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Learning • What is Learning? – Relatively permanent change in behavior that results from experience (behaviorist tradition) – Can there be learning that does not result in a change in behavior? • Types of Learning – Associative Learning (simple, passive, external) – Cognitive Learning (complex, strategic, internal)

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Learning. What is Learning? Relatively permanent change in behavior that results from experience (behaviorist tradition) Can there be learning that does not result in a change in behavior? Types of Learning Associative Learning (simple, passive, external) - PowerPoint PPT Presentation

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Page 1: Learning

Learning

• What is Learning? – Relatively permanent change in behavior that

results from experience (behaviorist tradition)– Can there be learning that does not result in a

change in behavior?• Types of Learning

– Associative Learning (simple, passive, external)– Cognitive Learning (complex, strategic, internal)

Page 2: Learning

Associative Learning

• Classical Conditioning – associating two stimuli

• Operant Conditioning – associating a behavior and its consequences

Page 3: Learning
Page 4: Learning

Classical Conditioning

• Pavlov’s serendipitous discovery• Associating 2 stimuli

– The first stimulus is “neutral” – does not produce any response

– The second stimulus produces a reflex (unconditioned) response

• After the 2 stimuli become associated, both will produce the unconditioned response

Page 5: Learning

Pavlovian Classical Conditioning

Before Conditioning

During Conditioning

After Conditioning

UCS UCR

Neutral Stimulus No Response

CS CR

CS UCRUCS

Page 6: Learning

Pavlovian Classical Conditioning

Before Conditioning

During Conditioning

After Conditioning

Food (UCS) Salivation (UCR)

Tone (NS) No Salivation

Tone (CS) Salivation (CR)

Tone (CS) Salivation (UCR)Food (UCS)

Page 7: Learning

Classical Conditioning to Cure Bed-Wetting

Before Conditioning

During Conditioning

After Conditioning

Alarm (UCS) Wake up (UCR)

Full Bladder (NS) No waking up

Full Bladder (CS) Wake up (CR)

Full B. (CS) Wake up (UCR)Alarm (UCS)

Page 8: Learning

Further Concepts that Apply to Classical Conditioning

• Generalization: CR is given to stimuli that are similar to the CS

• Discrimination: CR not given to stimuli that are dissimilar to the CS

• Extinction: If the CS is presented repeatedly without being followed by the UCS, the CR will diminish or cease

• Spontaneous Recovery: Following extinction, the CR will spontaneously re-appear after a delay

Page 9: Learning

Classical Conditioning as Simple Associative Learning

• Temporal Contiguity was thought to be sufficient – the CS simply needs to occur immediately prior to the UCS for conditioning to take place

• Equipotentiality: any two stimuli could be associated through conditioning

Page 10: Learning

Equipotentiality Falsified

• Some stimuli are easier to associate than others

• Taste Aversion – only foods become associated with illness, not other stimuli– Garcia & Koelling, 1966 – the “Sweet, bright,

noisy water study”

Page 11: Learning

Garcia & Koelling, 1966

• CS = flavor, light, and click (sweet, bright, noisy water)

• UCS: 2 conditions– Group 1: UCS = illness (from X-rays)– Group 2: UCS = shock

• CR = avoidance (not drinking the water)• After conditioning, tested which features of

the CS were associated with each UCS

Page 12: Learning

Garcia & Koelling: Results

• Both Groups: CS (sweet, bright, noisy) CR (avoidance)

• Group 1(UCS = shock)– Sweet water No avoidance– Bright noisy water Avoidance

• Group 2 (UCS = illness)– Sweet water Avoidance– Bright noisy water No avoidance

Page 13: Learning

Temporal Contiguity is Not Enough

• Contingency: The CS must reliably predict the occurrence of the UCS (Rescorla, 1966)

• Informativeness: The CS must provide new information for predicting the occurrence of the UCS

Page 14: Learning

Contingency (Rescorla, 1966)

• UCS = shock (S), UCR = fear• CS = tone (T)• Training: two conditions

– Random Condition: S TS S T TS S T TS– Contingent Condition: TS TS TS

• Results: Rats learned to fear the tone only in the contingent condition, when the tone predicted the shock

Page 15: Learning

Informativeness: Blocking

• If an organism has already learned that one CS predicts the UCS, that will block the conditioning of a new CS if the new CS does not provide any additional information

• Example: Fear conditioning of a tone blocks conditioning of a light

Page 16: Learning

BlockingTraining 1 Training 2 Test

-none- Tone & Light, shock

(CR = fear)

Light Fear

Tone, shock(CR = fear)

Tone & Light, shock

(CR = fear)

Light No Fear

Page 17: Learning

Rescorla-Wagner Model (1972)

• A mathematical model of the “strength of association” produced in classical conditioning

• Can account for all of the classical conditioning phenomena we have just seen

• Uses just one single equation!

Page 18: Learning

Rescorla-Wagner Model

ΔVn = c (Vmax – Vn)V = the strength of association between a CS and a

USΔVn = the change in the strength of association

between the CS and US on a given trialVmax = the asymptote for CS-US association strength

after learningc = rate of conditioning (how fast the association is

learned)

Page 19: Learning

Rescorla-Wagner Model

00.20.40.60.81

1.2

0 1 2 3 4 5 6 7 8 9 10

Conditioning Trials

Asso

ciat

ive

Stre

ngth

be

twee

n CS

and

US

Page 20: Learning

Cognitive Interpretation of Classical Conditioning

• Classical Conditioning is more than simple association

• The concept of information could explain contingency and blocking

• They are not just associating stimuli, they are seeking information from one stimulus to predict the occurrence of the other

Page 21: Learning

Operant Conditioning

• The law of effect: behaviors that are followed by good things happen more often

• Association: Things that occur together become associated

Page 22: Learning

Basics of Operant Conditioning

• Operant – freely emitted behavior operating on the organism’s environment; NOT a reflex response

• Reinforcement Contingencies – the consequences that follow a behavior– Reinforcement: increases the frequency of the

behavior– Punishment: decreases frequency of behavior

Page 23: Learning

Reinforcement & Punishment

• Positive reinforcement• Negative reinforcement• Positive punishment• Negative punishment

Page 24: Learning

Reinforcement Schedules

• Continuous vs. Partial• Fixed vs. Variable• Interval vs. Ratio• Examples

– Fixed ratio: vending machine– Variable ratio: slot machine– Fixed interval: checking mailbox– Variable interval: checking email

Page 25: Learning

Explaining Complex Learning with Operant Conditioning

• Secondary reinforcers - association• Shaping – simple learning in small

increments• Chaining – small increments plus

secondary reinforcement• Language – association and reinforcement

(Skinner’s Verbal Behavior, 1957)

Page 26: Learning

Learning that Could not be Explained by Behaviorism

• Latent Learning – learning without reinforcement (Tolman & Honzig, 1930)

• Observational Learning – learning without behaving or being reinforced (Bandura, 1977)

• Overjustification – when rewards decrease the frequency of behavior (but see Eisenberger & Cameron, 1996 for an opposing view)

• Language Acquisition – Chomsky’s critique

Page 27: Learning

Latent LearningTolman & Honzig, 1930

 Group 1: never a food rewardGroup 2: always a food rewardGroup 3: food reward after 10 days

 

0

1

2

3

4

5

6

7

8

9

10

Day 1 Day 4 Day 8 Day 12 Day 16

Group 1Group 2Group 3

Page 28: Learning

Behaviorism Falls Short: Language

• Chomsky: “Action in the past” as a property of stimuli is sneaking mental representations in the back door

• Association is insufficient to explain language learning: The evidence points to learning RULES

• Evidence: Over-regularization (“goed”)• Conclusion: Mere associations between words can

not explain language; any adequate theory of meaning must hypothesize internal representations of the rules of language (grammar)

Page 29: Learning

So What was Behaviorism Lacking?

• Symbolic Representation – we have internal (mental) representations for things in the external world

• Structure – we learn sets of rules for combining symbols (e. g., grammar), not just associations between pairs of symbols

Page 30: Learning

Associative Learning Rises Again?

• LSA – Latent Semantic Analysis– A theory of meaning, and a method for computer

analysis of the meanings of texts– The meaning of a word = all of the words that co-occur

with it in a sample of written text (roughly)– Meaning is just a function of associations of words, not

structure (syntax)– How much of language meaning can LSA account for?

A surprisingly large amount.

http://lsa.colorado.edu/

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