Week 2

Question 1

Endel Tulving

Answer 1

Episodic Memory: autobiographical knowledge about personal past, unique to the individual

Semantic Memory: general knowledge about the world that all members of a culture possess

Question 2

Penguin

Answer 2

First saw penguin at toronto zoo at 1985 (Episodic)

A bird that cannot fly (Semantic)

Question 3

Definitions

Answer 3

Conceptual knowledge doesn’t remember specific events like birthdays but general things such as what consists in a wedding

You have the idea what is involved in particular event or object

Allows us to use this knowledge to understand what we are looking at and what is the appropriate actions to perform.

A concept is a mental representation of an idea.
A dog is a concept
General idea, category is specific exemplar

Cateogorization is putting things into groups
Category of cats for example
Very related

Question 4

Why Categories Are Useful

Answer 4

Help to understand individual cases not previously encountered
* “Pointers to knowledge”
* Categories provide a wealth of general information about an item
* Allow us to identify the special characteristics of a particular item

Allows us to deal with new events or objects

Experiencing something new, got to understand what you are going to do in this situation

Use previous knowledge and react according to what the similar thing was

Figure out what the appropirtate response is
E.g., a bird has a beak, wings, creates nest, lays eggs etc

Allows us to figure out unique characteristics

Within a category, there are exceptions such as birds that cannot fly

Categories are useful for guiding us

Question 5

Definitional Approach

Answer 5

Determine category membership based on whether the object meets the definition of the category
* Let’s make a definition for “chair”
* A chair is a piece of furniture for one person to
sit on. Chairs have a back and four legs.

Idea that what you are doing, you figure out what the rules are for that category

Essentially try to define that category
E.g., the definition of chair

Typically a chair is a piece of furniture, meant for a single person

We look at different objects and see if they fit into the chair category

Hard to properly define a category

In practice, this not work quite well

People realized that the better way of approaching this would be resemblance, things in this category are similar with eachother

Question 6

Prototype Approach

Answer 6

Prototype - an average of category members encountered in the past
* “typical” member of a category
* Characteristic features that describe what members of that concept are like

Ellanoar rosh

We determine if something is apart of a category if our item is an average of categories from the past
E.g., ideas of birds and averaged together and create a prototype

Essentially the typical member of the category but this average does not actually exist

This is computed from mind but there is not actual prototype

Average will have features that will are mostly common with members of that category

Question 7

Prototype Perception

Answer 7

Closer to the prototype the easier to process
* Fluent processing of a stimulus can be mistaken for:
* Recognition
* Preference
* Truth and Beauty

Idea is that the closer the item is to the prototype, the easier it is to process it into the category
E.g., two patches of green or light green, faster to process the prototype green

Typicality effect
Number of studies that looked at fluency of processing and the things that are processed most fluently, it is something that was experienced before

Larry gecobiate, gave people list of names and had to make decisions about them and the next day, they were given another list and that list contained famous names and some from the previous day along with new names and had to decide who the famous people but when looked at the new names, they were much more likely to falsely claim that the previous day people were the famous people (false fame effect)

Situations in zyomph, looking at line drawing objects such as furniture, presented these at 1 millisecond per item and replaced by new item, for the person watching, this was a blur, after watching this, ziomph asked people to decide preference of chair which one of was in the blur and people chose the one from the blur

Now that item is processed more fluently

Propaganda effect, sir francis calton in 1860s, he took faces of a bunch of people and morphed them together which made an image about people and asked people for opinion of beauty, people preferred the composite face to the individual face

Idea was that the prototype (average) was found the most beautiful

Matches the face the best hence why you chose it

Question 8

Exemplar Approach

Answer 8

Concept is represented by multiple examples (rather than a single prototype)

Examples are actual category members )not abstract averages)

To categorize, compare the new item to stored examples

Idea is what you store all experiences that you had placed into a specific category in memory and when a new memory is formed, you compare it to a subset of existing memories

Multiple comparison

Not comparing to the average, comparing to actual members of categories
J
udging how similar it is to every members then make decision

Question 9

Cont.

Answer 9

Similar to prototype view
- representing a category is not defining it
Different: representation is not abstract
- descriptions of specific examples

The more similar a specific exemplar is to a known category member, the faster it will be categorized (family resemblance effect)

Both of these approaches talk about similarity, no specific rules that must be met

Question 10

Cont. 2

Answer 10

Easily takes into account atypical cases
easily deals with variable categories

Basically saying how well does it resemble to category

Compare it to average and each member

Both can explain typicality effect since the more similar it is, the better it is going to match and faster to process it.

Exemplar effect has advantage of taking atypical cases into account

Such as larger amounts of things, exceptional cases

You can also deal with categories that are very specific
E.g., category of games

Idea of what games are but there are many things that take into account

Very hard to create a prototype of this, but with exemplar, all these instances can be compared to the new item and see if they match

Question 11

Prototype or Exemplars

Answer 11

May use both

Prototypes may work best for larger categories (general)

Exemplars may work best for small categories (exceptions)

Probably use prototypes initially, since its easy and might work for categories that are general

Exemplar is good for taking care of exceptions,

beneficial for smaller categories since you do not need to compute an average

Recognition & Recall

Question 12

Production Vs. Verification

Answer 12

Verification = indicating the truth of a test item

Production = retrieving an instance from memory when given a cue

People look at reaction time since accuracy is at ceiling

You have to say if this fits: e.g., fruit - peach,

Analgouge of recognition

Analgouge of recall is production
E.g., fruit - A

People use these tasks in investing semantic memories

Question 13

Allan Collins & Ross Quillian

Answer 13

1969, 1970

First model of semantic memory

Question 14

Hierarchical Network Model (Collins & Quillian, 1969)

Answer 14

Semantic memory consists of a network of basic elements (nodes) connected by pointers which express relations between elements

Stored with each element is a list of properties that define the features of each concept

Claimed that semantic memory is a network, involves nodes and these nodes are connected to one another

These connections describe the relationship between elements

Argued that concepts are the nodes themself but connected to the elements are the properties associated with these elements (features apart of the concept)

Organization of these elements are in the hierarchy

General point at top and it gets more specific as you go down

Very limited in computing powers and digital, so they said that the system is trying to be sufficient as possible,

add cognitive economy to help

You store features at highest level of hierarchy.

Everything below it inherits the features

Question 15

Semantic Network

Answer 15

e.g., Animal -> skin, moves

Bird -> wings, flies

Question 16

HNM

Answer 16

You can enter the model at some particular hierarchy and make a decision between 2 elements, it takes time to move levels

If you want to access features at any level, that will also take time (comparing something in lower level, must go up)

Argued that the more levels you have to move, slower the responses will be

Faster to make decisions about category membership, than for properties in higher hierarchy

6 different questions for the example

Canary to bird is one up on hierarchy

Bird to animal is another one up

P2 means you have to go up 2 levels

Question 17

HNM Test

Answer 17

Tests of the model: Sentence verification. True? Yes or No

Category Membership (supersets)
S0: a canary is a canary
S1: a canary is a bird
S2: a canary is a animal

Properties:
P0: a canary is yellow
P1: a canary can fly
P2: A canary has skin

Question 18

Results (Collins 1970)

Answer 18

Higher on hierarchy means slower reaction time (property)

Green is lower than red so green is faster than property (superset faster than property)

Question 19

Priming

Answer 19

Found that people who got canary can fly were faster at making decision about canary is a bird compared to people who had canary is yellow

1 deficiency in this model

Question 20

Problems with HNM

Answer 20

Model does not explain the typicality effect: Faster to verify typical members of a category than atypical members

In model, typical and atypical members are at same level of hierarchy, so should take the same time

Not every member of category was equal

If we make people say robin vs. chicken is a bird, people will say robin

But in hierarchy model, all of them would be at lower level so none of them would preferred so they would have the same response time

Question 21

More Problems

Answer 21

Another study found problematic results

Is a pig a animal were quicker than a pig is a mammal
1268 vs. 1476

Each level that you traverse should take extra time

No reason why two levels should be quicker than 1 level

Question 22

Feature Comparison Model (Ripps, Shoben, Smith, 1973)

Answer 22

Developed to explain results that HN cannot explain

All concepts in SM are represeted as sets or lists of features

No cognitive economy, same features stored with different concept

Question 23

FCM

Answer 23

Argued that there is no hierarchy

Each concept was a unique item and each node has all features/properties connected to it

Argued that each concept, there were two types of features

Defining features - things that are needed to define the concept (bird - feathers)

Characteristic features - features that are common to many members but are not essential (birds - fly)

You have to figure out if there is a relationship between concepts

There is no physical connection between concepts in this model

You have to compute if there is a relationship

You do this by comparing the features

Question 24

FCM: Stage 1

Answer 24

Idea is you go to robin category and retrieve features from that category and then go to concept of birds and get those characteristics and then you look at the common alibies

Are the features retrieved from robin also the ones received from bird?

If match is very low, less than 20% e.g, make decision that there is low similarity and if there are high, 80%, then decide if things are highly likely related

Low = false
High = true

At second stage, only look at defining feature

Question 25

Stage 2

Answer 25

When you have typical member of category, it shares a lot of features
E.g., robin and bird

A pencil is bird would draw very little similarities

Ostrich is a bird would draw medium similarity as it shares defined but characteristic features

Samething with bat but this time its similar with characteristic features but not actual defining features

Question 26

FCM

Answer 26

This can easily explain typicallity effect but cannot define priming effect

There is no memory of what you have done on FCM, no reason of why what you did earlier should impact what you did now

You retrieve characteristic and defining feature of canary but not yellow

Question 27

Spreading Activation Network Model (Collins & Loftus, 1975)

Answer 27

Concepts organized in network, but organization not hierarchical

Features also stored with concepts

Length of links between concepts represents strength of associations

Assumes activation spreads between concepts

Essentially revised hiearcheral model

Recognized there is a network of semantic information

Also abandoned cognitive economy idea, argued that we have concepts and features and all are nodes that are connected with eachother

Closer to one another in hierarchy

Idea is figuring out if two thing are related in hierarchy

Question 28

Example

Answer 28

All are put in network

Things that are highly associated with another will be closer than things that are less related
E.g., cherries are closer to apples than red

Explain technicality effect as cherries and red would cause activation as there is a relation and meet up fairly quickly

Longer the connection, longer the time it takes to establish connection

Explain priming since activation remains in system, faster to establish length the second time

An improvement

Question 29

Meyer and Schvaneveldt (1971)

Answer 29

Lexical Decision Task

RT measure

NO trials (usually) not of interest

Are YES trials faster when preceded by a related word? Yes = PRIMING

Task required people presented with things on screen and asked to determine if it was a word or not

Measured reaction time

Focused on the yes responses

Presented 2 items and determine if they were words or not
E.g., Fundt and Glurb. Chair, money, nurse, doctor etc

Some pairs of words were related to one another

Associated words caused faster responses

This was evidence of the priming effect

Activation in these 2 nodes and this connection is better and closer

Priming will spread and lead to priming effect quicker

Question 30

Neely (1977)

Answer 30

Presented indivuiduals with words with specific category and another word where they had to make a lexical decision

Timing between varied (150 msec to 2000msec)
Conditions were involved

No shift - if particular prime word was given, know ahead of time that lexical word would be a member of the category
E.g., bird as prime (no shift), ⅔ is robin, other ⅙ of time, it would be other category would be body part or building piece so ⅓ of either

If prime word was body
⅔ building, ⅙ body and ⅙ bird, so if body was given, expect building (shift)
Building as prime would be ⅔ of body, ⅙ of building and ⅙ bird

These shifts varied across the individuals

How much of semantic priming is due to automatic processing and how much is due to control processes

Question 31

Results

Answer 31

No shift - bird ⅔ -

Bird and robin, across the board, there are benefits since they are close

Faster to verify target as they were both members of the prime category

Faster at short and long SOA

Bird-arm: performance deteriorated

Results suggested that priming effect benefited the bird group

Suggested automatic spreading activations at the short SOAs

Suggested that long SOAs has combination of automatic processes and expectanceis

Violates expectations which delays reaction time since it wasnt the item that you were expecting

Initial evidence that automatic spreading occurs and as time increases, expectancy effects come into play

Question 32

Shift Condition

Answer 32

Body - ⅔ building

For the thing you expect, at the shortest soas, no benefit and no cost

In order for benefit, you have to make connection of that body ⅔ time is building, essentially figure out the connection

With a short SOA, you don’t have enough time

Body and heart, earliest SOAs, there is a facilitation effect

Body and hand leads to a benefit even though body parts only happen ⅙ of the time

Even though it was a very rare event, there was a benefit
Over time, benefit went away and became a cost
S
hows that there was a initial activation of automatic processing but eventually expectation leads to a defit to cost

Although sparrow still had benefit

Question 33

Connectionist Approach

Answer 33

Idea that you have network of nodes that represents how things are connected and know idea is that model is supposed to be better analouge to the brain

Spreading activation network, concepts are each of nodes
E.g., cat doesnt activate single point in brain, it activates a lot since its a distributed network

Created network that has activation associated with particular concept is represented by activity across entire network

People argue that between nodes and network, there are links and the strength of them are based on how close things are

The more times neurons are fired, the stronger the connection will be with nodes

If one fires, it decreases

Question 34

Cont.

Answer 34

Input nodes come from outside world

Output nodes are from hidden units and determines the actual response from network

Network sends activity to connected node and depending on strength could cause them to fire which fire onto output nodes

Question 35

CA

Answer 35

How learning occurs:
- network responses to stimulus
- provided with correct response
- error signal
Different between actual activity of each output unit and the correct activity

Modifies responding to better match correct response via back propagation

The process repeats until error signal is 0

Training Phase: give network particular stimulus, sometimes there are educated guesses but often just randomly set all the weights

Provided input on gets to hidden and output

Compare output to correct response and calculate the error signal and see how off the current settings are

Adjust connection between the nodes

Idea is that modifying the weights, you can get actual settings better so in future you can get more accurate responses at output level

Neural Networks: Error is calculated at the very end, not at each layer of the neural network