Module 2

Question 1

Perception

Answer 1

How the external, physical world gets represented in the brain

Question 2

Agnosia

Answer 2

Inability to recognize objects visually; not a language deficit; does not affect prior memory about objects. Existence of different agnosias suggests that different visual processing uses different brain areas.

Question 3

Apperceptive Agnosia

Answer 3

Inability to name, match, or discriminate visual objects. Cannot combine basic bits of info into complete images (cannot read)

Question 4

Associative agnosia

Answer 4

Inability to associate visual patterns with meaning. Can describe, copy, and use items but cannot name the object.

Question 5

Experience Error

Answer 5

FALSE assumption that what you see is all that exists in the world (ex: illusions)

Question 6

Steps of Perception

Answer 6

Sensation/input of stimulus, assembling basic visual components, associating meaning

Question 7

Fixation-saccade cycle

Answer 7

Example of experience error; we use lots of quick jerky eye movements to scan a scene when not tracking an object with smooth pursuit, but we perceive it as continuous.

Question 8

Bottom-up processing

Answer 8

Data driven; see patterns by building up from basic perceptual input

Question 9

Top-down processing

Answer 9

Conceptually driven; input is influenced by prior knowledge and experience from memory; allows faster processing. Based on expectations about the world and context of surrounding stimuli

Question 10

Computational Approach to Perception

Answer 10

Bottom-up theories; perceiving items based purely on sensational input

Question 11

Template matching theory

Answer 11

We match input with stored templates of items in memory in order to recognize them.
Pros: works to program computer vision, but only if you can control the input.
Cons: Too many templates would need to exist for human perception to function

Question 12

Feature matching theory

Answer 12

We analyze and match each distinct feature of a visual item to distinguish a whole item, supported by the existence of feature detection neurons

Question 13

Pandemonium model (1959)

Answer 13

Layers of little ‘demons’ report each feature of an image. Image demon (receives input) -> feature/signal demon (detect and report features) -> cognitive demons (combine features and ‘yell’ if they share many features) -> decision demon (picks the loudest cognitive demon as the perceived image/item)
Pros: Breaking apart and recombining features, serial processing, does not rely on exact matches, parallel processing among ‘demons’
Cons: no explanation on how the features are recombined

Question 14

Recognition by Components theory (Biederman)

Answer 14

Expansion of matching theory to 3D objects. All objects are made of up 3D shapes called geons. Made to program computer vision for 3D shapes and viewpoint invariance (recognition from any angle of an object)

Question 15

Non-canonical viewpoints

Answer 15

Viewpoints that are unusual where geons are not perceptible. Makes it difficult or impossible to define an object

Question 16

View-based recognition

Answer 16

Humans are faster at identifying objects from canonical viewpoints (viewpoint centered). We have neurons specified to certain viewpoints

Question 17

Cons of Bottom-up Processing

Answer 17

Take too long to process some tasks, can’t explain category discrimination, only covers machine vision

Question 18

Gestalt Approach to Perception

Answer 18

“The whole is different than the sum of its parts”; how we combine features into objects or events. Heavily observation based, mostly bottom-up

Question 19

Gestalt grouping principles

Answer 19

Proximity (grouped by distance)
Similarity (grouped by shared features)
Common region (grouped by shared space)
Experience (prior knowledge can affect grouping of certain images forever)

Question 20

Perception/action approach to Perception ( adapted from Gibson)

Answer 20

Embodied approach, opposes computational approach (argues computers are too far removed from the real world). Argues goal of perception is determining action in the world, not items, and all you need to figure out the world is motion perception. All info comes from the ambient optic array, and objects are recognized by their affordances

Question 21

Ambient optic array

Answer 21

Structure the world gives to light before it hits the eye (reflections off of objects). We can perceive motion by comparing changes over time in the ambient optic array.

Question 22

Optic flow

Answer 22

Changes in the ambient optic array (motion)

Question 23

Distal Stimuli

Answer 23

Anything physically located in the real world

Question 24

Proximal Stimuli

Answer 24

Mental representations of physical objects

Question 25

Affordance

Answer 25

What we can do with an object. Affordances vary based on the perceiver of the object

Question 26

Modern Adaptation of Perception/Action Approach

Answer 26

Both action and representations are needed for perception; action influences how we perceive the world. Perception is embodied, but we don’t know how.

Question 27

Gibson’s Direct Perception Approach

Answer 27

Affordances of objects directly connect action and perception without need of cognitive processes; representations and memory do not play a role

Question 28

Ventral Pathway (What)

Answer 28

Object recognition; temporal lobe. Contains specific processing areas (FFA, PPA, etc.)

Question 29

Fusiform Face Area

Answer 29

Cortical area specified for human faces; right hemisphere

Question 30

Parahippocampal Place Area

Answer 30

Cortical area specified for recognizing places

Question 31

Dorsal Pathway (Where/how/action)

Answer 31

Knowing procedural knowledge for objects

Question 32

Ideomotor Apracia

Answer 32

Deficit in action towards objects/demonstrating how to use objects (can still recognize the object visually)

Question 33

Blindsight

Answer 33

Lack of conscious vision caused by cortical damage, not eye damage. Can still perform actions towards objects despite not ‘seeing’ them.

Question 34

Face Processing

Answer 34

We process configurally (as a whole rather than as features), unlike object perception. Facial recognition performance on mutated faces will always be poorer than on an unchanged face. Unknown cause.

Question 35

Face Inversion effect

Answer 35

We are faster at recognizing upright faces than upside down faces.

Question 36

Innate view of face perception

Answer 36

We are born with a special face perception system

Question 37

Experience view of face perception

Answer 37

We have become experts at recognizing faces because we do it so often and have since birth

Question 38

Diamond and Carey Expert Experiment (1986)

Answer 38

Had dog experts identify upright and upside down images of dog species. Testing if expertise acts the same as face processing. Found that experts did show an inversion effect like face processing. Shoddy replication.

Question 39

Gauthier and Tarr FFA theory

Answer 39

FFA is evolved to discriminate between highly similar stimuli. Tested with the Greeble experiments (made up shape ‘people’ and testing discrimination of features). Concluded that FFA is for ‘expertise’