven Bottom up processing → data driven

Question 31

Knowledge representations

Answer 31

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

Question 32

Reading fonts

Answer 32

- 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)

Question 33

Feature net

Answer 33

- 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

Question 34

Activation of feature detectors

Answer 34

- 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

Question 35

Activation level

Answer 35

- 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

Question 36

Efficiency vs. accuracy | Effect of inferences

Answer 36

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

Question 37

McClelland and Rumelhart Model

Answer 37

- 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

Question 38

Speed reading

Answer 38

- 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