wk11 - Learning + Knowledge

Question 1

What are the four experimental effects which show us that attention is important for learning?

Answer 1

1. Trade offs b/w salience & valdiity
2. Blocking
3. Highlighting
4. Learngin rules of different complexcity

Question 2

What is salience in regards to attention??

Answer 2

How much a cue grabs your attention when all other things are equal.

• In the absence of validity, high salience cues will attract your attention.
• Low salience cues will not attract attention.

Question 3

What evidence did Kruschke & Johansen (1999) provide for the interaction between salience and validity of cues and their utilisation in a manipulated Posner cuing task? (hint: see image for cues).

Answer 3

Posner Cuing task:

• Pts task is to learn which of the cues to attend in order to respond to a target as quickly as possible.
• Two Cues = High Salience Cue (C1) & Low Salience Cue (C2).
• Validity of the cues are manipulated
  
  • High validity (HV) / Low validity (LV)
• How much do they use each cue? (utilisation).

Results:

• Condition 1: C1 - HV & C2 - LV = utilisation is All C1 vs none C2.
• Condition 2: C1 - & C2 have equal validity (.8 prediction each) = C2 used a little more & C1 used a little less > C2 detracts C1.
• Condition 3: C1 .8 validity & C1 .9 validity = equal utilisation

Inference:

• There is a trade-off between salience & validity.
• Increased validity = increased utilisation.
• Decreased validity = decreased utilisation
• Increased salience = increased utilisation
• Decreased salience = Decreased utilisation
• BUT validity & salience interact
  
  • Increased utilisation of one cue decreases utilisation for other cues.

Question 4

What is Blocking? (attention/learning)

Answer 4

When learning occurs in a classical conditioning paradigm, an early learning task produces an association between red light (A) and reward (X) is created. A > X

• In a late learning task, A + a bell (B) are paired with X.
• AND an alarm (C) and a blue light (D) now predict a new reward (y).
  
  • A.B > X
  • C.D > Y.

Test: B (bell) & D (blue light) are presented to see which reward the mice will go to.. X or Y? 50/50 chance. BUT mouse consistently choose Y.

Why?

• All attention is focused on A > X
• When AB > introduced - not attention left for B - i.e. A blocks B - attention on one thing blocks learning of another.
• Cue D therefore drives final response

Question 5

What is highlighting? (learning/attention)

Answer 5

Classical Conditioning Paradigm:

• Early Training:
  
  • A.B > X
• Late Training:
  
  • A.B > X (twice as likely as below to get reward)
  • A.D > Y
• Test:
  
  • B.D > Y
  • Expected to go to X more.
  • But preference for Y (guided by D).

Why:

• A & B are already paired with X
• Attn is shifted to D because it alone predicts the unusual event at Y.
• Cue D drives the final response.
• D learning HIGHLIGHTS unusual event.

Highlighting: prior learning about cue A highlights the fact that cue D predicts something different than cue A.

Question 7

What is Simple Learning Theory?

Answer 7

• Where co-occurences lead to a strengthening b/w cues & outcomes.

Question 8

What is attentional learning theory? And how is it different from simple learning thoery?

Answer 8

• Differes by also incorporating the ability to differentially weight cues according to their relevance.
• i.e. co-occurring cues are down-weighted because they don’t show how two cues might different.. therefore not relevant.

Question 9

In the Filtration/Condensation categorisation experiment, which learning theory (simple or attention) better predicts the outcome of learning differences between type 1 (filtration) and type 2 (condensation) categories?

Answer 9

Real Data

• Filtration categories only have 1 feature (height) to focus on & are learned faster.
• Condensation categories have several features & are slower to learn.

Predictions of Learning Theories

• Simple Learning Theory predicts no difference in accuracy & speed of learning between the two types of categories
  
  • As it relies of co-occurrence alone - not taking into consideration simplicity vs complexity of cateogry features.
• Attention Learning Theory predicts Type 1 categories will be learned more accurately & faster.
  
  • Takes into account only relevant dimension.

Attention Learning Theory better fits the real data.

Question 10

What is one-shot/fast mapping?

Answer 10

Learning by exclusion, based on what is already known (our expectations)

Question 11

When given data about a relationship that goes against our intuition, and that data is noisy (not a perfect correlation) - what are we likely to do when trying to integrate that information into learning? (wind-speed to fire task & light-switch correspondence task).

Answer 11

We are likely to rely on our intuition to guide our expectations.

Wind-speed to fire spread task:

• in which pts asked to predict the spread of the fire based on the strength of the wind.
• real r/ship is that greater wind = smaller spread - which is counter-intuitive.
• When pts were given this data and then completed the prediction task, they produced imperfect results.
• These results were passed onto subsequent participants - & on & on - creating noisier data that was attenuated by the pts.
• Until finally, the predicted data began to perfectly represent the intuitive model - Geater wind = greater fire spread.

Similar finding was found for counterintuitive light-switch task

Question 12

What is the difference between Holmesian deduction & judicial exoneration?

Answer 12

Holmesian deduction: once you eliminate the impossible, whatever remains - however improbable - must be the truth.

• Only hypotheses which explain the data are plausible candidates for an explanation.

Judicial exoneration; If one suspect confessed, then we let the other go.

• If one hypothesis clearly explains the data, then other candidates are considered less likely.

Question 13

What is inference to the best explanation?

Answer 13

• Explanation is hypothesis evaluation.
• Have a prior belief in a hypothesis H, which predicts that we observe data D.
• Observe D then H is supported.
  
  • Even if there are others alt h’s that predict D - you believe your H more.

Question 14

What is Baye’s Rules?

Answer 14

• Prior Beliefs X Likelihood of Observed Event.
• How much should I update my belief in a hypothesis after observing some data?
• “Your updated belief should be proportional to your prior belief x the likelihood of the observed data, i.e. how much did you hypothesis actually predict the event?”
• Possible explanations should be combined w/ the data to update our belief in each hypothesis - data of our observations & things which require explaining

Coin Flip Example.

• Fair coin - P(HEADS) = .50
• Two-headed Coin - P(HEADS) = 1.0
• Two-tailed Coin - P(HEADS) = 1.0

Flips

• After 2 flips - 2 x tails - Two-head coin hypothesis gone.
• After 9 flips - 9 x tails - hmmm could be double two-tailed coin.
• 10th flip is tail = fair coin.

Question 15

What is Occam’s Razor?

Answer 15

• People prefer explanations that explain more data with a minimal number of assumptions
• “The simplest explanations (that fits the observed data) is probably the correct one”.