Memory: What’s it good for? Solving problems Functions need memory (must hold inputs and outputs)...

Memory: What’s it good for?

•Solving problems

•Functions need memory (must hold inputs and outputs)•Function themselves must be stored somewhere

•Turing machine•Strip of paper•Rules or procedures•State memory

•Neural networks•Activation of nodes•Connections between nodes *

A B C D

Problems that take Memory

• Raven’s Progressive Matrices• Towers of Hanoi• Spatial relations problems• Mental arithmetic• Syllogisms• Understanding complicated sentences

There is a sewer near our home who makes terrific suits.



The horse raced past the barn fell.



The cat hiding under the bed yawned.



There is a correlation between how well people do on different tests

There is a negative correlation between test results and age

Controlling for memory, there is no correlation between testresults and age!

Memory test

Memory test

recall

position in list

primacyeffectrecencyeffect

The Structure of Memory

Working and Long-Term Memory: Double Dissociation

recall

position in list

primacyeffect

recencyeffect

Lengthen wait before recall: influences R.E., not P.E.

Speed presentation:influences P.E., not R.E.

The Structure of Memory

Working and Long-Term Memory: Double Dissociation

Behavioral Studies:

Patients: H.M. can store things in W.M., but not L.T.M. K.F. can store things in L.T.M., but not W.M.

anterogradeamnesia

….example study: Free Recall Task….

Computational Models and Memory

Symbolic Models: ProductionMemory

DeclarativeMemory

WorkingMemory

storageretrieval matchexecution

perception action

Neural Network Models:

working memory

long-term memory

Working Memory

Baddeley’s Theory

CentralExecutive

PhonologicalLoop

VisuospatialBuffer

Dissociating Visual and Phonological...

Visual Task: Remember the figure, mentally trace it, and say “yes” when you come to a corner at the top or bottom, and “no” at other corners

*



*

yes



*

yes yes



*

yes yes no



*

yes yes no no no no no no yes yes(answers are spoken or pointd to)

Verbal Task: Remember the sentence, and say whether each word, left to right, is a noun or not.



*



Billy ate the cat for dessert.

yes no no yes no yes(spoken or pointed to)



*



Billy ate the cat for dessert.

yes no no yes no yes(spoken or pointed to)

Verbal response: FASTSpatial response: SLOW

Verbal response: SLOWSpatial response: FAST

Working Memory

CentralExecutive

PhonologicalLoop

VisuospatialBuffer

Working Memory

CentralExecutive

PhonologicalLoop

VisuospatialBuffer

• Phonological Representation Evidence: phonological confusion, articulatory suppression• Limited Capacity 7±2 chunks grouping by “chunks” helps

2 seconds you remember fewer long words

phonological buffer rehearsal

double dissociation….. again…. (pp 240-241)

Working Memory

CentralExecutive

PhonologicalLoop

VisuospatialBuffer





• Visual Representation Evidence: uses visual brain areas, rotation and distance on memory representations influence response times

Working Memory

CentralExecutive

PhonologicalLoop

VisuospatialBuffer





• Visual Representation Evidence: uses visual brain areas, rotation and distance on memory representations influence response times

?

Chunking in Working Memory

• Memory slots are in “chunks”

• Chunks can be many different format

In-Class Demo

• F BIV IPG NPC BSCIA

• FBI VIP GNP CBS CIA

• “1225ATFFBI31415”

Evidence for Chunking: SF

• Trained to chunk

• Used race times to chunk

• Digit Span of about 80

Experimental Evidence for Chunking

• Chase and Simon studied memory for chess• Start with a “snapshot” of a game in play,

quick glance, then memory test• Grand masters much higher memory score• But wait - aren’t these guys smart? (Big

memories)• Used random positions -- no advantage for

masters

Working Memory: Single Cell Recording

Sample

Delay

Choice

Cell-Recording -- Neurons in Inferior Temporal Cortex

• After training - responded only to “red”

• Responded only during delay

• Stopped responding when trial end

More Evidence of Working Memory Neurons

• Cool-down inferior temporal neurons

• This impairs monkey’s performance on this task

Semantic Memory: Concepts

Geometric Approach: Concepts and items are representedas points in a high-dimensional space. Similarity between itemsis the inverse of distance between the points. Categorization isthe task of finding which concept point is closest to the point thatrepresents the item in question (i.e. “is it a cat?” is a question ofwhether the point representing “it” is close to the “cat” point than any other point in the space).

• cat• dog• horse

• pig

• duck

closer together =

more similar


Geometric Axioms:

• Minimality: Similarity between an object and itself is always maximum ( d[A,A] = 0 )

• Symmetry: Similarity between A and B is the same as between B and A ( d[A,B] = d[B,A] )

• Triangle Inequality: If A is similar to B and B is similar to C, then A can’t be too dissimilar to C. ( d[A,C] d[A,B + d[B,C] )

S(apple,apple) > S(pomogranite, pomogranite)

Familiar things are moresimilar to themselves thanunfamiliar things.

Unfamiliar things are moresimilar to familiar things than vice-versa.

S(pomogranite,apple) > S(apple, pomogranite)

Things can be similarto for different reasons.

(Jamaica, Cuba, North Korea example)

DON’T WORK FOR PEOPLE!!!...


Featural Approach: Concepts and items are representedas lists of features. Similarity between items is given by:

S(A,B) = a features(A&B) - b features(AnotB) - c features(BnotA)

So similarity increases as two items have more in common,and decreases as each has it’s own non-shared features.

Notice there can be biases: coefficients a, b, and c can beweighted differently, so that features in each category canhave different effects.

So, this model can account for the violations of the metric axioms...


Feature apple orange banana pomograniteedible + + + +has a skin + + + +round + + +red + +edible skin +edible seeds + +good for pies +good for juice + +

Suppose the equation is: S(A,B) = 1*(A&B) - 1*(A~B) - 0.5*(B~A)

S(apple,apple) = 7-0-0 = 7S(pomagranite,pomagranite) = 5-0-0 = 5

S(apple,pomogranite) = 4-3-0.5*1 = 0.5S(pomograntite,apple) = 4-1-0.5*3 = 1.5

violation of minimality

violation of symmetry


How can we implement the featural model in a network?• Units represent concepts and features, with links for connecting concepts that are related, and features that describe them.• Assume spreading activation: when one unit is activated, it automatically spreads to all of the connected units over time• Assume the fan effect: the more units activation has to spread across, the weaker it becomes.

When we compare two things, both units are activated, and activation spreads outward from them. Their similarity isinversely proportional to how long it takes for a certain amountof activation from the two sources to overlap.


How can we implement the featural model in a network?• Units represent concepts and features, with links for connecting concepts that are related, and features that describe them.• Assume spreading activation, and the fan effect.

apple pomogranite

ediblehas a skinroundrededible skinedible seedsgood for piesgood for juices

Units not shared decrease overlapping

activation, by spreading it thinner

(fan effect)

The more units are shared,

the more activation will overlap


This model can also account for categorization andtypicality effects:• Categorization: It takes longer to verify “A dog is an animal” than “A dog is a mammal”, because it has farther to travel.

• Weights between units can indicate how typical an instance is of a superordinate category, changing how strongly activation from one is spread to the other.

animal

birdmammal

dog cat

animal

birdmammal

robinpenguin


What do feature lists leave out?• Causal relations (e.g. the fact that fertilizer tends to grow plants)• Relational dependencies (e.g. the fact that only small birds sing)• In short: Feature lists leave out structured information.

We recall from our discussion of Episodic Memory, this problemca be solved with the use of schemata: complex structured frameworks.

Thus, schemata can be used to semantic memory, too, to tell uswhat kinds of items are typically found in offices, what kinds ofevents typically happen in a restaurant, and so on.

Modeling Schemata?Challenge for the future: How to represent structuredrelational information in a network?• Relational information (e.g. “Chris loves Pat”) has a problemin networks with distributed representation, similar to thebinding problem: the catastrophic superposition problem.

Suppose this pattern:and this pattern:and this pattern:and this pattern:and this pattern:

Then this pattern:and this pattern:and this pattern:

represents “Chris”represents “Pat”represents “Harry”represents “Sally”represents “loves”

represents “Chris loves Pat”represents “Pat loves Chris”represents “Harry loves Sally”

There is no way to tell the difference!

Modeling Schemata?

One Answer: Temporal synchrony•

Suppose this pattern:

and this pattern:

and this pattern:

represents “Chris”

represents “Sally”

represents “loves”

But how do we distinguish between “Chris loves Sally” and “Sally loves Chris”?•

Modeling Schemata?

Need to combine structural and semantic information

LISA (Learning and Inference with Schemas and Analogies) – Hummel & Holyoak

Binds semantic information (e.g. “Chris”) to roles (e.g., “loves” Agent)

Can then make inferences like we do

Chris loves Mary. Chris gives flowers to MaryBill likes Sally. Bill gives candy to ??? Sally

•

Procedural Memory

Memory for procedures, rather than facts.• Skill Memory / Skill Learning• Knowing How (rather than “knowing that”)• Muscle Memory / Motor Memory• Implicit Memory / Implicit Knowledge

All of these are different terms of a third kind of memory.

How do we know it’s a separate kind of memory?

• You can read all the facts about how to do something and still not be good at it• H.M. completely lost semantic and episodic memory, but still had procedural.

Practice and the Power Law

Stages of Skill Learning

• Declarative Stage: You know what to do, like a list of instructions. You rely mostly on declarative memory. You are slow and make a lot of errors.

• Knowledge Compilation Stage: You have developed some procedures, but action is still divided into small individual steps, and is not fluid. You still rely some on declarative memory. You improve in speed and accuracy.

• Procedural Stage: Individual procedures and actions get streamlined and fluid, make less and less errors and get faster. Almost no reliance on declarative knowledge. As a result, it is hard to explain what you are doing, and hard to pick up again if you are interrupted (because movement is fluid).

proced

uralization

ACT*

ProductionMemory

DeclarativeMemory

WorkingMemory


perception action

I’m driving!

Stop sign!

ACT*

WorkingMemory


perception action

I’m driving!

Stop sign!

Pressing the brake slows the carThe brake is to the left of the gasTo stop you have to slow downYou have to stop at a stop sign…...

IF at stop sign THEN subgoal: slow the car.IF subgoal to slow the car AND foot is over gas, move foot leftIF subgoal to slow the car AND foot is over brake, press!!

ACT*

WorkingMemory


perception action

I’m driving!

Stop sign!


IF at stop sign THEN subgoal: slow the car.IF subgoal to slow the car AND foot is over gas, move foot leftIF subgoal to slow the car AND foot is over brake, press!!

CHUNKING!

ACT*

WorkingMemory


perception action

I’m driving!

Stop sign!


IF at stop sign THEN brake!

Memory: What’s it good for? Solving problems Functions need memory (must hold inputs and outputs)...

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Transcript of Memory: What’s it good for? Solving problems Functions need memory (must hold inputs and outputs)...