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 V N + V L ).

Blocking  First a noise is conditioned so that V N = 1.0  Next a light is added. The formula predicts its associative strength:  V L =  ( – ΣV) ΣV = V N + V L  If we assume that  and V N is 0 because no learning has occurred yet, then:  V L =.2[1.0 – ( )] = 0

Unblocking  As before, a noise is conditioned so that V N = 1.0  A stronger US is presented with the new CS (V L ).  As before, the formula predicts its associative strength:  V L =  ( – ΣV) ΣV = V N + V L  Again, we assume that  and V N is 0 but now the stronger US is 2.0 instead of 1.0:  V L =.2[2.0 – ( )] =.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:  V N =  ( – V) but is now 0 (due to extinction)  V N =.2[0 – 1] = -.2  The associative strength is decreasing. Use the decreased value for V N (1-.2) for the next trial.

Inhibition  During inhibition, a second CS L is presented that has never been associated with the UCS (V = 0). The formula predicts the associative strength for both CS’s:  V N =  ( –  V) and  V L =  ( –  V)  V N =.2[0 – ( )] = -.2  V L =.2[0 – ( )] = -.2  V = V N + V L.

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.

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.

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.

Conditioned Inhibition  A CS can signal the presence of a UCS. This is called excitation, CS +  A CS that never appears with the UCS signals the absence of the UCS. It becomes an “all clear” signal. This is called inhibition, CS -  In fear conditioning an excitor produces anxiety, an inhibitor produces relief or safety.

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.