Lecture 7 - Perceptual and Statistical Learning Flashcards
associative learning
both classical and operant conditioning (what goes with what?)
Both classical and operant conditioning require
the detection and prediction of contingencies (the presence of one thing relies on the presence of another) in the environment
For classical conditioning, the conditioned stimulus (CS) predicts the
unconditioned stimulus (US).
In operant conditioning, the operant behavioral response (R) predicts
the outcome (O).
In both cases, the association will become weaker (extinction) if
the prediction is less informative.
Paired-associate learning
is a testing procedure developed by Mary Whiton Calkins (1894).
• You repeatedly present pairs of items (usually
words or letter patterns) and allow the subject to build an ‘association’ between them.
- see how many repetions necessary to make the association
• Behaviorists later adopted a S –> R –> O model
in an attempt to explain this.
EX: trying to learn “rabbit” and “el conejo”
• Behaviorists say:
“rabbit” (S) –> “el conejo” (R) –> “good job!” (O)
paired-associate learning doesn’t quite fit the behaviorist model (difficult to explain this kind of learning).
1) The learned association is bidirectional.
o Rabbit –> el conejo AND el conejo –> rabbit.
- you can trigger the other response even if it hasn’t been reinforced
2) People use top-down information to facilitate the paired associations (Atkinson, 1975).
- people can use some kind of visual element (imagery) to combine and embody the two words: come up with an image that links the two
- problem for behaviorists because they argue it’s only the enforcer
“rabbit” and “el conejo” (rabbit wearing an ice cream cone)
Classical and operant conditioning provided some great experimental techniques, but had serious theoretical flaws:
- Couldn’t readily explain individual or species differences in learning.
- Ignored top-down influences on learning.
- Contingencies can sometimes be bidirectional.
- In general, had difficulty dealing with computational complexity (hard to constrain learning outside of the laboratory).
However, there does seem to be something important
about how we TRACK REGULARITIES in the environment.
tracking regularities
we are good at seeing what goes with what
How accurate are we at detecting contingencies?
experiment by Smedslund (1963)
• nurses were asked to sort through 100 patient cards to see if a particular symptom predicted
the presence of a specific disease (outcome).
• Although the symptom is present more often overall, it has no real
predictive value for the presence of the disease (not contingent).
• 85% of the nurses determined that when the symptom was present,
the disease was more likely. This is an illusory correlation.
shows that humans have a tendency to find causal links and if they’re not there we impose them.
we’re constantly looking for association and we find them (whether they’re real or not)
Much of our learning is driven by
detecting regularities in the
environment following repeated exposures/experiences (non-associative).
- the perceptual system just looks for regularities (id and distinguish things in the environment)
perceptual learning
we become better able to distinguish similar stimuli with experience.
- Chicken sexers are able to distinguish sexes at 1 day of age. Many can’t verbalize how this is done.
- This learning may be due to (1) mere exposure or (2) discrimination training.
In mere exposure learning (Gibson & Gibson, 1955)
there is no explicit training during the presentation of stimuli.
- learning to make visual discrimination just by seeing things a lot
• Participants were shown a series of cards and indicated when they
matched a target (circular scribble). No feedback was given.
• Participants were initially good at identifying the target, but mistakenly identified the similar items.
• With repeated exposures, they were able to distinguish the target
from very similar distracters.
With discrimination training
feedback is provided to
improve accuracy. This may combine perceptual learning with operant conditioning.
Fiorentini & Berardi (1981)
discrimination training
- trained participants to match complex visual images rotated by 1 degree.
- After initial training, they rotated the images 90 degrees…
By changing the orientation of the images (but not the
features of the image), performance dropped back to chance.
* This is an example of learning specificity. * initial learning might interfere (when the stimuli are very similar)