PSY 402 Theories of Learning Chapter 4 – Theories of Conditioning.
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Transcript of PSY 402 Theories of Learning Chapter 4 – Theories of Conditioning.
Rescorla-Wagner Model Classical conditioning occurs only if the US
(UCS) is surprising to the organism. If the UCS is already predicted by a CS, then it is
not surprising – it is expected. When the CS predicts the UCS perfectly, no
further learning occurs. The asymptote (lambda, ) is the point where the
learning levels off (no increase in learning occurs).
Parts of the Model V = ( – V) V is the Associative Strength (amount of
learning). V is the change in learning (increase in
Associative Strength. and are the salience of the CS and UCS – V is the surprisingness of the US (the
distance away from the asymptote).
Multiple Conditioned Stimuli (CS’s) The basic model explains changes in learning
with one UCS and one CS. This doesn’t explain what happens during
blocking and unblocking, with multiple CS’s. V = ( – ΣV) When multiple CS’s are present, V is the
sum of the associative strengths of all of the CS’s (such as VN + VL).
Blocking First a noise is conditioned so that VN = 1.0 Next a light is added. The formula predicts its
associative strength: VL = ( – ΣV)
ΣV = VN + VL
If we assume that and VN is 0 because no learning has occurred yet, then: VL = .2[1.0 – (1.0 + 0)] = 0
Unblocking As before, a noise is conditioned so that VN = 1.0
A stronger US is presented with the new CS (VL). As before, the formula predicts its associative
strength: VL = ( – ΣV)
ΣV = VN + VL
Again, we assume that and VN is 0 but now the stronger US is 2.0 instead of 1.0: VL = .2[2.0 – (1.0 + 0)] = .2[1.0] = .2
Extinction During extinction, the CS is presented without the
UCS. This is the same as presenting a UCS with intensity = 0.
The formula predicts the associative strength during extinction: VN = ( – V) but is now 0 (due to extinction)
VN = .2[0 – 1] = -.2
The associative strength is decreasing. Use the decreased value for VN (1-.2) for the next trial.
Inhibition During inhibition, a second CSL is presented
that has never been associated with the UCS (V = 0). The formula predicts the associative strength for
both CS’s: VN = ( – V) and VL = ( – V) VN = .2[0 – (1.0 + 0)] = -.2 VL = .2[0 – (1.0 + 0)] = -.2
V = VN + VL.
Protection from Extinction When extinction of an excitor takes place
together with extinction of an inhibitor, the excitor is never fully extinguished. This is called protection from extinction.
To fully extinguish an excitor, and to extinguish it faster, pair it with another excitor (another CS associated with the US).
The model predicts both of the these results.
Overexpectation Effect The value of a model is that it predicts new
findings. If you pair two previously conditioned CS’s
(excitors) on the same trial, V for each will decrease until V equals . This is because V “overexpects” the UCS.
Similarly, if a new CS (X) is added to the pair, it will become an inhibitor.
Contextual Cues Contextual cues consist of everything in the
environment in addition the CS and UCS. They cannot be ignored simply because the
experimenter is not manipulating them. Whenever a CS or a UCS appears “alone,” it is
still being paired with the context. When the context is considered another CS,
then ideas about blocking explain learning. Zero contingency occurs because context is
blocked.
4.6 (A) Negative contingency between CS and US; (B) Zero contingency between CS and US
CS becomes an inhibitor
No learning occurs
Comparator Theories An alternative theory to Rescorla-Wagner proposes
that the CS and UCS are associated and the UCS and context are associated.
The two sets of associations are compared to determine the amount of responding to the CS. The comparison determines the responding, not the
learning. Strengthening or weakening the context, after learning,
affects the amount of responding, supporting the theory.
Problems with Rescorla-Wagner It predicts that presenting an inhibitory CS
without the UCS should lead to extinction, but it doesn’t.
The model cannot account for latent inhibition (preexposure to the CS).
Mackintosh demonstrated that animals learn to ignore redundant stimuli – the model doesn’t predict this learning.
4.7 (A) Mackintosh-Turner experiment; (B) Results of exposure to LN-shock trials
More learning
Less learning
The Mackintosh Model Mackintosh proposed that the amount of
learning depends on how much attention the animal pays to the CS.
The attention to the CS is the term in the Rescorla-Wagner model.
Alpha increases when the CS is the best predictor and conditioning occurs to the best predictor of the UCS.
Criticisms of the Mackintosh Model The model does a good job of explaining
latent inhibition and its own criticisms of Rescorla-Wagner, but other problems arose.
While attention is important, it doesn’t necessarily increase when a CS becomes the best predictor. Hall & Pearce showed that preexposure to a tone
that was a good predictor of weak shock didn’t help learning when a stronger shock was used.
4.8 (A) A Hall and Pearce experiment design; (B) Results of conditioning during Phase 2
More learning
Less learning
Group 1 should have done better, but didn’t
Pearce Hall Model Animals don’t waste attention on stimuli
whose meaning is already well understood. Instead, they devote attention to understanding
new stimuli. For their model, the value of alpha depends
on how surprising the UCS was on the previous trial. If the UCS is surprising, the CS is not well
understood. Alpha is high when this occurs.
4.9 A rat orienting toward a light CS (Part 1)
This is orienting behavior – the rat is paying attention to the light